Handbook of Behavioural Economics and Smart Decision-Making: Rational Decision-Making Within the Bounds of Reason 1782549579, 9781782549574

Citation preview

HANDBOOK OF BEHAVIOURAL ECONOMICS AND SMART DECISION-MAKING

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Handbook of Behavioural Economics and Smart Decision-Making Rational Decision-Making within the Bounds of Reason

Edited by

Morris Altman Professor of Behavioural and Institutional Economics and Dean and Head, Newcastle Business School, University of Newcastle, Australia

Cheltenham, UK • Northampton, MA, USA

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© Morris Altman 2017 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA

A catalogue record for this book is available from the British Library Library of Congress Control Number: 2016957242 This book is available electronically in the Economics subject collection DOI 10.4337/9781782549598

ISBN 978 1 78254 957 4 (cased) ISBN 978 1 78254 959 8 (eBook)

02

Typeset by Servis Filmsetting Ltd, Stockport, Cheshire

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Contents

List of contributors Foreword by Vernon L. Smith Acknowledgements 1

Introduction to smart decision-making Morris Altman

PART I

2

ix xix xxi 1

SMART DECISION-MAKERS, DIFFERENT TYPES OF RATIONALITY AND OUTCOMES

Rational inefficiency: smart thinking, bounded rationality and the scientific basis for economic failure and success Morris Altman

11

3

Rational mistakes that make us smart Nathan Berg

43

4

Rational choice as if the choosers were human Peter J. Boettke and Rosolino A. Candela

68

5

Smart predictions from wrong data: the case of ecological correlations Florian Kutzner and Tobias Vogel

86

6

Heuristics: fast, frugal, and smart Shabnam Mousavi, Björn Meder, Hansjörg Neth and Reza Kheirandish

101

7

The beauty of simplicity? (Simple) heuristics and the opportunities yet to be realized Andreas Ortmann and Leonidas Spiliopoulos

119

Smart persons and human development: the missing ingredient in behavioral economics John F. Tomer

137

8

PART II 9

10

ASPECTS OF SMART DECISION-MAKING

Behavioral strategy at the frontline: insights and inspirations from the US Marine Corps Mie Augier Feminist economics for smart behavioral economics Siobhan Austen

157 173

v

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11

How regret moves individual and collective choices towards rationality Sacha Bourgeois-Gironde

188

12

Is it rational to be in love? Paul Frijters and Gigi Foster

205

13

Behavioral economic anthropology Giuseppe Danese and Luigi Mittone

233

PART III 14

DEVELOPMENT AND GOVERNANCE

Do changes in farmers’ seed traits align with climate change? A case study of maize in Chiapas, Mexico C. Leigh Anderson, Andrew Cronholm and Pierre Biscaye

251

15

Rationality, globalization, and X-efficiency among financial institutions Roger Frantz

275

16

The evolution of governance structures in a polycentric system Edward McPhail and Vlad Tarko

290

PART IV

TAX BEHAVIOUR

17

Taxation and nudging Simon James

317

18

Income tax compliance Erich Kirchler, Barbara Hartl and Katharina Gangl

331

PART V

SMART MACROECONOMICS AND FINANCE

19

Financial decisions in the household Bernadette Kamleitner, Till Mengay and Erich Kirchler

349

20

Employing priming to shed light on financial decision-making processes Doron Kliger

366

21

Experimental asset markets: behavior and bubbles Owen Powell and Natalia Shestakova

375

22

To consume or to save: are we maximizing or what? Tobias F. Rötheli

392

PART VI

DIMENSIONS OF HEALTH

23

Time orientation effects on health behavior Jannette van Beek, Michel J.J. Handgraaf and Gerrit Antonides

413

24

Behavioral aspects of obesity Odelia Rosin

429

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Contents 25

26

Time inconsistent preferences in intertemporal choices for physical activity and weight loss: evidence from Canadian health surveys Nazmi Sari Suicide among smart people Bijou Yang and David Lester

PART VII

27

28

29

30

449 464

SOCIOLOGICAL DIMENSIONS OF SMART DECISIONMAKING

Seeing and knowing others: the impact of social ties on economic interactions Astrid Hopfensitz Weakness of will and stiffness of will: how far are shirking, slackening, favoritism, spoiling of children, and pornography from obsessivecompulsive behavior? Elias L. Khalil The role of identity, personal and social capital in community crime prevention Ambrose Leung and Brandon Harrison Norms, culture, and cognition Shinji Teraji

PART VIII

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492

515 526

MORALS AND ETHICS

31

Rational choice in public and private spheres Herbert Gintis

543

32

Ethics and simple games Mark Pingle

557

Index

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Contributors

Morris Altman is Dean and Head of the Newcastle Business School and is Professor of Behavioural and Institutional Economics at the University of Newcastle, Australia. He is also Professor Emeritus at the University of Saskatchewan, Canada. Morris was the Head of the School of Economics and Finance and Professor at Victoria University of Wellington, New Zealand. He earned his PhD in economics from McGill University, Montreal, Canada in 1984. A former visiting scholar at Cambridge (Elected Visiting Fellow), Canterbury (Erkine Professor), Cornell, Duke, Hebrew, Stirling and Stanford Universities, he served as Editor of the Journal of Socio-Economics for ten years and is currently the co-founder and Associate Editor of the Review of Behavioral Economics. He is also past President of the Society for the Advancement of Behavioral Economics and of the Association for Social Economics. Morris has published over one hundred refereed papers and given over 150 international academic presentations on behavioural economics, x-inefficiency theory, institutional change, economics of cooperatives, economic history, methodology and empirical macroeconomics and has published eight books including: Handbook of Contemporary Behavioral Economics, Behavioral Economics for Dummies, Economic Growth and the High Wage Economy and Real-World Decision Making: An Encyclopedia of Behavioral Economics. Morris is on the International Co-operative Alliance (ICA) international committee on research as well as that for the Asian-Pacific region. C. Leigh Anderson is the Marc Lindenberg Professor for Humanitarian Action, International Development and Global Citizenship at the University of Washington’s Evans School of Public Policy and Governance, USA. Anderson’s research focuses on how individual and household decision-making is affected by economic and attitudinal factors including poverty, rural isolation, agricultural livelihoods, and preferences over risk, time and social standing. Of interest is how policy and programmatic interventions can be best designed and delivered to improve the lives of the poor and food insecure. Gerrit Antonides is an Emeritus Professor of Economics of Consumers and Households at Wageningen University, the Netherlands. He has published in the areas of behavioural economics, economic psychology and consumer behaviour. He has been an editor of the Journal of Economic Psychology and has authored and co-authored several textbooks on consumer behaviour and economic psychology. The behavioural aspects of consumer decision-making concerning issues of finance, household, environment and health are an important part of his current research activities. Mie Augier is Associate Professor at the Naval Postgraduate School, USA. Her scholarly and academic research interests include strategy, organizations, innovation, interdisciplinary social science, how organizations cultivate innovation capability (including the role of strategic organizational design), the influence of culture and globalization on strategic decision-making, and the past and future of management education and business schools. Her research has been published in more than 50 articles and book chapters in outlets ix

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such as Organization Science, Industrial and Corporate Change, Journal of Management Inquiry, Management International Review, Organization Studies; Research Policy and California Management Review, among others. With collaborators she has published on topics such as the history of business schools (including her 2011 book with James March, The Roots, Rituals, and Rhetorics of Change, Stanford University Press) and the organizational mechanisms leading to the rise (and decline) of novelty and innovation in organizations (‘The flaring of intellectual outliers’, 2015, Organization Science). Active research interests include: (1) organizational and strategic analysis of the US Marine Corps as an organization, how they have evolved and organized for innovation, and their strategic decision-making; (2) the evolution of the teaching of ethics and values within the history of business schools and management education; and (3) behavioural strategy as a field. Siobhan Austen is Professor of Economics and Director of Women in Social and Economic Research (WiSER) at Curtin University Perth, Western Australia. She works on feminist and institutional economics, with a particular focus on the circumstances and experiences of women in labour markets. Nathan Berg is Associate Professor of Economics at University of Otago in Dunedin, New Zealand. Berg’s work appears in Journal of Economic Behavior and Organization, Psychological Review, Social Choice and Welfare and Review of Behavioral Economics. Berg was a Fulbright Scholar in 2003 and Visiting Research Scientist at the Max Planck Institute-Berlin in the 2000s. His research has been cited in the Financial Times, Business Week, Canada’s National Post, The Village Voice, The Advocate, Science News, Slate and the Atlantic Monthly. Pierre Biscaye is the Research Coordinator for the Evans School Policy Analysis and Research Group (EPAR) at the University of Washington, USA. He manages and supports research looking at issues across agricultural development, poverty reduction, financial inclusion, global health, and development policy. Pierre received a Master of Public Administration (MPA) from the University of Washington’s Evans School of Public Policy and Governance. For his capstone, he developed a monitoring and evaluation system and implementation plan for a small non-profit organization supporting education projects in Sierra Leone. Peter J. Boettke is a University Professor of Economics and Philosophy at George Mason University, a BB&T Professor for the Study of Capitalism, and the Director of the F.A. Hayek Program for Advanced Study in Philosophy, Politics, and Economics at the Mercatus Center at George Mason University, USA. He is Co-Editor-in-Chief of The Review of Austrian Economics and President of the Southern Economic Association. Sacha Bourgeois-Gironde is Professor of Economics at Université Paris II and research faculty member of Institut Jean-Nicod at Ecole Normale Supérieure, France. His work lies at the interface between decision-theory and cognitive sciences. The first aim is to understand how recent developments in formal decision-theory can supply new testable psychological insights on our use (or non-use) of probabilities, indeterminacies of our beliefs and values, and long-term rational purposes in life. The second aim, using interdisciplinary approaches (computer science, neuroscience and experimental psychology), is to

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probe how bounded cognitive systems can adapt to complex decisional environments and how the interaction between the two brings about the emergence of particular institutions. Rosolino A. Candela is a PhD candidate in Economics at George Mason University and a Graduate Research Fellow in the F.A. Hayek Program for Advanced Study in Philosophy, Politics, and Economics at the Mercatus Center at George Mason University, USA. He holds a BA in History from St John’s University and an MA in Economics and International Political Economy from Fordham University. Previously, he was also a visiting PhD student in the Department of Political and Social Sciences at the European University Institute and a Charles G. Koch PhD Fellow at Suffolk University, where he was also a Koch Summer Fellow at the Beacon Hill Institute. Andrew Cronholm is an analyst with the King County Office of Performance, Strategy and Budget in Washington State, USA, where he provides policy, finance, and budgeting expertise. Andrew previously held analytical roles supporting the US Environmental Protection Agency and the City of Seattle’s Department of Transportation. Originally hailing from Massachusetts, Andrew received a Bachelor of Arts in Political Science from Drew University and obtained his Master of Public Administration (MPA) and Certificate in Environmental Management from the University of Washington’s Evans School of Public Policy and Governance. He currently resides in Seattle, Washington. Giuseppe Danese is a Fellow at CEGE, the research centre of Católica Porto Business School in Porto, Portugal. He holds a PhD in Economics from Simon Fraser University. His research interests are social norms, organizations, property rights, and the psychophysiological roots of decision-making. Gigi Foster is an Associate Professor with the School of Economics at the University of New South Wales, Australia. She works in many literatures, including education, social influence, corruption, laboratory experiments and time use. With support from the Australian Research Council and other bodies, she published a holistic behavioural economics treatise with Cambridge University Press (An Economic Theory of Greed, Love, Groups, and Networks, jointly with Paul Frijters) in 2013 and has authored over 25 academic papers published in a range of outlets such as the Journal of Public Economics, Quantitative Economics, Human Relations and Journal of Economic Psychology. Roger Frantz is Professor of Economics at San Diego State University, USA and Founding Editor of the Journal of Behavioral Economics for Policy. He has edited the Handbook of Behavioral Economics (London: Routledge, 2016). He is Editor of Renaissance in Behavioral Economics. He is co-editor, with Leslie Marsh, of Minds, Models, and Milieux: Commemorating the Centennial of the Birth of Herbert Simon and, with Robert Leeson, of Frederick Hayek and Behavioral Economics. He has also authored Two Minds: Intuition and Analysis in the History of Economic Thought and X-Efficiency: Theory, Evidence, and Applications. His work has been published in many journals, including the Journal of Socio-Economics, Journal of Economic Psychology, American Economic Review, Papers & Proceedings, Economics and Philosophy, Public Choice, Journal of Post Keynesian Economics, Journal of Behavioral Economics and the Southern Economic Journal. Paul Frijters is a Professorial Research Fellow at the Wellbeing Program within the Centre for Economic Performance at the London School of Economics, United Kingdom and Project

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Director of the LSE’s World Wellbeing Panel. Paul holds a PhD in welfare and well-being in Russia from the University of Amsterdam and has a wide range of research interests, having published over 70 papers and books in fields including happiness, labour markets, health economics, behavioural economics and econometrics. Before joining the LSE, he was the Research Director of the Rumici Project, an international project into the migration from the countryside to the cities in China and Indonesia, sponsored by ministries, the World Bank, the Ford Foundation and many others, tracking 20 000 individuals for many years. In 2009 Paul was awarded the Economic Society of Australia’s Young Economist Award (best economist under 40 in Australia). He regularly comments on economic issues in the national and international media, including the New York Times and the BBC. Katharina Gangl is Assistant Professor at the University of Göttingen, Germany, as the Chair of Economic and Social Psychology. She received her Diploma and PhD in Economic Psychology at the University of Vienna, Austria, and was a visiting scholar at the Queensland University of Technology, Australia. Her main research areas are ethical decision-making in organizations and tax behaviour. Herbert Gintis is External Professor at the Santa Fe Institute, Santa Fe, New Mexico, USA. His recent books include Game Theory in Action (with Stephen Schechter) (Princeton University Press 2016), A Cooperative Species (with Samuel Bowles) (Princeton University Press 2011), The Bounds of Reason (Princeton University Press 2009), Game Theory Evolving (Princeton University Press 2009), and Moral Sentiments and Material Interests (MIT Press 2005). His most recent book is Individuality and Entanglement: The Moral and Material Bases of Social Life (Princeton University 2016). His recent work on market dynamics includes: ‘The stability of general equilibrium with decentralized prices’ Journal of Mathematical Economics (with Antoine Mandel, 2016); ‘Stochastic stability in the Scarf economy’, Mathematical Social Sciences (with Antoine Mandel, 2014); and ‘The dynamics of general equilibrium’, Economic Journal (2007). His work on the unification of the behavioural sciences includes: ‘Zoon politikon: the evolutionary origins of human political systems’ (with Carel van Schaik and Christopher Boehm), Current Anthropology (2015); ‘Inclusive fitness and the sociobiology of the genome’, Biology & Philosophy (2014), ‘Homo socialis: an analytical core for sociological theory’ (with Dirk Helbing), Review of Behavioral Economics (2015); ‘The biology of cultural evolution’, Quarterly Review of Biology (2013), and ‘The evolutionary roots of property rights’, in Kim Sterelny et al. (eds), Cooperation and its Evolution (MIT Press 2013). Professor Gintis is a top reviewer of scientific books at Amazon.com and was recently cited as a gold star reviewer for Nature. Michel J.J. Handgraaf received his PhD in Social Psychology from Leiden University. Since 2011 he has been an Associate Professor at the Economics of Consumers and Households Group of Wageningen University, the Netherlands. Most of his research uses (field) experimental methods and surveys, can be described as ‘behavioural economics’ and mainly deals with differences between what rational economic theories would predict and the psychology behind deviations from such predictions. Besides research on fairness and ethics, Handgraaf’s current research focuses on decisions in the environmental domain. These decisions typically feature uncertainty, temporal trade-offs and social trade-offs.

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Contributors xiii Brandon Harrison obtained a Bachelor of Arts Criminal Justice (Honours) from Mount Royal University in Calgary, Alberta, Canada. Brandon is currently enrolled in the Faculty of Law at Thompson Rivers University in Kamloops, British Columbia, Canada. Brandon is interested in criminal law and energy law. Barbara Hartl holds a post-doctoral position at the Institute of Organization and Global Management Education at the Johannes Kepler University, Linz, Austria. Her research interests include cooperation in social dilemma, sustainable consumption and psychology of tax behaviour. She follows a multi-method approach, including laboratory experiments, survey studies, interviews and free associations. Astrid Hopfensitz is currently a Lecturer in Economics at the Toulouse School of Economics (TSE) in France. Her main research interest concerns the influence of emotions and psychological dimensions on economic decision-making and behaviour. In her work she employs economic experiments in combination with psychological methods of measuring emotions and character traits. Since 2012 she has also been affiliated with the Institute of Advanced Study in Toulouse (IAST). Simon James is an Associate Professor of Economics, Department of Organisation Studies, University of Exeter Business School, United Kingdom. He has held visiting positions at six universities overseas, published many research papers and his 16 books include a four-volume edited collection of papers Taxation: Critical Perspectives on the World Economy, 2002, A Dictionary of Taxation, 2nd edition 2012, The Economics of Taxation: Principles, Policy and Practice (with Christopher Nobes) 16th edition 2016 and, jointly edited with Adrian Sawyer and Tamer Budak, The Complexity of Tax Simplification: Experiences from around the World, 2016. Bernadette Kamleitner is Professor of Marketing at WU Vienna University of Economics and Business, Austria. She is head of the Institute for Marketing and Consumer Research at WU and President of the Austrian Forum Marketing. Her internationally published research is positioned at the cross-section of psychology, marketing and economics. Her particular research interests comprise the psychological underpinnings and consequences of experiences of ownership and financial decision-making. Elias L. Khalil is an Associate Professor of Economics at Monash University, Australia. He received his PhD in Economics from the New School for Social Research in 1990. His research focus is building a unified theory of human action, a theory that is based on rationality, virtue and an expanded notion of the self. His papers appeared in journals such as Economic Inquiry, Behavioral and Brain Sciences, Biology and Philosophy, Biological Theory, Theory and Decision, Journal of Economic Behavior and Organization,  Cambridge Journal of Economics, Journal of Evolutionary Economics, International Negotiation, Theoria, Philosophy, Economic Modelling and Economics and Philosophy. Reza Kheirandish is Associate Professor of Economics at the College of Business, Clayton State University, Morrow, Georgia, USA, and is affiliated with the Center for Adaptive Behavior and Cognition, Max Planck Institute for Human Development, Berlin, Germany. He received his PhD in economics from Virginia Tech and his BSc degree in electrical engineering from Sharif University of Technology. Reza has been a (summer)

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Visiting Researcher at the Max Planck Institute for Human Development in Berlin since 2014. He has served as the Director of the Center for Research in Economic Sustainability and Trends (CREST) at CSU (2010–15), the Program Co-Chair for the SABE/IAREP (2013) and the President, Vice-President, Treasurer/Secretary and Program Chair of SEINFORMS. He is the 2017 Program Chair for the SEDSI and has been a board member and webmaster of SABE since 2010. Erich Kirchler is Professor of Economic Psychology at the University of Vienna, Faculty of Psychology, and Guest Professor at WU Vienna University of Economics and Business, Austria. He is head of the Department of Applied Psychology: Work, Education, Economy at the Faculty of Psychology and past President of the International Association of Applied Psychology (IAAP), Division 9 (Economic Psychology) and the Austrian Psychological Society. His research is positioned at the cross-section of psychology and behavioural economics. His particular research interests comprise financial decisions in the household and psychology of tax behaviour. Doron Kliger is the Chair of the Economics Department at the University of Haifa, Israel, specializing in finance and behavioural economics. While in the US, he has been affiliated with the Wharton School, University of Pennsylvania. Kliger has published in a wide range of journals in finance, economics, insurance and probability, on topics including asset pricing, behavioural economics and finance, bond rating, decision-making, industrial organization and insurance pricing. He is a co-author of the book Event Studies for Financial Research, helping readers to obtain valuable hands-on experience with event study tools and to gain required technical skills for conducting their own studies. Florian Kutzner received his doctoral degree in psychology from the University of Heidelberg. After a research stay at the Warwick Business School he is currently affiliated with the Department of Cognitive Research in Social Psychology (CRISP) at the University of Heidelberg, Germany. His research focuses on the descriptive models of decision-making and learning in the context of social stereotypes and sustainable behaviour. David Lester has doctoral degrees from Cambridge University, United Kingdom, in social and political science and Brandeis University, USA, in psychology. He is Emeritus Professor of Psychology at Stockton University in Galloway, New Jersey, USA. He is a former President of the International Association for Suicide Prevention. He has published extensively on economic issues and suicide, including Suicide and the Economy (Nova Science, 1997) and ‘Calculating the economic cost of suicide’ (Death Studies, 2007, 31, 351–61). Ambrose Leung is Associate Professor at the Department of Economics, Justice, and Policy Studies, Mount Royal University in Calgary, Alberta, Canada. Ambrose received his PhD in Economics from Carleton University in Ottawa, Ontario, Canada. His main fields of research include socioeconomics, economics of crime, economic psychology and economics education. Ambrose has also acted as a consultant for the Department of Justice Canada. Edward McPhail is Professor of Economics at Dickinson College in Pennsylvania, USA. He has published papers in the American Journal of Economics and Sociology, Challenge, Eastern Economic Journal, European Journal of Political Economy, History of Economics

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Review, History of Economic Ideas, Historical Journal, Journal of Economic Behavior and Organization, Review of Political Economy and others. Björn Meder is a research scientist at the Center for Adaptive Behavior and Cognition (ABC) at the Max Planck Institute for Human Development in Berlin, Germany. His research interests include judgement and decision-making, causal inference, information search and cognitive modelling. Björn holds a PhD in psychology from the University of Göttingen, Germany. Till Mengay was Research Assistant at the Institute for Marketing and Consumer Research, WU Vienna University of Economics and Business, Austria. Currently he is working at the Federal Ministry of Education, Department for Adult Education, Austria. His particular research interests are sustainable consumption and sharing within groups. Luigi Mittone (PhD, Bristol) is Full Professor of Economics at the University of Trento, Italy. At the University of Trento he is Director of the Doctoral School in Social Sciences, Director of the Cognitive and Experimental Economics Laboratory and coordinator of the International Master in Economics (MEC). He is also coordinator of the research project in ‘Experimental economics and nudging’ at the Bruno Kessler Research Centre. He has published extensively on fiscal evasion, consumer behaviour, mental modelling of uncertain events, intertemporal choices, cooperation, and fiscal system dynamics with heterogeneous agents. Shabnam Mousavi’s research revolves around making sense of the ways in which people make their decisions. She holds a PhD in economics and one in statistics from Virginia Tech, USA, serves on the Faculty of Business at the Johns Hopkins University, USA, and is a researcher at the Max Planck Institute for Human Development, Berlin, Germany. At the moment she is writing her first book, Fast-and-Frugal Decision Making. Hansjörg Neth is Lecturer in Social Psychology and Decision Sciences at the University of Konstanz, Germany and an associate member of the Max Planck Institute of Human Development, Berlin, Germany. His theoretical and experimental research focuses on the analysis of adaptive behaviour, interactive cognition and ecological rationality, as well as applied aspects of choice, and heuristic decision-making under uncertainty. He has served as acting Chair of Cognition, Emotion, and Communication at the University of Freiburg, taught cognitive and decision sciences at the University of Göttingen, and was Research Assistant Professor in Cognitive Science at the Rensselaer Polytechnic Institute. He holds a PhD in psychology from Cardiff University, UK. Andreas Ortmann is Professor of Experimental and Behavioural Economics in the School of Economics at the UNSW Australia Business School, Sydney, Australia. He was formerly Professor at CERGE-EI, Prague, Czech Republic, and Researcher at the Center for Adaptive Behavior and Cognition. Mark Pingle has been a member of the University of Nevada, Reno Department of Economics, USA, since 1990. He was appointed Conjoint Professor of Economics to the Department of Economics, University of Newcastle in 2016. He received his PhD in Economics in 1988 from University of Southern California. Professor Pingle has

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published in the areas of behavioural economics, experimental economics and macroeconomics. He is Book Editor for the Journal of Behavioral and Experimental Economics, Associate Editor for the Review of Behavioral Economics and is a past President of the Society for the Advancement of Behavioral Economics. Owen Powell is an Assistant Professor in the Department of Economics at the University of Vienna, Austria. He holds a PhD in economics from the University of Tilburg. His research interests include experimental finance, growth and growth accounting, and computational economics. His work has been published in the Journal of Econometrics, the Review of Finance and the Journal of Behavioral and Experimental Finance. Odelia Rosin is a health economist. She holds a PhD in economics from Bar-Ilan University, Israel. Her doctoral dissertation dealt with obesity, its behavioral economic aspects and related public policy. Her research interests are health economics, behavioral economics, nutrition and public health. Odelia is a Lecturer in the College of Management – Academic Studies (COMAS) in Israel. She also serves as the academic head of one of COMAS’s campuses. Tobias F. Rötheli is Professor of Macroeconomics at the Department of Economics of the University of Erfurt in Germany. He holds a doctorate and a Venia Legendi in Economics from the University of Bern. His research focuses on behavioural models of expectations. Much of this work is built on the concept of pattern recognition and combines experimental methods and applied econometrics. A further area of research is the modelling of boundedly rational agents and their role in financial boom–bust dynamics. Finally, in historical studies Rötheli investigates the coevolution of quantitative methods in academic economics and in business practice. Nazmi Sari is Professor in the University of Saskatchewan, Department of Economics, Canada. In addition to his primary appointment at the university, he is a faculty associate with the Canadian Center for Health Economics, University of Toronto, and an adjunct scientist at the Health Quality of Council. His research interests are economics of physical activity and smoking, quality and efficiency in hospital markets, provider reimbursements and healthcare financing reforms. He has published articles in health economics, public health, and health policy journals. Natalia Shestakova is an Assistant Professor in the Department of Economics at the University of Vienna, Austria. She holds a PhD in Economics from the Center for Economic Research and Graduate Education – Economics Institute (CERGE-EI). Her research interests include behavioural economics, contract theory and experimental economics. Leonidas Spiliopoulos received a BA in Economics from Yale University in 1997, an MSc from the Athens University of Economics and Business in 2003 and a PhD in Economics from the University of Sydney in 2008. He is currently a Visiting Research Fellow at the Max Planck Institute for Human Development (Center for Adaptive Rationality), Berlin, Germany, where he also served as an Alexander von Humboldt Experienced Research Fellow. He previously held a Vice-Chancellor’s Postdoctoral Research Fellowship at the University of New South Wales, an Endeavour Cheung Kong Research Fellowship at the Hong Kong University of Science and Technology, and lectured at the University of

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Athens. His work focuses on how economics, game theory, cognitive psychology/neuroscience and artificial intelligence can inform models of decision-making and learning. Vlad Tarko is Assistant Professor of Economics at Dickinson College in Pennsylvania, USA. He is the author of Elinor Ostrom: An Intellectual Biography (Rowman & Littlefield International, 2017) and co-author with Paul Aligica of Capitalist Alternatives: Models, Taxonomies, Scenarios (Routledge, 2015). He has published papers in the American Political Science Review, Academy of Management Papers and Proceedings, Comparative Economic Studies, Kyklos, Constitutional Political Economy, Review of Austrian Economics and others. Shinji Teraji is Professor of Economics, Yamaguchi University, Japan. His research is mainly concerned with a synthesis of behavioural and institutional economics. He is the author of Evolving Norms: Cognitive Perspectives in Economics (2016). John F. Tomer is Emeritus Professor of Economics at Manhattan College, USA. He was born in 1942 and grew up in New Jersey. He has a PhD in Economics (1973) from Rutgers University, New Brunswick, New Jersey. He is a founder and past President of the Society for the Advancement of Behavioral Economics. His research areas are behavioural economics and human capital. He has written four books and 50 articles. His recent research integrates human capital with human development. Jannette van Beek completed a dissertation on time orientation in relation to both eating and exercising behaviour. The main aim of this dissertation is to provide insight into the relationship between time orientation and both eating and exercising behaviour in order to better understand individuals’ intertemporal decision-making in the health domain and ultimately stimulate healthy eating and exercising behaviour. Currently, Jannette works as a Lecturer at both the Economics of Consumers and Households Group and the Marketing and Consumer Behaviour Group of Wageningen University, the Netherlands. Tobias Vogel received his doctoral degree in psychology from the University of Heidelberg. After research stays at the Universities of Louvain-la-Neuve, Belgium, Basel, Switzerland, and San Diego, USA, he is currently affiliated with the Department of Consumer and Economic Psychology at the University of Mannheim, Germany. His research focuses on the psychology of evaluative judgements, with an emphasis on the cognitive processes underlying attitude acquisition and construction. He is author of the book Attitudes and Attitude Change (Vogel and Wänke, 2016). Bijou Yang has BA and MA degrees in economics from the National Taiwan University and MA and PhD degrees in economics from the University of Pennsylvania. Her dissertation was on econometric forecasting of the world economy, and she worked for Wharton Econometric Forecasting Associates (WEFA) before returning to academia. She joined Drexel University, USA, in 1987. Her research has focused on contingent employment, e-commerce and the behavioural economic approach to suicide and criminal behaviour. She served as Treasurer of the Society for the Advancement of Behavioral Economics (SABE) from 1986 to 2010 and as President from 2006 to 2008. She has published two books and some 190 scholarly articles and notes. Her research has appeared in Applied Economics and the Journal of Socio-Economics, most recently ‘Personality traits and economic activity’ in Applied Economics, 2016, 48, 653–57.

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Foreword

In the study of decision-making by people in the world, the laboratory, in surveys, or in all of the above, many scholars have derided our decisions as irrational, uninformed, biased or vulnerable to illusions, if not delusions, that steer us off track. You won’t find that simplistic reduction in this book. You will find plenty of cases of error, sometimes random, sometimes systematic, and sometimes in the models that are alleged to specify rational behaviour. You will also find penetrating analyses of institutions and other social systems that have made us smart, or smart enough to muddle through in an uncertain world. For me this shift in methodology from the search for anomalies that prove that the standard model is wrong – a search that was assured of finding what was sought – to a closer examination of the circumstances that make for success or failure is particularly welcome. Both experimental economics and the anomalies literature grew out of a welcome new wave of empirical investigation that can only be understood against the intellectual backdrop of a hundred-odd years of equilibrium theory development. That development had been jump-started by the marginal utility revolution of the 1870s, devolving into powerful new theory by the mid-twentieth century. Theoretical insights into topics ranging from individual decision-making and two-person games to the determinants of prices in markets invited new experimental investigations by psychologists and economists in the 1950s and 1960s. Both verifying and falsifying evidence surfaced as part of these investigations. When you are looking to verify the predictions of a theory and get glaring contrary evidence proving your beliefs are wrong, it changes the way you think about the phenomenon. In retrospect, neoclassical economic theory provided insights so powerful and influential that it displaced rather than supplemented the classical economic perspective. Under the influence of neoclassical theory, my first supply and demand experiments were designed to show that complete information – a pillar of theory at the time – was necessary to observe efficient competitive outcomes. However, the experiments demonstrated the opposite. We were wrong. Inadvertently, I was rediscovering that process is what matters. ‘The propensity to truck barter and exchange’ is a process; empower people with a trading institution, and they will use it to discover rich forms of specialization that otherwise did not exist. Neoclassical equilibrium in outcomes displaced rather than supplemented socioeconomic interactive processes of change, prominent in the writings of David Hume and Adam Smith. I see this book as a return to that perspective, but driven by new and exciting ways of modelling and thinking about the great issues that have created the modern world. Vernon L. Smith, Professor of Economics and Law George L. Argyros Endowed Chair in Finance and Economics 2002 Nobel Laureate in Economics Chapman University, USA xix

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Acknowledgements

This was a huge project and one that breaks from various conventional perspectives on economic theory and behavioural economics. I must thank Matthew Pitman, our publishing editor, for supporting this project and for his helpful advice. Of course, it goes without saying that all contributors devoted so much time and effort towards constructing some enriching and excellent chapters. Thanks for your contributions and support. The meticulous work from the whole Edward Elgar team was invaluable. I also express gratitude to life partner and wife, Louise Lamontagne, for her comments and suggestions. Many thanks as well to our daughter, Hannah Altman, now blossoming into a first-rate economist and health scientist.

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To the late Professor Harold (H.R.C.) Wright, brilliant and modest teacher-scholar, my Master’s and PHD supervisor, teacher, mentor and friend at McGill University.

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Introduction to smart decision-making Morris Altman

This is an original contribution of essays on behavioural economics, which builds upon the research of Herbert Simon and, more generally, the Carnegie-Mellon school of behavioural economics. This perspective can be referred to as the bounded rationality methodological approach to behavioural economics (Altman 1999, 2005, 2015, 2017). In this perspective, the prior assumption is that decision-makers are relatively rational, intelligent and smart (satisficing, boundedly rational and evolutionarily rational). As one of the intellectual leaders of the Carnegie-Mellon school, James March (1978, p. 589), stated, it is of primary importance to determine if we can explain human behaviour in terms of rationality, broadly defined, even if at first glance such behaviour does not appear rational and might even appear to be error-prone or ‘biased’. More generally, I refer to this methodological approach as smart decision-making, which encompasses bounded rationality, procedural rationality, fast and frugal heuristics, the brain as a scarce resource (following the insights of Friedrich Hayek) and the institutional, sociological and psychologicalneurological determinants of decision-making. This is counter-posed to the world view of conventional or neoclassical rationality as well as the heuristics and biases perspective on behavioural economics, pioneered by Kahneman and Tversky (Kahneman 2003, 2011), that dominates contemporary behavioural economics. Smart decision-making encompasses intelligent or smart decision-makers or agents, who develop or adopt decision-making processes and make decisions given their cognitive limitations, the decision-making mechanism of the brain, individuals (or economic agents) decision-making capabilities, decision-making experience, environmental factors, which include institutional and legal parameters, culture and norms, relative power in the decision-making process and related sociological factors. It is also recognized that cognitive limitations are affected by technology (computers and calculators, for example), the capabilities to effectively use new or improved technologies and the learning processes that affect how the brain is hardwired (neuroplasticity). Smart decision-makers or agents do the best they can, given the pertinent circumstances that affect the decision-making process and related outcomes. Herbert Simon refers to the act of doing the best we can as satisficing behaviour. Satisficing need not result in the best possible or optimal outcomes for the firm, household, society or individual; but it can, depending on circumstances. Deviations from optimality do not imply that decision-makers are not smart and, in this sense, irrational. Nor does establishing that decision-makers are smart imply that decision-making outcomes are optimal. Here rationality, broadly defined, relates to the choices people make and the decision-making processes adopted by individuals given their various constraints and opportunities as well as their decision-making environment. Optimality in production and consumption at an individual, firm, household or social level need not necessarily flow from smart decision-making. Smart decision-making, however, would often be a necessary but not a sufficient condition for optimality to be 1

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obtained. What these sufficient conditions might be are critically important to research that stems from the smart agent or smart decision-making perspective. Inadequate decision-making environments, for example, would preclude smart agents from achieving optimal results from their own and from society’s perspective (where externalities exist). For example, you might wish to increase your savings for retirement, but you invest in high-risk high-return financial paper because of the false or misleading financial information provided to you, resulting in you losing much of your savings. Women might want to have one child, but they end up giving birth to four or five, because they are not empowered to realize their preferences. A firm’s productivity might not be maximized because decision-makers are maximizing a complex utility function that includes managerial slack and short-term returns. None of the above is a product of irrationality. They are a product of preferences, decision-making capabilities, experience and the overarching decision-making environment. Conventional theory’s point of focus is on very generalized concepts related to how humans should behave and are expected to behave to generate optimal outcomes. As long as the analytical prediction is correct, all is well. This is effectively the correlation-based analysis promoted by Friedman (1953). If you get the prediction correct, you can assume for reasons of simplicity that humans behave as if they are maximizing profits, minimizing costs and maximizing utility (which is often assumed to be identical wealth maximization, controlling for risk). The realism of the simplifying assumptions we make about decisionmakers, the decision-making processes and the decision-making environment are not of importance from this perspective. We can simply assume that individuals behave as if they are maximizing profits or utility, as long as the analytical prediction is the correct prediction. The assumption here is that individuals ideally should behave ‘neoclassically’, if they are rational, which they are assumed to be. Rationality is defined in terms of neoclassical rationality. Apart from this, what transpires in the decision-making process is not of substantive interest. We simply abide methodologically with neoclassical simplifying assumptions of how individuals behave within the firm and in the household. Moreover, it is further assumed that the decision-making environment allows for the realization of optimal outcomes, given neoclassical rationality, for the individual, the household and the firm. The analytical focus, therefore, is on correlation as opposed to true causation, where the latter relates to determining what particular behaviours and decision-making environments generate particular outcomes. Modelling true causation would address issues of spurious correlation, omitted variables and the possibility of alternative behaviours, yielding similar sustainable outcomes. What is key is the determination of what specific behaviours, decision-making processes and institutional and sociological variables yield specific outcomes. This deeper modelling agenda is part of the bounded rationality approach to behavioural economics. The bounded rationality tradition in behavioural economics plays particular attention to identifying the actual decision-making process that generates particular outcomes. It ventures into the black box of the firm, the household and the individual. Only by understanding how individuals actually behave, how they make decisions, can we determine if these decisions are smart and in this broad sense rational. Hence, rationality here is contextualized. Benchmarks for what is rational are, therefore, not constructed by some imagined ideal unrelated to the decision-making capabilities and environments of the individual, household or firm.

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Introduction to smart decision-making 3 For this reason, a core attribute of the approach taken in this book is, following from Simon, the overall importance of reasonable, reality-based, simplifying modelling assumptions for robust economic analysis. Related to this is the significance of situating our definition of rationality and smart decision-making in context. Simon writes (1986, p. S209): The judgment that certain behavior is ‘rational’ or ‘reasonable’ can be reached only by viewing the behavior in the context of a set of premises or ‘givens.’ These givens include the situation in which the behavior takes place, the goals it is aimed at realizing, and the computational means available for determining how the goals can be attained. In the course of this conference, many participants referred to the context of behavior as its ‘frame,’ a label that I will also use from time to time. Notice that the frame must be comprehensive enough to encompass goals, the definition of the situation, and computational resources.

The smart agent, smart decision-making approach to decision-making and behavioural economics not only stands in contrast to what we find in much of conventional economics, it also stands in contrast, as mentioned above, to a theme running through much of contemporary behavioural economics where much of the typical individual’s behaviour is deemed irrational and error-prone. This is the heuristics and biases approach pioneered by Kahneman and Tversky (Kahneman 2003, 2011). A common thread running through this approach and conventional economics is adopting neoclassical benchmarks for rationality and, flowing from this, benchmarks for optimal outcomes in the domain of consumption and production (although the latter is not a point of focus in the heuristics and biases approach). In the heuristics and biases approach, as in conventional economics, these various benchmarks are not empirically derived. Rather, they are taken for granted. As in the conventional approach, causal analysis is not the point of focus, and it appears that analytical prediction (correlation analysis) is of greatest significance. However, in the heuristics and biases approach psychological factors are introduced into the modelling framework to supplement or replace economic variables. Typically such new variables are said to generate deviations from neoclassical optimality and, therefore, errors in decision-making. This is often derived from assumed, but not proven, hardwired biases in the human decision-maker. However, in terms of the derivation and introduction of psychological variables, these are often not predicated upon an assessment of how individuals behave within the household and the firm. Rather, they are generalized descriptors of human behaviour introduced into the modelling framework to produce improved analytical predictions or predictions that are as robust as those generated in conventional models, but now contain more realistic behavioural assumptions. To reiterate, the realism of these new assumptions is typically not tested against how individuals actually behave in the real world of decision-making. A point of commonality between the bounded rationality approach, the broader smart agent approach and the heuristics and biases approach is recognizing that real-world decision-makers typically do not behave like the individuals in the traditional economic models. We should note that Gary Becker (1996), for example, makes a similar point with regard to neoclassical models ignoring sociological variables to their analytical and scientific peril. He argues that neoclassical predictions are often wrong because they systematically ignore how social context impacts the decisions of rational agents. Douglass North (1971) makes a similar point with respect to neoclassical economics systematically ignoring the importance of institutional variables to decision-making by rational agents.

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Especially with respect to the heuristic and biases approach, a large scholarly industry has developed documenting the extent to which actual human behaviour deviates from predicted neoclassical behaviour. More generally, experimental economics, often done in classroom settings, has documented significant deviations from neoclassical norms. The fact that individuals tend not to behave neoclassically is no big surprise, even to many neoclassical economists. The latter simply assume that individuals behave as if they make decisions and choices based on neoclassical norms, not that they actually behave in this fantastical manner. Still this research remains important as it disabuses economists (theoretical and applied), model users and various types of practitioners, including policy-makers, from the notion that humans behave neoclassically. The big question is what does this actually means for analysis and policy? Experiments suggest that, on average, individuals engage in a wide array of behaviours that are contrary to what conventional economics assumes. For example: ● ● ● ● ● ● ● ● ● ● ●

Individuals weigh losses more than gains. Emotions and intuition drive much of decision-making. Individuals are willing to self-sacrifice to punish those who they deem are treating them unfairly. Individuals are willing to punish or hurt those they don’t like. Individuals are willing to self-sacrifice for those towards whom they feel sympathy. Ethical concerns play a role in economics decision-making. Wealth maximization, even when controlled for risk, finds many exceptions. Framing affects choices. Relative positioning often matters more than absolute levels of income or wealth. Sentiment or animal spirits often matter more to decision-making than ‘real’ economic variables. Individuals often follow the leader when making decisions (herding).

Are these ‘average’ human traits a sign of hardwired cognitive biases, yielding suboptimal choices, as the heuristics and biases approach intimates? Or, are these characteristics of smart agents given their capabilities, experience and decision-making environment, even when some of their decisions are wrong, at least in the first instance (a one-shot game)? This is where the smart agent or smart decision-making approach and bounded rationality approach part company with the heuristics and biases approach. From the smart agent approach, deviations from neoclassical norms typically imply that rational decisionmakers do not abide by these norms for good rational reasons that need to be identified and understood to better engage in robust causal analysis. From the heuristics and biases approach, deviations from the neoclassical norms imply systemic biases and errors in decision-making, typically a function of how the brain is hardwired. Humans do not and typically cannot behave the way they should behave to obtain optimal outcomes. Free will in decision-making can result in perverse socio-economic outcomes that can sometimes be corrected by experts nudging individuals to behave in the appropriate fashion as defined and articulated by the expert (referred to in the literature as the choice architect) (Thaler and Sunstein 2008). From the smart decision-making perspective, errors in decision-making can and do

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Introduction to smart decision-making 5 occur. There can be biases in decision-making, individuals can make decisions that are not in their own self-interest or they can make decisions in their self-interest but not in the interest of their group, organization or society, and preferences can be inconsistent across individuals and within an individual across historical time. All such non-traditional behaviours can be consistent with the hypothesis that economic agents are smart and, broadly speaking, rational. Moreover, these smart agents need not generate choices that are in any sense efficient. This is in stark contrast to the conventional approach wherein being ‘rational’ implies efficient outcomes. However, rationality need not imply efficiency or optimality in either consumption or production. Not conforming to neoclassical behavioural norms need not be symptomatic of irrationality, and free will in choice behaviour in and of itself need not result, therefore, in perverse socio-economic outcomes. Errors and biases and suboptimal socio-economic outcomes, for example, can be the product of inadequacies in decision-making capabilities, suboptimal decision-making environments and lack of experience. In this sense rationality does not mean perfection in actual behaviour or outcomes. Of critical importance is the determination of the conditions under which decisions and the decisionmaking processes can be improved upon; under what circumstances can rational or smart decision-making result in efficiency or optimality in either consumption or production? Identifying these circumstances is a critically important research agenda. Also, non-neoclassical behaviours can generate superior outcomes to those that flow from traditional neoclassical norms, such as narrowly maximizing behaviour. In other words, conforming to neoclassical behavioural norms can generate suboptimal outcomes and might therefore even signal irrationality in behaviour or at least serious biases and errors in decision-making. Gerd Gigerenzer (2007) and his colleagues have articulated this perspective in their fast and frugal heuristics narrative. Heuristics (decision-making short  cuts), often considered to be biased and error-prone in the heuristics and biases narrative, is argued to exemplify superior decision-making processes in the fast and frugal modelling of decision-making. From this perspective individuals have evolved decisionmaking processes that are partially derived from the fact that the brain is a scarce resource, has a particular processing capability and processes information within a particular decision-making environment. A prior assumption here is that individuals are broadly speaking rational. Hence, it is important to investigate whether, and the extent to which, non-neoclassical behavioural norms (such as fast and frugal heuristics) yield superior outcomes, and under what circumstances. At one extreme it could be argued that not only are individuals always rational, but their decision-making processes and decisions are always optimal as well. This perspective is derived from Hayek and his notion of ecological rationality (Hayek 1948; Smith 2003; Gigerenzer 2007). But it is critical to determine benchmarks for smart or broadly rational behaviour and, moreover, contextualized benchmarks for efficiency and optimality in decision-making outcomes. Smart decision-making is not a necessary and sufficient condition for efficiency and optimality. Kahneman (2011) has articulated a categorization of different types of decisionmaking, which he refers to as slow and fast thinking. He is basically looking at when particular thought processes yield better outcomes. Sometimes these might be fast thinking; very often these would be slow thinking. Some would argue that individuals do not know which type of thinking best serves their own self-interest and that of their organization

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and, all too often, individuals make the wrong choices as to which thinking decisionmaking platform to use. This would be contrary to the fast and frugal approach that maintains that typically individuals make the right choices with regard to decision-making platforms. From the smart decision-making perspective, it is a testable hypothesis as to which thinking platform would be best. This hypothesis needs to be contextualized by the capabilities and experience of individuals and their decision-making environment. A critical point here is that the thinking platform the individual should adopt is not determined a priori by the expert or by theory. It is context dependent. The smart decision-making approach has differential implications for policy and approaches for structuring decision-making. The conventional wisdom is, in its extreme, very ‘hands off’ on policy, both in terms of government and even on suggestions of what can be done inside the firm and household to improve decision-making processes and decision-making outcomes. The prior assumption is that ‘free’ markets plus rational agents would generate optimal results. So, government could intervene to make markets ‘freer’ and perhaps to better secure property rights. If individuals are hardwired to be error-prone and biased (the heuristics and biases approach) then intervention must be much more proactive, nudging or more forcefully driving individuals to make what are deemed optimal or at least better decisions. With the smart decision-making or smart agent approach, it is assumed, at least as an analytical starting-point, that individuals are rational. Hence, we need to address issues of capabilities, decision-making environments, experience and externalities to determine what is required to facilitate best practice, but also context informed, decision-making processes. Barring externalities, it becomes critically important to construct decisionmaking capabilities and environments to facilitate and nurture informed decisions, based on the free choice of decision-makers. Therefore, it also becomes important to understand the circumstances under which individuals lose the capacity (or this capacity is severely reduced) to make informed choices, such as possibly severe addictions and mental illness, and perhaps even more importantly the power and even the legal rights to make informed choices. These methodological differences between the smart agent–smart decision-making approach to behavioural economics (related to the concept of bounded rationality), the heuristics and biases approach to behavioural economics and conventional economics are illustrated in Figure 1.1. The smart decision-making approach incorporates and is informed by bounded rationality, process rationality and institutional design. These are informed by a variety of variables, inclusive of human capital, mental models, preferences, information, power and learning. Smart decision-making can result in either optimal or suboptimal outcomes depending on the above economic, sociological and institutional variables. Both these outcomes can be ‘rational’ from the perspective of the individual, but they can generate socially inefficient outcomes. We can have what I refer to as rational inefficiency, but this can be corrected (more often than not) by changing some of the key variables mentioned above. However, benchmarks for what yields optimal outcomes is largely unrelated to neoclassical behavioural norms. Rather, it is reality based. In contrast, the heuristics and biases approach predicts that what is often hardwired behaviour yields deviations from conventional norms for optimal behaviour, that is, from neoclassical rationality. The latter is retained as the gold standard for achieving optimality for the individual, the household, the firm and society. Deviations from the neoclas-

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Introduction to smart decision-making 7 Suboptimality

Optimality

Persistent errors Neoclassical rationality Smart decision-making

Bounded rationality

Process rationality

Conventional methodology

Deviations from neoclassical rationality

Heuristics and biases

Institutional design

Hardwired Human capital

Mental models

Preferences Nudging

Information

Figure 1.1

Power

Learning multi-shot games

Not predicted Information

Decision-making models

sical rationality yield persistent errors in decision-making, hence suboptimal outcomes. This can be corrected by nudging (which can involve varying degrees of paternalism) and, sometimes, by correcting for failures in institutional design. The latter includes improvements to information. Also, the latter as well as institutional design are critically important to the smart decision-maker approach to behavioural economics. Neoclassical models predict neoclassical rationality and optimal outcomes. They do not predict persistent deviations from neoclassical rationality which have been well documented in the literature. This book covers a wide range of themes from micro to macro, sub-disciplines within economics, economic psychology, heuristics, fast and slow thinking, experimental economics, the capabilities approach, institutional and sociological dimensions, methodology, nudging, ethics and morals, and public policy. The book is divided into a number of parts: ‘Smart decision-makers, different types of rationality and outcomes’; ‘Aspects of smart decision-making’; ‘Development and governance’; ‘Tax behaviour’; ‘Smart macroeconomics and finance’; ‘Dimensions of health’; ‘Sociological dimensions of smart decision-making’; and ‘Morals and ethics’. The authors critically explore the modelling, methodological and policy implications of a smart decision-making or smart agent approach to behavioural economics. This alternative approach to behavioural economics, rooted in the tradition established through the research of Herbert Simon and his colleagues, holds much promise, incorporating learning from the bounded rationality approach, the heuristics and biases approach as well as important insights from other disciplines, such as psychological, institutional and sociological analyses and neuroscience.

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REFERENCES Altman, M. (1999), ‘The methodology of economics and the survivor principle revisited and revised: some welfare and public policy implications of modeling the economic agent’, Review of Social Economics, 57 (4), 427–49. Altman, M. (2005), ‘Behavioral economics, power, rational inefficiencies, fuzzy sets, and public policy’, Journal of Economic Issues, 39 (3), 683–706. Altman, M. (2015), ‘Introduction’, in M. Altman (ed.), Real-World Decision Making: An Encyclopedia of Behavioral Economics, Santa Barbara, CA: Greenwood, ABC-CLIO. Altman, M. (2017), ‘A bounded rationality assessment of the new behavioral economics’, in R. Frantz, S.-H. Chen, K. Dopfer, F. Heukelom and S. Mousavi (eds), Routledge Handbook of Behavioral Economics, New York: Routledge, pp. 179–94. Becker, G.S. (1996), Accounting for Tastes, Cambridge, MA: Harvard University Press. Friedman, M. (1953), ‘The methodology of positive economics’, in M. Friedman (ed.), Essays in Positive Economics, Chicago, IL: University of Chicago Press, pp. 3–43. Gigerenzer, G. (2007), Gut Feelings: The Intelligence of the Unconscious, New York: Viking. Hayek, F.A. (1948), Individualism and the Economic Order, Chicago, IL: University of Chicago Press. Kahneman, D. (2003), ‘Maps of bounded rationality: psychology for behavioral economics’, American Economic Review, 93 (5), 1449–75. Kahneman, D. (2011), Thinking, Fast and Slow, New York: Farrar, Straus and Giroux. March, J.G. (1978), ‘Bounded rationality, ambiguity, and the engineering of choice’, Bell Journal of Economics, 9 (2), 587–608. North, D.C. (1971), ‘Institutional change and economic growth’, Journal of Economic History, 31 (1), 118–25. Simon, H.A. (1986), ‘Rationality in psychology and economics’, Journal of Business, 59 (4), S209–24. Smith, V.L. (2003), ‘Constructivist and ecological rationality in economics’, American Economic Review, 93 (3), 465–508. Thaler, R.H. and C. Sunstein (2008), Nudge: Improving Decisions about Health, Wealth, and Happiness, New Haven, CT and London: Yale University Press.

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PART I SMART DECISION-MAKERS, DIFFERENT TYPES OF RATIONALITY AND OUTCOMES

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Rational inefficiency: smart thinking, bounded rationality and the scientific basis for economic failure and success Morris Altman

INTRODUCTION The core argument of this chapter is that individuals (economic agents) can behave inefficiently in a number of domains, at both the micro or macro (social) level. But this behaviour can be considered to be rational in the sense that such inefficiency can be a product of smart or considered choice behaviour. Smart people can be efficient or inefficient. From a smart-rationality assumption, we cannot necessarily derive choices that will have efficient outcomes. Moreover, what might appear to be irrational and, therefore, inefficient behaviour from the perspective of conventional economics might very well be, and often is, rational, smart, intelligent, considered and even purposeful behaviour from a smart agent perspective. Rational or, more generally speaking, smart behaviour should also not be confused with socially rational behaviour. What is rational from the individual’s perspective might very well be irrational from the social perspective as preferences across individuals and social groups might, and typically do, differ dramatically. Maximizing the preferences of a chief executive officer (CEO) need not be consistent with the long-term viability of the firm. Maximizing the well-being of the male partner in a relationship can be inconsistent with maximizing the well-being of the female partner. In addition, it is important to differentiate rational individual choice behaviour from behaviour that is error-free or decisions that are not subject to regret. Making mistakes and regretting these errors in decision-making can be consistent with rational or smart behaviour. Much depends on the decisionmaking capabilities of the individuals and the relevant decision-making environment. This chapter presents a modelling narrative on rational choice behaviour from a bounded rationality perspective. This builds on the pioneering work of Herbert Simon (1959, 1978, 1986, 1987) integrating the concepts of bounded and procedural rationality, and overlaps and is informed by the research and research orientations of Gerd Gigerenzer (2007), Friedrich Hayek (1944, 1945, 1948), Harvey Leibenstein (1957, 1966, 1979) and Vernon Smith (2003, 2005), as well as my own research on the subject (Altman 1999, 2005, 2006a, 2010, 2011, 2012, 2015, 2017). It is also informed by the research of Daniel Kahneman (2003, 2011; Kahneman and Tversky 1979; see also Tversky and Kahneman 1981); the heuristics and biases perspective. However, the smart decision-making approach generates results and orientations that contravene the heuristics and biases approach to decision-making and behavioural economics, which maintains conventional economic benchmarks for rationality and efficiency.

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INTRODUCING RATIONALITY AND RATIONAL INEFFICIENCY To proceed in this narrative we have to clarify what is meant by rationality and by efficiency and what benchmarks we have to meet to be deemed rational and efficient. This remains a gap in the literature critical of conventional economics and, indeed, of the literature critical of the heuristics and biases approach and of the nudging approach to behavioural economics. Conventional economics is relatively clear on what is meant by rationality, what rational behaviours are, and what the expectations are for rational decision-making and its relationship to choices and outcomes. Conventional economics not only has reasonably clear benchmarks for rationality (discussed below), it also predicts rationality in human decision-making and hence in the outcomes emanating from these decisions. However, we should acknowledge that market failures remain a theoretical possibility even within the domain of conventional or neoclassical rationality when negative or positive externalities are present and not internalized by the decision-maker. Overall, conventional economics hypothesizes that rational inefficiency should not occur. This is predicated on the assumption that decision-makers are neoclassically rational (related to conventional economics definitions of rationality, discussed below). Moreover, given the prior assumption of the neoclassical rationality of decision-makers, it is assumed or predicted that the choices made by such rational agents will be efficient (assuming, for simplicity, no externalities exist). Given this overarching assumption of neoclassical rationality, we can predict that an individual’s choices yield optimal or ‘best’ outcomes given the constraints faced by the individual. By the prior assumption of rationality and de facto optimality on the part of individual decision-makers, we end up predicting that outcomes must be efficient and optimal. That which exists is presumed to be efficient by assumption as opposed to determining the extent of efficiency by empirical analyses. In this modelling scenario, it becomes possible to presume efficiency and optimality even when they actually do not exist. This can detract scholars from actively pursuing an analysis of the actual state of affairs, be it efficient or not, and its specific determinants. Note that in this approach, rationality is defined such that rational choice behaviour yields efficient outcomes. A core argument in this chapter is that smart decision-making is rational, but not necessarily neoclassically so. Rational behaviour can be inconsistent with ‘neoclassical’ behaviour and need not yield efficient outcomes. The conventional methodological approach fits nicely with what is referred to as the Humean fallacy, articulated in the A Treatise of Human Nature (Hume 1738 [2014], p. 576). Hume raises the problem of individuals deducing from what is, what ought to be (efficiency) and then attributing particular causes to the assumed efficiency, in this case a particular type of rationality. Since these deductions are not empirically based, they represent fallacies according to Hume. In fact, assuming that outcomes are necessarily efficient when choices are neoclassically rational is merely a testable hypothesis. This Humean fallacy is rooted in the dominant methodology in economics best articulated by Milton Friedman (1953, pp. 21–3) in his classic work on the praxis of positive economics. He argues that economically efficient outcomes are invariably the product of neoclassically rational behaviour. Hence only particular types of choices yield efficient outcomes. The derivative of this is that we do not have to investigate how actual humans behave or the process by which choices are made. It is good enough to assume that indi-

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Rational inefficiency 13 viduals behave as if they are neoclassically rational. Why? Because only efficient agents can survive on the market. If they survive they must be efficient. This efficiency can only be a product of neoclassically rational behaviour inside the firm. ‘Natural selection’, additionally, forces neoclassical rationality (in this case, what Friedman refers to as maximization-of-returns consistent behaviour) to dominate the behaviour of firm members and more specifically the decision-makers inside the firm. Therefore, the evidence in favour of rationality, joined with efficiency, is revealed by the survival of existing firms. Moreover, the persistence and dominance of the maximization-of-returns cum rationality assumption in the literature, both scholarly and popular, aided and abetted by no ‘credible’ alternative hypothesis explaining firm survival is, according to Friedman, further evidence of the scientific validity of the maximization-of-returns assumption. This type of argument is also developed in Alchian (1950) who argues that market forces create an environment wherein efficient choices are imposed on decision-makers, at least those with a preference for surviving on the market. There is no need for individuals to attempt to explicitly or carefully maximize profit of utility. Firms that survive are relatively efficient, because they survive. Hence, the behaviour of firm decision-makers must be consistent with such outcomes. This line of argument can be situated within the analytical domain of a Humean fallacy; any behaviour consistent with survival is considered to be acceptable and appropriate. For Alchian (1950), survival of the firm is evidence of relative efficiency, but there is no theory of human choice behaviour to benchmark which type of behaviours can yield, or should yield, relatively efficient outcomes. As with Friedman, there is little interest in how individuals actually behave. What is of concern is that any such behaviour yields economically efficient outcomes, at least in the long run. Alchian argues (1950, p. 213): Realized positive profits, not maximum profits, are the mark of success and viability. It does not matter through what process of reasoning or motivation such success was achieved. The fact of its accomplishment is sufficient. This is the criterion by which the economic system selects survivors: those who realize positive profits are the survivors; those who suffer losses disappear. The pertinent requirement – positive profits through relative efficiency – is weaker than ‘maximized profits,’ with which, unfortunately, it has been confused. Positive profits accrue to those who are better than their actual competitors, even if the participants are ignorant, intelligent, skilful, etc.

The conventional world view (and there are variations of this) is that individuals either behave in a fashion consistent with neoclassical rationality or they behave as if they are so doing. Ultimately it is expected that the outcomes will be economically efficient or utility maximizing either because market forces will guarantee this outcome or because individuals are hardwired to behave in this manner. The latter stronger assumption is all too often made. Typically, this is done implicitly. The end result is that a dominant prior assumption in the conventional wisdom is that outcomes will be economically efficient or utility maximizing. Moreover, it is assumed that because individual neoclassical rationality results in micro-level economic efficiency, this morphs into macro-level or social economic efficiency. It is then no longer analytically important to determine how individuals make their choices and which choices are made, or whether or not outcomes are in some identifiable sense efficient. Even institutional design and policy lose their importance if neoclassical rationality is predicted to yield economic efficiency in and of

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itself – appropriate institutional design and policy are assumed to evolve naturally in an accommodating manner. Mancur Olson elaborates on this point with regards to social or macro-level economic efficiency derived from assumptions pertaining to micro-level neoclassical rationality and economic efficiency. Olson (1996, pp. 4–5) writes: The idea that the economies we observe are socially efficient, at least to an approximation, is not only espoused by economists who follow their logic as far as it will go, but is also a staple assumption behind much of the best-known empirical work. In the familiar aggregate production function or growth accounting empirical studies, it is assumed that economies are on the frontiers of their aggregate production functions. . . If the ideas evoked here are largely true, then the rational parties in the economy and the polity ensure that the economy cannot be that far from its potential, and the policy advice of economists cannot be especially valuable.

As evidenced above, critical to this neoclassical or conventional rational economic efficiency perspective is the assumption that the survival of economic entities is proof of economic efficiency and, correlated to this, economic efficiency is demonstrated by survival being proof of rationality. This survival principle builds upon the assumption that only efficient economic entities, which also happen to be rational, can survive in the market. To the extent that inefficient economic entities can survive in a moment in time (cross-sectionally) and over time, then survival can no longer serve as proof of efficiency or neoclassical rationality – critical to the conventional efficiency-rationality narrative. Survival would imply neither efficiency nor rationality. Moreover, if neoclassical rationality is not necessary to economic efficiency, then economic efficiency is not proof of economic agents being neoclassically rational. Another point that is important to note, and which will be elaborated on further below, is that the rationality–inefficiency–efficiency narrative can be applied to the realm of consumption or consumer behaviour. Conventional economics assumes that the revealed preferences of consumers through their choices off and on the market coincide with their true preferences – their wants and desires. In the realm of consumption this assumption represents an important aspect of consumption efficiency. Moreover, it is assumed that the process by which choices are made are consistent with the carefully calculating and prescient behaviour of consumers assumed in conventional economics. Hence consumption efficiency presumes the identity between revealed preferences and true preferences and these preferences being actualized within the parameters of neoclassical behavioural processes. However, even if we assume neoclassical processes, this is not sufficient to guarantee that revealed preferences are identical to the true preferences of decision-makers. This prior assumes that neoclassical processes are indeed the most effective means for achieving preferred ends. This is typically not the case in the real world of complex, costly and asymmetric information. The assumption equating revealed and true preferences builds implicitly upon very strong and unrealistic assumptions about the necessary conditions required for this equality to hold. Institutional parameters are critical in determining the extent to which revealed preferences are below optimal preferences. Moreover, unlike the reference to firm rationality and efficiency, market forces cannot guarantee that individuals should adhere to neoclassical behavioural decision-making protocols. There is no so-called survival of the fittest in the domain of consumption, even if we accept the

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Rational inefficiency 15 assumption that competitive forces can drive such neoclassical outcomes in the domain of production (Altman 2010). From the perspective of the heuristics and biases approach (Kahneman and Tversky 1979), there would be such suboptimal consumer behaviour, but this would be a product of the hardwired cognitive limitations of decision-makers, not the ‘inefficiency’ of institutional parameters, for example. Also, we expect suboptimal behaviour to be the rule, not the exception. This is most clearly elaborated and expressed in the Nudge approach to decision-making, which is well articulated in Thaler and Sunstein (2008). The more Simon-related behavioural economists situate consumer decision-making (as they do all types of decision-making) in the ‘environmental’ space, broadly speaking, within which decision-making takes place. Given this space, outcomes are considered to be optimal even though the realization of such outcomes does not follow neoclassical decision-making processes. This is now referred to as ecological rationality. Different decision-making processes (non-neoclassical processes) are expected to generate these optimal outcomes in the realm of consumption. So, what might appear to be irrational or error-prone and biased behaviour from the perspective of both the conventional wisdom and from heuristics and biases approaches could very well be both rational and optimal given the decision-making environment. This particular approach championed by Gerd Gigerenzer (2007), referred to as fast and frugal heuristics and ecological rationality, has roots in the work of Herbert Simon. Still, it remains an empirical question if, when and where particular heuristics yield optimal outcomes from the perspective of the individual or society. However, clearly, the benchmarks for what is consumer efficiency and rationality and what are the expected outcomes of the decision-making process are quite different in the conventional wisdom compared to what would be the case from the various perspectives in behavioural economics. Also, of significance to the smart agent approach to decision-making is an understanding of how sociological variables impact on choice behaviour, affecting the constraints and opportunities that frame the decision-making process and the choices made by economic agents. What is rational choice behaviour must also be contextualized by sociological variables such as peers, families, social norms and culture, for example (Becker 1996; Akerlof and Kranton 2010). Changing the social context of the choice environment impacts on what choices a smart individual will make. More generally, the conventional world view as well as most other methodological perspectives in economics, inclusive of the ‘new’ behavioural economics (heuristics and biases and related nudging approaches) and heterodox modelling, often also tend to rely, analytically, on the typical agent, household and firm where these are supposed to be the equivalent of representative agents. Where it is assumed that all economic agents are neoclassically rational and economically efficient, it is the typical agent, household and firm that is assumed to be so. This typical agent more often than not implicitly or explicitly refers to the average behaviour of economic agents, households and firms. However, the average cannot represent typical behaviour unless most individual behaviour is identical to the average. This assumption is unlikely to be true and cannot be assumed to be true without empirical validation. A critical focus on the typical or average is clearly articulated by Alchian (1950) – attention is placed on accurately predicting average outcomes and then imputing economic efficiency from these outcomes. However, if the objective is to determine the extent of economic efficiency and its

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determinants (very much a function of the choices made by agents given their constraints, opportunities and capabilities), we have to go beyond the average and analyse the various empirical slices that comprise the average. There might be slices that comprise the average or typical that are efficient and others that are not; and there might be different means by which economic efficiency is achieved, which are smart but not neoclassically rational. Moreover, within each analytical slice, agents might be facing different opportunities and constraints and might possess different capabilities. These will affect what we mean by rational or smart decisions. The latter must be contextualized by the decision-makers’ overarching decision-making environment.

INSTITUTIONS, EFFICIENCY AND RATIONALITY Institutional frames are vitally important to a discussion of rationality and efficiency at both the micro and the macro (social) level. Whether or not decision-making is rational, and the extent to which efficiency is achieved, can only be determined if the decisionmaking process and the choices that flow from this process are contextualized by the institutional environment within which decisions are made. This is typically not done by conventional economists or even by economists off the mainstream. Not only must decision-making be institutionally contextualized, the framing must be empirically based. This entails that the framing must be derived from the actual institutional parameters within which the decision-making process takes place. We cannot assume that optimal institutional parameters are in place or will evolve willy-nilly. This is a point addressed by Simon (1987), articulating the importance of institutional parameters for decisionmaking processes and outcomes. Douglass North, one of the founding ‘fathers’ of what is often referred to as the new institutional economics, critiques conventional economics for paying no attention to the role institutions play in affecting choice behaviour and thereby economic outcomes, especially when decision-making is a dynamic process taking place through historical time. This is exactly the type of environment within which decision-making is embedded. Conventional theory is more concerned with stipulating equilibrium conditions given a particular institutional environment; often conditional upon an assumed institutional design that yields optimal economic outcomes. North (1994, p. 359) remarks: Neoclassical theory is simply an inappropriate tool to analyze and prescribe policies that will induce development. It is concerned with the operation of markets, not with how markets develop. How can one prescribe policies when one doesn’t understand how economies develop? . . . The very methods employed by neoclassical economists have dictated the subject matter and militated against such a development. In the analysis of economic performance through time it contained two erroneous assumptions: (i) that institutions do not matter and (ii) that time does not matter.

North (1991, p. 97) provides one possible definition of institutions and it would be this framework that North argues is ignored or assumed to be analytically irrelevant to cogent economic analysis: ‘Institutions are the humanly devised constraints that structure political, economic and social interaction. They consist of both informal constraints (sanctions, taboos, customs, traditions and codes of conduct) and formal rules (constitutions, laws, property rights).’

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Rational inefficiency 17 The reason why institutions are of analytical importance, argues North, is because they affect the incentive environment within which decision are made. For example, relative prices and relative opportunity costs of various types are conditional upon institutional parameters. North writes (1991, p. 97): Institutions and the effectiveness of enforcement (together with the technology employed) determine the cost of transacting. Effective institutions raise the benefits of cooperative solutions or the costs of defection, to use game theoretic terms. In transaction cost terms, institutions reduce transaction and production costs per exchange so that the potential gains from trade are realizeable [sic]. Both political and economic institutions are essential parts of an effective institutional matrix.

North continues that optimality in outcomes is not inevitable even if one assumes neoclassically rational agents. The types of institutions that are constructed, monitored and enforced determine outcomes. North remarks (1991, p. 110): When economies do evolve, therefore, nothing about that process assures economic growth. It has commonly been the case that the incentive structure provided by the basic institutional framework creates opportunities for the consequent organizations to evolve, but the direction of their development has not been to promote productivity-raising activities. Rather, private profitability has been enhanced by creating monopolies, by restricting entry and factor mobility, and by political organizations that established property rights that redistributed rather than increased income.

In North’s take on institutional economics, institutional design plays a pre-eminent role in determining the choices taken by decision-makers. Institutions, therefore, play a critical role in determining whether the outcomes of these institutionally derived choices are economically efficient. It is important to reiterate that North’s decision-makers can be neoclassically rational. However, such rationality need not generate economic efficiency at a micro level or at a social level, but given the institutional parameters imposed by a particular institutional design, such rationality would be utility maximizing, at least broadly speaking, from the perspective of the decision maker. North makes the case for utility maximizing rational inefficiency, contingent on whether or not institutional design incentivizes such inefficiency. Another pre-eminent economist, often associated with the conventional world view, also makes the case for rational inefficiency, contingent upon institutional design. Mancur Olson argues that the evidence is overwhelming that there are trillions of dollars lying on the sidewalk – something that should not occur in a world of rational wealth cum utility maximizing agents (1996, 19): The evidence from the national borders that delineate different institutions and economic policies not only contradicts the view that societies produce as much as their resource endowments permit, but also directly suggests that a country’s institutions and economic policies are decisive for its economic performance.

A key point made by Olsen is that even given the assumption of individual-based neoclassical rationality, societies can be socially inefficient and socially ‘irrational’, and they are socially irrational because they are socially inefficient. Institutional design determines the extent to which neoclassically rational agents generate socially inefficient economic

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outcomes. He guestimates that such rationally inefficient outcomes are more the rule than the exception. Olson writes (1996, p. 23): ‘Some important trends in economic thinking, useful as they are, should not blind us to a sad and all-too-general reality: as the literature on collective action demonstrates . . . individual rationality is very far indeed from being sufficient for social rationality.’ At a very basic level the new institutional economics is incompatible with the core modelling assumptions of conventional economics. It makes the point that economic inefficiency can be rational and that economic efficiency requires particular institutional parameters to be in place. We can have billions, if not trillions, of dollars lying on the sidewalk even in the long term without rational or smart people picking these up. The incentive environment need not be appropriate for socially optimal behaviour to take place among utility maximizing individuals. Private utility maximization, which can take the form of rent-seeking behaviour, for example, can be consistent with social inefficiency. An economic agent, a decision-maker, might be maximizing utility, operating along her or his utility function, while the economy is operating in the interior of the economy’s production possibility frontier. Given a person’s utility function and given the institutional environment, it makes sense for the individual to maximize utility and profits through redistributing wealth as opposed to wealth creation. Taking this argument further, derivative of the ‘old’ institutional economics, I argue that such suboptimal (inefficient) outcomes could easily and predictably take place even with more appropriate (lower) transaction costs and more secure private property rights where agents are relatively secure that their legal gains from trade or their assets will not be arbitrarily confiscated by the state or by private agents. The capabilities of individuals, the preferences of decision-makers and the power relationship between decision-makers and potential decision-makers can impact the efficiency of economic outcomes, even given effective property rights and competitive market structures being in place. For example, inefficiency producing preferences, which would be a consequence of a preference for managerial slack (firm inefficiencies) or rent-seeking (social inefficiencies), can dominate even across different institutional parameters (Altman 2005). This social and power perspective to institutional economics is also absent not only from the new institutional economics, but also from the current and various perspective in behavioural economics.

DIFFERENT TYPES OF RATIONALITY It is important to have an understanding of what conventional economists tend to agree are the behavioural norms for optimal behaviour; that is, behaviour that yields efficient economic outcomes. Not everyone would completely agree on what these norms are. However, there are certain core assumptions that are often made reference to by both neoclassical and conventional economists, and by behavioural economists. What I outline below is not a straw ‘man’ that’s easy to attack and shoot down, and there are variations and modifications to this narrative. However, many would agree that the following is representative of the assumptions underlying much of conventional economic modelling: 1.

Individuals can and do make consistent choices across all possible bundles of goods and services and through time.

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Rational inefficiency 19 2.

3.

4. 5. 6.

7.

It is assumed that all individuals have a thorough knowledge of all relevant available options at any given point in time and they all have the means to process and understand this information in a timely manner – the brain is assumed not to be a scarce resource and individuals’ computational ability is assumed to be unlimited with respect to the decision-making process and problem in hand. Individuals can forecast the implications of their decisions through time and hence calculate, at least in a measurable probabilistic sense, the consequences of their choices. Individuals are assumed to make choices across alternatives that maximize utility or well-being, hence choices should not be subject to regret. It is typically assumed either explicitly or implicitly that, controlling for risk, utility maximization is consistent with wealth or income maximization. It is assumed that individuals are effective and efficient calculating machines, or at least they behave as if they are, irrespective of age, experience, education or social context. It is assumed that all individuals independent of context should behave in the same calculating manner (following conventional behavioural norms) to maximize utility or efficiency.

Herbert Simon rejects this neoclassical or conventional economics definition of rationality in favour of what he refers to as bounded rationality and, related to this, satisficing. Simon considers the conventional definition to be completely unrealistic and therefore useless with respect to constructing models that can speak to causation (as opposed to correlation) and to actual normative requirements to achieve optimal decisions and choices. Bounded rationality refers to smart or considered choice behaviour in the context of the choice environment and the decision-making capabilities of the decision maker. Hence, for Simon there is no unequivocal benchmark for rationality. It is context dependent and recognizes that decision-making capabilities differ across individuals, firms, households, ethnicities, cultures, religions, regions and nations. Satisficing is doing the best possible with what means are realistically available given the reality of bounded rationality. A key message here is that simply because decision-makers are not behaving neoclassically in their decision-making processes, this does not imply that they are irrational or inefficient. Indeed, behaving neoclassically might very well be irrational given the decision-making environment, yielding suboptimal outcomes (Simon 1978, 1986, 1987). This point is clearly articulated by James March, a close colleague of Simon. Rationality cannot be defined and modelled outside the context of the decision-making environment and the decision-making capabilities of decision makers (March 1978, p. 589): Engineers of artificial intelligence have modified their perceptions of efficient problem solving procedures by studying the actual behavior of human problem solvers. Engineers of organizational decision making have modified their models of rationality on the basis of studies of actual organizational behavior . . . Modern students of human choice behavior frequently assume, at least implicitly, that actual human choice behavior in some way or other is likely to make sense. It can be understood as being the behavior of an intelligent being or group of intelligent beings.

Vernon Smith, a pioneer of experimental economics, makes a related point, basing his understanding of rational behaviour on what works in effectively generating the preferred

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outcomes of decision-makers. Such non-neoclassical behaviour, which we might refer to as satisficing, would be the most rational course of action for smart agents and should form the basis for constructing general norms for best practice behaviour or decisionmaking processes. Smith (2005, pp. 149–50; see also Smith 2003) writes: It is shown that the investor who chooses to maximize expected profit (discounted total withdrawals) fails in finite time. Moreover, there exist a variety of nonprofit-maximizing behaviors that have a positive probability of never failing. In fact it is shown that firms that maximize profits are the least likely to be the market survivors. My point is simple: when experimental results are contrary to standard concepts of rationality, assume not just that people are irrational, but that you may not have the right model of rational behavior. Listen to what your subjects may be trying to tell you. Think of it this way. If you could choose your ancestors, would you want them to be survivalists or to be expected wealth maximizers?

We can also refer to Gerd Gigerenzer (2007), who developed the concept of fast and frugal decision-making. The latter refers to decision-making processes that appear to be efficient in spite of being inconsistent with neoclassical processes. Fundamentally, the argument presented here is that decision-making must be contextualized and evaluated in terms of the decision-making environment and the decision-making capabilities of the individual (Gigerenzer refers specifically to Simon’s conceptualization of bounded rationality). Todd and Gigerenzer (2003, pp. 147–8) argue: ‘[B]ounded rationality can be seen as emerging from the joint effect of two interlocking components: the internal limitations of the (human) mind, and the structure of the external environments in which the mind operates. This fit between the internal cognitive structure and the external information structure underlies the perspective of bounded rationality as ecological rationality – making good (enough) decisions by exploiting the structure of the environment . . . Heuristics that are matched to particular environments allow agents to be ecologically rational, making adaptive decisions that combine accuracy with speed and frugality. (We call the heuristics ‘fast and frugal’ because they process information in a relatively simple way, and they search for little information.) The study of ecological rationality thus involves analyzing the structure of environments, the structure of heuristics, and the match between them.

The foundational behavioural economists, led by Simon, made a point of emphasizing that they do not dispute that human beings acting in the economic sphere (economic agents) are rational. They do not dispute this assumption of conventional economics, but they disagree on how conventional economics defines rationality. On rationality, Simon writes (1986, p. S210): I emphasize this point of agreement at the outset-that people have reasons for what they dobecause it appears that economics sometimes feels called on to defend the thesis that human beings are rational. Psychology has no quarrel at all with this thesis. If there are differences in viewpoint, they must lie in conceptions of what constitutes rationality, not in the fact of rationality itself. The judgment that certain behavior is ‘rational’ or ‘reasonable’ can be reached only by viewing the behavior in the context of a set of premises or ‘givens.’ These givens include the situation in which the behavior takes place, the goals it is aimed at realizing, and the computational means available for determining how the goals can be attained.

Simon further elaborates on rationality with regard to other social sciences, emphasizing that the conventional economics definition of rationality is a significant outlier in the social sciences (Simon 1986, p. S210):

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Rational inefficiency 21 In its treatment of rationality, neoclassical economics differs from the other social sciences in three main respects: (a) in its silence about the content of goals and values; (b) in its postulating global consistency of behavior; and (c) in its postulating ‘one world’ that behavior is objectively rational in relation to its total environment, including both present and future environment as the actor moves through time.

In defining rationality relative to decision-making Simon (1986, p. S211) points out that: The rational person of neoclassical economics always reaches the decision that is objectively, or substantively, best in terms of the given utility function. The rational person of cognitive psychology goes about making his or her decisions in a way that is procedurally reasonable in the light of the available knowledge and means of computation.

Simon elaborates on his concept of bounded rationality, making it more specific and nuanced. This brings him to a discussion of process rationality, which refers to the process of and the procedures used in arriving at a decision given the decision-making environment, the capabilities of the decision-maker and the objectives of the decision-maker. Moreover, process rationality takes into consideration that decision-makers’ understanding of what is best practice or optimal might be misconstrued or flat out wrong, but they rationally act upon such a misperception. Simon (1986, p. S211) argues that: if we accept the proposition that knowledge and the computational power of the decision maker are severely limited, then we must distinguish between the real world and the actor’s perception of it and reasoning about it . . . we must construct a theory (and test it empirically) of the processes of decision. Our theory must include not only the reasoning processes but also the processes that generate the actor’s subjective representation of the decision problem, his or her frame . . . The rational person of neoclassical economics always reaches the decision that is objectively, or substantively, best in terms of the given utility function. The rational person of cognitive psychology goes about making his or her decisions in a way that is procedurally reasonable in the light of the available knowledge and means of computation [it is context dependent].

Bounded rationality, satisficing and process rationality, all fit into a modelling paradigm that has as its core assumption that decision-makers are fundamentally smart. There can be exceptions to this rule. However, of critical importance is that we need to begin the analysis with a premise of smart agents doing the best they can, given their circumstances, their preferences, their understanding of available choices and their understanding of the best or optimal means of achieving their objectives. Deviations from neoclassical behavioural norms should not imply irrationality or inefficiency. More nuanced contextdependent norms need to be constructed for rational behaviour and what this implies for economic efficiency. This also implies a better understanding of how social context, social relationships, social norms and cultural factors, most of which can be reconfigured, impact on the rational choices that individuals make (Becker 1996; Akerlof and Kranton 2010). The ‘new’ behavioural economics, emanating from the initial research outcomes and initiatives of Kahneman and Tversky (1979; Kahneman 2003, 2011; see also Tversky and Kahneman 1981), sets out to develop theories that are better able to describe human behaviour, where often such behaviour is related to economic issues. This heuristics and biases approach rejects the neoclassical prediction that decision-makers will behave in a manner that will generate predicted ‘optimal’ choices. In this vein, for example, they

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developed prospect theory as an alternative to subjective expected utility theory. Certainly, Kahneman and Tversky view their scientific project as bearing down on better describing choice behaviour than conventional economic theory. In the Kahneman and Tversky approach, such descriptive theories are typically related to the behaviour of the average individual. The focus on the average has also been a mainstay of conventional economics. This implicitly assumes that the average is the most appropriate point of reference for descriptive and analytical purposes. This ‘new’ behavioural economics also interprets the ‘average’ individual’s deviations from the conventional economic norms for optimal decision-making to be error-prone and biased, and typically persistently so. On the one hand, this perspective on behavioural economics maintains and adheres to a fundamental premise of conventional economics, that there is particular way of behaving in the economic realm resulting in a particular set of choices and therefore outcomes that are optimal (most, effective, efficient and unbiased). However, it represents a big break with conventional economics in that individuals tend not to behave optimally in a large array of choice scenarios. It is argued that individuals tend to engage in biased and error-prone behaviours; but they do so because they do not conform to conventional or neoclassical behavioural norms. Hence, the heuristics and biases approach retains neoclassical normative benchmarks for efficient and rational behaviour (although little mention is made of the term rational) (Berg and Gigerenzer 2010; Berg 2014). In the bounded rationality or smart agent approach to behavioural economics, errors and biases are not hardwired. There are those individuals with mental disabilities who engage in hardwired-biased behaviour – but these are clearly the exception to the rule. Overall, there are rational reasons that would explain most such biased and error-prone behaviour. At least this is the starting point of the smart agent perspective to economic modelling. What is meant by rational and even by efficiency (at least in the domain of consumption) would be different from that specified by conventional wisdom and by the heuristic and biases approach to behavioural economics (Altman 2017). Two key points need to be made and developed further. One is that it is important to specify or to think through (or model) the conditions under which rational decisionmakers generate either persistent local or social inefficiencies. It is important to also specify the extent to which such rational inefficiencies are a product of preferences of decision-makers, gaps in their capabilities, and/or biases or problems with institutional design. This is true for both the production and the household and consumer space. Modelling the necessary conditions for rational inefficiencies is the mirror image of modelling the necessary conditions for rational efficiencies. The focus of most conventional and behavioural economists has been on the process of achieving efficiencies often based on unrealistic assumptions of rationality, often decontextualized from pertinent institutional parameters. The second key point is the importance of better articulating the benchmarks for rationality and efficiency. For behavioural economists, following on from the Simon or bounded rationality perspective, this is a much more nuanced and complex narrative from what one finds in conventional economics or from the heuristics and biases approach, which rely largely on conventional benchmarks (Altman 2017).

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Rational inefficiency 23

PRODUCTION INEFFICIENCY In production, inefficiency can be defined as not making the best use of resources that are available for the task at hand. Hence, we would be operating inside the production possibility frontier. Or, we would be operating along a production isoquant that is further removed from the origin than it need be. In the latter case we would be using more inputs than required to generate a given level of output. This is also referred to as x-inefficiency in production (following the researches of Leibenstein 1966, 1979) as opposed to allocative inefficiency. The latter is a function of a distortion to relative prices, typically caused by oligopolistic market structures and presumed government distortions of the price mechanism. This leads to the misallocation of resources and hence to lower levels of productivity below that which would be the case when market prices are not distorted. However, it appears that allocative inefficiency is only of marginal importance as compared to x-inefficiency (Frantz 1997; Leibenstein 1966). Leibenstein considers x-inefficiency to be a product of irrational behaviour largely because decision-makers deviate from the norms of rational neoclassical behaviour (see also Cyert and March 1963). Leibenstein maintains that x-inefficient firms are a product of decision-makers, such as managers, not maximizing profits or minimizing costs as they should and would if they behaved in accordance with conventional economic norms. However, Leibenstein’s definition of rationality, although consistent with the overarching perspective of the heuristics and biases approach (using neoclassical behavioural benchmarks), is not at all related to whether decision-makers are making smart decisions given their constraints and opportunities and their preferences. Rationality is narrowly defined as it is in the conventional approach and in the heuristics and biases perspective. More importantly, Leibenstein creates an analytical space for persistent economic inefficiency by modelling x-inefficiency as a product of the preference function of decision-makers, where there is a preference for leisure as opposed to maximizing profits and minimizing costs. Here we have a preference function embodying managerial slack, yielding x-inefficiency in production. Decision-makers are, broadly speaking, maximizing their utility which, given their preferences, yield x-inefficiency. An important assumption in the conventional model is that preference functions of decision-makers are consistent with there being x-efficiency in production – firms using the fewest inputs possible to produce a given level of output. In reality, preferences of decision-makers are all too often not consistent with x-efficiency in production. This conventional benchmark for x-efficiency, minimizing inputs per unit of output, is a reasonable one, unlike the assumption of agents being super-calculators with prescience and perfect knowledge (in the relevant decision-making domain). I have argued that preferences inconsistent with x-efficiency (minimizing inputs per unit of output) are consistent with rational or smart behaviour. Agents can be purposeful, deliberative and even calculating, whilst still making choices that yield economic inefficiency (Altman 1999, 2005, 2006a, 2015). Leibenstein introduces the concept of effort discretion into the modelling of economic agents, something that runs contrary to conventional wisdom’s typical exposé of the economic agent. Effort should be, at a minimum, constant or even constant at some maximum according to the conventional wisdom. However, when managers organize the firm such that effort inputs diminish, then productivity falls and, ceteris paribus, average

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cost increases. The firm is better off if it is x-efficient but, in this case, x-inefficiency in production is consistent with the preferences of decision-makers and, hence, with these agents maximizing their utility. Rational or smart agents attempt to ‘maximize’ their utility even if this results in suboptimal outcomes for the firm and society at large. This point can be illustrated in the equation 2.1, representing a simple economy with labour as the only factor input. Fundamental results do not change as we add other factor inputs to the production function. Ac 5

w Q a b L

(2.1)

AC is average cost; w is the wage rate or, more generally, the unit cost of inputs; (Q/L) is the average product of labour; Q is total output; and L is labour input measured in terms of hours worked. Reducing productivity by, for example, increasing managerial slack will, ceteris paribus, increase average cost (AC). Leibenstein assumes that w remains constant in the face of changes to productivity and average cost. Another way to visualize this argument is as follows: De 1 D(Q/L) 1 DAC

(2.2)

Going to the basic point, changes in effort input (e) yield changes in labour productivity (Q/L) which, in turn, yield changes in average costs (AC). In this model, maximizing effort inputs maximizes average product and, thereby, minimizes average cost. This would be consistent with x-efficiency in production. Such effort maximization is possible when the preferences of decision makers are consistent with this particular objective. I argue that effort maximization is rational or smart only under certain circumstances. Hence, economic efficiency (maximum x-efficiency), even among rational agents, should not be assumed as the natural state of things, given that economic inefficiency can be consistent with the preferences of decision-makers. And such preferences cannot be assumed to be irrational simply because they are not consistent with effort maximization. Leibenstein maintains that, given that decision-makers prefer the easy way out (managerial slack), unless product markets are highly competitive, x-inefficiency will persist. Since most markets are not highly competitive, he argues, x-inefficiency should be expected to dominate at different rates in different sectors, with a predicted strong positive causal relationship between more competition and more x-efficiency. However, Leibenstein argues that the political economy of market economies (which includes lobbying) would preclude product markets from being competitive enough for economic efficiency to be achieved. Within the context of imperfect product markets, managerial preferences play a key causal role in determining the extent of x-inefficiency. We can take this one step further. Smart agents and their preferences have a critical role in determining the extent of economic inefficiency because less than maximum levels of effort need not yield higher average costs, hence potentially threatening such firms’ survival on the market. The point made in Altman (2005, 2006a, 2010, 2011, 2012, 2015) is that managerial decisions (the extent of managerial slack, for example) affect the quality

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Rational inefficiency 25 and quantity of effort inputs among the ‘community’ of economic agents that comprise the firm as a unit of production. However, changes to effort levels are a costly process, affecting the levels of compensation to economic agents as well as investments in the quality of the work environment. Moreover, fixed costs are incurred if the system of management is transformed to change the level of productivity. This being said, if effort levels decrease this can be accompanied by lower wages and deteriorating working conditions and we would anticipate higher wages and improvements to working conditions when effort levels increase. From equation 2.1, we would expect w to be positively related to changes in productivity (Q/L). We would anticipate cost offset changes in effort levels. Rational or smart agents, therefore, have significant discretion as to how efficient firms end up being in long-run equilibrium since even with highly competitive product markets, inefficient firms can remain competitive and efficient firms need not have a cost advantage over less efficient firms. In this scenario, even competitive market forces cannot enforce economic efficiency on economic agents where this is incompatible with the preferences of the firm’s decisionmakers. Simply introducing more competitive product markets need not generate optimal economic efficiency. We can end up, as the evidence suggests we do, with firms ranging from highly inefficient to highly efficient even when the highly efficient outcomes are feasible and viable given current institutional parameters. We have multiple equilibrium in outcomes that flow from a multiple equilibrium in preferences (Altman 2016). In this narrative the preferences of members of the firm, whose preferences dominate the decision-making process, become of primary importance. This argument is illustrated in Figure 2.1. In this figure, aLCM represents our cost curve for the conventional firm if wages increase (effort levels are held constant) and for the Leibenstein model when effort levels are unrelated to changes in wages. In both cases the level of x-efficiency is held constant. And, average cost would increase in both scenarios. For the firm to survive, they would need protection, at a maximum of PLPL*.

Protection and multiple equilibrium

P’

P

LCM Extent of protection

d

Average cost

n del a

PL l mo iona t n e onv model ity) st: c variabil e co enstein g a r (effort b t e i c u e v d o L A e pr inal Averag orig PL* a

BM’

BMT

c

BM

Average cost (behavioural model with cost offsets)

0

d

X-efficiency

b

Induced technical change g

Wages and different levels of x-efficiency

Figure 2.1

Multiple equilibrium in production

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Leibenstein’s x-inefficient firms can only survive when such firms are protected either through government policy or through imperfect product markets; but such protection is often afforded to inefficient firms. Alternatively, x-inefficient firms can survive by offsetting lower productivity related higher costs, by reducing labour costs. Along aBM, x-inefficient firms at different levels of x-inefficiency all produce at the same average cost as the x-efficient firm, given at point b. Cost offsets allow for multiple equilibrium with respect to x-inefficient and x-efficient firms. Market forces need not eliminate x-inefficient firms, even in the absence of product market imperfections and government protection, and even in the long run. The other side of the coin is that higher wages and improved working conditions need not generate higher average costs if compensated for by higher effort inputs which, in turn, yield compensating higher levels of labour productivity, here given by aBM’. In this scenario, higher levels of x-efficiency are consistent with higher labour costs. Indeed, the latter might be the cause of the former, forcing a reduction in the level of managerial slack, for example. Such higher labour cost firms can generate further cost offsets if higher labour costs induce technological change, which is illustrated by a shift in the average cost curve from aBM to ABMT (Altman 2009). This modelling narrative is consistent with what is articulated in the traditional prisoner dilemma (PD) model wherein particular ‘common knowledge’ assumptions yield social outcomes that represent a worst case scenario, even given the assumption of neoclassical rationality. In the realm of production, the worst case social outcome is one where productivity or output is at some minimum – the PD solution. It occurs when each participant in the game believes (common knowledge) that the other invests the least possible amount of time and effort in the process of production; maximizes her or his gains. This is consistent with narrowly self-interested maximizing behaviour (neoclassical rationality). In this narrative, we can increase (and maximize) our own individualized benefits by behaving in very narrowly self-interested fashion, if the other party actually contributes more than the anticipated minimum to the process of production. This is the case even though pie size is less than it might be otherwise (x-inefficiency in production). On the other hand, if we choose to behave in a manner that increases the size of the economic pie we risk a reduction in individualized benefits if the other party acts in a narrowly self-interested fashion. If the common knowledge is that the other party will act in a narrowly self-interested fashion, it would be rational to do the same, for only in this way can we minimize any potential losses to ourselves. Only if we change the common knowledge of the other’s behaviour will it be rational to behave in a fashion consistent with the common or social good, increasing the size of the economic pie. With increased pie size, each player of the economic game could see her or his real income increase – everyone is a potential beneficiary. This would be a cooperative solution to the economic problem, in direct contrast with the PD solution. Non-cooperative solutions are possible, as discussed above, when non-cooperative firms are protected from market forces or when they are able to trade-off low productivity with low wages and poor working conditions. Both PD and cooperative outcomes are sustainable and rational, given the preferences of decision-makers and the decision-making environment within which their decisions are made. It also needs mention that the constraints on decision-making within the firm are set by members of the firm hierarchy in the traditional investor-owned firm. If joint preferences of

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Rational inefficiency 27 the firm hierarchy are of the non-cooperative type wherein utility is maximized, the PD solution is inevitable. If a cooperative solution is what maximizes joint utility, then a cooperative solution would follow. In cooperative (worker-owned) firms the joint preferences would veer towards the cooperative solution. Also, power dynamics within the firm can effect which solution dominates. More bargaining power in the hands of workers can, but does not guarantee, a more cooperative solution as members of the firm hierarchy must find the means to increase productivity to offset the increasing direct costs of production that often follow when the bargaining power of employees is enhanced. Also, for firms where employees have a more substantive say on managerial and corporate decisions (a mixed hierarchical model), a cooperative solution is more likely. The same would be the case if owners and managers have a joint preference in favour of more cooperative outcomes (Altman 2002). Further related to the neoclassical assumption of what comprises rational behaviour within the firm, in behavioural-type models of the firm, simplistic formulations of profit maximization or cost minimization, especially in their mathematical presentation, tell us little about what is required for firms to be economically efficient. Being efficient is not a matter of equating marginal revenue to marginal cost. For example, even within the framework of a very simple model, assuming that firm decision-makers can actually and effectively do this calculation in a dynamic fashion, we can equate marginal cost and marginal revenue without effort being maximized. For any given level of effort input we can do this calculation. Hence, the firm could be economically inefficient even when marginal cost equals marginal benefit. The relevant marginal cost and marginal revenue functions would simply be different from what they would be if effort input was maximized. Also, in this type of modelling, the decision-makers would be maximizing their utility at different levels of effort input. The utility maximizing level of effort input is given by the preferences of the decisionmakers. Hence, any model that is scientifically robust must incorporate the conditions under which effort levels inside the firm are higher or lower, since these conditions are critically important for any determination of why and how rational or smart agents generate a particular level of effort input and, therefore, a particular level of productivity. The details of what transpires inside of the ‘black box’ of the firm becomes critically important because it is in the black box that we can deconstruct the methods adopted by decision-makers to achieve their chosen ends. There may also be alternative means to achieve efficiency, all of which might be consistent with the generic and often vacuous normative directive that efficiency is achieved when economic agents equate or behave as if they equate marginal costs to marginal benefits. There are those who argue that a heavily monitored and punitive environment where labour costs are minimized (such as wages and quality of the work environment) serves to maximize labour productivity. However, there is strong evidence to suggest that a more collaborative work environment based on teamwork, trust and reciprocity is better able to achieve economic efficiency. Here there is a more equitable (but not equal) distribution of power and income inside the firm. Both organizational structures and related processes could be rational from the perspective of the dominant decision makers (and their preferences), even though neither adheres to the behavioural processes that fit into the simplistic marginal cost equals marginal benefit narrative of conventional economics (Altman 2002). Related to the conventional prediction that rational behaviour should yield economic

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efficiency, there is the commentary of Richard Posner (2009) on the (2007–08) global economic crisis. Posner was a leading proponent of the efficient market hypothesis and neoclassical rationality as the best way of modelling the economy and the relationship between law and the economy. He shifted theoretical ground to overlap with the Simon or bounded rationality modelling perspective. His perspective also overlaps with the view that rationality should not be interpreted as neoclassical rationality. Among the critical points made by Posner is that decision-makers’ rational behaviour in terms of efforts to maximize income need not take the form of neoclassical processes (they could involve emotion, intuition and herding). All these rational behaviours, however, can cause longrun harm to the firm, even while generating significant short- and even long-run benefits to the individuals engaging in such rational behaviours. Posner (2009, p. 111) elaborates: In sum, rational maximization by businessmen and consumers, all pursuing their self-interest more or less intelligently within a framework of property rights and contract rights, can set the stage for an economic catastrophe. There is no need to bring cognitive quirks, emotional forces, or character flaws into the causal analysis. This is important both in simplifying analysis and in avoiding a search, likely to be futile, for means by which government can alter the mentality or character of businessmen and consumers.

Posner argues that to prevent an economic meltdown, or at least to reduce the probability of one, we should not attempt to re-wire decision-makers so that they behave more neoclassically. Neither should we attempt to re-wire them so that they become less greedy or less narrowly self-interested – which Posner argues is very difficult to operationalize with substantive effect on the economy. To prevent or minimize the probability of narrowly rational income, wealth maximizing or ‘greedy’ individuals (those attempting to maximize their private income or wealth) causing social harm, which incorporates reducing longrun firm real income, wealth and/or productivity, governments must change the institutional environment. This goes beyond simplistic references to improvements to property rights and reducing transaction costs, which is often the focus of the new institutional economics. Also of importance would be providing decision-makers with improved information sets, improved information processing and analytical capabilities, and better understanding of viable organization options (low wage versus high wage, for example), and internalizing externalities to the firm and individual decision-makers (hence reducing the probability of moral hazard). Shiller (2008, 2012) argues for the improvements in the legally enforceable and regulated provision of transparent, accurate and understandable information to be important to a well functioned and socially efficient market economy. In summary, rational inefficiency is a very reasonable outcome given the preferences of dominant decision-makers and the institutional environment within which they are embedded. By acknowledging the possibility of rational inefficiency and its underlying determinants, we can suggest means of achieving more efficient outcomes. Moreover, if we are able to better model the conditions underlying rational inefficiencies, we can better identify when and where they exist as opposed to assuming ex ante that decision-makers make choices that yield economically efficient outcomes. Thus far, I have discussed rationality and efficiency in terms of productive sectors as opposed to rent-seeking sectors of the economy. However, it is important to note that even if all agents behave in an economically efficient fashion, this does not preclude this result-

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Rational inefficiency 29 ing in x-inefficiency in production in the economy as whole. One can have x-efficiency in sectors of the economy that are of a rent-seeking nature, wherein the firm’s wealth is a product of transferring resources from one sector to another or from one individual to another. Here, the non-productive sectors are x-efficient, but the economy as a whole is operating below its production possibility frontier as a consequence of the efficiency in the rent seeking sectors. What is important to note also is that criminal behaviour, lobbying, corruption and war machines that engage in income transfers to the conquering population can be run in an economically efficient manner. Institutional parameters can make such organizational forms more attractive (profitable) to economic agents. Economic efficiency, even when agents are neoclassically rational at the organizational level, in no way necessarily translates into social efficiency. At the extreme, we can have a rent-seeking based society, run by rational agents, that is efficient at the level of the organization but which is socially inefficient. Rationality implies neither efficiency nor inefficiency in production. Rationality also does not imply neoclassical behavioural norms in the realm of production. Smart decision-makers can deliver firm and socially efficient outcomes in production, contingent on the preferences of decision-makers, decision-making and organizational capabilities, and the overall incentive environment; but these conditions all too often do not prevail. From a smart agent perspective, it is a critically important scientific task to identify those conditions conducive to economic efficiency at both the firm and social levels. Smart agents can generate x-efficiency at both the firm and social levels given the appropriate circumstances.

CONSUMPTION INEFFICIENCIES Although I have devoted considerable attention to the rationality–efficiency–inefficiency narrative in the domain of production, contemporary behavioural economics devotes considerable energies to rationality–efficiency–inefficiency in the realm of consumptionrelated behaviour. A fundamental prior in contemporary microeconomic theory is that the revealed preferences of individuals represent their true and, related to this, utility maximizing preferences. Moreover it is assumed that these true preferences can be realized through the choices a person makes given her or his income and given relative prices. I have referred to this as choice x-efficiency (Altman 2010). Conventional economics assumes that choice x-efficiency is the rule in any given society and at any given point in historical time. In brief, true preferences represent those preferences of an individual that are formed in an environment wherein he or she has access to relatively complete and truthful information pertaining to pertinent choice decisions, has the capabilities to process and understand such information, and where this person’s preference formation and choices are not constrained by coercive circumstances. These assumptions are layered over the assumption that individuals are rational in their decision-making process. All these assumptions must hold for choice x-efficiency to prevail. Harsanyi (1982), one of the pioneers of choice theory, makes similar points with regard to the necessary conditions for revealed preferences to equal what we might refer to as true preferences (see also, Altman 2010).

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I have argued that rational or smart choice behaviour requires the prevalence of the above preference formation and choice environment. However, to be realized, such rational choice behaviour only requires boundedly rational behaviour as opposed to the unreasonable and unobtainable prescient and super-calculating behaviour of conventional neoclassical economics. Even smart decision-makers cannot realize choice x-efficiency unless the appropriate preference formation and choice environment prevails. Hence, we can end up, under very reasonable circumstances, with rational inefficiencies (what I refer to as choice x-inefficiencies) in the realm of choice. Even if we can form ‘true’ preferences, rational individuals may not have the power to translate these preferences into choices or revealed preferences. In this scenario individuals are not free to choose. For example, a women may want to have one child, but may be forced into having six, or a parent might want her daughter to learn to read and write but is not empowered to do so, given social norms and legal parameters. Building on a bounded rationality platform, we can model conditions wherein choice x-inefficiencies/x-efficiencies can be obtained. Only under particular institutional/ environmental/social circumstances can choice x-efficiency be realized. Hence we should be able to identify the circumstances under which choice x-inefficiencies (with smart decision-makers) exist and how such circumstances need to be changed for revealed preferences to converge to an individual’s true preferences. It is important to note that true preferences, these utility maximizing preferences, irrespective of how ‘rational’ they might be, need not be socially rational. Choices that cause harm to others can be rational and reveal the true preferences of the individual decisionmaker. This socially suboptimal behaviour represents a form of market failure wherein externalities are not internalized by the individual decision-maker. Such market failures are not part of the conventional narrative even when we can legitimately assume that revealed preferences equal true preferences. This is the case even though the ‘forefathers’ of preference theory recognized this very real possibility (Harsanyi 1982). But market failure of this type can be easily incorporated into a modelling of preference formation and choice realization, as articulated above. In my modelling of choice x-inefficiencies and choice x-efficiencies, as in the conventional wisdom, there is the possibility of revealed preferences being identical to true preferences; but there is also the possibility that this equality need not hold – there is an analytical space for rational choice inefficiencies, irrespective of whether or not one models agency from a neoclassical or boundedly rational perspective. Moreover, there is a possibility that individuals do not have the capabilities and are not in a decision-making environment for true preferences to be formed. There is also the possibility of market failure in the domain of choice. This modelling narrative, therefore, does not accept as a prior working assumption that choice efficiency at an individual and a social level prevails everywhere and always. Its existence and prevalence is an empirical question, very much contingent upon the necessary institutional parameters (inclusive of appropriate power relationships) and individual decision-making capabilities being in place. Freedom of choice philosophically underpins the conventional wisdom’s normative preference for the revealed preference-utility maximizing modelling of decision-making. The individual’s preferences determine choice that, in turn, allows the individual to maximize her or his utility. Here freedom of choice is valued as core to the ability of individuals to ‘maximize’ their utility, their level of well-being. Modelling preference for-

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Rational inefficiency 31 mation and choice from a critical and bounded rationality perspective does not obviate a normative focus on the critical importance of individual freedom for utility maximization (accept when the latter causes harm to others). This overlaps with the critical approach taken by Sen and Nussbaum (Sen 1985; Nussbaum 2011) on this matter applying their capabilities analytical framework. Here, too, freedom of choice is critically important. The problem is that this freedom only exists if the institutional and individual decisionmaking capabilities are present. Once these conditions are met then, in this modelling framework, as in the conventional wisdom, we would predict that individuals’ choices should be ‘maximizing’ their utility or level of satisfaction. However, in the bounded rationality-choice x-efficiency approach, public policy would be required to assure that conditions for choice efficiencies and hence for freedom of choice are met. In the conventional approach it is typically assumed, ex ante, that such conditions are present everywhere and always. This normative approach has been challenged by the stream of behavioural economics linked with the heuristics and biases analytical framework developed by Kahneman and Tversky. A key point made here is that individuals are hardwired to be error-prone in decision-making. The capacity to form and execute our true preferences, therefore, will not preclude persistent errors and biases in decision-making. If anything, such freedom (even assuming that there are no choice x-inefficiencies – true preferences can be realized) can predictably cause more harm than good. This perspective has been most forcefully and poignantly developed and articulated by Thaler and Sunstein (2008) in their nudge approach to behavioural economics and public policy. They argue that there are clear objective benchmarks for what it means for individuals to be better off or maximizing their utility. These benchmarks appear to be universal, running across individuals, but it is argued that these universal benchmarks for utility maximizing, ‘best-practice’ behaviour cannot be realized by the typical individual exercising free choice. This is in part because individuals are not properly hardwired to do so – hence the persistent biases in choice behaviour, resulting in individuals’ choices yielding suboptimal outcomes for the individual decision-maker and society at large. An important assumption in this modelling is that preferences are the same across individuals – homogeneous preferences. Hence what is good for all individuals is based upon what is deemed to be good from the perspective of the expert. Individual preferences do not inform the content of what is ‘good’. The baseline for what is good is largely based on ‘neoclassical’ benchmarks and a depth of knowledge and emotionless understanding beyond the limit of typical human decision-makers. However, it is assumed that the expert has the capabilities, knowledge and understanding to identify what is truly utility or welfare maximizing and the means to achieve this in a most efficient and effective manner. Thaler and Sunstein (2008, p. 176) maintain: We intend ‘better off’ to be measured as objectively as possible, and we clearly do not always equate revealed preference with welfare. That is, we emphasize the possibility that in some cases individuals make inferior choices, choices that they would change if they had complete information, unlimited cognitive abilities, and no lack of willpower.

Critical to this interpretation of what is good for the individual and what is the baseline for the good is choice architecture and the choice architect. An important feature of choice architecture is reconfiguring the choice environment in a manner that induces

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or, in more extreme circumstances, forces the individual to make choices that the expert deems to be in the individual’s best interest. Note that each individual does not have her or his own specific choice architecture. The latter is generic, as all individuals are assumed to be homogeneous in preferences. Actual differences in preferences across individuals are not recognized here (a type of simplifying assumption). The choice architect is the expert who designs the choice environment nudging the individual to make choices that will make her or him better off (higher level of utility, satisfaction or well-being) from the perspective of the choice architect or expert. This would be the case even if the affected decision-maker did not believe that her or his nudged choices increases her or his level of utility or satisfaction – making this person better off. The expert – the choice architect – knows best. A fundamental policy implication of the heuristics and biases approach is that people opposing choice architecture do so because they make the assumption that each individual knows what is in her or his best interest. This assumption is fundamentally flawed from the heuristics and biases approach. That is, this approach contests a fundamental world view of conventional neoclassical economics as well as that of the boundedly rational-smart decision-maker perspective articulated here. In the conventional perspective revealed preferences always (or almost always) reveal the true preferences of the individual, which equates with behaviour that maximizes an individual’s level of utility or satisfaction or well-being. Smart decision-makers would do what is in their best interest if they have the capabilities to form and then to realize their true preferences. However, from the heuristics and biases approach, true preferences are expected to be inconsistent with what is in the best interest of the decision-maker (Thaler and Sunstein 2009, p. 6): [A]lmost all people, almost all of the time, make choices that are in their best interest or at the very least are better than the choices that would be made by someone else. We claim that this assumption is false. In fact, we do not think that anyone believes this on reflection.

It is important to recognize that the nudging perspective contains many elements, some of which are paternalist, and others which are consistent with creating the conditions for the formation and realization of true preferences. However, the focus has been on the paternalist component, inducing or forcing individuals to make choices consistent with the expert’s preferences. An important component of the nudging approach is framing options such that individuals make expert-consistent choices. This could involve forcing organizations to re-frame options available to consumers so that consumers make expertconsistent choices. A fundamental argument put forth by Thaler and Sunstein is that choice options are always framed and that there is always someone who constructs the frame. Little analytical attention has been paid to framing because conventional economics assumes that framing does not affect the choices made by decision-makers. The implicit assumption in the nudging approach is that different frames contain no new information pertinent to a particular decision. Hence, individuals are easily manipulated by changing the framing of a choice option even when the revised frame is not substantively different from the prior frame. A classic example given is that of the framing of pension options. If the default option is not to invest in a pension, then most employees will not invest. However, if the default is to invest, most people will invest. The frame, in this case, is the default option. Simply changing the frame appears to have a huge impact on whether or not individuals

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Rational inefficiency 33 invest in a pension. The assumption is that individuals are indifferent in terms of utility between the two different frames and simply make their choices based on the different frames while their utility remains constant. However, it is further assumed that one of the frames yields choices that make the individual and society better off (they are both individually and socially optimal and welfare improving). Hence the positive view of the interventionist role of the expert, of the choice architect. This apparently clear-cut example appears to demonstrate the case for soft and, even, hard paternalism. However, at a minimum, in a world of complex, asymmetric and even misleading information, and the limited decision-making capabilities of decision-makers (partially based on learning deficiencies), defaults can represent signals to decision-makers as to which choice or choices have the highest probability of making them better off. When the default is not to invest in pensions (especially in a particular pension plan), this signals that experts deem this not to be the best idea, and the opposite if the default is to invest. Hence, the decision-maker is relying on the integrity of those setting the default to inform the decision-maker on which choice might be the best choice. By changing the default from non-investing to investing, the ‘expert’ must assure that the investor knows what he or she is getting into, such as various opportunity costs and risks. Re-framing is not simply changing the frame from one to another wherein no substantive information is being changed. Re-framing typically involves making substantive changes to the information affording to the decision-maker. This is why it is rational for decision-makers to change their behaviour when frames are changed. When the default is not investing, the onus is on the decision-maker to determine what is in her or his best interest, as the expert is not signalling to invest in this instance. Even here, framing becomes important, but not in the sense emphasized by the heuristics and biases or nudging perspective (Altman 2011, 2012; Gigerenzer 2007). How choices are framed is important because frames contain information fundamental to the determination of choice. Hence, for choices to be ‘optimal’ requires that the frame provides the individual with truthful, comprehensible and accessible information so that the individual can make the best possible decision given the choice-set available. This alternative, smart agent approach to framing focuses on providing decision-makers with an environment wherein they can better form their preferred choices and exercise these choices. This approach would not apply to situations when an individual’s optimal choice causes harm to others. Examples of this would be smoking in public spaces, taking heroin while pregnant, being abusive to your spouse and children, closing factories and asset stripping to maximize short-term gain for major shareholders, and cheating and deceiving customers. The smart agent approach focuses on improving the preference formation and decisionmaking environment, while accounting for and incorporating negative and positive externalities in this endeavour. Although there is some overlap between this and the nudging approach, the prior working assumption of the smart agent approach is that individuals’ preferences should, for the most part, be respected and that when choices are suboptimal even for smart decision-makers, they tend to be so for reasons of institutional design, for environmental reasons or for reasons of capabilities. Hence the focus is on institutional design, capabilities development and empowerment of decision-makers. This approach also pays attention to the enforcement of rules and regulations that can contribute towards an improved decision-making environment. From this perspective errors in

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decision-making can and are often made. However, this is related to environmental and capabilities issues as opposed to the hardwiring of the human brain. The expert plays a role in this analytical construct by contributing to improvements in the decision-making environment as opposed to determining which decisions individuals should make. This bounded rationality smart agent approach is libertarian in orientation, but recognizes the importance of various levels of government and expert intervention to improve the overall decision-making environment and the decision-making capabilities of decision-makers as well as developing an incentive environment that accounts for negative and positive externalities. Martha Nussbaum, the co-developer, along with Amartya Sen, of the capabilities approach, makes a similar point. She argues (1999, p. 49): ‘Government is not directed to push citizens into acting in certain valued ways; instead, it is directed to make sure that all human beings have the necessary resources and conditions for acting in those ways. By making opportunities available, government enhances, and does not remove, choice.’ Related to my narrative on choice x-efficiency and smart decision-making, what Nussbaum is referring to is the creation of optimal preference formation and decisionmaking environments, as opposed to experts determining the choices that people should make.

MACROECONOMIC CHOICES AND RATIONAL BEHAVIOUR As with microeconomic behaviour, in the macroeconomic domain, conventional economics makes the case that decision-makers must be neoclassically rational. The evidence suggests this is not how individuals behave and this has had some major repercussions in the construction of macroeconomic theory and for finance theory (Akerlof 2002; Akerlof and Shiller 2009). However, these reconstructions are rejected outright by those who remain strict adherents to the conventional assumptions of rationality combined with assumptions related to flexible factor prices and the capacity of micro decisions having direct and immediate impact in the macroeconomic domain. The latter ‘school of thought’, in their pre-Keynesian incarnation, has been dubbed the classical school, whereas their modern equivalents have been referred to as the new classical school of macroeconomics. Many of the underlying revisions to classical macroeconomic theory were made decades ago by Keynes in his articulation of business cycle theory, more specifically, his theoretical narrative on the making of deep recessions and the mechanism involved in the economy transitioning from a deep recession or depression to recovery. Keynes’s narrative is largely based on the assumption of smart agents making decisions in a world of complex and asymmetric information with asymmetric power relationships across decision-makers. Keynes introduces the notion of ‘animal spirits’ as important to the determination of the timing and depth of recessions and upturns. His narrative suggests that animal spirits as a determinant of decision-making are rational in the sense that decision-makers are doing their best, given their decision-making environment. Hence, Keynes recognizes the importance that non-economic variables can play in determining economic (macroeconomic) outcomes (Keynes 1936). In the conventional wisdom, such non-economic variables are assumed away. Keynes

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Rational inefficiency 35 also recognizes the importance of sticky prices as being a possible and possibly important determinant of recession/depression, given negative demand shocks. However, the relative importance of sticky prices in determining economic downturns, especially severe downturns, is subject to heated debate among those writing in the Keynesian tradition, but there is no denying the empirical significance of sticky prices. Most recently Akerlof (2002), in theory, and Bewley (1999), empirically, have made the case that sticky prices in the face of a negative demand shock are rationally determined. This is based on what is referred to as efficiency wage theory, first modelled by Harvey Leibenstein (1957). Smart agents make local (within the firm) utility and profitmaximizing decisions that have negative macroeconomic consequences, such as persistent unemployment. Firms do not cut real wages for fear that workers will retaliate by cutting effort inputs, thereby reducing productivity. Here effort is a variable in the production function. Employers are also concerned that their best workers will quit, given the opportunity, for what are perceived to be fairer firms, also damaging firm productivity; but workers maximize their utility by taking such action, which is common knowledge to employers. Akerlof considers sticky-price related unemployment to be involuntary. The employed do not want to lose their jobs or keep others unemployed even though their locally rational decisions have this effect. It is important to note that classical economists, old and new, pay no attention to this efficiency wage modelling of unemployment, but they could interpret such behaviour as a reflection of labour’s preference for leisure, at least on the margin. There would be the assumption that in the face of negative aggregate demand shocks, employment would be restored by cutting real wage below where it was prior to a particular negative demand shock. The assumption is also made that workers can determine their real wages as opposed to simply their nominal wages. In terms of the narrative of this chapter, what is critical for causal analysis and policy is whether or not decisions-makers are rational, and the implications of this for analysis and policy. The pre-Keynesian and new classical economics perspectives assume that rational agents would endeavour to clear all markets (prices are flexible) and behave as if prices are flexible. Hence, if unemployment exists or if it increases, this is related to the rational decision to keep real wages too high, for example. Here, unemployment or increases in unemployment are voluntary. There can no substantive demand-side problem, especially in the longer run. The assumption here is that increases in the unemployment rate are a product of changing preferences of workers in favour of more leisure or non-labour market activities or government interventions that make labour markets less flexible and/ or increase the real wage above what would be generated in a ‘pure’ market economy. The increased real wage is predicted to increase the rate of unemployment. Such institutional interventions (such as minimum wages and unions) increase the structural rate of unemployment. Here, too, the demand side is not important. A popular rendition of this perspective was put forward in Friedman’s classic 1968 article, making a case for supply-side determinants of macro outcomes. He focuses on what he refers to as the natural rate of unemployment, which is determined by the structure of real wages. Not much attention is paid to severe negative demand shocks. Ultimately, if workers wanted more employment they should and would cut their real wages. It is assumed that this would not have a knock-on effect of reducing aggregate demand and therefore further increasing the rate of unemployment. Moreover, as unemployment

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increases, even dramatically so, it can be attributed to changing preferences of workers in favour of more leisure time or changing government policy that permanently increases real wages to higher levels – facilitating workers’ preferences for more leisure time. Notice that among the old and new classical economists, and among many Keynesian economists, there is a prior assumption that decision-makers are rational, but the understanding of rationality differs across schools of thought, with significant implications for policy. Across the board, Keynesians regard spikes in unemployment yielding substantive increases in the unemployment rate to be involuntary. These increases in unemployment would be impossible for the market to deal with quickly and efficiently, that is, in the real world of complex and asymmetric information, limited foresight, inflexible prices and the consequential reliance (to a lesser or greater extent) on decision-making heuristics, such as herding. There is no evidence that markets naturally clear swiftly after a severe demandside shock. However, the classical economists assume that this reflects the preferences of decision-makers (there is very little modelling attention paid to different preferences and different power relationships across agents). This adds weight to the argument that our definition of rationality, what it means to be a smart decision-maker, and the realism of our modelling of the decision-making process, is vitally important for causal analysis and, in the macro domain, for public policy. A core Keynesian argument is that increasing demand either through monetary or fiscal policy will restore the economy to full employment in a relatively quick and efficient manner. Hence, the excessive demand-side related unemployment would be eliminated and the economy restored to the prior and lower natural rate of unemployment. The higher unemployment rate that is realized during a depression or deep recession is not the natural rate of unemployment – which is the claim of classical economists, old and new. A critical assumption made by Keynesian economists is that for involuntary unemployment to be eliminated, workers must accept lower real wages, as increasing employment requires the formerly employed less-productive workers (lower marginal product) to accept lower real wages. It is assumed here that a downward sloping marginal product of labour curve, over its relevant portion, characterizes the representative firm, which is a very big short-run assumption indeed. The decreased real wage must coincide with adequate increases in aggregate demand. Classical economists argue that accepting lower real wages would not be the rational response of the typical worker. Hence, increasing aggregate demand can have no real effect on the economy, measured by increased employment. However, the side-effect of such activist demand-side policy would be increased prices or increasing the rate of inflation. Akerlof has attempted to provide a scientific quasi-rational basis for government policy to restore employment towards its pre-recession levels (Akerlof 2002). He maintains that workers in some sense suffer from money illusion (quasi-rationality) and will therefore not pay attention to reductions in real wages that are a function of low rates of inflation. Basically, the transaction costs of computing the impact of low rates of inflation on real wages are not worth the benefits. Hence, increasing aggregate demand to increase employment should be effective as long as we buy into the realism of this transaction-cost based money illusion argument. Decades earlier, Keynes rejected any presumption of money illusion on the part of

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Rational inefficiency 37 workers, although he accepted the assumption that real wages need to be decreased for pre-recession or depression rates of unemployed to be restored. Workers would accept cuts to real wages that were generalized across sectors and occupations, as these would be seen as fair especially when accompanied by increased employment. This could be achieved through aggregate demand-side induced inflation. Workers, themselves, could not orchestrate such a cut in real wages. This would have to be effected through macroeconomic government policy. There is no money illusion here at all. Moreover, Keynes theorizes that self-imposed cuts to money wages would simply reduce aggregate demand, further dampening animal spirits and, thereby, further increasing unemployment. Keynes (1936, pp. 14–15) argues: [T]hey [workers] do not resist reductions of real wages, which are associated with increases in aggregate employment and leave relative money-wages unchanged, unless the reduction proceeds so far as to threaten a reduction of the real wage below the marginal disutility of the existing volume of employment. Every trade union will put up some resistance to a cut in moneywages, however, small. But since no trade union would dream of striking on every occasion of a rise in the cost of living, they do not raise the obstacle to any increase in aggregate employment which is attributed to them by the classical school.

Simply because nominal wages are sticky in no way implies that real wages are not flexible enough in a world of rational (smart) agents, for employment to be restored to prerecession levels through monetary and fiscal policy. Increased longer-term unemployment need not be a product of workers suddenly shifting their preferences towards more leisure but, rather, of misconstrued macro policy that equates sticky nominal prices (especially wages) with sticky real wages. On a related note, given the empirics and theory underlying x-efficiency theory, even if real wages increase as aggregate demand increases, if this is accompanied by compensating increases in labour productivity (a rational response by economic agents), increasing real wages would not impede the employment of more workers as aggregate demand increases. In this case, increasing real wages will not affect the economic capacity of the firm to hire more workers on the margin. The marginal product of the labour curve shifts to the right as real wages increase (Altman 2006b). Here, too, by assuming rational individuals, we cannot logically deduce that increasing unemployment is a function of workers’ preference for more leisure. Rather, a large reduction of aggregate demand requires a compensating increase in aggregate demand, given that rational or smart workers pose no fundamental obstacle to restoring employment to its pre-recession levels. This x-efficiency perspective strengthens the rational worker approach presented by Keynes in his narrative on workers accepting generalized fair cuts to real wages, given the expectation that employment will increase as a consequence. In this instance, rational inefficiency becomes a product of government not pursuing a policy that restores aggregate demand, in the face of rational decision-making at the firm level. The latter is a product of the belief by government decision-makers in the capacity of markets to self-correct and that the ultimate source of the persistence in the increased level of unemployment following a severe economic downturn is the unwillingness of workers to reduce their real wages. This belief in the classical model might be rational given the information set of decision-makers. However, they yield economic inefficiencies

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at the macroeconomic level, keeping unemployment rates unnecessarily high and output well below what it might otherwise be.

CONCLUSION A key argument presented in this chapter is that smart individuals (economic agents) can make decisions that are economically inefficient in the realm of production and consumption, and at both the micro and macro level. Being smart and being rational, from this boundedly rational perspective, does preclude outcomes being inefficient and suboptimal. In the conventional wisdom, rational efficiencies are assumed away at the micro level. Rational agents behaving in accordance with the dictates of neoclassical theory should produce results that are both economically efficient in production and utility maximizing, reflecting the true preferences of decision-makers in the firm and the household in both the realm of production and consumption. However, if individuals were to deviate from neoclassical behavioural norms we would expect inefficiencies in both production and consumption, as they would be behaving irrationally at least from the perspective of conventional wisdom. However, the more empirically based smart agent approach redefines rationality more broadly in terms of smart decision-making. This builds upon the contributions of Simon and the bounded rationality/procedural rationality modelling platform that he developed. Here, a rational baseline for decision-making is predicated on the capabilities of the individual and the decision-making environment. This introduces a different set of norms for what is rational and even for what is efficient. Moreover, in the narrative presented in this chapter, economic inefficiencies can flow from rational or smart behaviour in both the realm of production and consumption. Such inefficiencies can be a function of the preferences of decision-makers and the decisionmaking capabilities of smart individuals and their decision-making environment. In fact, even given optimal decision-making capabilities and optimal decision-making environments, inefficiencies can arise given the preferences of smart decision-makers. Economic efficiency cannot be achieved simply by constructing appropriate decision-making capabilities and environments. The latter two serve as the necessary but not sufficient conditions for economic efficiency. Overall, the different approaches to behavioural economics empirically unmask the fact that individuals typically do not behave as predicted and as is normatively preferred by conventional economics. However, the heuristics and biases approach to behavioural economics, which feeds into and overlaps with the nudging approach, regards such deviations from conventional norms as indicators of suboptimal behaviour, typically hardwired into the human brain. From this perspective, modelling choice behaviour requires investigating and documenting deviations from the conventional norms and determining means of inducing decision-makers to behave in accordance with these norms for optimal behaviour to be achieved. Hence, the heuristics and biases approach, although critical of the conventional assumption that individuals behave ‘rationally’, retain the conventional economic benchmarks for rationality. Building upon the evidence, the argument presented in this chapter is that although smart people do not behave in accordance with conventional economic norms, this should not imply that such behaviour and the choices flowing from it are irrational, suboptimal or

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Rational inefficiency 39 inefficient, given individuals’ capabilities and their decision-making environment. Smart people make boundedly rational decisions. Benchmarks for what is rational, smart and intelligent need to be based upon what makes sense given the decision-makers capabilities and their decision-making environment. There are no specific optimal decision-making norms that apply across time, space and individuals, although there might be general behavioural normative rules of thumb. The approach taken in this chapter, and implicit in Simon’s notion of bounded and procedural rationality, is that individuals can make mistakes and can even be biased, but this is not part of the human condition – hardwired in the human brain. Environmental factors and decision-making capabilities, which can be altered, play a determining role. We can determine the conditions under which optimal decisions can be achieved by individuals, households and firms. Herein lies a critical role for societal (from community to state to international) interventions in economy and society; to facilitate the provision of improved decision-making environments and capabilities. Also, it is important to correct for externalities, positive and negative, many of which are related to information imperfections and coordination failures as well as preferences that, if realized, cause harm to others. Some of the differences and similarities of the different approaches to rationality and their implications for understanding the source and determinants of the relative inefficiencies in production and consumption are illustrated in Figure 2.2. Conventional economics presumes that narrowly defined rationality best explains human behaviour, and yields substantive predictions of production and consumption efficiencies across time and space. Public policy is of limited importance apart from assuring competitive markets and secure property rights. The heuristics and biases approach, while retaining conventional normative benchmarks for optimal behaviour and efficient choice outcomes, documents the persistent deviations from conventional norms. Hence, we have persistent inefficiencies (errors and biases in decision-making), typically a function of behaviours hardwired into the human brain. This yields policy prescriptions designed to nudge individuals towards what experts (choice architects) deem to be in the best interest of the decision-maker. The smart agent approach, building upon the bounded rationality contributions to the decision-making literature, rejects many of the conventional norms for optimal behaviour, while agreeing with the heuristics and biases proponents that humans typically do not behave in accordance with these norms. However, here rationality is defined relative to the capabilities of the decision-makers, the decision-making environment, preferences and power relationships, as well as recognizing differences in these variables across agents and across time and space. In this smart agent modelling, rational agents can make errors and be biased in their decisions, and generate inefficiencies in the domain of production and consumption. But these suboptimal outcomes can be affected by, for example, changes to individual capabilities and the overall decision-making environment. This underlines the significance of public policy in facilitating choices that yield more efficient outcomes while increasing the ability of agents to form and realize their true preferences.

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40

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Figure 2.2

Capabilities and environmental based benchmarks

Capabilities decision-making environment

Deviations from neoclassical norms

Rationality and inefficiency

▪ Improve capabilities and decision-making environments ▪ Improve markets and property rights ▪ Internalize externalities

Policy

Extent of production and consumption inefficiencies

Fast and frugal heuristics

Bounded and procedural rationality

Smart agents

▪ Nudging and reframing ▪ Hard and soft paternalism

Policy

Hardwired errors and biases

Neoclassical behavioural benchmarks

Heuristics and biases

Libertarian: ▪ Minimize government Less libertarian: ▪ More competitive markets ▪ Development and protection of property rights ▪ Internalize externalities

Policy

Production and consumption efficiencies

Assumed neoclassical behaviour

Neoclassical rationality

Rational inefficiency 41

REFERENCES Akerlof, G.A. (2002), ‘Behavioral macroeconomics and macroeconomic behavior’, American Economic Review, 92 (3), 411–33. Akerlof, G.A. and R.E. Kranton (2010), Identity Economics: How Our Identities Shape Our Work, Wages, and Well-Being, Princeton, NJ: Princeton University Press. Akerlof, G.A. and R.J. Shiller (2009), Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, Princeton, NJ: Princeton University Press. Alchian, A.A. (1950), ‘Uncertainty, evolution, and economic theory’, Journal of Political Economy, 58 (3), 211–21. Altman, M. (1999), ‘The methodology of economics and the survivor principle revisited and revised: some welfare and public policy implications of modeling the economic agent’, Review of Social Economy, 57 (4), 427–49. Altman, M. (2002), ‘Economic theory, public policy and the challenge of innovative work practices’, Economic and Industrial Democracy: An International Journal, 23 (2), 271–90. Altman, M. (2005), ‘Behavioral economics, power, rational inefficiencies, fuzzy sets, and public policy’, Journal of Economic Issues, 39 (3), 683–706. Altman, M. (2006a), ‘What a difference an assumption makes: effort discretion, economic theory, and public policy’, in M. Altman (ed.), Handbook of Contemporary Behavioral Economics: Foundations and Developments, Armonk, NY: M.E. Sharpe, pp. 125–64. Altman, M. (2006b), ‘Involuntary unemployment, macroeconomic policy, and a behavioral model of the firm: why high real wages need not cause high unemployment’, Research in Economics, 60 (2), 97–111. Altman, M. (2009), ‘A behavioral-institutional model of endogenous growth and induced technical change’, Journal of Economic Issues, 63 (1), 685–713. Altman, M. (2010), ‘A behavioral and institutional foundation of preference and choice behavior: freedom to choose and choice x-inefficiencies’, Review of Social Economy, 68 (4), 395–411. Altman, M. (2011), ‘Behavioural economics, ethics, and public policy: paving the road to freedom or serfdom?’, in J. Boston (ed.), Ethics and Public Policy: Contemporary Issues, Wellington: Victoria University Press, pp. 23–48. Altman, M. (2012), Behavioral Economics for Dummies, Mississauga, Ontario: Wiley. Altman, M. (2015), ‘Introduction’, in M. Altman (ed.), Real-World Decision Making: An Encyclopedia of Behavioral Economics, Santa Barbara, CA: Greenwood, ABC-CLIO, pp. xv–xxxi. Altman, M. (2016), ‘Multiple equilibria, bounded rationality, and the indeterminacy of economic outcomes: closing the system with institutional parameters’, in R. Frantz and L. Roger (eds), Minds, Models and Milieux: Commemorating the Centennial of the Birth of Herbert Simon, Basingstoke: Palgrave Macmillan, pp. 167–85. Altman, M. (2017), ‘A bounded rationality assessment of the new behavioral economics’, in R. Frantz, S.-H. Chen, K. Dopfer, F. Heukelom and S. Mousavi (eds), Routledge Handbook of Behavioral Economics, New York: Routledge, pp. 179–94. Becker, G.S. (1996), Accounting for Tastes, Cambridge, MA: Harvard University Press. Berg, N. (2014), ‘The consistency and ecological rationality approaches to normative bounded rationality’, Journal of Economic Methodology, 21 (4), 375–95. Berg, N. and G. Gigerenzer (2010), ‘As-if behavioral economics: neoclassical economics in disguise?’, History of Economic Ideas, 18 (1), 133–66. Bewley, T.F. (1999), Why Wages Don’t Fall During a Recession, Cambridge, MA, and London: Harvard University Press. Cyert, R.M. and J.C. March (1963), A Behavioral Theory of the Firm, Englewood Cliffs, NJ: Prentice-Hall. Frantz, R.S. (1997), X-Efficiency Theory, Evidence and Applications, Boston, MA, Dordrecht and London: Kluwer Academic. Friedman, M. (1953), ‘The methodology of positive economics’, in M. Friedman (ed.), Essays in Positive Economics, Chicago, IL: University of Chicago Press, pp. 3–43. Friedman, M. (1968), ‘The role of monetary policy’, American Economic Review, 58 (1), 1–17. Gigerenzer, G. (2007), Gut Feelings: The Intelligence of the Unconscious, New York: Viking. Hayek, F.A. (1944), The Road to Serfdom, Chicago, IL: University of Chicago Press. Hayek, F.A. (1945), ‘The use of knowledge in society’, American Economic Review, 35 (4), 519–30. Hayek, F.A. (1948), Individualism and the Economic Order, Chicago, IL: University of Chicago Press. Harsanyi, J. (1982), ‘Morality and the theory of rational behavior’, in A. Sen and B. Williams (eds), Utilitarianism and Beyond, Cambridge: Cambridge University Press, pp. 39–62. Hume, D. (1738), A Treatise on Human Nature, London, reprinted 2014, Some Good Press Kindle file. Kahneman, D. (2003), ‘Maps of bounded rationality: psychology for behavioral economics’, American Economic Review, 93 (5), 1449–75.

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Kahneman, D. (2011), Thinking, Fast and Slow, New York: Farrar, Straus and Giroux. Kahneman, D. and A. Tversky (1979), ‘Prospect theory: an analysis of decision under risk’, Econometrica, 47 (2), 263–91. Keynes, J.M. (1936), The General Theory of Employment, Interest, and Money, New York: Harcourt, Brace and Company. Leibenstein, H. (1957), Economic Backwardness and Economic Growth, New York: John Wiley and Sons. Leibenstein, H. (1966), ‘Allocative efficiency vs “x-efficiency”’, American Economic Review, 56 (3), 392–415. Leibenstein, H. (1979), ‘A branch of economics is missing: micro-micro theory’, Journal of Economic Literature, 17 (2), 477–502. March, J.G. (1978), ‘Bounded rationality, ambiguity, and the engineering of choice’, Bell Journal of Economics, 9 (2), 587–608. North, D.C. (1991), ‘Institutions’, Journal of Economic Perspectives, 5 (1), 97–112. North, D.C. (1994), ‘Economic performance through time’, American Economic Review, 84 (3), 359–68. Nussbaum, M. (2011), Creating Capabilities: The Human Development Approach, Cambridge, MA: Harvard University Press. Nussbaum, M.C. (1999), Sex and Social Justice, Oxford and New York: Oxford University Press. Olson, M. (1996), ‘Distinguished lecture on economics in government: big bills left on the sidewalk: why some nations are rich, and others poor’, Journal of Economic Perspectives, 10 (2), 3–24. Posner, R.A. (2009), A Failure of Capitalism: The Crisis of ’08 and the Descent into Depression, Cambridge, MA, and London: Harvard University Press. Sen, A. (1985), Commodities and Capabilities, Amsterdam: North-Holland. Shiller, R.J. (2008), The Subprime Solution: How Today’s Global Financial Crisis Happened, and What to Do about It, Princeton, NJ: Princeton University Press. Shiller, R.J. (2012), Finance and the Good Society, Princeton, NJ: Princeton University Press. Simon, H.A. (1959), ‘Theories of decision making in economics and behavioral science’, American Economic Review, 49 (3), 252–83. Simon, H.A. (1978), ‘Rationality as process and as product of thought’, American Economic Review, 68 (2), 1–16. Simon, H.A. (1986), ‘Rationality in psychology and economics’, Journal of Business, 59 (4), S209–24. Simon, H.A. (1987), ‘Behavioral economics’, in J. Eatwell, M. Millgate and P. Newman (eds), The New Palgrave: A Dictionary of Economics, London: Macmillan, pp. 221–5. Smith, V.L. (2003), ‘Constructivist and ecological rationality in economics’, American Economic Review, 93 (3), 465–508. Smith, V.L. (2005), ‘Behavioral economics research and the foundations of economics’, Journal of SocioEconomics, 34 (2), 135–50. Thaler, R.H. and C. Sunstein (2008), Nudge: Improving Decisions about Health, Wealth, and Happiness, New Haven, CT, and London: Yale University Press. Todd, P.M. and G. Gigerenzer (2003), ‘Bounding rationality to the world’, Journal of Economic Psychology, 24 (2), 143–65. Tversky, A. and D. Kahneman (1981), ‘The framing of decisions and the psychology of choice’, Science, 211 (4481), 453–58.

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3

Rational mistakes that make us smart Nathan Berg

[E]very intelligent system makes good errors; otherwise it would not be intelligent. The reason is that the outside world is uncertain, and the system has to make intelligent inferences based on assumed ecological structures. Going beyond the information given by making inferences will produce systematic errors. Not making these errors would destroy intelligence. (Gerd Gigerenzer 2005, p. 199)

I describe theoretical and empirical examples of errors – both in games against nature and in strategic settings – that confer individual-level and, in some cases, Pareto-improving benefits to an entire economy or social system. My goal is to demonstrate the wide range of mechanisms by which we individually and collectively benefit from behaviors that many behavioral economists have been too quick to label as mistakes, simply because those behaviors do not conform to the orthodox rational-choice standard of rationality based on internal logical consistency. I want to invite you to reconsider the interpretations that can and should be attached to behavioral patterns that are commonly described by many behavioral economists as decision-making errors. I argue that mistakes are vital for strengthening and maintaining valuable relationships and enabling perceptual, inferential, social and financial success. Making mistakes ex ante, that is, as part of our planned behavior, is an expected and regular characteristic of smart behavior. Smart people must (that is, descriptively, as a logical consequence of the requirements of success) and should (that is, prescriptively) make mistakes. How could success require mistakes? Are such notions of ‘mistakes’ merely a semantic parlor trick that disappears once proper definitions of success are introduced? Does the claim that smart people must make mistakes sound like a bad joke? In fact, the mistake of telling a ‘bad joke’ (that is, an ill-chosen attempt at humor that unintendedly winds up annoying or offending someone you care about and want to feel good) illustrates the point precisely that smart people must err (cf., the examples and arguments in Gigerenzer 2005).

BAD JOKE Consider what happens if I tell my girlfriend a story that I heard from an entertaining and rough-talking (read ‘severely politically incorrect’) friend of mine. It turns out that my girlfriend does not like the joke and finds it deeply offensive. Normally, she would update her beliefs about any person (for example, me) heard to have spoken those specific words aloud. My girlfriend might, in fact, be interpreted here as a Bayesian updater whose subjective belief that I am a high-quality, worthy person (after conditioning on the historical sequence of speech acts by me that she has observed). Normally, her conditional assessment of my value would decline sharply, conditional on my telling of the bad joke. The joke was so bad that, conditional on observing me tell it, her updating function abruptly 43

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downgrades the level of subjective esteem associated with the speaker (given the finite number of virtuous acts that were previously observed in our shared history). I am assuming that she also uses a threshold condition to accept men worthy of dating (that is, satisficing on mate choice while updating beliefs according to Bayes’ law). Her updated level of esteem for me, conditional on the bad joke, now falls strictly below her minimum threshold required for mate acceptance. She would normally reject me out of hand as a partner based on her usual belief-updating system. The bad joke I told would therefore normally exclude me from her consideration set and lead to a breakup of the relationship already in progress. What should I expect comes next? Instead of breaking up, she decides to forgive me. She says, ‘I didn’t like the words I heard you say, but I forgive you. Please don’t say it again.’ Just like that, something outside the normative performance metrics introduced in the model so far newly enters the analysis. Like bones that heal stronger than in their previously unbroken state or an immune system that heals stronger, apparently relationships, too, can grow deeper, richer, more valuable and stronger – in love, business, science, and friendships of many kinds – thanks to the event of a mistake followed by forgiveness (or other means of relationship repair). I consider several modeling strategies with the goal of representing the mechanism of relationships whose depth or robustness benefits from mistakes and shared adversity in their shared history. Among my reasons for raising this example are the subtleties it raises regarding the methodological conventions of constrained optimization and game-theoretic reasoning that behavioral economists typically use as the benchmark of perfect rationality in relation to which deviations are thought to measure irrationality, a-rationality, or various normative gradations of irrationalities (Berg 2003, 2014a; Berg and Gigerenzer 2006, 2010). Next I will present contrasting representations of this interaction corresponding to different views of other players’ action sets, whether those action sets include the possibility of intentionally versus randomly bad jokes (as a result of ‘nature’s move’); and whether the continuation value of the relationship itself is included explicitly, possibly strengthened as a result of withstanding a threatening event and then recovering thanks to forgiveness and repair.

SMALL WORLD WITH NO INTENTIONAL TELLING OF BAD JOKES AND NO INDIVIDUAL CONTROL OVER PROBABILITY, P Figure 3.1 shows a simple, small world with no possibility of intentionally telling a bad joke. Bad jokes are modeled in Figure 3.1 as a random event detached from any other variable under the joke teller’s control such as effort. Note, too, that there is no explicit model of risk preferences, cautionary motives, pro-social affections or anti-social motives such as spite. The only decision variable that the joke teller, referred to throughout as Agent 1, has to make in Figure 3.1 is whether to tell a joke or not. The second player whose payoffs are represented in Figure 3.1 is Agent 2, the receiver of Agent 1’s joke (for example, my girlfriend in the discussion above). Without loss of generality, both players’ payoffs are normalized to zero at the left-most no-joke node, so that the payoffs associated with the other two terminal nodes, corresponding to bad-

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Rational mistakes that make us smart 45 1

Joke

No joke Nature

Prob (bad joke) = p (b1, b2)

(0, 0)

Figure 3.1

Prob (good joke) = 1 – p (g1, g2)

Small-world event tree with no possibility of intentionally telling a bad or offensive joke, with bad jokes occurring as an act of nature with probability p, 0 0. Assuming from now on that b1 < 0 and b2 < 0, should we then agree with conventional wisdom in behavioral economics that the bad-joke outcome is always best avoided if possible? In Figure 3.1, there is nothing in either player’s choice set that enables him or her to control the probability of the bad-joke outcome. However, if there were, would rationality then trivially require (that is, by definition) that agents avoid making the mistake of telling a bad joke? The next model gives Agent 1 clairvoyance to focus on the question of whether he would ever rationally choose a bad joke he has perfect control to avoid.

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AGENT 1 HAS CLAIRVOYANCE AND THEREFORE PERFECT ABILITY TO AVOID TELLING THE BAD JOKE In Figure 3.2, Agent 1 is clairvoyant. An equivalent assumption is that nature moves first and in a manner that is visible to both players, which determines the quality of the joke before Agent 1 decides whether to tell it. Therefore, Agent 1 knows in advance if the joke will land in Agent 2’s ears as a bad or good joke. An own-payoff-maximizing Agent 1 will never choose to tell a bad joke in the model depicted in Figure 3.2. If a bad joke occurs, then, because the joke teller is clairvoyant, Agent 2 knows that Agent 1 actually intended harm or offence. The bad-joke outcome in Figure 3.2 can never be accidental. Therefore, the possibility of spite or malevolence is now an unavoidable consideration for Agent 2 upon observing the bad joke. The unnatural abstraction of the payoffs from the context of the agents’ relationship shows up starkly and reflects a razor-edge view of what can be rational in Figures 3.1 and 3.2. The missing context of relationship remains in the next representation, which returns to the setup in Figure 3.1 but endows Agent 1 (in Figure 3.3) with the capacity to choose cautionary effort x in a way that reduces the bad-joke probability p(x). Nature

Prob (bad joke) = p

Prob (good joke) = 1 – p 1

1

Figure 3.2

No joke

Joke

No joke

Joke

(0, 0)

(b1, b2)

(0, 0)

(g1, g2)

Nature moves first (deciding whether Agent 1’s joke will turn out to be good or bad) or, equivalently, Agent 1 is clairvoyant

1 No joke

(0, 0)

Figure 3.3

x1 Joke x2 Agent 1 chooses bad-jokeavoidance effort x xi xn Nature Prob (bad joke) = p(x) Prob (good joke) = 1 – p(x) (b1, b2)

(g1, g2)

Joke teller chooses a continuously-valued cautionary effort variable, x P [0, ∞), such that the bad-joke probability is p(x) 5 e–ax

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JOKE TELLER CHOOSES CAUTIONARY EFFORT X SUCH THAT BAD-JOKE PROBABILITY P(X) IS DECREASING IN X Figure 3.3 is an extension of Figure 3.1 (returning from now on to the original assumption of no clairvoyance) in which the joke teller is endowed with a continuously-valued cautionary effort variable, x P [0, ∞), that effectively reduces the probability of a bad joke. Therefore, p(x) is assumed to be a decreasing function of x. For simplicity, the specific functional form p(x) 5 e-ax is introduced as a specific case drawn from the more general family of decreasing (that is, controllable) bad-joke risk functions. Assuming that the unit cost of cautionary effort is measured by parameter k, then Agent 1’s expected payoff objective (conditional on telling a joke) can be written as follows: p(x) 5 p(x)b1 + (1 − p(x))g1 − kx,

(3.2)

which Agent 1 seeks to maximize by choosing x such that telling a joke is better than no joke (that is, p > 0) and that p(x) 5 e-ax. The constrained maximization problem just described has a global maximum at x* 5 [ln(a) + ln(g1 − b1) − ln(k)]/a (in the dense subset of the parameter space of b1, g1 and k satisfying the conditions that x* > 0 and p(x*) > 0). In the models of Figure 3.1 and Figure 3.3, we have not considered Agent 1’s reasoning about Agent 2’s conditions for continuing the relationship or any inferences she makes about Agent 1’s intentionality. The interactions so far represented are one-offs unless the payoff parameters are interpreted as depending on both agents’ valuations of continuing the relationship in future rounds of interaction. Before proceeding fully toward the fundamental issue of modeling the intentionality of the joke teller, I now model Agent 2’s decision to either break off the relationship (that is, not continue) versus continue. Introducing Agent 2’s continuation decision turns out to be enough to generate the possibility of the relationship increasing in value following forgiveness in the bad-joke outcome.

AGENT 2 CHOOSES ‘NO’ OR ‘YES’ TO CONTINUING THE RELATIONSHIP Figure 3.4 shows an extension of the basic model in Figure 3.1 (without a cautionary effort choice x that influences bad-joke probability p), which now includes the possibility of forgiveness and relationship repair in addition to the possibility that Agent 2 chooses to end the relationship by choosing ‘no’. New notation introduced in Figure 3.4 includes each agent’s valuation of the relationship itself, ex payoffs from the joke-telling interaction. The agents’ continuation values whenever Agent 2 chooses ‘yes’ to continue are denoted r1 and r2, respectively. Down the event branch in which a bad-joke outcome occurs and Agent 2 decides ‘yes’ to continue nevertheless, then both agents’ valuations of their relationship increase to R1 and R2, respectively. In keeping with the previous three figures where payoffs represent changes relative to the no-joke state, the continuation value of the relationship does not show up in this representation along the continuation path but instead as a lost continuation value whenever Agent 2 chooses ‘no’.

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Handbook of behavioural economics and smart decision-making Value to R1 > r1 and R2 > r2, respectively 1 No joke

Joke

Nature

Prob (bad joke) = p

Prob (good joke) = 1 – p

2

2

2 No

(–r1, –r2)

Figure 3.4

Yes

(0, 0)

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Yes

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Yes

(b1 – r1, b2 – r2) (b1 + R1 – r1, b2 + R2 – r2) (g1 – r1, g2 – r2)

(g1, g2)

Same as Figure 3.1, but Agent 2 now chooses whether to continue the relationship (‘no’ not continue or ‘yes’ continue), depending on whether her relationship valuation r2 remains positive, with forgiveness of bad jokes having the effect of increasing both agents’ relationship

The loss of the relationship value shows up at nodes where Agent 2 chooses not to continue. At the left-most node, for example, the payoffs are now written as (−r1, −r2) if Agent 2 chooses to not continue and (0, 0) if Agent 2 chooses to continue following the no-joke outcome. If, for whatever reason, Agent 2 perceives negative continuation value, then it is rational for her to discontinue because discarding the negative value achieves a positive payoff relative to continuation (r2 < 0 implies −r2 > 0). At the two nodes following the good-joke branch, the relationship value is assumed not to change and therefore does not show up in the payoffs (g1, g2) but is instead deducted from the payoffs as the lost value of continuing the relationship in the payoffs (g1 − r1, g2 − r2). Presumably, Agent 2 never has any reason to rationally choose ‘no’ following a good-joke outcome so long as r2 > 0. The real innovation and main point of focus in the analysis of payoffs in Figure 3.4 are those that follow the bad-joke outcome and Agent 2’s decision ‘yes’ continuing with the relationship nevertheless. In this case, in addition to the bad-joke payoffs (b1, b2), each agent sees something new about their respective assessments of the value of continuing the relationship. The changes in their relationship values, R1 − r1 and R2 − r2, respectively, are added to the bad-joke payoffs. Note here there is a distinct new possibility that the agents who are most well-off are those who endured the bad joke and recovered from it – or have simply chosen to continue the relationship, thereby revealing to each other greater continuation values than would otherwise have been observable to either player without the failure or mistake. The performance advantage referred to here as ‘most well-off’ could be interpreted as individuals who enjoy the strongest, most durable relationships founded on joint awareness that they are mutually valuable to each other – enough so to withstand a large range of negative-payoff events and nevertheless retain positive relationship value. The ideas here draw on the pioneering work by Rapoport and Chammah (1965), Rapoport (1984) and Axelrod (1984).

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Rational mistakes that make us smart 49 Result 1 In the interaction represented in Figure 3.4, if Ri − ri > bi − gi, then Agent 1 is better off after a bad joke is mistakenly told and Agent 2 chooses ‘yes’ to continue the relationship than Agent 1 would have been without Agent 1 having made the mistake. It follows from Result 1 that, if the inequality holds for both agents, then the mistake causes a Pareto improvement (that is, mistakes can unambiguously increase the size of the economic pie by revealing otherwise latent information about the strength of social ties). The possibility that mistakes can strengthen social ties through such a transformatively positive (that is, relationship-strengthening) act of forgiveness modeled along the bad-joke-‘yes’ path in Figure 3.4 brings with it profound implications. Note, too, that instead of forgiveness, the transformative event that occurs can be interpreted as information revelation – that the mistake simply reveals otherwise latent (that is, unobservable) information about others’ subjective valuations of their relationships with us. From this observation, a large set of new mechanisms that map mistakes into aggregate-value expanding outcomes emerges. For example, if I fail to deliver on a contractual commitment to a key business partner in a repeated interaction, and that partner expresses understanding and agrees to continue even though I see that my mistake imposed large costs on the partner, then I may be willing to take joint risks with that partner that I would not have otherwise. The reason for the shift in willingness to undertake value-generating risk may be that the process of dealing with my past failure and the hardships it caused both of us transformed the relationship or focused our attention on jointly observing the value of our collaboration. Or it could have simply revealed otherwise unobservable information about my partner’s willingness to endure joint losses and remain committed to continuing together, which, in turn, triggers my own willingness to take on new projects where our joint actions expose each other to new risks. I want to make the case that the interaction in Figure 3.4 and its scope for generating welfare-enhancing mistakes can be rather broadly interpreted. Telling a bad joke; failing to deliver on a contractual obligation; or being very late in delivering a promised book chapter to an editor whom I respect greatly, whose friendship is dear to me and whose book project I feel great passion for – these examples are illustrative of smart people’s rational mistakes. I discuss additional examples below. Before considering more examples, I want to begin addressing the as yet unexamined question of intentionality and why the mechanism of welfare-enhancing mistakes generally breaks down if this mechanism is deliberately exploited.

MODEL IN WHICH AGENT 1 CAN CHOOSE TO DELIBERATELY TELL A BAD JOKE If a prototypical Agent 1 looks at the payoffs in Figure 3.4 and perceives that the highest possible outcome is indeed the path along which a bad joke occurs and Agent 2 forgives, then could Agent 1 rationally pursue this outcome as his goal? Certainly if Agent 2 knew that Agent 1 were hurting her intentionally in order to coax her into forgiving and revealing her high latent value for continuing with him, then she would likely modify her assessed continuation value of the relationship downward. If Agent 1 is not sociopathic,

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then he likely experiences some guilt (denoted g > 0, representing Agent 1’s psychic cost of deliberately telling the bad joke or otherwise intentionally hurting Agent 2). To represent intentionality, I introduce the notation w P{B, G} to code Agent 1’s intention to tell a bad or good joke. Figure 3.5 depicts, by now, the rather elaborate joke-telling interaction featuring two distinct bad-joke branches which correspond to intentional versus accidental bad jokes. The dotted oval represents Agent 2’s uncertainty: when she observes the bad-joke outcome, she does not know whether Agent 1’s intention was to tell a bad one or not – intentionally offending and hurting her, or, alternatively, intending to tell a good joke that led accidentally to causing offense or hurt. Because Agent 2’s valuations of continuing the relationship now depend on, and vary with, intentionality type w (through the functions r(w) and R(w)) while holding constant the bad-joke outcome, the model therefore becomes non-consequentialist. That is, agents’ subjective rankings depend not only on the final outcome but also on the process that led to that outcome. Figure 3.5 expresses payoffs corresponding to each of the two bad-joke outcomes that differ only in Agent 1’s intention w. But Agent 2 is not clairvoyant and does not know Agent 1’s intention (or intentionality type w) with perfect certainty. In the second dotted oval below the main payoff nodes, Figure 3.5 also provides Agent 2’s expected payoffs (in her state of uncertainty about w), which depend on Agent 2’s probabilistic belief b that Agent 1 is a bad-intention type. The function R(w) represents Agent 2’s assessment of the value of continuing the relationship with Agent 1 following a bad-joke outcome as a function of Agent 1’s type. The function r(w) represents Agent 2’s assessment of the value of continuing the relationship with Agent 1 along any other discontinuation or continuation path that does not involve the bad-joke outcome and forgiveness. Note that when Agent 1 is a bad-intention type, both continuation values are assumed to take on very negative and nearly equal payoff values: R(B) 5 r(B) r(G) > 0 and R(G) − r(G) > 0 (when Agent 1 is a good-intention type). In Agent 2’s state of uncertainty about Agent 1’s type, w, and having observed the bad-joke outcome, we can compute the difference between Agent 2’s expected payoff from choosing ‘yes’ (to continue) minus her expected payoff from choosing ‘no’ (to not continue), which I denote Dyes−no|bad-joke: Dyes−no|bad-joke 5 (1 − b)R(G) + br(B).

(3.3)

Agent 2 (assumed to be an expected payoff maximizer with belief b that measures her subjective probability that 1’s type is bad) chooses to continue if, and only if, Dyes−no|bad-joke ≥ 0 (assuming continuation whenever ‘no’ and ‘yes’ have equal expected payoffs) and discontinue otherwise. That is, Agent 2’s continuation decision in the face of being exposed to the bad joke and uncertainty about Agent 1’s intentionality type turns on the upwardly revised relationship value and Agent 1’s intentionality type being good, R(G), weighted by 2’s belief that 1 is in fact a good type − and then comparing this positive expected continuation value to the negative expected value if 1 were a bad type, r(B), weighted by 2’s belief that 1 is in fact a bad type. Under what circumstances will 1 deliberately cause 2 harm under the expectation that 2 will forgive and continue, thereby yielding a greater player-1 payoff than by trying to tell a good joke: b1 + R1 − r1 − g > p(b1 + R1 − r1) + (1 − p)g1? Recall that 1 effectively chooses his intentionality type w. Figure 3.5 assumes that, if Agent 1 chooses w 5 B, then the

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Figure 3.5

(–r1, –r2)

No

Yes

(b1 + R1 – r1 – γ, b2)

Yes

(b1 – r1, b2 – r(G))

No

2

b2 – r(B) – (1 – )r(G)

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Nature

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Yes

(g1 – r1, g2 – r(G))

Prob (good joke) = 1 – p

(b1 + R1 – r1, b2 + R(G) – r(G))

Prob (bad joke) = p

Intend to tell good joke ( = G)

Agent 2’s expected payoffs after observing a bad joke but facing uncertainty about Agent 1’s intentionality type with belief Prob( = B) =

(b1 – r1 – γ, b2 – r(B))

No

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Agent 1 can deliberately tell a bad joke

(0, 0)

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bad joke

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joke will turn out bad with probability 1. If 1 chooses w 5 G, however, then we are back in the non-degenerate probabilistic world where p measures the probability of the badjoke outcome. A further condition is required if 1 is to believe that 2 will indeed choose to continue. That is, 1 must believe that Dyes−no|bad-joke ≥ 0. The issue arises as to whether intentions are ever conclusively observable. The model here reflects an attempt to model real-world scenarios where distinct sets of intentions are in fact observable. If an agent deliberately misrepresents his or her intentions in a repeated interaction setting, then the modeling exercise here rests on the assumption that such misrepresentation is eventually discoverable (for example, my partner overhearing me tell my friend that I deliberately told a bad joke or showed up late to test the extent of her forgiveness). Agent 2’s preferences are non-consequentialist, a fact made explicit through the dependence of the functions R(w) and r(w) on w. Agent 2’s view of her own payoffs is not invariant with respect to w holding the bad-joke outcome fixed. Agent 1’s intention – to deliberately tell the bad joke (w 5 B) or to at least try to tell a good joke (w 5 G) – matters quite explicitly to Agent 2. The next section applies a slightly different interpretation to the payoff schemes in the figures above to illustrate the general nature of the phenomenon described above in the figures and Result 1, namely, that individually and collectively welfare-improving mistakes are commonplace and broadly distributed throughout the decision environments people face. The integrity of the mistakes, as distinguished in Figure 3.5, matters. There are honest mistakes and fraudulent or deliberate mistakes. Smart agents should, in general, be adept at detecting fraudulent mistakes, although doing so is not necessarily easy in practice. The broader issue is that errors can be welfare improving when they elicit new information or provide opportunities for others to reveal more about the objectives they are pursuing.

YOU’RE LATE! One way I can learn how much you value my work, or my contribution to a joint venture, or our relationship, is by observing your willingness to forgive. I sometimes show up late (or as it turns out, deliver work or other outputs much later than originally promised, thereby testing the patience – completely unintentionally – of dear colleagues, people about whom I care deeply and hold in truly great esteem, for example, by delivering late a chapter for an edited volume on Rational Decision-Making within the Bounds of Reason). In response to my lateness, some colleagues may classify me as unreliable and choose not to engage with me on future projects; others may classify me as (once again) unreliable but nevertheless choose to continue engaging with me, effectively revealing that they forgive me for being late, or that they value my contributions highly enough to offset the substantial costs I unintentionally imposed on them with my lateness, regardless of whether forgiveness is formally expressed. In game-theoretic terms, the act of my colleague effectively forgiving my lateness sends an important signal about their implicit valuation of engaging with me relative to the respective costs that my lateness imposed on them (again, completely unintentionally on my part). Why do I emphasize my a priori intention to not be late followed by ex post lateness (that is, unintended lateness)? Consider re-labeling ‘bad joke’ outcomes in Figures 3.1–3.5

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Rational mistakes that make us smart 53 with ‘late’. The interactions represented in Figures 3.4 and 3.5, for example, can then be reinterpreted as: as long as I am not late, then my payoffs and those of my colleagues correspond to good-joke payoffs, (g1, g2), which represents the normal state of affairs based on the productivity of our relationship with no change in trust, no disappointments and no forgiveness. However, as soon as I violate my colleague’s expectation, the colleague’s decision about continuing can then be interpreted as a signal of forgiveness (or otherwise revealing additional mutual value in continuing). Then something new happens. There is an objective loss to both players: bi < gi for i 5 1, 2. My colleague bears the cost of my lateness equal to g2 − b2. I pay a cost g1 − b1 based on embarrassment, loss of reputation for punctuality, and perhaps stress over future opportunities now at risk. However, our aggregate payoffs are now mutually recognized as being greater – for both of us – if we continue, thanks to the synergistic interaction of both individuals (assuming the condition in Result 1 holds). Acknowledging that these costs of lateness can be, and often are, substantial, what then justifies Result 1 and its possibility of greater payoffs – for both of us – corresponding to the action profile of (late, forgive) with associated payoffs of (b1 + R1 − r1, b2 + R2(G) − r2(G)) > (g1, g2)? The answer must be the existence of an offsetting or compensating deepening of the value of our working relationship, where a signal has now been transmitted showing the intention to collaborate cooperatively into the future within a larger-than-expected space of perturbations in the form of missed expectations of various kinds. Another possibility is more direct: the incremental increase in well-being that follows from an expression of (relatively) unconditional acceptance. What have I learned by being seriously late and then receiving implicit forgiveness? I may have learned that my colleague enjoys interacting with me or benefits to a sufficient degree that he or she is willing to incur higher costs than I had perhaps previously realized to keep the working relationship alive. Given the benefit discovered by lateness and subsequent forgiveness, might I then pursue intentional lateness as a mechanism to force colleagues to reveal signals about their willingness to forgive transgressions and maintain working relationships? No. None of this works if lateness (or the bad joke, or any other setback, mistake, disappointment or missed expectation) is intentional. Suppose I am considering a sequence of lateness decisions, coded as binary for simplicity, with a new person in my life with whom a potentially valuable relationship might unfold. I would like to know how this other person regards me or, more crassly, assesses the potential value of our relationship. In other words, I have positive willingness to pay for a costly-to-fake signal of affection, esteem or some form of perceived value from continued engagement. The other person would also like such a signal from me. Could I test the other person by deliberately being late, or deliberately telling an offensive joke, for example, to get a live observation of the other person’s willingness to forgive? Intentional mistakes are no longer mistakes, however, and this strategy is unlikely to work. Problems include the high risk of being discovered and my guilt or embarrassment (g), not to mention the new risk of being discovered, the possibility of an extremely negative payoff that the other person would perceive if I were outed as a perpetrator of intentional lateness, deliberately offensive joke telling or some equivalently dis-pleasurable breach of the other person’s expectations. In case of rare sociopath types who apparently feel no remorse (that is, g 5 0), the model should be interpreted as representing a world

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where others engaged in repeated interaction should eventually be able to see the sociopath’s intention to deceive, leading non-sociopaths to break off the relationship and look for a normal human being with whom to continue interacting.

GAMES AGAINST (OR IN ACCORDANCE, COOPERATION, HARMONY WITH) NATURE Gigerenzer (2005) discusses physicist Feynman’s arguments in favor of violating invariance with respect to logically equivalent re-descriptions of the same problem. Feynman sought out scientifically useful framing effects by which different intuitions about the laws governing a set of variables became more readily apparent using different frames or logically equivalent re-descriptions. He wrote that these logically equivalent re-descriptions are valuable because ‘psychologically they are different’ (quoted in Gigerenzer 2005, p. 207). In contrast, behavioral economists largely adopt the opposite normative view: that framing effects and other patterns of making different inferences or taking different actions in response to logically identical re-descriptions of the ‘same’ decision problem constitute evidence of irrationality. Gigerenzer argues that the mind’s perceptual system similarly makes smart bets; the intelligence of those bets depends necessarily on making mistakes. For example, in making three-dimensional inferences based on two-dimensional visual input, the mind bets that there is only one source of light that is located above, implying that objects with dark shading below are likely to be ‘sticking out’ toward the observer. From this, Gigerenzer observes that the perceptual system correctly assumes that the world (that is, its threedimensional structure) is fundamentally uncertain (that is, we face the challenge of missing information about three-dimensional structure in our environments) and therefore use associational rules to make reasonable guesses. If instead the mind proceeded as an agnostic Bayesian and waited for irrefutable evidence before logically deducing the correct three-dimensional structure, it would be paralyzed. Similarly, if it had access to a veridical copy of all information required to produce an objectively accurate model of all relevant detail in its environment, the mind and its perceptual system would be overwhelmed. The functionality of the simple bivariate-association rule, ‘objects with dark shading below are sticking out toward me’, depends on its partiality and imperfection with respect to veridical descriptive accuracy. A hypothetically perfect (that is, veridically accurate) perceptual mechanism would still be too little, leaving perceptual holes when facing new or unknown environments (that is, situations where a quick action based on a snap perceptual bet is required without inputting the vast amount of information that a perfect perceptual mechanism would require). This perfectly veridical perceptual representation of the world would also be overwhelmingly too much, presenting the mind with paralysingly large volumes of spatial information. There are different representations of the truth with varying degrees of detail. Representations with more information, even when the additional information is perfectly valid, may induce strictly inferior judgments and decisions compared to less complete representations which enable minds to make quicker and more accurate inferences. The same goes for memory. Is more better? Not necessarily (for example, Schooler and Hertwig 2005, show that forgetting is beneficial in inference tasks). Gigerenzer

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Rational mistakes that make us smart 55 (2005) discusses individuals with unusually large recall memory that suffer, as a result of their special pneumonic endowment, with acute inability on tasks requiring abstraction. Perhaps having more recall memory means less practice at efficiently coding the gist of what is taking place, abstracting and forming equivalence classes in memory. Are larger consideration sets better than smaller ones? Among the successful entrepreneurs from whom data were collected in Berg (2014b), very small consideration sets with only three potential locations for a high-stakes investment decision were the rule rather than the exception. Larger consideration sets were associated with below-average investment performance. Less (that is, consideration of fewer feasible choices) was more (that is, greater than expected financial return). When choosing where to stand to catch a ball, three observations about how professional baseball players do it are worth noting in that they deviate from how robots would be programmed to do it using a veridical causal model based on initial velocity, wind speed, rotation and so on. Many researchers believe that veridical causal models stand unquestioningly as the gold standard for rational choice. In that view, deviations from how an idealized robot would do it are automatically labeled as mistakes. This view forces the interpretation that the deviations of professional baseball players – who are the best in the world at what they do – are prima facie evidence of irrationality rather than intelligence and high functionality. If the mind were essentially solving the physics problem of where to stand to catch the ball based on initial velocity, wind speed and rotation, then players who can reliably catch the ball in this way should be able to point to and predict the landing point without actually running to catch the ball. They cannot (see references in Gigerenzer 2005, and Berg and Gigerenzer 2010 on as-if behavioral economics). If players’ minds were evolved to approximate the veridical causal mechanism, then they should also run straight to the ball’s landing spot and do so as fast as they can to leave time for last-minute adjustments. Instead, they use a gaze heuristic that requires no causally relevant information at all and no precisely optimal angle (but, rather, allows for a large and forgiving range of angles that function just fine) at which to fix their gaze. The gaze heuristic is a process model: fix the angle of one’s gaze to the ball, start running and maintain the angle. It requires no causally relevant information. And it works.

BIAS–VARIANCE TRADE-OFF The bias–variance trade-off well known in statistics, machine learning and, more recently, psychology, implies that deliberate bias is, in general, a requirement of virtually any wellperforming statistical procedure that fits unknown parameters on a training set and then measures performance in generalization tasks requiring out-of-sample prediction. This trade-off forces the conclusion that insisting on zero bias will lead inexorably to maximal variance which, in any application with a single, finite dataset, violates most notions of ‘well performing’.

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LEXICOGRAPHIC ORDER OF ASYMPTOTIC CONSISTENCY OVER VARIANCE REDUCTION IN ECONOMETRICS Classical econometrics is still taught in many if not most economics PhD programs as if there is a unanimous tacit agreement that the normative criteria of an estimator being unbiased or consistent (asymptotically converging to the correct value with probability 1) is infinitely more important than variance (not to mention performance in out-ofsample prediction). Orthodox econometric pedagogy advances a lexicographic order over biasedness and variance, ranking any estimation technique that is biased (or inconsistent) as strictly inferior, no matter what compensating characteristics (for example, speed, accuracy, low information requirements, lower variance, and so on) it may offer. Therefore, orthodox economic methodology applies lexicographic preferences over the methodological variables that economists choose when doing their work, while, in contrast, assuming that the consumers and firms in their models (with utility and profit functions satisfying the usual smoothness conditions) are never lexicographic in the way they reason about high-stakes decisions they face. This odd juxtaposition of methodological norms seems worth noting. Conditional mean functions are specified as flexible but always compensatory functions of the vector of conditioning variables. Utility functions are used that assume preferences cannot be lexicographic. In contrast, in econometrics, economists work under the assumption that lexicographic preferences over the characteristics of estimators are reasonable (that is, unbiasedness and consistency trump any comparisons of variance).

MORE INFORMATION NOT NECESSARILY BETTER EVEN IN GAMES AGAINST NATURE How much information should we pay attention to? When is it rational to ignore relevant information even when facing no cognitive constraints or costs of conditioning information? Berg and Hoffrage (2008) provide a formal definition of an economic or psychological environment and the matching concept of ecological rationality. They demonstrate that there are dense sets of environments in which, because payoffs and probabilities cancel out under the expected payoff operation, a non-redundant predictor or decision cue X that is veridically correlated with future payoffs may nevertheless drop out of optimal action rules, giving rise to the phenomenon of rational ignoring environments. Berg et al. (forthcoming) present data collected from economists that measure both individual-level belief consistency with respect to Bayes’ rule and belief accuracy with respect to published point estimates for disease frequencies in the medical literature. Which economists had the most objectively accurate beliefs about prostate cancer risks? It was not the economists whose conditional beliefs were perfectly Bayesian. Formal analysis of the analytic measures of belief consistency and belief accuracy, as well as the empirical data, show that performing well by one of these two distinct criteria does not imply good performance on the other. In many settings the multiple normative criteria that are observable in choice data may be negatively correlated. Perfect time consistency may arise mostly as a result of consistently impatient behavior (so that time consistency and the present value of lifetime wealth or laboratory earnings are negatively correlated).

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Rational mistakes that make us smart 57 Perfect conformity with the Savage Axioms may arise primarily as the result of consistently risk-averse choices with far-below average mean earnings. Perfect conformity with transitivity may result primarily as a very clear orientation toward leisure over money, implying that transitive types are, on average, less wealthy, less entrepreneurial and lower earning in laboratory experiments. When might the ‘mistake’ of failing to maximize expected utility and satisficing instead lead to social welfare improvements? Berg and Gigerenzer (2007) demonstrate just such an environment. Their model provides a thought experiment about a benevolent social planner who wants to achieve the greatest possible individual and aggregate payoffs for all stakeholders in her society. Now suppose she were able to choose whether the agents were expected utility maximizers or satisficers. Would the benevolent social planner follow behavioral economists’ preference for constrained optimization and advocate that individual members of society strive to be expected utility maximizers as opposed to satisficers? Berg and Gigerenzer (2007) show that the society of satisficers is unambiguously better off according to the same social welfare function. The satisficers achieve higher social welfare and require far less paternalistic intervention when compared from the vantage point of a Benthamite social-welfare metric.

STRATEGIC GAMES AGAINST SELF-INTERESTED COMPETITORS Mistakes can make an agent’s behavior less predictable and therefore thwart exploitative attacks. Like Columbo’s feigned ineptitude and lack of cleverness, agents that adopt decision styles that allow for and plan on committing errors can induce their adversaries into less cautious play, less aggressive best-response functions and greater revelation of information. To clarify: the errors considered in this chapter so far have nothing to do with strategically portraying anyone as stupid. However, it is worth including feigned irrationality in this list of examples that illustrate the breadth of mechanisms through which mistakes confer genuine value added. If others are convinced that I am stupid, then I may have more freedom to discover information or trade in markets without others strategizing against me. Inflated or wrong beliefs can make me a stronger negotiating partner. Mistakes lead to discoveries when the environment (for example, the reward- or payoffgenerating process) is changing (Bookstaber and Langsam 1985).

MARKETS AND SOCIAL SYSTEMS THAT BENEFIT FROM LOGICAL INCONSISTENCY AND OTHER ALLEGED ERRORS At the species level, suboptimal individual decisions may be rewarded by what is effectively a species-level portfolio diversification effect. There are some individuals failing to maximize in today’s environment, which may seem like a suboptimal waste. In the event that the payoff environment is buffeted by shocks so that previously optimal behaviors can no longer survive, however, then the currently suboptimal individuals may come into their own. Suppose the energy yield from grazing on the north side of the lake is 80 but only 20 on

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the south side. What is individually rational is, of course, to graze at the north side. At the group level, however, when attacks, pests or poisons can appear on one side or the other, it is adaptive for some individuals to graze on the low-energy-yielding south side. This individual-level mistake averts group-wide cataclysm had all individuals chosen north and an unexpected attack takes place on the north. Market liquidity itself depends on noise or liquidity traders. Behavioral, belief and preference heterogeneity are primary reasons underlying why trade (that is, exchange itself) creates economic value. Berg and Lien’s (2005) model of Pareto-improving overconfidence in the precision of information possessed by insiders (in beliefs among the uninformed) shows that overconfidence in experts, while sometimes damaging, can generate surprising liquidity benefits in financial markets. These positive externalities in the form of lowered transactions costs more than offset the individual costs of having wrong beliefs. The model features informed experts and uninformed non-experts who may be overconfident in experts’ expertise (that is, the precision of experts’ information about future payoffs). If these agents’ (otherwise typical) payoff functions were alternatively interpreted as representing evolutionary fitness functions, then a striking conclusion emerges: there is no sense in which rational expectations (that is, objectively accurate subjective probabilistic beliefs) are adaptive; overconfident belief profiles support equilibria that Pareto dominate the rational expectations equilibrium. Sampling to learn about a changing environment is another benefit of making mistakes. That may explain why experimental subjects who switch their responses (perhaps randomly) to the very same decision tasks at different experimental sessions have been observed to earn more, on average, than do consistently impatient and consistently riskaverse individuals. The consistent types’ behavior passes the rationality test according to the norm of internal logical consistency, which is the sole claimant to rationality in rational choice orthodoxy. These consistent individuals earn significantly less, however (Berg et al. 2010b). Such contrasts, once again, highlight the multiple normative standards that economists employ, whether tacitly or explicitly (Berg, 2014), in characterizing the rationality of observed choice data. Randomization may confer other surprising benefits. For example, in social systems that offer opportunities for random face-to-face encounters, Berg et al. (2010a) show that agents who use a simple lexicographic heuristic for judging the acceptability of potential neighbors based on face recognition are capable of achieving stable multi-ethnic neighborhoods and preventing Schelling-type location-choice dynamics that tend toward absolute segregation. In public goods games, behavioral economists alternate in their interpretation of what constitutes mistaken behavior. Usually, failing to free ride, as required by the Nashequilibrium strategy (under the assumption that all players maximize standard rational choice own-payoff objective functions), is cast as an alleged mistake and serves as one of the main outcome variables that behavioral economists focus on. Kameda et al (2011) report evidence of strikingly intelligent behavior in the nonlinear public goods games they study. The ‘error’ in this case would be choosing an action that is different than the actions that everyone else in one’s group chooses even though all group members face exactly the same payoff functions and resource endowments. (That is, the game is completely symmetric, but Nash equilibrium requires an asymmetric profile of actions that theorists view as being very difficult to achieve). The equilibrium in the symmetric public goods games

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Rational mistakes that make us smart 59 they study requires asymmetric action profiles. Therefore, some means of coordinating or deciding which group member will volunteer to be the sole contributor and agree to be free-ridden upon is required. Rather than widespread pathology, their data reveal a wide range of individual and group intelligence. Regulation to prevent overuse of a commons is another longstanding question in public economics. The less-is-more principle underlying individual intelligence in Gigerenzer’s heuristics reappears, once again, as relevant to regulatory policy across multiple settings. For example, Berg and Kim (2015) show that permissive regulation that places fewer restrictions on the use of a commons (for example, road transportation networks, fisheries, or bandwidth for stock-price quotation networks that are exploited by high-frequency trading algorithms) can, counterintuitively, be more effective at mitigating overuse than stricter restrictions would have been, given imperfect enforcement of the regulation. A similar surprise regarding what looks mistaken through one lens of benefit–cost calculus becoming rational when viewed from another such lens shows up in models of social dynamics that include positive payoffs for coordinating with like types (as well as potentially negative externalities possibly resulting from extreme racial and religious segregation). The Kahneman-inspired normative position of much of behavioral economics condemning human judgment and decision making as generally pathological can be turned on its head once again: rather than widespread pathology as the default normative assessment of behavior that deviates from simple rational choice models and their assumed consistency criteria, there is as yet much intelligence that can be observed in apparently mistaken behavior. Take, for example, the money sacrificed on religious products for which there is an intrinsically equal-value substitute available at substantially lower price. Such behavior can, even without intrinsic benefit, provide socially valuable signaling and coordination functions (for example, Berg and Kim 2014, show that paying a higher premium for Islamic banking services can provide a signaling service that makes it worth paying for among highly pious types).

SINGULAR VERSUS PLURAL NORMS USED IN DEFINING RATIONALITY? It sounds paradoxical and unbelievable to many behavioral economists, which makes it worthwhile to reiterate: rational choice orthodoxy underlies much of behavioral economics, and the two share a methodological commitment to there being a single normative standard of rationality that does not depend on context or domain but instead is decided based solely on internal logical consistency (Berg 2003, 2014a; Berg and Gigerenzer 2010). The Kahneman-inspired biases literatures within behavioral economics and the field of judgment and decision making typically focus on deviations from some standard of logical consistency. Behavioral economists working in this vein are generally interested in the observational phenomenon of deviations from such a standard of internal logical consistency. Rather than question whether this normative standard used to define bias and deviations, the normative validity of the rational choice benchmark remains largely unquestioned among both what appears to be most behavioral economists and proponents of the rational choice orthodoxy. Their shared singular normative standard defines the

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deviations that comprise the main outcome variables of interest to many behavioral economists. Such standards of rationality based solely on logical consistency include: logical invariance as the rationality standard against which framing effects become interestingly pathological; transitivity as the core component of the rational preference standard against which studies of intransitive and incomplete preference gain traction; Bayes’ rule in papers about non-Bayesian beliefs; the logic of set theory in investigations of the conjunction fallacy; and, even, Nash equilibrium as a benchmark of rationality in hundreds of studies by behavioral economists that report non-Nash play frequencies as the main dependent variable without ever comparing dollar payoffs (or comparisons by other normative metrics) among Nash versus non-Nash subsamples. In this chapter, I am considering the rationality of mistakes and errors of the kinds described above. To do so automatically implies that a newly pluralistic set of normative concepts are required. Ecological rationality is explicitly pluralistic by requiring good-enough (that is, satisficing levels) of match between a decision procedure and the environment in which it is used. This standard asks that, in a well-specified set of task environments, the decision procedure performs to a functional and pragmatic standard such that, despite and sometimes thanks to making mistakes, the procedure is readily seen as sensible, purposeful and, yes, rational! In the ecological rationality framework, a particular decision procedure or heuristic is, in itself, neither rational nor irrational. Unlike the rational choice and behavioral economics standard in which a single pair of intransitive choices or violation of logical invariance earns the universal assessment of irrationality, a choice procedure in the ecological rationality framework has performance characteristics that are alternatively rational and irrational depending on the external environment in which it is considered. It is only once the decision procedure is embedded in a particular environment that Herb Simon’s two blades of the ecological rationality scissors (decision procedure and external environment that jointly determine reasonable performance metrics for defining what is good enough to achieve success) can do their work at identifying boundaries that circumscribe the set of task environments in which a particular decision procedure achieves ecological rationality. It appears that any normative framework integrating the possibility of beneficial mistakes, as categorized above, necessarily implies that pluralistic normative metrics and the adaptive toolkit approach to defining what rationality means are in play.

WHICH ORGAN IN THE HUMAN BODY IS BEST? Does it make any sense to ask which organ in the human body is the best or most valuable? Using the massively interdependent body as an analogy, the behavioral phenomena of interest to social scientists will generally require multiple normative metrics akin to separately measuring and considering kidney function, liver function, cholesterol, triglycerides, blood glucose levels, and so on. Would it make sense to integrate all known organ-specific performance metrics or results from standard blood panels into a single, scalar-valued assessment, perhaps using a label such as generalized aggregate physiological (GAP) score? We would be hard pressed to think of any application where such aggregated summaries that compress the body’s multiple interdependent systems into a single

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Rational mistakes that make us smart 61 scalar-valued metric would be more informative or pragmatically useful than the disaggregated components considered as a fundamentally multivariate normative outcome. By analogy, when we ask the normative question using experimental choice data or theoretical models whether an observed set of behavioral patterns could be rationalized as if it were maximizing some scalar-valued objective function with newly exotic preference parameters to more flexibly mop up variation in the data, we are most likely asking a similarly wrong question. The standard analysis of a scalar-valued normative metric asks us to rely on the optimal choice function (that is, the program that maps exogenous parameters into an endogenous inference or action maximizing the narrowly defined objective). This method leads to the erection of a dug-in methodological phalanx that severely limits behavioral economics to persisting in egregious repetition of what statistician John Tukey called a type-III error: providing the right answer to the wrong question. In a massively interactive and interdependent biological or social system, the right way to behave depends on context. Rationality norms must be pluralistic and thoughtfully well-matched to a specific (that is, explicitly delimited) class of decision problems where a particular (that is, explicitly defined, possibly multivariate) standard of rationality makes sense (cf. Simon’s, 1976, notion of procedural rationality).

IS ECONOMICS THE ONLY DISCIPLINE WITH A COMMITMENT TO MONO-METHODOLOGICAL SINGULARISM? Yes.

INFLUENCE BY AND PARALLELS WITH THE AXIOMATIZATION PROGRAM IN MATHEMATICS? The axiomatization program in economics was in part inspired by the axiomatization program of mathematicians such as David Hilbert, Whitehead and Russell, and the Bourbaki group, which overlaps with the consistency school of normative bounded rationality (Berg 2014b). This axiomatization program profoundly influences (that is, restricts) economists’ normative analysis (that is, the normative questions that can be asked) in subtle ways that go mostly unnoticed in methodological treatises on the realworld applicability of behavioral economics and bounded rationality. Economic studies of bounded rationality would benefit by noticing the waning trajectory of this axiomatization program in mathematics and, like many in mathematics have, choose instead to pursue applied problems and the informal mathematics described in Backhouse (1998). I define the axiomatization program in economics as the body of economic theory that seeks a short list of axioms (perhaps minimal in some sense) that exhaustively characterizes the rationality of: preference orderings; sets of observed choices or demanded bundles (the extensive literature on revealed preference typically associated with Paul Samuelson); or orderings on choice sets. This axiomatization program can be narrowed further to investigations that pursue the question of postulating maximally general axioms (that is, the weakest possible) that can ‘rationalize’ observed choice behaviour. The

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methodological priority of (topological) generality that characterized much of Hilbert’s program peaked in the latter half of the twentieth century. Since then, the dominance of the axiomatic program in mathematics has waned, whereas its methodological force in economics appears to have remained relatively undiminished. The history of the axiomatization program in economics reflects numerous borrowings and inspirations from mathematicians: David Hilbert, Bertrand Russell and the Bourbaki group all sought to rid mathematics of the possibility of inconsistencies. Bertrand’s paradox provides a primary motivation for early twentieth-century mathematicians’ program of eliminating inconsistency. That well-known paradox posits a collection of all sets that do not belong to themselves. The contradiction turns on ambiguity in the definition of the aforementioned ‘collection’ enjoying the status of set. By restricting the definition of a set to exclude some otherwise well-defined collections of mathematical objects, Frege, Whitehead and Bertrand, and Fraenkel introduced a new formalism into mathematics to resolve such paradoxes, most often beginning with axiomatization. There are even earlier links in the works of mathematicians such as Georg Cantor in the late 1800s (and axiomatization programs in set theory which followed) to the later axiomatization program in economics, based on the goal of providing a minimal list of conditions to ‘rationalize’ choice data. The ‘characterization’ of rationality and the ‘rationalization’ strand of the axiomatization program in economics can be thought of as beginning with a set of axioms and a universe of observable patterns of behavior and then projecting the graph that characterizes all allowable patterns of behavior that satisfy the axioms, which is a strict subset of the larger universe of possible patterns of behavior. This can be backward engineered as follows: Given the observed set of choices or behavior patterns, what axioms must this set of choice data satisfy in order to (1) recover a preference ordering that could have generated the choice data, and (2) assuming a preference ordering exists for ranking vector-valued bundles or payoff distributions in the case of risky choice, what axioms must the data satisfy for the rankings of multidimensional objects to be representable as scalar-valued utility or value scores? Note that this rationalization subset of the axiomatization program in economics contains, for example, Tversky and Kahneman’s (1992) loss-averse cumulative prospect theory, specifically, versions of it that attempted to rationalize the choice data generated in Allais’ paradox (which are interpreted as anomalous with respect to expected utility theory). Rationalizing anomalous choice data is described by Gerd Gigerenzer, Werner Güth and Reinhardt Selten as a repair program. The goal is to take choice data (from binary choices over pairs of risky gambles in the case of prospect theory as a resolution to Allais’ Paradox) that cannot be represented with an expected utility function, and then show that those data could have been generated by prospect theory, for some unspecified but theoretically possible parameters that determine the shape of the value-function and the nonlinear function mapping objective probabilities into decision weights. Note that this rationalization project, or repair program, bears some similarity to the fallacy of ranking regression models according to their R-squared. Finding a list of axioms that ‘can explain’ choice data is analogous to a regression model with more right-hand-side variables fitting a dataset better. As econometric textbooks correctly caution, a model that fits the data better may not necessarily make more accurate out-of-sample predictions. Fit can always be made to reach 100 percent if enough free parameters are added to the model specification, one for each observation in the fitting or training sample. Arguments

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Rational mistakes that make us smart 63 in favor of prospect theory that use different parameterizations to fit each new sample, for example, do not typically discipline that model to the risks of out-of-sample prediction. Instead, they use the flexibility of the additional parameters in prospect theory (compared with standard expected-utility theory) to increase ‘fit’ in a manner entirely analogous to increasing R-squared by trivially increasing the number of free parameters used in a regression model. Cantor proved – more than century ago – that if a binary relation is linearly ordered, then it is also embeddable as an isomorphism in the real numbers. Technically, this is almost identical to the intellectual work of writing down axioms (that is, restrictions on the preference ordering) that guarantee representability with utility, expected utility or prospect-theory value-function scores. Ragnar Frisch is credited as the first economist to define preferences as binary relations. Contemporary graduate textbooks use very different notation (deleting Frisch’s more broad-ranging ‘choice field’ formulation, which distinguishes commodity space from what Frisch referred to as the decision maker’s problem space). Frisch played a leading role in the founding of the Econometric Society and the journal Econometrica, advocating formalism and math modeling as a primary source of ‘rigor’ needed to put economics on a ‘scientific’ footing (Bjerkholt and Dupont 2010). Despite his view that mathematizing economics was needed to displace the ‘verbal’ approaches of institutionalists, his sophisticated appreciation of the fact that the decisions modeled as constrained utility maximization (exhaustively searching through a feasible set in commodity space) are embedded in a larger problem space that includes problems perhaps not best handled by the techniques of constrained optimization is striking. This notion of a larger ‘problem space’ foreshadows the notion of ‘environment’ used by writers such as Gigerenzer and Vernon Smith in advocating ecological rationality. Frisch’s concept of constrained maximization in commodity space as only one decision domain embedded in a larger problem space notably does not appear in most contemporary PhD textbooks, which instead emphasize the flexibility and universality of preference maximization devoid of context specificity. As is well known to economic methodologists and historians, early representation theorems in utility theory sought to address debates in economics between those who interpreted utility as a potentially measurable psychological metric of hedonic satisfaction and those influenced by logical positivism wanting to remove psychological notions (Bruni and Sugden 2007). Early representation theorems establishing utility as a purely ordinal concept devoid of cardinal meaning led to representation theorems in expected utility theory, axiomatizations of Bayesian updating as rational belief functions, and, more recently, weaker axiomatizations that can account for (as bounded rational) some well-known anomalies with respect to rational choice theory. It is this last subliterature of economists writing on rationality axioms in behavioral economics and making reference to Herbert Simon’s phrase ‘bounded rationality’ that is relevant to this chapter’s focus on bounded rationality and smart people’s rational mistakes. It is instructive to recall that the central motivation of Hilbert and Whitehead and Russell’s (1927 [2009]) axiomatization program was to formalize mathematics and philosophy with the explicit goal of eliminating inconsistency. Hilbert and Russell undertook this program and advocated that others join them to rid mathematics – and science – of the possibility of generating inconsistent statements, whether those statements be abstract or detailed descriptions of the world.

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While Hilbert’s move toward formalism profoundly influenced mathematics (and at the same time attracted well-established critics), it eventually waned as new subfields in mathematics applying methods outside the Hilbert program grew up and gained acceptance as making substantial contributions to mathematics. Applied problem solving, combinatorics, category theory and subfields of mathematics overlapping with computer science achieved influence and prominence, while other theorists working in the constructivist and intuitionist traditions similarly produced new knowledge that followed distinct methodological priorities. The methodological influence of formalism and the axiomatization program in economics followed an arguably equal if not more profound influence in economics (see Backhouse 1998, regarding formalism in economics versus informal mathematics). One minor parallel between the trajectories of formalism in mathematics and economics was the desire to shed old interpretations (for example, the interpretation of points, lines and planes in geometry and the psychological or hedonistic interpretation of utility in nineteenth-century economics in favor of utility as a purely ordinal device). Another speculative parallel that can be seen in the restrictions that choice axioms placed on what had been a previously more libertarian view of consumer sovereignty is to see them echoing the restrictions that Frege, Whitehead and Russell, Fraenkel and Hilbert applied to the definition of a set (in order to avoid paradoxes such as Russell’s). Beyond these similarities, however, the differences in the historical trajectories of axiomatization programs in mathematics and that of economics are many. Formalism in economics (until very recently) did not have a long struggle with concepts such as the definition of a set as its core methodological problem, syntactical formalism, the incompleteness theorems of Gödel, and many others. The mathematical issues in the development of economists’ formalism were, by and large, far simpler mathematically, and focused on applying topological formalisms already established in mathematics to preferences and representations of preferences. In the axiomatization program in economics, the role of interpretation and motivation of axioms were the primary objects of notable theoretical economists’ writing. Critiques and crises over the roles of an axiomatization program (and the ‘interpretation-free’ view of mathematics as a content-free set of primitives and a formulaic set of statements based on definitions of operations juxtaposed or concatenated to generate all permutations allowed by the axioms) did not surface or echo in economics, at least in obvious ways. These differences, however, serve to cast into sharp relief the one overriding similarity between the axiomatization programs of math and economics: internal logical consistency as the pre-eminent normative value.

BEHAVIORAL ECONOMICS IS NORMAL SCIENCE PORTENDING NO PARADIGM SHIFT IN NORMATIVE ANALYSIS Some argue that behavioral economics should be interpreted as a paradigm shift or otherwise momentous contestation in reaction to the axiomatization program in economics. Behavioral economists’ work could, if such an interpretation were granted, be seen as echoing earlier methodological shifts in mathematics following the rise and decline of

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Rational mistakes that make us smart 65 the Bourbaki group’s influence in mathematics in the twentieth century. I argue against this methodological view of behavioral economics as a paradigm shift and instead demonstrate that its overriding normative value remains firmly rooted in the axiomatization program’s normative view, namely, that the central concern is, and should be, internal logical consistency. See Berg (2014a) or Berg and Gigerenzer (2010) for further detail distinguishing different camps within behavioral economics, and Berg (2003) on Thaler’s and Kahneman’s changing positions regarding the normative implications of implicit and explicit methodological challenges that behavioral economics put to orthodox rational choice models. Those who see behavioral economics and modelers of bounded rationality acting as an ensemble to ‘expand’ or ‘loosen’ the methodological strictures of rational choice theory miss a crucial difference in the normative views of the consistency and ecological rationality schools as I have defined them in earlier work (Berg 2014a). Behavioral economists in the consistency school propose radically narrow normative definitions of rationality and use the label ‘bounded rationality’ (in a manner that would seem to contradict Herbert Simon’s normative view). The result is to harden the methodological commitment to internal consistency as the sole criterion that economists are expected to use in characterizing what it means to make rational decisions – and in prescriptive policy proposals that paternalistically intervene, aiming to induce people’s private actions to more closely conform to axiomatic models of rationality. Backhouse (1998) reminds us that axiomatization, mathematicization and formalization are distinct. Gigerenzer and Selten’s (2001) ecological rationality program provides a clear example of normative decision analysis that draws on quantitative data to produce theories that can be expressed in the language of mathematics, yet have nothing to do with axiomatization. Backhouse notices (as many other writers on mathematics and philosophy, and the history of mathematics have) that mathematics itself can be either formal or informal. In the development of proofs of Euler’s theorem, for example, which relates the numbers of vertices, faces and edges of a polyhedron, Backhouse (1998, p. 1848) describes different authors’ proofs as somewhere ‘between formalism and irrationalism. . ..There is more to mathematics than driving the properties of formal systems’. The implication would seem to be that applied economics, welfare economics and prescriptive policy analysis cannot be entirely about deductive logic (Berg 2007). Indeed, the proper role of deductive logic led to animated and productive debates about mathematical methodology and philosophy regarding the Hilbert program among constructivists, intuitionists (including Hilbert’s students Brouwer and Weyl), subsequent work in proof theory, category theory and those inspired by Turing on computability. Given these prolific bodies of work by mathematicians that raised questions about consistency as the core methodological concern in mathematics, it would seem wrong for economists to draw the lesson from mathematics – in the name of ‘providing rigor’ or ‘putting economics on a more scientific basis’ – to insist on applying consistency alone as the ultimate methodological value. What are we to make of the long tradition among neoclassical economists – and now behavioral economists – who seem to follow Hilbert’s singular normative premise in pursuit of logical consistency? I think we can note the positions of economists like Debreu and Binmore as playing a role similar to Hilbert’s role in mathematics. Their staunch position in favor of consistency as a singular methodological and normative-prescriptive

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value is simply one among multiple, competing normative claims within economics. Heterogeneity of methodological priorities is a positive symptom of productive scientific investigation. In light of the productivity generated by those who raised questions and took positions against Hilbert’s consistency program in mathematics, however, economists might also notice that competing normative claims are likely to play a similarly productive role in economics. Such methodological debate is no small side issue but rather a substantial object of core investigation in normative economics.

CONCLUDING REMARKS Casual empiricism and the theoretical economics, biological sciences and biostatistics literatures provide a rich collection of source material from which one finds a broad range of mechanisms by which smart people make rational mistakes. Also, economies that generate value added and nurture richly multidimensional measures of well-being generate numerous opportunities by which aggregate performance is enhanced thanks to systematic deviations from standard rationality criteria based solely on internal logical consistency. I have provided examples that hopefully give a sense of the technical, substantive and historical range of context-specific mechanisms in which alternative normative criteria that allow for welfare-enhancing deviations from logically consistent axiomatic rationality can be given even-minded consideration. May further study of this important phenomenon bloom forth and melt away the methodological strictures unnecessarily limiting behavioral economists’ evaluations of rationality.

REFERENCES Axelrod, R. (1984), The Evolution of Cooperation, New York: Basic Books. Backhouse, R.E. (1998), ‘If mathematics is informal, then perhaps we should accept that economics must be informal too’, Economic Journal, 108 (451), 1848–58. Berg, N. (2003), ‘Normative behavioral economics’, Journal of Socio-Economics, 32 (4), 411–27. Berg, N. (2007), ‘Behavioural economics, business decision making and applied policy analysis’, Global Business and Economics Review, 9 (2–3), 123–5. Berg, N. (2014a), ‘The consistency and ecological rationality schools of normative economics: singular versus plural metrics for assessing bounded rationality’, Journal of Economic Methodology, 21 (4), 375–95. Berg, N. (2014b), ‘Success from satisficing and imitation: entrepreneurs’ location choice and implications of heuristics for local economic development’, Journal of Business Research, 67 (8), 1700–1709. Berg, N. and G. Gigerenzer (2006), ‘Peacemaking among inconsistent rationalities?’, in C. Engel and L. Daston (eds), Is There Value in Inconsistency?, Baden-Baden: Nomos, pp. 421–33. Berg, N. and G. Gigerenzer (2007), ‘Psychology implies paternalism? Bounded rationality may reduce the rationale to regulate risk-taking’, Social Choice and Welfare, 28 (2), 337–59. Berg, N. and G. Gigerenzer (2010), ‘As-if behavioral economics: neoclassical economics in disguise?’, History of Economic Ideas, 18 (1), 133–66. Berg, N. and U. Hoffrage (2008), ‘Rational ignoring with unbounded cognitive capacity’, Journal of Economic Psychology, 29 (6), 792–809. Berg, N. and J.Y. Kim (2014), ‘Prohibition of riba and gharar: a signaling and screening explanation?’, Journal of Economic Behavior and Organization, 103 (July), 146–59. Berg, N. and J.Y. Kim (2015), ‘Quantity restrictions with imperfect enforcement in an over-used commons: permissive regulation to reduce over-use?’, Journal of Institutional and Theoretical Economics, 171 (2), 308–29. Berg, N. and D. Lien (2005), ‘Does society benefit from investor overconfidence in the ability of financial market experts?’, Journal of Economic Behavior and Organization, 58 (1), 95–116.

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Rational mistakes that make us smart 67 Berg, N., G. Biele and G. Gigerenzer (forthcoming), ‘Consistent Bayesians are no more accurate than nonBayesians: economists surveyed about PSA’, Review of Behavioral Economics (ROBE). Berg, N., C. Eckel and C. Johnson (2010), ‘Inconsistency pays? Time-inconsistent subjects and EU violators earn more’, working paper, University of Texas-Dallas, Dallas, TX. Berg, N., U. Hoffrage and K. Abramczuk (2010a), ‘Fast acceptance by common experience: FACE-recognition in Schelling’s model of neighborhood segregation’, Judgment and Decision Making, 5 (5), 391–410. Bjerkholt, O. and A. Dupont (2010), ‘Ragnar Frisch’s conception of econometrics’, History of Political Economy, 42 (1), 21–73. Bookstaber, R. and J. Langsam (1985), ‘On the optimality of coarse behavior rules’, Journal of Theoretical Biology, 116 (2), 161–93. Bruni, L. and R. Sugden (2007), ‘The road not taken: how psychology was removed from economics, and how it might be brought back’, Economic Journal, 117 (516), 146–73. Gigerenzer, G. (2005), ‘I think therefore I err’, Social Research, 72 (1), 195–218. Gigerenzer, G. and R. Selten (2001), Bounded Rationality: The Adaptive Toolbox, Cambridge, MA: MIT Press. Kameda, T., T. Tsukasaki, R. Hastie and N. Berg (2011), ‘Democracy under uncertainty: the wisdom of crowds and the free-rider problem in group decision making’, Psychological Review, 118 (1), 76–96. Rapoport, A. (1984), ‘Game theory without rationality’, Behavioral and Brain Sciences, 7 (1), 114–15. Rapoport, A. and A.M. Chammah (1965), Prisoner’s Dilemma, Ann Arbor, MI: University of Michigan Press. Schooler, L.J. and R. Hertwig (2005), ‘How forgetting aids heuristic inference’, Psychological Review, 112 (3), 610–28. Simon, H.A. (1976), ‘From substantive to procedural rationality’, in S.J. Latsis (ed.), Method and Appraisal in Economics, Cambridge: Cambridge University Press, pp. 129–48. Tversky, A. and D. Kahneman (1992), ‘Advances in prospect theory: cumulative representation of uncertainty’, Journal of Risk and Uncertainty, 5 (4), 297–323. Whitehead, A.N. and B. Russell (1927), Principia Mathematica, 3 vols, Cambridge: Cambridge University Press; 2nd edn, 1925 (vol. 1), 1927 (vols 2, 3), vols 1, 2 and 3 originally published in 1910, 1912 and 1913; 1st edn reprinted 2009 by Merchant Books, USA.

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Rational choice as if the choosers were human Peter J. Boettke and Rosolino A. Candela

1

INTRODUCTION

In a recent paper entitled ‘Principles of (behavioral) economics’, economists David Laibson and John List claim that ‘behavioral economics is a series of amendments to, not a rejection of, traditional economics’ (2015, p. 385), which studies ‘how people try to pick the best feasible option, including the cases in which people, despite their best efforts, make mistakes’ (2015, p. 389, original emphasis). For a classroom of undergraduates they would summarize the principles of (behavioral) economics in this way: If you want to boil behavioral economics down for a classroom summary you might say that most people are located somewhere between Mr. Spock and Mr. Simpson (aka Homer). Like Mr. Spock, Mr. Simpson is also an optimizer – he tries to choose the best feasible option. He’s just not good at it. We need to study and model all optimizers: the good, the bad, and those in between. (Laibson and List 2015, p. 389)

Oddly enough, these statements made by Laibson and List about human decision-making parallel strongly with the following statement made by Ludwig von Mises, one of the strongest proponents of rationality in the history of economic thought: It is a fact that human reason is not infallible and that man very often errs in selecting and applying the means. An action unsuited to the end sought falls short of expectation. It is contrary to purpose, but it is rational, i.e., the outcome of a reasonable – although faulty – deliberation and an attempt – although an ineffectual attempt – to attain a definite goal. The doctors who a hundred years ago employed certain methods for the treatment of cancer which our contemporary doctors reject were – from the point of view of present-day pathology – badly instructed and therefore inefficient. But they did not act irrationally; they did their best. (Mises 1949 [2007], p. 44)

From the quotes stated above, both Laibson and List and Mises seem to be depicting rational choosers as if they were human. However, if traditional economics does indeed need to be amended by behavioral economics, as Laibson and List argue, then the question is what notion of man has occupied ‘traditional’ economics? Implicitly, it would seem that the notion of man they have in mind for traditional economic analysis is none other than Homo economicus. The concept of economic man, or Homo economicus, has been under assault throughout much of the history of the discipline. It has often been a criticism intimately tied to an effort to discount the lessons that can be learned from economics for the practical understanding of public policy. Since economics as a discipline stresses scarcity and thus choice within constraints, the debate often turns on how competent people are in making choices, and how binding those constraints are. In an idealized world, the argument goes, individuals are fully informed and perfectly rational in making their decisions, and the constraints 68

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Rational choice as if the choosers were human 69 they face are hard and unyielding. Thus, correct decisions will not just ‘tend’ to be made, but will inevitably be made. Since this applies equally to all in the economy, the equilibrium that results will exhibit exchange efficiency, production efficiency, and product-mix efficiency. In short, all the gains from trade will be exhausted, all the gains from technological improvement will be incorporated into production, and the array of products that buyers are willing to pay for would be available on the market. Perfectly rational actors interacting freely in a frictionless environment produce an efficient outcome. With that narrative in the background, then consider the argumentative strategy of those who wanted to critique the free market system – they can go after the notion of the rational actor, they can go after the frictionless environment, and they can challenge the ethical status of the efficiency standard. Throughout the history of the discipline, all three intellectual strategies have been pursued. The easiest target has been the bogeyman of Homo economicus. Economic man is a bogeyman in two ways – the claim that the concept implies that economic ends, or monetary motives, enter the decision calculus, and that the model implies that the decision makers are imbued with omniscience with respect to all the relevant factors to the decision. ‘The hedonistic conception of man’, Veblen wrote, ‘is that of a lightning calculator of pleasures and pain, who oscillates like a homogenous globule of desire of happiness under the impulse of stimuli that shift him about the area, but leave him intact’ (1898, p. 389). Or consider how Keynes in his rhetorical brilliance was able to link both perfect rationality and perfect markets together and dismiss both claims in ‘The end of laissez faire.’ As he put it: Let us clear from the ground the metaphysical or general principles upon which, from time to time, laissez-faire has been founded. . ..The world is not so governed from above that private and social interests always coincide. It is not so managed here below that in practice they coincide. It is not a correct deduction from the principles of economics that enlightened self-interest always operates in the public interest. Nor is it true that self-interest generally is enlightened; more often individuals acting separately to promote their own ends are too ignorant or too weak to attain even these. (Keynes 1926 [1978], pp. 277–8, original emphases)

Human actors to Keynes are not rational as the ‘model’ presumes, and the market system does not function as smoothly as the ‘model’ suggests assuring that private and public interests align. The choice Keynes provides is binary – either perfect actors and perfect markets, and thus laissez-faire, or imperfect actors and imperfect markets, and thus activist government policy as a corrective. There simply is no way in his intellectual schematic that imperfect actors operating in an imperfect world could be stumbling upon coping mechanisms for the complex reality in which they find themselves, enabling them to realize the productive gains from specialization and peaceful cooperation without the activist hand of enlightened government. Keynes brilliantly identified the ‘dark forces of time and ignorance’ (1936 [1964], p. 155) in The General Theory, but in his depiction human actors are unable to navigate in that world. The debates over individual rationality and system-level efficiency have proceeded along these lines ever since. By modelling the actor as a close-ended decision maker and the economy as exhibiting a single exit, our result is the deterministic model of rational ‘choice’ depicted in a standard economics textbook, in which the human actor is devoid of any cognitive ability. If we introduce some form of imperfection, either with the actor (say, informational asymmetries) or in the market structure (say, monopolistic competition),

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then the welfare conclusions derived from the determinant solution shifts. The government as a corrective either at the actor level to provide the requisite information, or at the structural level to provide the requisite regulatory measures, seems to follow naturally from the model. However, the strict binary intellectual choice that a Veblen or Keynes imposed on the economic conversation need not be followed. This is true for the contemporary discussion of behavioral economics, renewed calls for paternalism, and the entire practice of nudges. In this chapter, we contribute to the theme of this book by evaluating how rational human choosers are in fact ‘smart’ in the decision-making process once we have taken into account the particular institutional context within which they are evaluating the costs and benefits of their choices. As Morris Altman states, ‘conventional economics assumes that people’s choices are made in a vacuum’ (2012, p. 4), which is not only institutional, but also historical and cultural in nature as well. What we hope to demonstrate is that there has been from Adam Smith to Vernon Smith a tradition of economic scholarship that is grounded in the decision calculus of individuals, but requires neither the heroic assumptions of omniscience, nor that the individuals are interacting with others in frictionless environments. Instead, they see man as pursuing their varied purposes and caught, as they often are, between alluring hopes and haunting fears, and interacting in institutional environments that are constituted by vaguely and imperfectly understood rules that are often poorly enforced and path-dependent on the imprint of culture and history. Yet the filtering mechanisms of this institutional environment are guided to act in ways that coordinate their activities with those of others to realize the mutual gains from social cooperation. It is precisely because these scholars emphasize the open-endedness of choice that they can identify the role that even imperfect institutions play in coordinating economic affairs through time. Section 2 provides a survey of economic thinkers who rejected the caricature of Homo economicus that critics claimed was in fact held by classical and neoclassical economists, but who nevertheless defend the rational choice perspective in the social sciences, and economics in particular. These will include figures such as Frank Knight, Ludwig von Mises, F.A. Hayek, James Buchanan, Douglass North, Vernon Smith and Elinor Ostrom. Section 3 focuses on how Hayek, Buchanan and Ostrom develop the argument to move to the rules level of analysis in human decision making and human interaction. Section 4 discusses the concept of path dependency and imperfect institutions as developed by North and Ostrom. Section 5 concludes.

2

METHODOLOGICAL INDIVIDUALISTS WHO REJECT HOMO ECONOMICUS BUT EMBRACE RATIONAL CHOICE

In his Epistemological Problems of Economics, Ludwig von Mises (1933 [1960]), writing before the rise of the Keynesian revolution in macroeconomics and the growing emphasis on mathematical formalism and equilibrium analysis in microeconomics, claimed that there had been a consolidation of certain core propositions from different strands of economic thought that had emerged from the Marginal Revolution of 1871. These developments in neoclassical economics, according to Coase (1992, p. 713), were rooted in filling ‘the gaps in Adam Smith’s system, to correct his errors, and to make his analysis vastly

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Rational choice as if the choosers were human 71 more exact’. Choice within constraints had been a staple of economic analysis since at least the eighteenth century, but the Marginal Revolution led to a deeper understanding of the subjective nature of utility, the unit of analysis being the individual, and the choice calculation on the margin of decision. With the intellectual revolution in value theory, any Ricardian notion that long run costs of production determined value and price was to be jettisoned, and fallible yet competent human decision makers became the focal point of economic analysis. As Mises argued: Within modern subjectivist economics it has become customary to distinguish several schools. We usually speak of the Austrian and the Anglo-American Schools and the School of Lausanne . . . these three schools of thought differ only in their mode of expressing the same fundamental idea and that they are divided more by their terminology and by peculiarities of presentation than by the substance of their teachings. . .Today we have only one theory for the solution of the problems of catallactics, even if it makes use of several forms of expression and appears in different guises. (1933 [1960], pp. 214–15)

The ‘one theory’ for the analysis of catallactics that Mises had emphasized constituted a set of positive propositions that led to the further development of ‘mainline’ economics (see Boettke 2012). These propositions, which were held in common by economists from classical political economists such as Adam Smith to modern experimental economists such as Vernon Smith, explained the emergence of social order based on invisible hand theorizing that reconciled a broad notion of self-interest (that is, purposive action) with the public interest via institutional analysis. Figure 4.1 illustrates this point. Rational individuals, though imperfect in their cognitive capabilities, yet guided by the institutional prerequisites of private property, money prices, and profit/loss, will nonetheless coordinate their subjective plans through the unintended design of the invisible hand, yielding a social order. These mainline economists did not explain social order or disorder by collapsing selfinterest on to the public interest or by assuming the super-human cognitive capabilities, or lack thereof, upon individuals. However, in textbook presentations of mid-twentieth century microeconomics (and unfortunately true till this day) the argument is that social order results if, and only if, actors are fully informed and perfectly rational, and the market structure is perfectly competitive. Otherwise, decision making and system-wide efficiency will be lacking, and in need of correction.1 These two views of what has become ‘mainstream’ economics are illustrated in Figure 4.2. Rather than utilizing a behaviorally contingent explanation, their analysis was based on an institutionally contingent process Self-interest

Figure 4.1

Institutional filter

Social order (i.e. public interest)

Sequence of causation in mainline economics

Perfectly rational self-interest Irrational self-interest

Figure 4.2

Invisible hand

Perfect market structure Imperfect market structure

Social order Social disorder

Sequence of causation in mainstream economics

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of reconciliation via exchange between fallible but capable individuals within a context of private property, freedom of contract, and the rule of law. All the developments that we are talking about are, as we quoted Coase above as saying, seen as filling in the gaps of Adam Smith’s scientific system. However, as the mainstream of economics deviated significantly from the mainline of economics as developed by the classical political economists and early neoclassical economists, acts of scientific entrepreneurship were initiated to try to place the individual once again at the center of economic analysis, and to resurrect institutional analysis as critical in explaining observed patterns of social order (disorder). It is in these acts of scientific entrepreneurship that we see ‘schools of thought’ playing out their function – in our narrative this includes the ‘Austrian school’, the ‘property rights school’, the ‘public choice school’, and the ‘new institutional school’ of contemporary economic thought. For Mises and the Austrians of the 1930s, the major opponents of this mainline notion of economic theorizing were perceived ‘not as being the followers of Walras or of Marshall, but as being the historical and institutionalist writers’ (Kirzner 1988, p. 9) who had criticized mainline reasoning by presuming that catallactics was behaviorally dependent on a notion of Homo economicus. For example, Institutionalist economist Thorstein Veblen criticized neoclassical economists for basing economic theory upon ‘a faulty conception of human nature’, which he rejected as a ‘hedonistic’ conception of man as a lightning calculator of pleasure and pains, namely because ‘under hedonism the economic interest is not conceived in terms of action’ (1898, p. 394). Remarking on such renditions made by Institutionalists and Historicists during the Methodenstreit, Mises asserted that: It was a fundamental mistake of the Historical School . . . in Germany and of Institutionalism in America to interpret economics as the characterization of the behavior of an ideal type, the Homo oeconomicus . . . Such a being does not have and never did have a counterpart in reality; it is a phantom of a spurious armchair philosophy. No man is exclusively motivated by the desire to become as rich as possible; many are not at all influenced by this lean craving. It is vain to refer to such an illusory homunculus in dealing with life and history. Even if this really were the meaning of classical economics, the Homo oeconomicus would certainly not be an ideal type. The ideal type is not an embodiment of one side or aspect of man’s various aims and desires. It is always the representation of complex phenomena of reality, either men, of institutions, or of ideologies. The classical economists sought to explain the formation of prices. They were fully aware of the fact that prices are not a product of the activities of a special group of people, but the result of an interplay of all members of the market society. (Mises 1949 [2007], p. 62, original emphasis)

Just like the classical economists, neoclassical economists of the twentieth century also were preoccupied with explaining the formation of prices, but they were also increasingly occupied with conceptualizing price determination along Walrasian and Marshallian lines, both of which take cost curves to be objective and therefore measurable. It is from this backdrop that a debate emerged over the use of marginal analysis, in which the criticisms of institutionalist economists against the principles of neoclassical economics would resurface. As Robert Prasch has argued, ‘this episode, now remembered as the “Marginal Cost Controversy”, presents us with something of an American Methodenstreit’ (2007, p. 815). During the 1940s, economist Richard Lester challenged the empirical reality of economic actors engaging in marginal decision making. According to Lester, survey data of labor markets demonstrated that actors had no clue about weighing marginal benefits and

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Rational choice as if the choosers were human 73 marginal costs. For Lester, like the Institutionalists, economic generalizations could be inferred without theory from generalizations that could be verified empirically. Moreover, when ‘analytical concepts, including the competitiveness of the market, the nature of economic rationality, or the structure of a firm’s costs, are assumed or asserted without reference to widely understood and accepted facts, then that theory lacked genuinely scientific foundation’ (Prasch 2007, p. 814). Although Lester was rejecting marginalist principles, the premise of Lester’s argument rested implicitly on the notion that cost curves were objective in the sense that they were measurable by an outside observer. In this respect, Austrian economist Fritz Machlup responded that cost curves were subjective, and therefore his conclusions were invalid. Machlup’s position is consistent with that of Hayek’s presentation in ‘Economics and knowledge’ (1937), where the marginal conditions are not assumptions going into an analysis, but by-products that emerge out of decision making, ‘discovered anew’ within the process of market competition itself. Too often Machlup’s contribution is captured under the heading of ‘as if’ modeling. While Machlup often used the instrumentalist language of his day to try to communicate his point, a careful reader will note that he always makes subtle shifts in language, which was understood by those at the time as qualifiers, but which have failed to travel through time with him. Such a classic case is his shift in the debate over verification in economic science where he switches the claim about ‘predictability’ to one focused on ‘intelligibility’ (Machlup 1955; see also Boettke 2015; Zanotti and Cachanosky 2015). A similar subtle switch occurs when Machlup in the science wars argues that economics is as scientific as the natural sciences are, but it is just that in the sciences of man ‘matter can talk’, changing the epistemological problems that must be confronted in the practicing of the science. A comprehensive review of the marginal cost controversy is beyond the scope of this chapter (see also Lavoie 1990). What is important for our analysis of rational choice, in which economic actors are fallible, but capable, human beings and neither mechanistic automatons nor hopelessly disoriented actors, is that many textbook presentations of the Lester–Machlup debate present Machlup as the winner, but present his argument in the ‘as if’ tradition of thinking championed by Milton Friedman (1953). Individuals act ‘as if’ they balance marginal benefits and costs even if they do not explicitly do so in their own minds. However, this misses the point in the sense that the debate has been understood in terms of behavioral assumptions of whether or not individuals are profit maximizing or not. What was lost in the exchange was an analysis of the institutional conditions within which individuals are enabled to or inhibited from pursuing maximum profits, not only pecuniary but also non-pecuniary. The marginal conditions have little or nothing to do with how individuals actually make decisions. Rather, the marginal conditions emerge as a by-product of the market process within an institutional context of private property, prices, and profit/loss accounting. We do not have direct access to motivations of individuals. What we can study is the systemic effect of different institutional arrangements on the incentives that actors face. However, we cannot detail what motivates individuals. As the renowned Chicago economist Frank Knight has argued: We really know very little about human motives, and still oversimplify them disastrously in nearly all discussion . . . The larger problem is to arrange things so that people will find their

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Among those institutional arrangements, Knight not only emphasized the formal constraints such as private property, freedom of contract, and the rule of law, but also understood that such formal institutions are dependent on informal norms that are conducive to self-interest ‘rightly understood’ as the harmony or dovetailing of individual ends among members of society: From the falsity of the atomistic-individualistic view of human nature and human desires it is an easy inference that any mechanical theory of social organization is subject to narrow limitations. The most potent agency of social control, even today, in spite of all the obstacles thrown in its way by an antiquated and wooden system of association, is the moral control of the individual’s sense of decency and the pressure of the opinions of his fellows. (Knight 1920 [2011], p. 87)

Moreover, market interactions are not defined solely by monetary exchanges, but also encompass and depend upon a realm of voluntary, non-monetary associations, which Coase recognized are prohibitively costly to effect through monetary exchange because of the costs of defining separate contracts (Coase 1937). Because of these transactions costs, not only firms but also other institutions such as marriage and families emerge to avoid the costliness of pricing non-monetary attributes, such as love amongst marriage partners and parental devotion towards children: The great advantage of the market is that it is able to use the strength of self-interest to offset the weakness and partiality of benevolence, so that those who are unknown, unattractive, or unimportant, will have their wants served. But this should not lead us to ignore the part which benevolence and moral sentiments do play in making possible a market system. Consider, for example, the care and training of the young, largely carried out within the family and sustained by parental devotion. If love were absent and the task of training the young was therefore placed on other institutions, run presumably by people following their own self-interest, it seems likely that this task, on which the successful working of human societies depends, would be worse performed. (Coase 1976, p. 544)

Returning to Veblen’s critique of neoclassical economics, rational choice does not depend on ‘mechanical’ or ‘hedonistic’ responses to objective cost and profit functions. To counter Veblen, economics is in fact an evolutionary science, but one that is firmly grounded in an open-ended notion of rational choice that embraces both discovery under uncertainty. Alluding to the marginal cost controversy described above, Armen Alchian states the following in his classic article ‘Uncertainty, evolution, and economic theory’: While it is true that the economist can define a profit maximization behavior by assuming specific cost and revenue conditions, is there any assurance that the conditions and conclusions so derivable are not too perfect and absolute? If profit maximization (certainty) is not ascertainable, the confidence about the predicted effects of changes, e.g., higher taxes or minimum wages, will be dependent upon how close the formerly existing arrangement was to the formerly ‘optimal’ (certainty) situation. What really counts is the various actions actually tried, for it is from these that ‘success’ is selected, not from some set of perfect actions. The economist may be pushing his luck too far in arguing that actions in response to changes in environment and changes in satisfaction with the existing state of affairs will converge as a result of adaptation or adop-

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Rational choice as if the choosers were human 75 tion toward the optimum action that should have been selected, if foresight had been perfect. (Alchian 1950, p. 220)

What Alchian is arguing is that neither the behavioral assumption of profit maximization nor perfect foresight is necessarily required ex ante for human rationality. What is sufficient is awareness of the institutional conditions within which human rationality manifests itself ex post: Even if each and every individual acted in a haphazard and nonmotivated manner, it is possible that the variety of actions would be so great that the resulting collective set would contain actions that are best, in the sense of perfect foresight . . . The essential point is that individual motivation and foresight, while sufficient, are not necessary. Of course, it is not argued here that therefore it is absent. All that is needed by economists is their own awareness of the survival conditions and criteria of the economic system and a group of participants who submit various combinations and organizations for the system’s selection and adoption. (Alchian 1950, pp. 215–17)

Regardless of the behavioral assumptions, given the ubiquitous presence of scarcity, rational human action (that is, the continuous application of discovered means to individual aims) will generate the contextual knowledge, manifested through the price system, for the pursuit of maximum profits given the particular institutional context (Boettke and Candela 2015). The science of economics analyzes how fallible, but capable individuals do their best under particular institutional constraints. The art of economics, however, is uncovering those institutional constraints for understanding how in each particular case individuals attempt to do their best: Even if environmental conditions cannot be forecast, the economist can compare for given alternative potential situations the types of behavior that would have higher probability of viability or adoption. If explanation of past results rather than prediction is the task, the economist can diagnose the particular attributes which were critical in facilitating survival, even though individual participants were not aware of them. (Alchian 1950, p. 216)

The outside observer of human behavior who assesses some particular action as ‘irrational’ makes his or her evaluation based on either a value judgment of the ends pursued or narrowly defining the actor’s utility function to fit a close-ended model of choice. Criticisms of Homo economicus have been based both on the former, namely, by challenging efficiency as an ethical benchmark, as well as the latter by subjecting the model to narrowly defined monetary motives. As Elinor Ostrom states, this ‘thin model of rationality needs to be viewed . . . as the limiting case of bounded or incomplete rationality’ (1998, p. 9), that emerges only after all the gains from trade and specialization have been exhausted. However, ‘as we move away from these conditions we must explore not only the immediate consequences in terms of choices but particularly the kinds of institutions that will evolve in such contexts to structure human interaction’ (North 1993, p. 161). Rational action understood among mainline economists refers to the discovery of the means applied towards the fulfillment of a particular end; it does not necessarily depend on all our preferences and means being given or specified in one’s utility function. ‘Consistent with all models of rational choice is a general theory of human behavior that views all humans as complex, fallible learners who seek to do as well as they can given the

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constraints that they face and who are able to learn heuristics, norms, rules, and how to craft rules to improve achieved outcomes’ (Ostrom 1998, p. 9). It encompasses learning from our errors through time, without which institutions would not matter (North 1994). Thus, the innumerable manifestations of rationality depend on the institutional context within which learning through time takes place. Therefore, as Vernon Smith explains, what seems to be ‘irrational’ to the outside observer of human behavior is no more than a misunderstanding of the institutional context: Thus, if people in certain contexts make choices that contradict our formal theory of rationality, rather than conclude that they are irrational, some ask why, reexamine maintained hypotheses including all aspects of the experiments – procedures, payoffs, context, instructions, etc. – and inquire as to what new concepts and experimental designs can help us to better understand the behavior. (Smith 2003, p. 471)

Moreover, institutional analysis does not imply that rules will always be perfectly specified or that individuals respond passively to the institutional reward structure. Rather, because of the cost of defining all of the possible actions that may be prohibited or sanctioned by the institutional framework, entrepreneurial discovery by individuals will generate endogenous institutional solutions to problems that are institutional in nature, resulting in gradual changes to the institutional framework. In the next section, we elaborate on the insights of Hayek, Buchanan and Ostrom in shifting to the rule level of analysis in analyzing human decision making and human interaction.

3

DEVELOPMENT OF THESE INSIGHTS TO THE RULE LEVEL OF ANALYSIS

We have previously argued that exposition of economic phenomena in terms of competitive equilibrium as a description of reality rather than using equilibrium analysis as a heuristic tool had rendered institutional analysis of little concern to economists. By extending the pure logic of rational choice to closed-form solutions, real-world markets act ‘as if’ they were in competitive equilibrium, not only purging the analysis of institutional derivations of the logic of choice, but also resulting in economic analysis becoming increasingly reliant on behavioral assumptions characterized as Homo economicus, around which advocates and critics of the market order would base their arguments.2 By collapsing the gap between economic models and economic reality, the behavioral intentions of economic actors correspond one-to-one with the outcomes ‘predicted’ within the model (Boettke and Candela 2014). What Buchanan, Hayek, and Ostrom acknowledged was that a richer notion of economic theory included institutional analysis and that incorporating institutional analysis enabled economic analysis to explain a broader notion of rational choice as if choosers were human. Moreover, what distinguished them from earlier classical as well as neoclassical economists was their application of rational choice to the rule level of analysis as well. Unlike the behavioral and physical laws of nature, which they took as given, what they explicitly drew attention to was that ‘rules are interesting variables precisely because they are potentially subject to change. That rules can be changed by humans is one of their key characteristics’ (Ostrom 1986, p. 5).

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Rational choice as if the choosers were human 77 Hayek as early as 1937 in ‘Economics and knowledge’ recognized that rational choice analysis, or what he referred to as the pure logic of choice, was a necessary, though not a sufficient, condition for equilibrium analysis. What was sufficient for the derivation of equilibrating tendencies within the market order, however, was a shift to the rule level of analysis, or comparative institutional analysis. Fundamentally, the importance of rules to Hayek was to provide a framework of predictable guidelines within which individuals could adapt to unforeseen circumstances. As he states: We can produce the conditions for the formation of an order in society, but we cannot arrange the manner in which its elements will order themselves under appropriate conditions. In this sense the task of the lawgiver is not to set up a particular order but merely to create conditions in which an orderly arrangement can establish and ever renew itself. As in nature, to induce the establishment of such an order does not require that we be able to predict the behavior of the individual atom – that will depend on the unknown particular circumstances in which it finds itself. All that is required is a limited regularity in its behavior; and the purpose of the human laws we enforce is to secure such limited regularity as will make the formation of an order possible. (Hayek 1960, p. 161)

Ostrom also acknowledged that rules ‘are the result of implicit or explicit efforts by a set of individuals to achieve order and predictability within defined situations’ (1986, p. 5). More so than Buchanan and Ostrom, Hayek emphasized that rules emerged from a spontaneous order based on human action, though not of human design. However, like Buchanan and Ostrom, he also acknowledged that rules that have evolved spontaneously can also be improved upon by marginal deliberative choices on the level of rules to facilitate different patterns of interactions within those rules: At the moment our concern must be to make clear that while the rules on which spontaneous order rests may also be of spontaneous origin, this need not always be the case. Although undoubtedly an order originally formed itself spontaneously because the individuals followed rules which had not been deliberately made but had arisen spontaneously, people gradually learned to improve those rules; and it is at least conceivable that the formation of a spontaneous order relies entirely on rules that were deliberately made. (Hayek 1973, p. 45)

The rule level of analysis requires neither that rational agents are homogenous in their objectives, nor does it imply that they share only pecuniary aims, such as that attributed to Homo economicus. As Buchanan states: The central rationality precept states only that the individual choose more rather than less of goods, and less rather than more of bads. There is no requirement that rationality dictates choice in accordance with the individual’s economic interest, as this might be measured by some outside observer of behavior. The individualistic postulate allows the interests or preferences of individuals to differ, one from another. And the rationality postulate does not restrict these interests beyond the classificatory step noted. Homo economicus, the individual who populates the models of empirical economics may, but need not, describe the individual whose choice calculus is analyzed in constitutional political economy. (Buchanan 1990, pp. 14–15)

Buchanan argued that economists could analyze the derivation of that framework separately through the tools of rational choice political philosophy, namely, social contract theory, but differently from Hayek and Ostrom, argued that institutions were provided

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exogenously in the first place. Ostrom, building more from Hayek in this regard, saw the framework itself as an endogenous set of rules that emerge from the bottom up, through the individual and group striving to minimize conflicts and realize gains from cooperation. It is the notion of ‘constitutional craftsmanship’ that is foundational to the work of Ostrom that provides the conciliatory link between the dual spontaneous order analysis argued for here and the restriction of spontaneous order analysis to the market process, argued notably by Buchanan. Yet the common thread uniting their shift to the rule level of analysis was that the ability for individuals to coordinate their actions fell within a paradigm of exchange behavior to achieve a more preferred arrangement of rules in order to facilitate outcomes conducive to cooperation through a division of labor. What they did not share was a vision of political economy through an ‘allocation paradigm’ (Buchanan 1964), in which rational choosers were purged of any human deliberation as well as confined to perfectly defined constraints not subject to change and improvements by human rational choosers themselves.

4

THE INSTITUTIONAL IMPRINT, RATIONAL CHOICE, AND PATH DEPENDENCY

The fundamental task that has plagued economists since Adam Smith, both mainline and mainstream, has been to inquire into the nature and causes of the wealth of nations. Particularly since the collapse of communist regimes in Central and Eastern Europe after 1989, this inquiry has been increasingly marked by a dovetailing of the mainline and mainstream through its emphasis on comparative institutional analysis and institutional path dependency in understanding the lag in economic development not only among countries emerging from communism, but also in Asia, Africa, and Latin America. The plain fact is that the ultimate source of poor economic performance in third-world countries is the polity that specifies and enforces the economic rules of the game. As yet the study of third-world polities is as underdeveloped as their polities themselves. But one thing is for sure: not much progress is going to be made in modeling such polities without taking into account the limits of rational choice and the importance of ideologies. (North 1993, pp. 160–61)

The point that Douglass North makes in this quote is that not only do the formal rules of the game matter for the economic performance of a country, but also that informal constraints provide path dependency in cultural attitudes towards trade and exchange. As North argues, ‘once a development path is set on a particular course, the network externalities, the learning process of organizations, and the historically derived subjective modeling of the issues reinforce the course’ (North 1990, p. 99). In this respect, neoclassical economics could not underpin the reform of centrally planned economies for two particular reasons. First, the theoretical model of perfect competition operates as a behavioral filter of the limiting conditions that apply to individuals after all the gains from trade and specialization have been exploited. It illustrates an idealized world in which individuals are fully informed and perfectly rational in making their decisions, and that the constraints they face, such as prices and income, are taken as given. However, lacking any institutional filter of the structure of incentives that gener-

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Rational choice as if the choosers were human 79 ates tendencies towards such limiting conditions, deviations from this behavioral model can only lead the economist to conclude that individuals are behaving ‘irrationally’ and that the economy is prone to ‘market failure,’ characterized by the prevalence of asymmetric information, externalities, and monopoly power. As Ronald Coase argued, ‘These ex-communist countries are advised to move to a market economy, and their leaders wish to do so, but without the appropriate institutions no market economy of any significance is possible. If we knew more about our own economy, we would be in a better position to advise them’ (1992, p. 714). Secondly, as Coase alluded to above, without understanding the ‘tacit presuppositions’ (Buchanan 1997) that are embodied in the underlying norms, customs, and behavioral attitudes of individuals that reinforce the prevailing institutions of society, it would be unclear whether the institutional designs of economic reformers would have the intended effect on the economic performance of a country. James Buchanan clearly makes this point: In Western regimes, markets work tolerably well, within the political-legal framework of widely dispersed property rights, when the workings of ordinary politics do not interfere excessively. They do so because they have evolved through a long history which has been interpreted and understood by experts in such fashion as to reinforce the behavioural attitudes necessary to make such institutions function. In failed socialist regimes, markets have neither the history nor the interpretation-understanding that informs behavioural attitudes. It seems naïve in the extreme to assume that the market order is ‘natural’ to the extent that it can emerge full blown without history, without institutional construction and, most importantly, without understanding. (1997, p. 106)

It is not just that institutions matter, but history and ideas matter for understanding the feasible institutional opportunity set within which the economist is able to propose reforms. Furthermore, Buchanan makes a distinction between an ‘exchange culture’ and a ‘command culture’ to distinguish the underlying behavioral attitudes in Western and post-socialist economies, respectively. To understand this point, Buchanan is neither denying that individuals are choosing rationally, nor is relying on any notion of Homo economicus. Rather, it is the underlying informal norms prevalent throughout members of society that motivate the degree of toleration and acknowledgement of the mutually beneficial nature of trade under anonymity, which fundamentally extends the limit of the market, and widens the scope for rational self-interest to encompass activity beyond the behavioral confines of Homo economicus. In Western countries, Buchanan argues the following: The attitude of reciprocity in the market relationships remains relatively pervasive in Western economies, even in those settings where, in a behavioural sense, there remains little or no rational foundations for such attitude. The salesclerk in the Sheraton Hotel in Houston, Texas, offers me a postcard as if she, personally, has an interest in my purchase, even when both of us know that her wage or position depends only in some extremely remote sense on her behaviour in our exchange relationship. The exchange relationship tends to foster the attitude of reciprocity, even in as if settings, and behavior reflecting such an attitude tends in itself to promote a mutuality of expectations that becomes reinforcing. (1997, p. 97, original emphases)

In those countries that have failed to emerge successfully, in terms of economic growth, from the failures of socialism, the underlying informal norms of society are conducive to

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a command culture, one that ‘describes an idealization of collective reality, as this reality is interpreted by those who experience it’. It reinforces the idea that the ‘exertion of effort creates no claim to share in product. Effort is directed toward common purposes, and linkage between effort and reward becomes the source of envy rather than emulation’ (Buchanan 1997, p. 101, original emphasis). From this cultural context, it would therefore seem rational for individuals to regard the market order with suspicion, especially when viewed in the zero-sum terms of a command culture. Extending Buchanan’s point, Pejovich elaborates: In many parts of the region, gains from trade are seen as a redistribution of income rather than as rewards to innovators for creating new wealth. State authorities are more likely to impose price controls on producers and/or merchants who earn large profits than to seek ways to create incentives for others to emulate such individuals in competitive markets. The cultural heritage in [Central and Eastern Europe] supports an activist state. Historical development and nationalism are major reasons for cultural differences within the region . . . By feeding on the conviction that the community’s common good transcends the private ends of its members, nationalism in many [Central and Eastern European] countries has reinforced the culture of collectivism. (2003, p. 351)

Although our discussion thus far has emphasized a comparative institutional analysis between Western economies and the economies of Central and Eastern Europe, our observations have broader implications for income differences across time as well. Not only do the formal institutions matter for economic growth, but, perhaps more importantly, the fact that customary practice dictates the legitimacy of formal institutions is because informal rules ‘are not a policy variable’ (Pejovich 2003, p. 348) and, therefore, formal institutions must be crafted to be congruent, or ‘stick’ to the underlying informal rules of society. Although economic reformers may succeed in designing institutions that exhibit ‘institutional stickiness’ (Boettke et al. 2008) to informal institutions, this is by no means sufficient for generating economic growth: There is no guarantee that beliefs and institutions that evolve through time will produce economic growth . . . In fact, most societies throughout history got ‘stuck’ in an institutional matrix that did not evolve into the impersonal exchange essential to capturing the productivity gains that came from the specialization and the division of labor that have produced the Wealth of Nations. (North 1994, pp. 363–4)

With respect to the logic of rational choice, societies that exhibit path dependency towards economic stagnation does not imply irrationality on the part of economic actors within that society. Rationality must be understood as entirely subjective and forward looking; an individual’s perception of costs and benefits are shaped by the institutional payoffs: In every system of exchange, economic actors have an incentive to invest their time, resources, and energy in knowledge and skills that will improve their material status. But in some primitive institutional settings, the kind of knowledge and skills that will pay off will not result in institutional evolution towards more productive economies. (North 1991, p. 102)

As North elaborates on how institutional path dependence can be self-sustaining: In the case of economic growth, an adaptively efficient path . . . allows for a maximum of choices under uncertainty, for the pursuit of various trial methods of undertaking activities, and for an

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Rational choice as if the choosers were human 81 efficient feedback mechanism to identify choices that are relatively inefficient and to eliminate them . . . But so, too, can unproductive paths persist. The increasing returns characteristic of an initial set of institutions that provide disincentives to productive activity will create organizations and interest groups with a stake in the existing constraints. They will shape the polity in their interests. Such institutions provide incentives that may encourage military domination of the polity and economy, religious fanaticism, or plain, simple redistributive organizations, but they provide few rewards from increases in the stock and dissemination of economically useful knowledge. The subjective mental constructs of the participants will evolve an ideology that not only rationalizes the society’s structure but accounts for its poor performance. As a result the economy will evolve policies that reinforce the existing incentives and organizations. (North 1990, p. 99)

However, the observation that certain societies are locked into an institutional path unconducive to economic growth does not necessarily imply that intervention from reformers external to the particular institutional context will resolve the situation, namely, by establishing private property rights or transplanting other formal institutions that evolved within the historical context of Western economic development. As Ostrom has argued: When analysts perceive the human beings they model as being trapped inside perverse situations, they then assume that other human beings external to those involved – scholars and public officials – are able to analyze the situation, ascertain why counterproductive outcomes are reached, and posit what changes in the rules-in-use will enable participants to improve outcomes. Then, external officials are expected to impose an optimal set of rules on those individuals involved. It is assumed that the momentum for change must come from outside the situation rather than from the self-reflection and creativity of those within a situation to restructure their own patterns of interaction. (2010, p. 648)

Ostrom recognized that when the definition and enforcement of property rights are devised from the bottom up rather than from the top down, individuals will have a greater incentive to conserve on resources used in the process than when that process is imposed exogenously, not only because they exhibit greater residual claimancy over their actions, but also because they are able to utilize their contextual knowledge not often available to external reformers. The ability of individuals to craft rules that are effective within their own communities hinges upon the mutually agreed-upon rules of governance that then establish reliable expectations among the community. Elinor Ostrom emphasized the legitimacy of rules as essential to minimizing the enforcement and monitoring costs of rules (1990, p. 205). If rules are developed internally, by actors with local legitimacy and knowledge of the community’s history, then monitoring can be a ‘natural by-product’ of the system of rules (Ostrom 1990, p. 96). In addition, because of the path-dependent nature of bottom-up institutional solutions, formal enforcement of rules cannot run contrary to how individuals perceive and understand them: Without individuals viewing rules as appropriate mechanisms to enhance reciprocal relationships, no police force and court system on earth can monitor and enforce all the needed rules on its own. Nor would most of us want to live in a society in which police were really the thin blue line enforcing all rules. (Ostrom 1998, p. 16)

A summary of the core argument of this section can be restated as follows (Boettke 2001, pp. 251–9):

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1. 2. 3.

People respond rationally to incentives as they perceive them. Incentives and therefore economic performance are a function of the rules of the game, both formal and informal. Rules are only rules if customary practice dictates.

5

CONCLUSION

In this chapter, we have argued that the caricature of economic man as Homo economicus has been an invalid and unwarranted characterization of the individual in their interactions with other individuals within the market order. From Adam Smith to Vernon Smith there has been a common thread that has united economic thought on man’s epistemic and behavioral capacity, one that rests on institutional analysis and the emergence of customs, norms, and monetary prices to guide social interaction towards social order. Hayek best summarizes this common lineage in mainline economic thought: Perhaps the best illustration of the current misperceptions of the individualism of Adam Smith and his group is the common belief that they had invented the bogey of ‘economic man’ and that their conclusions are vitiated by their assumption of a strictly rational behavior or generally by a false rationalistic psychology. They were, of course, very far from assuming anything of the kind. It would be nearer to the truth to say that in their view man was by nature lazy and indolent, improvident and wasteful, and that it was only by the force of circumstances that he could be made to behave economically or carefully to adjust his means to his ends . . . The main point about which there can be little doubt is that Smith’s chief concern was not so much with what man might occasionally achieve when he was at his best but that he should have as little opportunity as possible to do harm when he was at his worst. It would be scarcely too much to claim that the main merit of the individualism which he and his contemporaries advocated is that it is a system under which bad men can do least harm. It is a social system which does not depend for its functioning on our finding good men for running it, or on all men becoming better than they now are, but which makes use of men in all their given variety and complexity, sometimes good and sometimes bad, sometimes intelligent and more often stupid. (1948, pp. 11–12)

The excessive preoccupation with the behavioral characteristics of man in economic analysis from the late nineteenth century to the mid-twentieth century resulted not only from misunderstandings about the role of theory and history (Mises 1957) in economic analysis, but also from depicting economic phenomena in terms of competitive equilibrium. Because facts are theory laden, the purpose of economy theory is to engage in historical explanation of facts. To argue that man is rational – that is, that he or she evaluates the marginal costs and benefits of undertaking an activity towards the fulfillment of a particular goal – does grant that individual infallibility or omniscience. This is the realm of price theory, consistent with the understanding of mainline economists discussed in this chapter. Rather, such decision making and the manifestations of rationality must be evaluated within its particular institutional context. This is the realm of history. The modern analytical narrative approach employed by Bates et al. (1998) embodies this distinction between theory and history that Mises specified: The process of deciding the appropriate individuals, their preferences, and the structure of the environment – that is, the right game to use – is an inductive process much like that used in modern comparative politics, by historical institutionalists, and by historians. Once that induc-

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Rational choice as if the choosers were human 83 tion is complete, we can use the deductive methods to study behavior within the context of the game. (Bates et al. 2000, p. 697).

However, when economic theory becomes dependent on behavioral assumptions, not only does this tend to crowd out institutional analysis, collapsing human intentions on to outcomes, but it also leads to misleading characterizations of human ‘irrationality’ when historical events, such as the Great Depression or more recently the Great Recession, are not ‘predicted’ by theory or do not correspond to some particular efficiency benchmark. As a result, arguments about the success of comparative economic systems, even those in defense of the market order, will hinge upon the behavioral capabilities of man. The predictive power of mainstream theorizing in macroeconomics, both Keynesian as well as new classical, no less depend on whether individuals are hopelessly irrational or perfectly rational, respectively. With the resulting disconnect of theory from history, new emphasis was drawn to the rule level of analysis, which had not only been emphasized by Adam Smith and Frank Knight, but was reincarnated as new insights manifesting themselves as the public choice of James Buchanan, new institutional economics of Armen Alchian, Ronald Coase and Elinor Ostrom, the market process of modern Austrian economics developed by Mises and Hayek, the experimental economics of Vernon Smith, and the new economic history of Douglass North. Each of these scholars, while rejecting the notion of Homo economicus, did not throw rational choice by the wayside either. Instead, their contributions to economics were built upon ‘Adam Smith’s view of man’ as ‘he actually is – dominated, it is true, by self-love but not without some concern for others, able to reason but not necessarily in such a way as to reach the right conclusion, seeing the outcomes of his actions but through a veil of self-delusion’ (Coase 1976, pp. 545–6). While we do not deny that ‘Adam Smith frequently wrote about the psychology of decision-making’ (Laibson and List 2015, p. 385), accepting this view of man, who is a fallible but capable individual, will draw the economist’s attention, whether mainline or mainstream, behavioral or traditional, to the realization that man’s capacity to foster social order and capture the gains from exchange and innovation depends not on his reason or lack of reason per se, but on rules that are discovered and crafted to marshal individual reasoning dispersed among individuals throughout society.

NOTES 1. Economist Mark Thoma, in a 2015 article, goes so far as to say ‘I believe in markets as much as anyone. But for markets to work well the conditions for perfect competition must be approximated’. The notion that markets only ‘work’ after all the gains from trade and exchange of goods and services (including information) have been exploited (that is, perfect competition) is a description of the end result of market competition, not an analysis of the economic forces at work. Moreover, this characterization of the market ‘working well’ not only commits the nirvana fallacy (see Demsetz 1969) of comparing imperfect markets to a perfectly efficient benchmark, but more importantly, it also lacks comparative institutional analysis of market forces under different conditions. 2. During the socialist calculation debate between the 1920s and 1940s, market socialists, arguing against Mises’s claim that rational calculation was impossible under socialism, utilized neoclassical equilibrium analysis to establish the invalidity of Mises’s claim. Mises’s as well as Hayek’s fundamental argument during the debate held that, absent the institutional prerequisites of private property, central planners would be precluded from the prices and contextual knowledge required to engage in economic calculation. However,

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REFERENCES Alchian, A. (1950), ‘Uncertainty, evolution, and economic theory’, Journal of Political Economy, 58 (3), 211–21. Altman, M. (2012), Behavioral Economics for Dummies, Mississauga: John Wiley & Sons Canada. Bates, R.H., A. Greif, M. Levi, J.-L. Rosenthal and B. Weingast (1998), Analytical Narratives, Princeton, NJ: Princeton University Press. Bates, R.H., A. Greif, M. Levi, J. Rosenthal and B. Weingast (2000), ‘Review: the Analytical Narrative Project’, American Political Science Review, 94 (3), 696–702. Boettke, P.J. (2001), ‘Why culture matters: economics, politics, and the imprint of history’, in P.J. Boettke (ed.), Calculation and Coordination: Essays on Socialism and Transitional Political Economy, New York: Routledge, pp. 248–65. Boettke, P.J. (2012), Living Economics: Yesterday, Today, and Tomorrow, Oakland, CA: Independent Institute. Boettke, P. (2015), ‘The methodology of Austrian economics as a sophisticated, rather than naive, philosophy of economics’, Journal of the History of Economic Thought, 37 (1), 79–85. Boettke, P. and R. Candela (2014), ‘Alchian, Buchanan, and Coase: a neglected branch of Chicago price theory’, Man and the Economy, 1 (2), 189–208. Boettke, P. and R. Candela (2015), ‘What is old should be new again: methodological individualism, institutional analysis and spontaneous order’, Sociologia, 49 (2), 5–14. Boettke, P. and K. Vaughn (2002), ‘Knight and the Austrians on capital and the problem of socialism’, History of Political Economy, 34 (1), 153–74. Boettke, P., C. Coyne and P. Leeson (2008), ‘Institutional stickiness and the new development economics’, American Journal of Economics and Sociology, 67 (2), 331–58. Buchanan, J. (1964), ‘What should economists do?’, Southern Economic Journal, 30 (3), 213–22. Buchanan, J. (1990), ‘The domain of constitutional economics’, Constitutional Political Economy, 1 (1), 1–18. Buchanan, J.M. (1997), Post-Socialist Political Economy: Selected Essays, Cheltenham, UK and Lyme, NH, USA: Edward Elgar. Coase, R. (1937), ‘The nature of the firm’, Economica, 4 (16), 386–405. Coase, R. (1976), ‘Adam Smith’s view of man’, Journal of Law and Economics, 19 (3), 529–46. Coase, R. (1992), ‘The institutional structure of production’, American Economic Review, 82 (4), 713–19. Demsetz, H. (1969), ‘Information and efficiency: another viewpoint’, Journal of Law and Economics, 12 (1), 1–22. Friedman, M. (1953), ‘The methodology of positive economics’, in M. Friedman, Essays in Positive Economics, Chicago, IL: University of Chicago Press, pp. 3–43. Hayek, F.A. (1937), ‘Economics and knowledge’, Economica, 4 (13), 33–54. Hayek, F.A. (1945), ‘The use of knowledge in society’, American Economic Review, 35 (4), 519–30. Hayek, F.A. (1948), Individualism and Economic Order, Chicago, IL: University of Chicago Press. Hayek, F.A. (1960), The Constitution of Liberty, Chicago, IL: University of Chicago Press. Hayek, F.A. (1973), Law, Legislation, and Liberty, Volume I: Rules and Order, Chicago, IL: University of Chicago Press. Keynes, J.M. (1926), ‘The end of laissez faire’, reprinted 1978 in E. Johnson and D. Mogridge (eds), The Collected Writings of John Maynard Keynes, Volume IX: Essays in Persuasion, New York: Cambridge University Press. Keynes, J.M. (1936), The General Theory of Employment, Interest, and Money, reprinted 1964, New York: Harcourt, Brace & World. Kirzner, I. (1988), ‘The economic calculation debate: lessons for Austrians’, Review of Austrian Economics, 2 (1), 1–18. Knight, F. (1919), ‘Review of Co-operation and the Future of Industry by Leonard W. Woolf’, Journal of Political Economy, 27 (9), 805–6.

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Rational choice as if the choosers were human 85 Knight, F. (1920), ‘Social organization: a survey of its problems and forms from the standpoint of the present crisis’, Research in the History and Methodology of Economic Thought, vol. 29-B, 65–88, reprinted 2011, Bradford: Emerald Publishing. Laibson, D. and J. List (2015), ‘Principles of (behavioral) economics’, American Economic Review, 105 (5), 385–90. Lavoie, D. (1990), ‘Hermeneutics, subjectivity, and the Lester/Machlup debate: toward a more anthropological approach to empirical economics’, in W.J. Samuels (ed.), Economics as Discourse: An Analysis of the Language of Economists, Dordrecht: Springer, pp. 167–87. Machlup, F. (1955), ‘The problem of verification in economics’, Southern Economic Journal, 22 (1), 1–21. Mises, L. von (1933), Epistemological Problems of Economics, reprinted 1960, Princeton, NJ: Van Nostrand. Mises, L. von (1949), Human Action: A Treatise on Economics, reprinted 2007, Indianapolis, IN: Liberty Fund. Mises, L. von (1957), Theory and History: An Interpretation of Social and Economic Evolution, New Haven, CT: Yale University Press. North, D. (1990), Institutions, Institutional Change, and Economic Performance, New York: Cambridge University Press. North, D. (1991), ‘Institutions’, Journal of Economic Perspectives, 5 (1), 97–112. North, D. (1993), ‘What do we mean by rationality?’, Public Choice, 77 (1), 159–62. North, D. (1994), ‘Economic performance through time’, American Economic Review, 84 (3), 359–68. Ostrom, E. (1986), ‘An agenda for the study of institutions’, Public Choice, 48 (1), 3–25. Ostrom, E. (1990), Governing the Commons: The Evolution of Institutions for Collective Action, New York: Cambridge University Press. Ostrom, E. (1998), ‘A behavioral approach to the rational choice of collective action’, American Political Science Review, 92 (1), 1–22. Ostrom, E. (2010), ‘Beyond markets and states: polycentric governance of complex economic systems’, American Economic Review, 100 (3), 641–72. Pejovich, S. (2003), ‘Understanding the transaction costs of transition: it’s the culture, stupid’, Review of Austrian Economics, 16 (4), 347–61. Prasch, R. (2007), ‘Professor Lester and the neoclassicals: the “marginalist controversy” and the postwar academic debate over minimum wage legislation: 1945–1950’, Journal of Economic Issues, 41 (3), 809–25. Smith, V. (2003), ‘Constructivist and ecological rationality in economics’, American Economic Review, 93 (3), 465–508. Thoma, M. (2015), ‘The problem with completely free markets’, accessed 2 July 2015 at http://www.thefiscal times.com/columns/2015/06/30/problem-completely-free- markets?onswipe_redirect5no&oswrr51. Veblen, T. (1898), ‘Why is economics not an evolutionary science?’, Quarterly Journal of Economics, 12 (4), 373–97. Zanotti, G. and N. Cachanosky (2015), ‘Implications of Machlup’s interpretation of Mises’s epistemology’, Journal of the History of Economic Thought, 37 (1), 111–38.

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Smart predictions from wrong data: the case of ecological correlations* Florian Kutzner and Tobias Vogel

INTRODUCTION With the economic crisis that hit the European continent in 2007, many were interested in the consequences for those affected, from economic hardship, to emotional well-being to family planning. Take the relationship between the severity of the crisis and family planning as an illustration. Across 28 European countries, it was found that an increase in unemployment rates from 2007 to 2011 correlated with a decrease in birth rates (Goldstein et al., 2013). To illustrate, we plotted changes in unemployment rates against the respective changes in birth rates (see Figure 5.1). The correlation of –.34 indicates that those countries with larger increases in unemployment rates are marked by larger decreases in birth rates. It seems there exists ‘a strong relationship between economic conditions and fertility’ (Goldstein et al. 2013, p. 85) and that ‘the extent of joblessness . . . does in fact have an effect on birth rates’ (BBC 2013). Can we conclude, however, that people who lost their job delayed having children? In this chapter we show that people respond ‘Yes’ to this question, that this answer can be wrong at times, and yet this response can still be smart or rational on average. The conclusion that the correlation between changes in unemployment and birth rates across countries also holds for individual people, that those becoming unemployed are

Change in birth rate

0.20 0.10 0.00 –0.10 –0.20 –0.30 –5%

0%

5%

10%

15%

Change in unemployment rate Note: Dashed line 5 regression line. Source:

Data from Eurostat.

Figure 5.1

Changes in unemployment rates (2011 minus 2007) in 28 European countries plotted against respective changes in birth rates 86

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Smart predictions from wrong data 87 more likely to delay having children, can be severely wrong. Yet, when considering real-life constraints on decision making, being ignorant about different levels of data aggregation offers a smart strategy. In many cases, using aggregated data is the only option owing to a lack of more appropriate data or a lack of resources for appropriate analyses. Also, even though some conclusions will be wrong, an illustrative demonstration lends credence to the fact that correlations across countries (or other ecologies) have some predictive value for correlations across individuals.

ECOLOGICAL CORRELATIONS AND AGGREGATION BIAS The term ‘ecological’ in ecological correlations is applicable to any grouping of observations. Talking about people, it is easy to conceive ecologies as macro variables such as geographical entities. Yet, many other ecological groupings of variables are almost equally plausible and prominent, including social characteristics such as income or age groups. An ecological correlation results when a correlation between two variables is computed using the variables’ mean values for different ecologies. In Figure 5.1 the ecologies are countries that are assigned mean values for two variables: change in unemployment rates and change in birth rates. Across 28 of these ecologies a correlation is computed. That is, statistically the correlation is based on 28 observations. At the same time, thousands of individuals are behind the mean values, each of which has either become unemployed or not, and has become a parent or not. This individual information, however, is lost when aggregated into a country index. Which inferences about individuals are still valid given such aggregated data has puzzled statisticians, epidemiologists and sociologists for decades (Hannan 1971; Hammond 1973; King 2013). With aggregation comes greater reliability for estimating mean values. Asking 30 individuals about their change in employment status will be a less reliable estimate of the mean tendency than asking 1000 individuals. At the same time, correlations between these mean values across a handful of ecologies do not become more reliable. Even more dramatic than losing the reliability benefit, the size and even sign of correlations can differ from before to after aggregation. This sets the stage for ‘aggregation biases’ or ‘ecological fallacies’ (Hammond 1973). An early demonstration of diverging correlations given individual and aggregated data can be found in Robinson’s work (1950). Across nine US districts Robinson showed that the correlation between the averages of African-Americans and illiterates living in these districts was about r ≈ .95. The districts with higher rates of African-Americans had almost certainly higher illiteracy rates. At the same time, using individual data showed that the likelihood of being illiterate was barely higher for African-Americans than for non-African-Americans, only resulting in a correlation of r ≈ 0.2. An analogous divergence can be found with the effect of the economic crisis on birth rates. An analysis based on the changes in job and parental status of individual people, across a comparable time span and set of countries, reveals that becoming unemployed does not, on average, affect becoming a parent (Schmitt 2012). There are several reasons for a possible divergence (Hannan 1971; Hammond 1973). Perhaps the most obvious is the presence of an unobserved third variable that influences both measured variables. For example, allocation of people to districts might be

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driven in part by socio-economic status. If status is correlated with ethnicity as well as with literacy, the percentage of African-Americans and the percentage of illiterates are likely to be correlated across districts. Within every district and in the whole population African-Americans can be as likely to be literate as their counterparts. In a similar vein, a third-variable – yet, with a different function – could explain the unemployment–birth rate correlation. If the economic downturn does increase subjective uncertainty, then everybody, not just those losing their jobs might delay having children. Yet, while many variables might cause a divergence between aggregated or ecological and individual-level correlations, it will be hard to know for a given question whether any of these are at work, especially for the incidental user of aggregated data. In the next sections of this chapter we elaborate on the use of ecological correlations from a descriptive and a normative perspective. We first report evidence to demonstrate that lay people are insensitive as to whether correlations are based on aggregated or individual-level data. Then, we will illustrate that such ecological inferences are potentially smart, and though error-prone, provide a good guess about individual-level correlations.

THE USE OF ECOLOGICAL CORRELATIONS IN LAY PEOPLE The technical term ecological correlations might obscure how prevalent they are as part of everyday life, especially in the media. The BBC coverage of the change in birth rates is but one example. Another widely covered article (Preis et al. 2012) found that the countrywide tendencies to Google for dates in the future, as compared to dates in the past, are positively related to a country’s gross domestic product (GDP). Conclusions such as ‘a focus on the future supports economic success’ (Preis et al., 2012, p. 2) were readily recited in the media (Wall Street Journal 2012; Guardian, 2013). But, again, what do readers conclude from such coverage? Ecological Inferences when Individual-level Data are Unavailable Before reviewing a host of pertinent laboratory research, let us present a brief and straightforward survey of what incidental consumers of the BBC news coverage conclude from the birth rate article. Shortly after the article appeared under the headline ‘Europe birth rates “have fallen” since economic crisis’, we had UK citizens (N 5 159) recruited online read either the headline or the entire article. The article included a figure similar to the one presented in Figure 5.1. Subsequently, participants were asked to answer the following question: ‘Who is more likely to delay having children?’ Respondents had three options: ‘The employed’, ‘The unemployed’ or ‘Don’t know’. A majority of readers of the headline (see the white bars in Figure 5.2) concluded that the unemployed would be more likely to delay having children. Only a few concluded that this was true for the employed or that they did not know. After only reading the headline, though, this pattern might results from a shared stereotype of what influences family planning. More interestingly, for those participants who were confronted with the whole article, including the ecological correlation, this trend intensified, c2 (2) 5 8.25, p 5 .016. That is, even though the correlation presented was not based on individuals, it made readers more certain that their conclusions about the correlation between employment

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Smart predictions from wrong data 89 ‘Who is more likely to delay having children?’ 100% BBC article

80%

BBC headline 60% 40% 20% 0% Unemployed

Employed

Don't know

Note: BBC headline 5 participants just receiving a headline stating ‘Europe birth rates “have fallen” since economic crisis’.

Figure 5.2

Responses to being confronted with an ecological correlation across countries (BBC article) between the change in unemployment rate and the change in birth rate

and parental status for individuals were correct. Of course, the conclusion is intuitively plausible and the argument well presented. Thus, the more general question is whether people draw similar conclusions in more controlled settings or when incentivized to be accurate. More systematic evidence for the use of ecological correlations by lay people stems from a series of controlled laboratory experiments. In one experiment (Vogel et al. 2013), we presented participants with information about a fictitious city. The graphical display drew on online newspaper formats. Participants saw a schematic map of the city, which was separated into nine districts. For each of the districts, there was information about the percentages of citizens belonging to an ethnic majority or an ethnic minority group, as well as about the percentage of people satisfied (versus dissatisfied) with their lives. As our critical manipulation, we varied the ecological correlation across districts. For half of the participants, the percentage of satisfied people increased with the number of majority members across districts. For the other half of participants, the percentage of satisfied citizens decreased with an increase of majority members. Asked about individual citizens, participants in the latter group judged majority members as less satisfied with their lives than minority members. This finding reversed for participants in the former group, who had been exposed to a positive ecological correlation between majority group and life satisfaction. A second experiment, using an interactive map corroborated this notion. The interactive format required participants to request the relevant information by clicking on the respective areas. Only participants who were motivated to compare different areas with regard to the relevant information learned the ecological correlation and transferred it to the individual judgments. Hence, using ecological correlations do not seem to reflect cognitive laziness. Instead, using ecological correlations requires attention to statistical regularities, which are taken to represent individuals as well.

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The media often provides us with aggregate rather than individualized data. As is evident from the experiments reported above, consumers of such content readily use the provided ecological correlations to predict individual behavior or attributes. Yet, what happens when individual-level correlations are available? Ecological Inferences in Spite of Individual-level Zero Correlations We might conjecture that the use of ecological correlations as a proxy for individual data is a necessary evil owing to missing individual data. Indeed, in the case of missing information, lay people as well as scientists use the ecological correlation as a best guess. However, would we expect lay people to continue relying on ecological correlations in the presence of individual data? This is what a plethora of experimental learning studies suggests (for a review, see Fiedler et al. 2009). In one of the first studies to test the use of ecological correlations, Fiedler and Freytag (2004) presented participants with information about psychiatric patients. Participants learned about each patient’s test score on two personality tests, called type X and type Y. Across individuals, high scores in one test implied neither an increased or a decreased chance of a high score on the other test. In technical terms, the correlation between the test scores across individuals was zero. Yet, there was also an ecological correlation. Patients were additionally classified as belonging to one of two groups or ecologies: those for which previous psychotherapy had been successful and those for whom it had been unsuccessful. Among the successfully treated patients, high levels of both personality traits were three times as frequent as for patients with unsuccessful prior treatment. This created a perfect ecological correlation. The mean levels of both personality traits varied hand in hand across groups. The critical finding was that participants expected individuals with a high score on personality trait X to have a high score on trait Y. Thus, even with individual-level data easily accessible, a zero correlation is ignored and substituted with a positive ecological correlation. Ecological Inferences in Spite of Conflicting Individual-level Correlations Thus far, we have considered evidence for ecological correlations being used to infer correlations across individuals. We have implied that this correlation is the same within the different ecologies. For example, there was a zero individual-level correlation between the personality traits within both groups, for patients with successful and for patients with unsuccessful prior therapy. Yet, this might not always be the case. Another related yet different type of ecological inference fallacy has been demonstrated when the individual-level correlation for an entire population is different from the individual-level correlations within ecologies of this population. This constellation, in which different or even reverse individual-level correlations exist within ecologies, is known as Simpson’s paradox (Simpson 1951). Studies typically found that participants act as if they do not take the ecological variable into account reproducing the overall correlation across the entire population (for example, Schaller 1994). This neglect of an ecological variable seems at odds with claiming there is a strong influence of ecological correlations. Intriguingly, evidence that has been used to support the idea that the ecological variable is neglected in a Simpson’s paradox can equally well

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Smart predictions from wrong data 91 be reconciled with the more recent idea that ecological correlations, not overall individuallevel correlations, are used. Meiser and Hewstone (2004) provided evidence that favors this ecological correlation explanation. In a setup typical for a Simpson’s paradox, their participants were presented with a series of positive and negative behavioral descriptions involving members of two groups, A and B, distributed over two towns, X and Y. While the individual-level correlation was positive across all individuals, say, being from group A was predictive of behaving positively, it was negative within both towns. Such a setup, however, implies that in one town group A members and in the other group B members form the majority. At the same time, one town is characterized by many and the other by few positive behaviors. Thus, in creating the Simpson’s paradox, an ecological correlation results between being a member of group A and positive behavior. This is because the average ‘group-A-ness’ for a town is perfectly predictive of the average positivity. When asked to make predictions about individuals belonging to either of the groups and residing in either of the towns, participants predicted group A members to be more likely to behave in a positive way, contrary to the correlation actually observed within the towns, but in line with both the ecological correlation and the individual-level correlation neglecting the town variable. Thus, either participants neglected the ecological town variable and made judgments based on an individual-level correlation or they relied on the ecological correlation. Speaking in favor of the ecological correlation explanation, it was particularly those participants who had accurately learned the ecological correlation, that is, which town was characterized by a majority of group A members and a prevalence of positive behaviors, who exhibited this tendency. Thus, the failure to ‘solve’ the rather complex Simpson’s paradox might not reflect an attempt to simplify the task by neglecting a confounding ecological variable. It might instead reflect a genuine attempt to use the ecological correlation as a smart and parsimonious proxy for individual-level correlations. Ecological Inferences in Spite of Monetary Rewards Converging evidence for the use of ecological correlations stems from even simpler operant conditioning experiments where accurate responses were rewarded with money (Kutzner et al. 2008). In these experiments, only one ‘ecology’ with two variables was encountered. Participants were repeatedly asked to predict whether a ‘left’ or a ‘right’ response was the correct reaction after one of two signals, a high- and a low-pitch sound. The sounds were not predictive of which was the correct reaction, that is, on the individual trial level there was a zero correlation between sound and correct reaction. At the same time, the whole setting or ecology that participants encountered was special. One of the sounds, say the high-pitch sound, preceded responses clearly more frequently than the low-pitch sound and, irrespective of the sound presented, one of the responses, say ‘left’, was rewarded clearly more frequently than the other. This strangely ‘skewed’ ecology contrasts with an ‘ignorant prior’ ecology where signals and correct reactions can be expected to be evenly distributed. As compared to this ignorant prior ecology, there is again a perfect ecological correlation. Knowing the mean value of the high-pitch sound in one ecology is perfectly predictive of mean value of ‘left’ being the correct response. The choice pattern in this operant conditioning scenario is in line with the use of an

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ecological correlation (see Fiedler et al. 2013 for a discussion of ecological inferences in single ecologies). Even though choices tended towards the more frequently rewarded option, this trend was less pronounced for the less frequently presented sound, significantly reducing participants’ payoff (Kutzner et al. 2008). Many more experiments testify to the use of ecological correlations that did not use monetary incentives but social incentives, calling on participants to form accurate and non-discriminatory impressions about others (McGarty et al. 1993; Eder et al. 2011; Kutzner et al. 2011). Ecological Inferences in Both Directions Given the robustness of inferences from ecological to individual correlations, the question is whether people are actually insensitive to the level of data aggregation or whether they are simply not able to assess individual-level correlations. In the end, ecological correlations require assessing less information saving time and processing costs. To answer this question we conducted two experiments (Vogel et al. 2014). We provided participants with information about the level of product demand in two supermarkets. Different ecologies were created in terms of eight different product segments (for example, cheese, fruit). A first study replicated evidence for the use of an ecological correlation. Participants acted as if high demand for a specific product in one supermarket was predictive of high demand for that product in the other supermarket. Yet, demand on product level was not predictive. Across the product categories, however, high average category demand in one supermarket predicted high average category demand in the other supermarket. In the second study, the reverse was true. Here, demand across product categories did not correlate between supermarkets but demand for individual products did.1 In that case, participants correctly identified the individual product-wise correlations. Additionally, participants also acted as if there was a correlation across ecologies, predicting higher average demand for product categories that had been highly demanded in the other supermarket. These experiments suggest that people substitute ecological correlations for individual-level ones just as readily as they substitute individual-level correlations for ecological ones, committing the so-called atomistic fallacy (Diez-Roux 1998). In that participants correctly identify existing individual-level correlations but readily and, in this case, incorrectly generalize them to a higher level of data aggregation, the results demonstrate a genuine insensitivity to the level of data aggregation. In sum, examining results across a variety of paradigms, inferences about correlations have revealed that people are insensitive as to whether aggregated or individual-level data provides the input to their judgments. Further studies have extended this evidence to content domains including scholastic achievement (Fiedler et al. 2007) and political attitudes (Vogel et al. 2013). Together, these studies demonstrate the robustness of the use of ecological correlations as a proxy for individual-level correlations by lay people. It appears that whenever there is an ecological variable (for example, countries) allowing for the grouping of individual observations, lay people will consider the ecological correlation to learn about the individual-level relationship.

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Smart predictions from wrong data 93

THE SMART POTENTIAL BEHIND ECOLOGICAL CORRELATIONS Given the resilience of the use of ecological correlations or, more generally, the indifference between aggregation levels, a pressing question is whether this indifference is potentially smart or, broadly speaking, rational? Parsimony Using ecological correlations to infer individual correlations is parsimonious. To make inferences, only the base rates of variable (how relatively frequently things happen) have to be assessed. This assessment can be based on experience gathered on different occasions. It is not necessary to wait for or remember combined observations of, for example, people becoming unemployed and becoming a parent. Such a complete data matrix, as ideally assumed in statistics books, is hardly ever available to the lay decision maker in real life. To illustrate, consider trying to assess which of four dichotomous variables are predictive of happiness. The four zero-order correlations already result in combining, recording, and remembering 16 types of observations (four for each correlation). Trying to avoid a Simpson’s paradox, taking into account each of these variables as moderating the others’ influence, creates 24 correlations (96 types of observations) to be handled (four variables related on each level of the other three variables). Beyond complexity that might prevent accurate processing, the data might not be available at the time of judgment. Trying to assess the correlation for a novel variable, say, engaging in volunteer work, might simply be impossible because the relevant observations have not (yet) been gathered. However, the base rate of people that volunteer in a given ecology should be recorded and recalled quite automatically (Hasher and Zacks 1984). Using these readily available base rates, ecological inferences seem designed to enable inferences about novel correlations. In sum, ecological correlations pose relatively low demands on either cognitive resources or the amount of data that has to be available. Thus using ecological correlations to infer individual correlations satisfies one of the components of being a smart inference strategy: they are feasible and efficient. The Validity of Ecological Correlations: The Case of Happiness Feasibility and efficiency alone, however, do not render an inference strategy smart. Only if the strategy used has some degree of validity can it be justified. Confounding variables, as in the case of the Simpson’s paradox, threaten the validity of taking ecological correlations for individual level correlations. Yet, checking for the presence of confounding variables usually amounts to making sure there is no needle in the haystack. A pragmatic workaround to the elusive analytical answer to the validity of ecological correlations in general is to quantify their validity for a specific question. Such an analysis is presented below. This analysis is but an example of how the validity of ecological correlations can be quantified. It makes no claim to their validity in general. Instead, it could inspire the classification of questions into those for which ecological inferences are valid and those for which they are not.

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The strategy put forward to assess the validity of using ecological correlations is simple. In essence, it compares the correlation of a criterion with a meaningful predictor assessed across individuals with the same correlation assessed across ecologies. This comparison is repeated for multiple predictors and multiple ways to form ecologies in the same data. Ecological correlations are deemed valid to the degree that the comparisons show correspondence between ecological and individual-level correlations in sign and size. To do so for a given question, we had to select (1) a criterion for which individual-level data are available, (2) variables used to predict or correlate with this criterion and (3) ecologies or ecological variables across which ecological correlations are computed. All of these selections will possibly change the validity of ecological correlations. Therefore, the resulting selection should be as representative as possible of what lay decision makers would do to make inferences important to them. As a criterion we selected happiness. Happiness studies have enjoyed a privileged status among scientists, resulting in representative large-scale surveys optimal to assess individual-level correlations.2 Prominent philosophers from Socrates, Meister Eckart (Meister Eckart and Davies 1995) and Mill (1863) to the Dalai Lama (Dalai Lama and Cutler 1998), but also economists (for example, Anielski 2007), lawyers (Bronsteen et al. 2015) and psychologists (for example, Maslow 1962; Seligman 2002) have dedicated their work to the identification of the very predictors of this arguably ultimate goal. Lay people, pursuing hedonic or utilitarian motives, are also likely to reason about the correlates of happiness to finally infer what causes it. At the operational level, we used happiness as represented in the sixth European Social Survey’s (ESS) question C1 ‘Taking all things together, how happy would you say you are?’ ranging from ‘00 Extremely unhappy’ to ‘10 Extremely happy’. To select predictors of happiness we relied on variables prominent in the scientific psychological literature. We selected health, social integration, income, religiousness, personal freedom, educational achievement and gender. Note that some of the documented correlations seem robust and strong, such as those for health and social integration, whereas others are feebler, such as those with income or gender (for a review, see Diener et al. 1999). Selecting ecological variables, we tried to reflect social categories that people might spontaneously think of when thinking about happiness. We included social categories that are generally considered readily available when thinking about the social world, such as nationality, gender and age (Gavanski and Hui 1992). We also tried to capture ‘warmth’ and ‘competence’, variables that are usually considered fundamental dimensions in social perception (Fiske et al. 2002). As proxy for warmth we used what a person’s main occupation is, including housework, work, being in school or retired. As proxy for competence we used a person’s income group. Our selection resulted in eight correlations with happiness, which we calculated five times, once at the individual level and four times for the four ecological variables. These ecological correlations include, for example, people thinking about, or being confronted with, data indicating that middle-aged people are, on average, least free and least happy. Would it be smart for decision makers to infer that high personal freedom is likely to go along with high personal happiness? In Table 5.1 we present the results for one nation, Slovenia. We focus on evidence within one nation because we assume that people’s information will be biased towards

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Smart predictions from wrong data 95 Table 5.1

Correlation table for predicting high levels of happiness in Slovenia

Units of observations Predictors Good health High income Good social integration High freedom High edu. achievement High religiousness Being female Higher age

Individuals (N 5 1257) 0.69 0.56 0.54 0.50 0.25 0.16 0.04 −0.38

Income Nationality Main Age Gender (N 5 5) (N 5 29) occupation (N 5 5) (N 5 2) (N 5 5)

Mean validity

1.00 − 0.98 0.80 1.00 −0.86 −0.97 −0.98

0.70 0.34 0.74 0.62 −0.05 −0.19 – −0.19

0.80 0.91 0.96 −0.24 0.29 −0.61 0.08 −0.75

0.93 0.84 0.97 −0.76 0.57 −0.63 −0.64 –

−1.00 −1.00 −1.00 1.00 −1.00 1.00 – 1.00

Validity for Slovenia % correct sign Rank order agreement

71 0.83

63 0.83

75 0.78

50 0.65

25 −0.61

57 0.49

Validity for all 29 countries % correct sign Rank order agreement

75 0.72

62 0.84

77 0.71

56 0.51

48 0.02

64 0.56

Note: Ecologies with N 5 5 are based on quintile ranks, except for ‘Main occupation’ which represents the groups’ paid work, education, housework, unemployed and retired. Rank order agreement 5 correlation with individual correlation coefficients.

their local social circle (Galesic et al. 2012). We focus on Slovenia because it represents a modal pattern of results for the validity of ecological correlations among the 29 countries we analyzed. As visible in the first column of Table 5.1, results largely replicate research on what correlates with happiness. Strong individual-level correlations can be found with health, income and social integration, whereas small correlations result for gender and even a negative one for age. These results also provide insights into the validity of ecological correlations. Consider, first, the ecological correlations with happiness across income ecologies. Even though treating an income group as ‘ecology’ might sound strange, contrasting rich and poor people when thinking about happiness might not. For example, we might conclude that health is lowest for those with little income and highest for those with high income. We might observe that happiness is also lowest for those with little income and highest for those with high income. In this case we have an ecological correlation between happiness and health across income groups. When computing exactly this ecological correlation for Slovenia, a perfect correlation of r 5 +1 results across the five income quintiles. As visible in Figure 5.3(a), every move upward in income goes along with a joint move upward in health and happiness. As compared to the r 5 .69 individual-level correlation between health and happiness (see the first column in Table 5.1), this ecological correlation is inflated but has the same sign. Yet, we also found evidence for the ecological and individual-level correlations to also diverge in sign. As visible in the first column of Table 5.1, being religious is correlated at r 5 +.16

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Mean happiness (0–10)

(a) Health-happiness correlation across income quintiles 8.0

Mid high

High

Average

7.5 7.0 Mid low

Low

6.5

3.4

3.6

3.8

4.0

Mean health (1–5)

Mean happiness (0–10)

(b) Religiousness-happiness correlation across income quintiles High

8.0 7.5

Mid high

Average

7.0

Mid low Low

6.5 2.6

2.7

2.8

2.9

3.0

Mean religiousness (1–5) Source:

Data from the sixth ESS survey for Slovenia.

Figure 5.3

(a) Ecological correlation across income groups (quintiles) between health and happiness; (b) ecological correlation across income groups (quintiles) between religiousness and happiness

with happiness in Slovenia. When analyzed across income quintiles, a lower mean level of religiousness almost always goes along with a higher mean level in happiness, r 5 –.86. Overall, however, five of seven predictors,3 71 percent, correlate in the same direction with happiness across income groups and across individuals. Additionally, the relative importance of the different predictors of happiness at the individual level should optimally also reflect in the ecological correlations. Comparing the ranks of the correlation coefficients in the first and second columns in Table 5.1 seems to support this. If a predictor’s coefficient has a high rank when computed at the individual level it tends to have a high rank among the ecological correlations as well. The correlation of correlation coefficients amounts to r 5 +.83 (see rank order agreement in Table 5.1). Thus, at least across income groups as ecologies, not only the sign but also the relative importance of the predictors tends to be preserved when going from individual level to ecological correlations. Similar levels of validity can be found when considering different ecological variables such as nationality, main occupation, and age (see third to fifth columns in Table 5.1). The only exception is gender where the validity in terms of detecting the correct sign or rela-

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Smart predictions from wrong data 97 tive importance is systematically off. There might be various reasons for this. Foremost, the difference in happiness between genders is, if present in the population, very small in the data set. This might render any ecological correlation computed across two gender categories an unsystematic chance gamble between +1 and –1. More substantially, for some correlations gender is likely to be a confounding variable. For example, as being female tends to be related to lower income but females are slightly happier in the data set, a perfect negative correlation between income and happiness across gender groups mainly reflects the correlation between income and happiness. In summary, for our representative case of Slovenia relying on ecological correlations to infer individual-level correlations has a high degree of validity for inferring which variables correlate with happiness and in what direction. Inferring the sign allows for 57 percent of correct predictions overall. Rank ordering the size of the correlations amounts to a validity of r 5 +.49. To generalize these results to the other countries in the data set, we averaged the validity indices separately for the five ecological variables across all 29 countries included in the ESS survey. As visible in the bottom lines of Table 5.1, on average the validities are even higher, allowing for 64 percent of correct sign inferences and a rank ordering with a validity of r 5 +.56. Only ecological differences across gender, possibly for the reasons discussed above, are on average uninformative for individual-level correlations. Further, for every country there is an overall positive validity in terms of the percentage of correct sign inferences and the rank ordering. The percentages and rank correlations range from 52 percent in Sweden and .00 in Iceland, to 88 percent and +.91 in the Czech Republic. Where does this leave us – is the glass half empty or half full? As is obvious from the imperfect rank correlations, decision makers using ecological correlations will sometimes mis-estimate the importance of a given predictor, and as evident from the percentage of sign inferences, ecological correlations also leave space for sign errors. However, in our data, ecological correlations are well above chance performance when inferring the sign and relative importance of individual-level correlations. Thus, to infer which variables correlate and how they correlate with happiness on an individual level, ecological correlations seem to be a valid inference tool overall. Of course, this analysis cannot be more than exemplifying evidence. Yet, it illustrates the potential validity of ecological inferences for variables meaningful to the passing consumer of information. These arguments add to the claim, though cautiously, that using ecological correlations represents a smart strategy. More systematic analyses of the circumstances under which they are smart are an endeavor for future research.

CONCLUSIONS This chapter deals with the use of ecological correlations as a proxy for individual-level correlations as a smart or, broadly speaking, rational inference strategy. In the first part we demonstrate that people, from passing consumers of newspaper articles to monetarily incentivized participants in laboratory studies, use correlations that are computed across ecologies, such as nations, supermarkets or social groups, to infer correlations at the individual level. This might be owing to the relative parsimony of ecological correlations in terms of the processing demands and the demands on the available data. In the second

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part, we show that in the example of inferring what variables correlate with happiness, using ecological inferences seem to be a valid strategy. Even though the case of ecological correlations might seem technical at first, our current social and informational world is ripe with aggregated information inviting their usage. Many different internet-based services, such as Google and Twitter allow for access to their aggregated user data. For example, using Google Trends (http://www.google.com/ trends/), everybody can, within minutes, visualize on a map how the propensity to search for, say, ‘sushi’ differs between regions in the US, and how this compares to the propensity to search for, say, ‘democrats’. If similar colors are generated on a map, an easily accessible form of ecological correlation is born. Most government-based statistical services, such as Eurostat or the European Social Survey, and government agencies now provide similar access. For example, the London police (http://maps.met.police.uk/) offer results for recent crime statistics mapped out across different suburbs, ready to be ‘ecologically’ correlated with, for example, knowledge about the population composition of these suburbs. Concluding, using ecological correlations to infer individual-level correlations appears to be a valid inference strategy in the absence of adequate data or processing abilities. Yet, it is to some degree error-prone. Whether this probabilistic inference strategy is smart, ultimately depends on the costs, social or material, incurred by inferring wrong correlations.

NOTES *

The research underlying this paper was supported by a grant from the Deutsche Forschungsgemeinschaft (KU – 3059/2-1). 1. The distribution used in Vogel et al. (2014) is compatible with an ecological variable moderating the correlation between two variables, that is, individual correlations actually differ between ecologies. There are many examples for this kind of aggregation bias in the economist literature (for example, Jaworski and Kohli 1993; Grewal et al. 2013). For the sake of simplicity, in this chapter, we only consider divergence between ecological correlations and individual contingencies pooled across ecologies, but omit discussing heterogeneity regarding within ecology correlations. 2. For the subsequent analyses, we rely on the sixth round of the European Social Survey conducted in 2012 with 54 637 respondents in 29 countries (including Switzerland, Russia and Israel). 3. We removed the correlation of income across income groups from the analysis. Even though possible to compute, an ecological correlation of average income across income groups seems implausible. Also, resulting high correlations seemed to artificially inflate the validity scores. The same was true for age and gender.

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Mallapragada (2013), ‘Environments, unobserved heterogeneity, and the effect of market orientation on outcomes for high-tech firms’, Journal of the Academy of Marketing Science, 41 (2), 206–33, doi:10.1007/s11747-011-0295-9. Guardian (2013), ‘Which countries are the most forward thinking? See it visualised’, Guardian, 8 February, accessed 5 March 2015 at http://www.theguardian.com/news/datablog/2013/feb/08/countriesmost-forward-thinking-visualise. Hammond, J. (1973), ‘Two sources of error in ecological correlations’, American Sociological Review, 38 (6), 764–78. Hannan, M.T. (1971), ‘Problems of aggregation’, in H. Blalock (ed.), Causal Models in the Social Sciences, Chicago, IL: Aldine, pp. 473–508. Hasher, L. and R. Zacks (1984), ‘Automatic processing of fundamental information: the case of frequency of occurrence’, American Psychologist, 39 (12), 1372–88, doi:dx.doi.org/10.1037/0003-066X.39.12.1372. Jaworski, B.J. and A.K. Kohli (1993), ‘Market orientation: antecedents and consequences’, Journal of Marketing, 57 (3), 53–70, doi:10.2307/1251854. King, G. (2013), A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data, Princeton, NJ: Princeton University Press. Kutzner, F., P. Freytag, T. Vogel and K. Fiedler (2008), ‘Base-rate neglect as a function of base rates in probabilistic contingency learning’, Journal of the Experimental Analysis of Behavior, 90 (1), 23–32, doi:10.1901/ jeab.2008.90-23. Kutzner, F., T. Vogel, P. Freytag and K. Fiedler (2011), ‘A robust classic: illusory correlations are maintained under extended operant learning’, Experimental Psychology, 58 (6), 443–53, doi:10.1027/1618-3169/ a000112. Maslow, A.H. (1962), ‘Introduction: toward a psychology of health’, in A.H. Maslow, Toward a Psychology of Being, Princeton, NJ: D Van Nostrand, pp. 3–8. McGarty, C., S. Haslam, J. Turner and P. Oakes (1993), ‘Illusory correlation as accentuation of actual intercategory difference: evidence for the effect with minimal stimulus information’, European Journal of Social Psychology, 23 (4), 391–410, doi:10.1002/ejsp.2420230406. Meiser, T. and M. Hewstone (2004), ‘Cognitive processes in stereotype formation: the role of correct contingency learning for biased group judgments’, Journal of Personality and Social Psychology, 87 (5), 599–614, doi:10.1037/0022-3514.87.5.599. Meister Eckart and O. Davies (1995), Meister Eckart – Selected Writings, Harmondsworth: Penguin Classics. Mill, J.S. (1863), Utilitarianism, London: Parker, Son, and Bourn. Preis, T., H.S. Moat, H.E. Stanley and S.R. Bishop (2012), ‘Quantifying the advantage of looking forward’, Scientific Reports, 2, art. 350, doi:10.1038/srep00350. Robinson, W. (1950), ‘Ecological correlations and the behavior of individuals’, reprinted 2009, American Sociological Review, 15 (3), 351–7, doi:10.1093/ije/dyn357. Schaller, M. (1994), ‘The role of statistical reasoning in the formation, preservation and prevention of group stereotypes’, British Journal of Social Psychology, 33 (1), 47–61, doi:10.1111/j.2044-8309.1994.tb01010.x. Schmitt, C. (2012), ‘A cross-national perspective on unemployment and first births’, European Journal of Population/Revue Européenne De Démographie, 28 (3), 303–35, doi:10.1007/s10680-012-9262-5. Seligman, M.P. (2002), Authentic Happiness: Using the New Positive Psychology to Realize Your Potential for Lasting Fulfillment, New York: Free Press. Simpson, E.H. (1951), ‘The interpretation of interaction in contingency tables’, Journal of the Royal Statistical Society. Series B (Methodological), 13 (2), 238–41.

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Vogel, T., F. Kutzner, K. Fiedler and P. Freytag (2013), ‘How majority members become associated with rare attributes: ecological correlations in stereotype formation’, Social Cognition, 31 (4), 427–42, doi:10.1521/ soco_2012_1002. Vogel, T., F. Kutzner, P. Freytag and K. Fiedler (2014), ‘Inferring correlations: from exemplars to categories’, Psychonomic Bulletin & Review, 21 (5), 1316–22, doi:10.3758/s13423-014-0586-5. Wall Street Journal (2012), ‘Europe takes digital lead; divide persists’, The Wall Street Journal, 6 April, accessed 5 March 2015 at http://blogs.wsj.com/tech-europe/2012/04/06/europe-takes-digital-lead-divide-persists/.

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Heuristics: fast, frugal, and smart Shabnam Mousavi, Björn Meder, Hansjörg Neth and Reza Kheirandish

Recent years have seen important new explorations along the boundaries between economics and psychology. For the economist, the immediate question about these developments is whether they include new advances in psychology that can fruitfully be applied to economics. (Simon 1959, p. 253)

INTRODUCTION Individuals often make smart decisions despite the inherent limitations of cognitive and material resources. Whereas mainstream economics has focused mainly on the allocation mechanisms of material resources by cognitively unbounded (fully rational) agents, behavioral economics aims to include allocation of cognitive resources by using the insights from the heuristics and biases program in psychology (Kahneman et al. 1982). In this chapter, we introduce another psychological program with a more optimistic perspective inspired by Simon’s view of bounded rationality and developed systematically in the study of fast-and-frugal heuristics (Gigerenzer et al. 1999).1 We make a distinction between human decision making in two situations: under uncertainty, in which case we reason that simple heuristics are successful strategies, and under risk, in which case we discuss the enhancing role of risk literacy and statistical thinking (for the ways in which information is processed and knowledge created under risk versus under uncertainty, see Mousavi and Gigerenzer 2014, Table 1; and for the realms of rationality see Neth and Gigerenzer 2015, Table 1). Our goal is to make sense of smart and efficient decisionmaking processes demonstrated by individuals who use their evolutionary developed and learned capacities. Which recent advances in psychology are important to economic theory and behavioral economics? This chapter has emerged from a series of dialogues between two psychologists and two economists exchanging views on the study of fast-and-frugal heuristics as it pertains to the methods of understanding economic behavior and decision making. Our discussions developed before a backdrop of what we view as the thrust of our fields as well as their overlaps in relation to formal treatments of human behavior. What economists now practice and profess as the basis and criterion for rigorous study of economic behavior traces back to the normative interpretation of subjective utility.2 Psychologists, by contrast, have often searched for systematic patterns of behavior in laboratory and case studies, often formulating models without subscribing to or aiming for accordance with universal maxims of behavior. The heuristics and biases research program (Tversky and Kahneman 1974) commenced with an inquiry into uncovering general cognitive mechanisms. Mainstream behavioral economics has combined the findings of this psychology program with the maxims of economics. The resulting body of work has been valuable in 101

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crossing disciplinary boundaries, inspiring scientific inquiry into untapped domains and generating potential for further discoveries. At the same time, like any other field of study, behavioral economics has carried baggage from its mother discipline, which generated byproducts and implications for methods of research. Our assertion is that the study of the ecological rationality of fast-and-frugal heuristics (Gigerenzer et al. 1999) can provide important insights and tools for alternative and complementary analyses of behavior. The framework of fast-and-frugal heuristics can be distinguished from the heuristics and biases approach by the following characteristics and standpoints. In our view, heuristics are indispensable strategies for successfully dealing with uncertain situations in the real world. Notably, most real-world situations do not allow identification of all alternatives, consequences, and probabilities, even subjectively, as required for finding the optimal solution. Moreover, the best solution from a social perspective does not necessarily accord with rational choice based on self-interest (for example, public goods). For these reasons, smart decision makers regularly develop and use heuristics, relying on the wisdom and experience that simple heuristics can outperform supposedly optimizing strategies in uncertain situations. More often than not, satisficing with respect to a good enough aspiration level turns out to be both rational and smart for boundedly rational agents. It is thus not irrational but intelligent to be less than fully rational, in the neoclassical sense, in many decision-making situations. A two-way influence and exchange between psychology and economics can build upon shared notions such as ecological rationality of simple heuristic strategies. This is the core around which we have structured this chapter. We start with juxtaposing economic and psychological views of ecological rationality (based on interviews with Vernon Smith and Gerd Gigerenzer, both leading researchers in their respective fields), pointing out the overlaps between the two views, and then extend questions pertaining to behavioral economics as a set of suggestions for advancing the dialogue between economics and psychology. Viewing heuristics as adaptive tools for decision making is discussed next. Although heuristic strategies can be used both under uncertainty and under risk, the simplicity of heuristics makes them particularly successful under the irreducible uncertainty of many decision situations. This point is illustrated by connecting the Knightian distinction between risk and uncertainty to inferential rules, amended by heuristics. The practical success of simple heuristics is then illustrated in the domains of financial investments and business decision-making. Next, we consider potential implications of ecological rationality in two applied scenarios: the current debate on nudging and the use of natural frequencies in risk communication. We close by providing a brief summary and extending our collaborative challenge to economists.

WHERE ECONOMIC RATIONALITY MEETS PSYCHOLOGY In his Nobel Prize lecture, Vernon Smith (2002) focused on two forms of rationality in economics and their functions with respect to the understanding of human behavior. The first form is constructivism, which is rooted in Hume’s and British empiricism; here the study of human behavior starts with observing an outcome and then reconstructing the steps with which such an outcome can be generated through a deliberate reasoning process. This reconstruction provides a variety of possibilities and options to choose

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Heuristics: fast, frugal, and smart 103 from, which are not sufficient for the realization of action. For that reason, Smith points out that ‘constructivism alone leads nowhere; its roots must find ultimate nourishment outside of [such] reason. Outside means knowledge derived from experience, from social interactions, and from unconscious sources and processes – the nexus that I have called ecological rationality’ (Smith 2008, p. 287). Interestingly, this second form of rationality, the notion of ecological rationality in economics, is shared with the psychological study of fast-and-frugal heuristic decision-making: The term ‘ecological rationality’ has been used fittingly by Gigerenzer et al. (1999) for application to important discoveries captured in the concept of ‘fast and frugal decision making’ by individuals: ‘A heuristic is ecologically rational to the degree that it is adapted to the structure of an environment.’ (p. 13). My application of the term is concerned with adaptations that occur within institutions, markets, management, social, and other associations governed by informal or formal rule systems – in fact, any of these terms might be used in place of ‘heuristic’ and this definition works for me. (Smith 2008, p. xix)

The similarities and some specific connections between the research traditions established by Vernon Smith in economics and Gerd Gigerenzer in psychology that evolve around this shared notion of ecological rationality and lead to a functional view of heuristics are juxtaposed in Table 6.1 (for a juxtaposition of Smith’s and Kahneman’s approach to the theory and modeling of human behavior, see Altman 2004). Moreover, Table 6.1 provides two items under each connected notion. The first item illustrates the overlap between the two views, and the second outlines research questions that relate the particular preceding topic to the core of behavioral economics inquiry. In the study of human action, Smith calls for supplanting the traditional constructivist framework of rationality with the ecological one. In a similar vein, Gigerenzer calls for ‘a better understanding of human rationality than that relative to content-blind norms’ (2008, p. 19). Constructivist rationality derives normative benchmarks from formal frameworks such as logics and probability theory, where the situation in which a choice is made is abstracted from its content. Thus, these norms are blind to the content of the decisionmaking situation. Regrettably, ‘these were of little relevance for Homo sapiens, who had to adapt to a social and physical world, not to systems with artificial syntax, such as the laws of logic’ (Smith 2008, p. 19). In cognitive science, the study of error has fallen prey to a major error by maintaining norms of logic and statistics, which despite their coherent and consistent elegance, and at the price of preserving this elegance, could lack meaningful association to evaluation of human decision-making behavior.3 Pointing out that this is an unjustified extension from the study of perceptual errors to the cognitive domain, Mousavi and Gigerenzer (2011; see also Gigerenzer 1991, 1996) argue for adopting and developing content-sensitive norms for the study of human cognition and behavior. For example, when the famous Wason selection task (Wason 1966) is given content by assigning two roles of employee and employers to the players who both are tasked with cheating detection, one group’s correct strategy aligns with the logical truth table associated with the conditional, but the other does not. Thus, logic appears to capture one part of the content and miss the other part. In this case, if judgment is evaluated based on logical truth, one group appears to have wrong judgment, whereas their judgment is completely correct with respect to the role (content) that they are assigned (Gigerenzer and Hug 1992). Thus, what counts as human rationality depends on the content and domain,

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Table 6.1

Ecological rationality and heuristics à la Smith (economics) and Gigerenzer (psychology)

Fast-and-frugal heuristics program

Constructivist versus ecological rationality in economics

A heuristic is ecologically rational to the degree that it is adapted to the structure of an environment Humans have to adapt to a social and physical world, not to systems with artificial syntax, such as logic

Ecological rationality is concerned with adaptations that occur within institutions, markets, management, and social and other associations governed by informal or formal rule systems

Overlap between psychology and economics: same definition of ecological rationality, when heuristic can be replaced by markets, management, or other rule systems Research questions for behavioral economics: what is the relationship between rule systems and heuristics? Do they overlap, or is one nested in the other? What can be learned from establishing such characterizations? Unbounded rationality can generate optimal solutions for simple situations, e.g., tic-tactoe; omniscience and omnipotence can also be used for theoretical examination of human behavior, but applying them as universal standard of rationality is a scientific error

Constructivism or reason provides a variety of ideas to try out but often no relevant selection criteria, whereas ecological process selects the norms and institutions that serve the fitness needs of societies

Overlap between psychology and economics: Norms produced by unbounded or constructivist rationality are not useful as selection criteria in complex situations; the ultimate evaluation comes from the real world, not from theoretical sophistication Research questions for behavioral economics: In the study of human behavior where does realism matter, and where does it not? If norms are chosen conditional to the situation, how can we judge across situations? Can ecological fitness be formalized? Experimental games are bound to study social behavior as rule-obeying behavior and not as rule-negotiating or rule-changing behavior

Observing how people actually behave reveals unanticipated system rules, e.g., hubs emerged unexpectedly (like an equilibrium) when airlines were deregulated

Overlap between psychology and economics: rules are to be discovered as they emerge from social behavior. Formal models can be used to provide a possible description of what was observed Research questions for behavioral economics: to what extent can field experiments improve the relevance of solution concepts used for the study of human behavior and specify their limitations? Fast-and-frugal heuristics are strategies triggered by environmental situations and enabled by evolved or learned capacities

Heuristics are a kind of cognitive capacity that we can access, although we are not completely aware of our access to it

Overlap between psychology and economics: The choice of heuristic strategy is often not fully deliberate. This does not exclude the possibility of training or altering the trigger conditions Research questions for behavioral economics: When is a heuristic successful, and when does it fail? In real-world situations, when is it not rational to be ‘rational’? Source: Based on interviews in Mousavi and Kheirandish (2014).

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Heuristics: fast, frugal, and smart 105 whereas logic is content-free. Extending on this point, Mousavi and Gigerenzer (2011, p. 102) argue that ‘cognitive scientists studied judgment errors in order to discover rules that govern our minds, just as visual errors were studied to unravel the laws of perception. This practice has generated a long list of so-called cognitive biases, with disappointingly little insight into how the human mind works’. Alongside Smith (2008, p. 31), we find that ‘[t]he failed objective of this constructivist adventure is cause for joy, not despair’. Behavioral economics is concerned with making sense of human behavior, with the goal of developing a framework for analyzing decision-making in real-world situations, and evaluating and predicting human choice and actions therein. Vernon Smith’s following remark refers to these tasks directly: ‘Whatever it is that people do, it is evident that they do not think about the problem the way an economist does, nor do they model it that way’ (Mousavi and Kheirandish 2014, p. 1784). As Gigerenzer elaborates, ‘the question is not whether it is good or bad to ignore information but what ignoring information does psychologically . . . “Why a certain strategy?” and “When does it work?” rather than assuming “It maximizes something,” and that something may be psychological’ (Mousavi and Kheirandish 2014, p. 1784). This view, which emphasizes the interplay between heuristics and environments, and relies on the notion of ecological rationality to evaluate the rationality of human behavior, provides an alternative way for understanding our adaptive minds where constructivist rationality reaches its limits.

HEURISTICS AS ADAPTIVE TOOLS FOR DECISION MAKING UNDER UNCERTAINTY A prevalent view in both psychology and behavioral economics, the heuristics and biases program (Tversky and Kahneman 1974), presumes that heuristics result from a trade-off between accuracy and effort and lead to flawed and biased thinking. Typically, the benchmarks used to corroborate these claims are formal frameworks such as logic, probability theory, and expected utility theory. These are presumed to provide normatively correct solutions, and deviations in human decision making constitute errors. We advocate a different view based on formal models of heuristics. Within the framework of fast and frugal heuristics (Gigerenzer et al. 1999), heuristics are adaptive tools that ignore information to make fast and frugal decisions that are accurate and robust under conditions of uncertainty (Neth and Gigerenzer 2015). Heuristics are successful when they exploit an ecologically rational match to the structure of information in the environment. In the previous section, we discussed the role of ecological rationality in understanding the success of simple individual and organizational strategies in the juncture of psychology and economics. Here, we turn our focus to two central concepts in the theory of decision-making, namely, uncertainty and knowledge. The path towards making a decision starts with a disequilibrium that triggers a search for solutions through processing information to create the knowledge we need (Dewey 1938 [1986]). Traditionally, we model this procedure in two forms, deductive and inductive, depending on the structural properties of the situation to be resolved (Goldman 1988). Also, we acknowledge the unknowns of the situation by specifying alternatives, consequences, and their probabilities, which leads to a characterization of the risk associated with the problematic situation. The way in which a problematic situation is

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Table 6.2

Decisions under risk versus uncertainty

Nature of unknown

Knightian probability

Decision process

Method

Generated knowledge

Risk

A priori (design; propensity)

Deductive

Risk

Statistical (frequencies in the long run)

Inductive (statistical inference)

Use probability theory to model the underlying structure; optimization Use statistical inference; optimization

Uncertainty

Estimate; conduct based on opinion; not fully reasoned

Heuristic

Deterministic knowledge (as in lotteries); e.g., objective odds Stochastic knowledge; e.g., estimates of correlations Satisficing solutions when optimizing is not feasible; intuition (as in entrepreneurship)

Select a heuristic that is ecologically rational for a task; exploratory data analysis

Source: Adapted from Mousavi and Gigerenzer (2014) with permission.

characterized, in turn, shapes and limits the type of solution that can be produced because it dictates the form of knowledge generated from the processing of information. This is illustrated in Table 6.2, wherein, in addition to deductive and inductive processes, a third heuristic process is proposed, which involves a less than exhaustive search for or consideration of information and leads to knowledge that is simply good enough for making a successful decision, but by no means exhausts information or optimizes across conditions. Note that the idea is not that these decision processes are mutually exclusive categories. Rather, the current categorization is meant to shed light on the nature of knowledge used and created in the process of decision making. For resolving disequilibrium, boundedly rational agents tend to use different types of strategies compared to fully rational agents (Simon 1955; Selten 1998). They restore the equilibrium by finding satisficing answers to their problems in situations with irreducible uncertainty, wherein exhaustive search is often unhelpful or even impossible. Heuristic decision-making, based on good-enough reasons to act, characterizes the observed behavior primarily with respect to a functional (rather than mirror image) match between the mind of the decision maker, the particular strategy employed, and properties of the task environment. A large number of situations involving unknowns are characterized by what we refer to as fundamental uncertainty that cannot be reduced to risk calculations. This fundamental uncertainty includes what Knight refers to as an ‘estimate’ and extends to situations where some options, outcomes, or probabilities are fundamentally unknown (Meder et al. 2013). Heuristics are then to be viewed as less than fully reasoned strategies to deal with complexities of such uncertain unknowns by not trying to assign a probability to (including zero for ignoring) every unknown, but just forming an opinion that allows an action, what Knight calls an estimate: Suppose we are allowed to look into the urn containing a large number of black and red balls before making a wager, but are not allowed to count the balls: this would give rise to

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Heuristics: fast, frugal, and smart 107 an estimate of probability in the correct sense; it is something very different from either the mere consciousness or ignorance on which we act if we know only that there are balls of both colors without any knowledge or opinion as to the numbers or the exact knowledge of real probability attained by an accurate counting of the balls. In the second place, we must admit that the actual basis of action in a large proportion of real cases is an estimate. Neither of these interpretations, however, justifies identifying probability with an estimate. . ..The exact science of inference has little place in forming the opinions upon which decisions of conduct are based, and that this is true whether the implicit logic of the case is prediction on the ground of exhaustive analysis or a probability judgment, a priori or statistical. We act upon estimates rather than inferences, upon ‘judgment’ or ‘intuition’, not reasoning, for the most part. (Knight 1921, p. 23)

Note that heuristics can be applied to a variety of situations. For instance, the priority heuristic (Brandstätter et al. 2006) provides a lexicographic strategy to choose among lotteries, the classical paradigm for decision-making under risk. The priority heuristic chooses between lotteries by comparing their probabilities and outcomes (gains or losses) lexicographically (that is, one at a time) instead of combining probabilities and gains in a weighted sum. Surprisingly, this simple model logically implies long-lasting anomalies of human choice behavior, such as the Allais paradox, the fourfold pattern of risk, and the certainty effect (Katsikopoulos and Gigerenzer 2008). Also, heuristic methods may be applied to a wide range of other problems, such as catching a ball. One way of solving the problem would be to compute the trajectory of the ball and move towards the inferred landing point, but owing to the number of causally relevant variables (for example, velocity and wind resistance) and the associated uncertainties, this is difficult to impossible. However, the problem can be tackled by a relatively simple algorithm according to which the catcher does not compute the landing point, but focuses on the ball and keeps a constant angle of elevation of gaze while running in the direction (McLeod and Dienes 1996). This example also illustrates the tight connection between heuristics and evolutionary or learned capacities. Applying the gaze heuristic requires certain capacities (that is, to fixate a moving object with your eyes, locomotion, and so on) that are necessary for using the strategy, which are far from trivial and cannot be reduced to merely computing the solution. The simplicity of heuristics is a feature, rather than a flaw. Heuristics are successful because of their simplicity, which involves a beneficial degree of ignoring information, not despite it – something that may puzzle many economists, when trying to make sense of the observed behavior through the lens of constructivist rationality, but is practiced regularly by laypeople. Whether the benefits of heuristics come at a prohibitive cost is not a matter of opinion but should be understood as an empirical question. In the following we turn to the world of business and finance as an example of an uncertain environment in which the successful use of heuristic strategies accords with the ecological notion of rationality.

SUCCESSFUL HEURISTICS IN FINANCE AND BUSINESS DECISION-MAKING The previous sections have provided theoretical arguments for a shift towards the norm of ecological rationality and proposed that heuristics are appropriate tools to tackle

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complex problems under conditions of uncertainty. Given that practitioners care more about measurable results than about abstract beauty or consistency with axioms, it is not surprising that some of the strongest examples for successful use of heuristics stem from the world of finance and business decision making. Any form of resource allocation faces two fundamental problems: (1) how should we distribute our assets over all available options, and (2) when should we switch from one option to another? Theoretically, the asset allocation problem is solved by the Nobel prize-winning mean-variance model of Markowitz (1952), which provides the optimal investment portfolio by maximizing profit for a given level of risk. By contrast, a dominant strategy employed by many people is a simple 1/N heuristic that allocates resources equally across all considered assets. When contributing to retirement savings plans, 1/N has been called ‘naive diversification’ and is believed to incur substantial costs to investors (Benartzi and Thaler 2001). However, when DeMiguel et al. (2009) compared Markowitz’s solution and its modern variants with 1/N, the heuristic performed at least as strongly as the mean-variance model. One reason for the surprising success of the simple 1/N heuristic lies in the so-called bias-variance dilemma (Geman et al. 1992), pertaining to minimizing the prediction error. The prediction error has two contributing components: bias and variance. The error due to bias has been at the center of behavioral economics, and has led to enlisting several debiasing techniques. The error due to variance, however, has not been receiving much attention. 1/N exemplifies a simple allocation mechanism, which is highly biased but has no variance, and overall generates less prediction error under certain circumstances. 1/N can be viewed as a special case of the Markovitz model, which implies that the flexibility of the Markowitz model comes at the potential cost of an increased estimation error (Neth et al. 2014; see Gigerenzer and Brighton 2009, for a general discussion of heuristics and the bias-variance dilemma). As the benefits of 1/N have been shown to generalize to investments in international stock markets and different asset classes (Jacob et al. 2013) it seems smart of Markowitz to have used 1/N himself, rather than his own method of portfolio optimization (Benartzi and Thaler 2001, p. 80). The 1/N heuristic is an instance of a more general equality rule (Messick 2008) that is also applied in parental investments (Hertwig et al. 2002). Regarding the switching problem (that is, when and how to switch between different options), biologists and psychologists have examined simple, yet highly effective stopping rules in animal foraging theory (Green 1984; Stephens and Krebs 1986) and research on human multitasking behavior (for example, Payne et al. 2007). An applied instance of a simple and successful temporal threshold rule is the hiatus heuristic (Wübben and von Wangenheim 2008), which allows directing marketing efforts by abandoning customers who have not purchased anything for a certain amount of time, say, a number of months. This period of time that sets the threshold is called the hiatus. Interestingly, heuristic models combine explanatory parsimony with higher predictive power for situations of uncertainty.4 This is in direct contrast with the prevalent method used by mainstream behavioral economics of adding flexible parameters to Bernoulli utility functions in order to incorporate psychological factors of observed behavior, which in turn adds to the complexity of the model but often costs predictive power (Berg and Gigerenzer, 2010). The abundance and ubiquity of successful heuristics in applied contexts raises the question whether existing heuristics can be used to create new or improve existing strategies.

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Heuristics: fast, frugal, and smart 109 One aspect of ecological rationality – as a research program – aims at teasing out the elements of successful strategies to adapt and refine them to novel situations. In addition, understanding how, when, and why heuristics work well can guide the design of intuitive decision systems that fit the strategies that people naturally use. For instance, highly transparent and teachable fast-and-frugal trees (Martignon et al. 2003) have been designed for coronary care unit allocations (Green and Mehr 1997), for diagnosing patients with clinical depression (Jenny et al. 2013), and for identifying vulnerable banks in financial regulation (Aikman et al. 2014; Neth et al. 2014). Thus, successful heuristics are not only discovered, but can also be specifically designed to create efficient and effective tools. Next, we demonstrate this transformative potential of ecological rationality in the context of public policy decisions and the communication of medical risks.

APPLIED LESSONS FROM THE STUDY OF ECOLOGICAL RATIONALITY In the following, we extend our discussion of ecological rationality to applied issues. First, we critically evaluate the idea of nudging, a policy-making tool rooted in the behavioral economics approach. Subsequently, we discuss probabilistic reasoning and risk literacy as an example of how successful decision engineering can be guided by psychological research that takes the match between cognitive processes and the information structure of the environment seriously. This approach is based on the idea of making people risk literate to help them make better, more informed decisions, rather than merely nudging them towards an externally specified goal. The Risk of Using Nudges in an Uncertain World How to conceptualize human rationality is not only an academic issue, but has strong implications for policy making and the question of how to help people make better decisions. A prominent example is the so-called ‘nudge’ approach, which (in the tradition of the heuristics and biases program) assumes that people frequently make inferior decisions because their thinking is fundamentally biased and error-prone (Thaler and Sunstein 2008). The proposed remedy is to structure the choice situation so that people are more likely to make better decisions, while retaining freedom of choice (Grüne-Yanoff and Hertwig 2015). Examples include nudging people towards healthier dietary choices by arranging food items (for example, in a canteen) such that healthier items are more readily available, or setting default options in retirement saving plans in a way that people automatically enroll in higher saving contributions, unless they deliberately opt out (for an alternative approach to public policy that advocates financial literacy, see Altman 2012). However, what are the implications of nudges in an uncertain world, where it may not always be clear what it means to make better decisions? Consider nudges in the health domain. In the past decades, several countries have set up screening programs (for example, for breast cancer and prostate cancer), with the long-term goal of reducing cancer-related mortality rates. The idea behind these programs is to detect cancer in early stages, in order to treat people earlier and more effectively (or at least more cost-efficiently). A key question is how to provide information to the target group to increase participation; one way

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of doing so is to resort to the nudge approach. For instance, different nudges have been used in the Danish breast cancer screening program to increase participation rates (Ploug et al. 2012; see Gøtzsche and Jørgensen 2013, for a related analysis of the British NHS breast cancer screening programme). Women in the target group received an invitation to participate, along with an information leaflet. The default was a pre-booked appointment, so that women needed to actively opt out. The leaflet also stated that after evaluating the pros and cons the Danish National Board of Health recommends participating in screening. These strategies aim at nudging people towards a goal defined by experts and policy makers, based on the assumption that it is in people’s interest to participate. It could be argued that people should be nudged to participate in screening – after all, is it not to their own benefit to participate if such a program can reduce the risk of dying from cancer (or at least lead to better treatment with less side effects)? What remains unclear, however, is whether participating in screening always serves people’s interests, given that there are different benefits and costs associated. In the case of breast cancer screening, the (currently) available data show that over a period of ten years, eight out of 2000 women who do not participate in screening die from breast cancer, compared with seven out of 2000 women who do participate (Gøtzsche and Jørgensen 2011). At the same time, however, screening entails potential and harms, such as overtreatment resulting from false positive test results (for example, unnecessary removal of the breast). Also, the overall mortality rate (that is, total number of women dying from all causes) does not vary between women participating and not participating in breast cancer screening (Gøtzsche and Jørgensen 2013). Yet this crucial information was omitted from the leaflet of the Danish breast-screening program, thereby undermining the possibility to make an informed decision based on considering and evaluating the potential benefits and harms (see Gigerenzer 2014a; Gigerenzer and Edwards 2003; Gigerenzer et al. 2007). For other screening programs, such as PSA-based screening for prostate cancer, the current evidence indicates that the potential harms actually outweigh the potential benefits. Consequently, the US Preventive Services Task Force explicitly recommends against prostate-specific antigen (PSA)-based screening for prostate cancer (Moyer 2012). This recommendation was issued after a period of uncertainty in which not enough evidence was available to determine whether PSA-based screening would be beneficial or not. These examples highlight critical issues in the foundation and application of nudges. An important precondition for the nudge approach is the possibility to determine – from the perspective of the choice architect – which decision is in the best interest of the decision maker. It may be self-evident that an apple is a healthier choice for a snack than a chocolate bar, but in other domains, such as medical treatments, determining which choice is in the decision maker’s best interest may be highly uncertain and dependent on individual preferences. A woman provided with the currently available evidence on breast cancer screening may decide that the benefits outweigh potential harms and therefore participate. However, she may also conclude that the potential harms outweigh the potential benefits and therefore decide that she would be better off by not participating. In each case, the decision will depend on how she values the associated benefits and costs. This, in turn, highlights the importance of providing people with the necessary information to make informed decisions, relative to their goals, values, and individual preferences, and not merely nudging them towards an externally specified goal. Importantly, not all informa-

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Heuristics: fast, frugal, and smart 111 tion is created equal, and identifying or designing transparent and intuitive information presentation formats is crucial for both psychology and behavioral economics (see below). In sum, the nudge approach rests on the assumption that there is one right way to make decisions, which applies to everybody, and that the choice architect knows what is in the best interest of the decision maker and can therefore enforce it. While this may well be true in some cases, we advise against an uncritical application of the approach to policy making. Nudges may be an effective tool in some circumstances, but like any tool (fastand-frugal heuristics included), they can cut both ways and need to be handled with care. In an uncertain and changing world, nudges may lead to adverse outcomes that are not in the best interest of decision makers. In our view, rather than precluding the possibility that people can make good decisions, the goal should be to develop means for communicating the relevant information in a way that facilitates people’s understanding of it, so that they can make better, more informed decisions. From the perspective of the nudge program, educating and informing people to make them risk literate (Gigerenzer et al. 2007; Gigerenzer and Muir Gray 2011) is likely to be ineffective, because the assumption (rooted in the heuristics and biases program) is that human thinking and decision making are fundamentally flawed. This view, however, neglects recent research that demonstrates how people can be helped to make better inferences (for example, inferring posterior probabilities, such as the probability of breast cancer given a positive mammogram) by conveying the relevant information in a transparent and intuitive way, without being patronizing (Gigerenzer and Hoffrage 1995; Sedlmeier and Gigerenzer 2001; Meder and Gigerenzer 2014). Similar views have been presented in other domains. In the equilibrium analysis of financial markets, although an equilibrium state is always Pareto optimal, this optimality does not necessarily coincide with the best ‘wanted’ outcome for all agents. This is illustrated in phishing equilibria, where ‘phools,’ who do not act according to what they want or is good for them, are systematically ‘phished’. Akerlof and Shiller (2015) argue that when information can be used systematically in forms that would deceive the consumers, the very structure of free markets provides opportunity for exploitation, a point overlooked by behavioral economists, [C]uriously, to the best of our knowledge, they [behavioral economists] have never interpreted their results in the context of Adam Smith’s fundamental idea regarding the invisible hand . . . It’s a major reason why just letting people be Free to Choose – which Milton and Rose Friedman, for example, consider the sine qua non of good public policy – leads to serious economic problems. (Akerlof and Shiller 2015, p. 6)

As we discuss next, the use of accessible and intuitive representative formats such as natural frequencies can improve decision making by enhancing their probabilistic reasoning abilities. Moving Beyond Nudges by Making People Risk Literate Understanding of and reasoning with probabilistic and statistical information is crucial for making good decisions. For instance, an informed decision on whether to participate in a screening program requires understanding of the relevant evidence regarding potential benefits and harms and the implications of medical test results. The nudge approach

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and the heuristics and biases program that provides its conceptual foundation presume that people lack this capacity. Since humans are assumed to be biased and error-prone when it comes to probabilistic thinking, the suggested remedy is to nudge people into making better decisions. However, is nudging the only way to help people make better decisions? Also, what does the psychological literature have to say about people’s capacity to reason with probabilistic and statistical information? In fact, the psychological literature to date has given very different answers to these questions. In the 1950s and 1960s, researchers began investigating experimentally whether people’s inferences correspond (approximately) to probability theory in general, and to Bayes’ rule in particular. For instance, Phillips and Edwards (1966) used (incentivized) bookbag and poker chip scenarios, in which they presented subjects with a sequence of draws that came from either a bag with more red than blue chips or a bag with more blue than red chips. The question of interest was whether subjects would update their beliefs regarding the bag in accordance with Bayes’ rule, given the observed data. This and other studies indicated that the human mind is able to deal with probabilistic inferences, although it was frequently observed that the amount of belief revision was not as extensive as prescribed by Bayes’ rule (a phenomenon referred to as conservatism; Edwards 1968). Peterson and Beach (1967) coined the term ‘man as intuitive statistician,’ mirroring the Enlightenment view that the laws of probability are also the laws of the mind (Daston 1988). This view stands in stark contrast to the conclusions drawn from later research in the heuristics and biases tradition: ‘In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of prediction’ (Kahneman and Tversky 1973, p. 237). A key empirical finding used to corroborate this claim was that people often do not seem to appreciate base rate information (prior probabilities) when making Bayesian inferences. A prominent example is the so-called ‘mammography problem’ (Eddy 1982; Gigerenzer and Hoffrage 1995). Figure 6.1 (left-hand side) gives an example of the task in which the goal is to derive the posterior probability of a woman having breast cancer given a positive mammogram, based on information about the base rate (prior probability) of cancer, the probability of obtaining a positive test result for a woman having the disease, and the probability of obtaining a positive test for women without cancer. The probability tree (Figure 6.1, middle left) visualizes the given information, which consists of a set of unconditional and conditional probabilities. The posterior probability can be inferred using Bayes’ rule (Figure 6.1, bottom left), according to which the probability of cancer given a positive test result is about 8 percent. Yet many people give much higher estimates in this particular scenario, which has been interpreted as neglect of the base rate. These and similar findings have led to the view that people’s probabilistic reasoning is fundamentally flawed (but see Koehler 1996 for a critical review). More recently, however, psychologists have begun to identify the conditions under which people are able to make sound probabilistic inferences. This is a case in point for successfully exploiting the ecological rationality of designed tools. Instead of focusing on human errors, the focus is shifted to human engineering: What can be done to help people with probabilistic reasoning? A key insight from this line of research is the power of presentation formats: The extent to which people are able to make sound probabilistic inferences crucially depends on the ways in which the relevant information is conveyed.

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Heuristics: fast, frugal, and smart 113 Task The probability of breast cancer is 1 percent for a woman at the age of 40 who participates in routine screening. If a woman has breast cancer, the probability is 80 percent that she will get a positive mammography. If a woman does not have breast cancer, the probability is 9.6 percent that she will also get a positive mammography. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer? ___ percent

Ten out of every 1000 women at the age of 40 who participate in routine screening have breast cancer. Eight of every 10 women with breast cancer will get a positive mammography. Ninety-five out of every 990 women without breast cancer will also get a positive mammography. Here is a new representative sample of women at the age of 40 who got a positive mammography in routine screening. How many of these women do you expect to actually have breast cancer? ___ out of ___

Representation Conditional Probability Tree

Natural Frequency Tree

1 woman

1000 women

Cancer

No cancer

1%

Cancer

No cancer

10

99%

990

80%

20%

9.6%

90.4%

8

2

95

895

Test positive

Test negative

Test positive

Test negative

Test positive

Test negative

Test positive

Test negative

Inference P(cancer | test positive) =

=

P(test positive | cancer) × P(cancer) P(test positive)

P(cancer | test positive) =

0.8 × 0.01 ≈ 0.08 0.08 × 0.01 + 0.096 × 0.99

=

N(test positive cancer) N(test positive) 8 ≈ 0.08 (8 + 95)

Note: The middle panel shows two types of task representations, a conditional probability tree (left) and a natural frequency tree (right). The bottom row shows two (mathematically equivalent) ways of deriving the posterior probability of having cancer given a positive mammogram, P(cancer|test positive), either by using Bayes’ rule (left) or by deriving it from the natural frequency information. Source: Task descriptions (top row) are taken from Gigerenzer and Hoffrage (1995).

Figure 6.1

Example of a simple probabilistic reasoning task

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Particular frequency formats, presented verbally or graphically, have been shown to foster people’s inferences, in the laboratory and outside of it. Consider the variant of the mammography problem shown in Figure 6.1 (top right), adapted from Gigerenzer and Hoffrage (1995). Here, instead of using conditional probabilities, information is presented in terms of natural frequencies. The key difference to conveying information in terms of conditional probabilities is that natural frequencies preserve base rate information. The natural frequency tree (Figure 6.1, middle right) illustrates this. This tree represents information as it would result from natural sampling (Kleiter 1994), providing a joint frequency distribution over the two variables (cancer and test result) that reflects the base rate of cancer in the sample (as opposed to systematic sampling, which fixes base rates a priori). Several studies have shown that presenting information this way strongly improves the accuracy of people’s inferences (for a review, see Meder and Gigerenzer 2014). One reason is that the provided information makes it easier to calculate the desired quantity, namely that of 103 women who receive a positive mammogram (95 + 8), only eight actually have breast cancer (Figure 6.1, bottom right). This echoes Simon (1978; see also Larkin and Simon 1987), who noted that two representations are informationally equivalent if one representation can be translated into the other without losing information, but that this does not imply that they are computationally equivalent. Importantly, these findings have guided the development of efficient tools and teaching methods to help people deal with statistical information. Key examples include the use of natural frequencies for understanding the implications of diagnostic tests (Hoffrage and Gigerenzer 1998; Labarge et al. 2003) and forensic evidence (Lindsey et al. 2003), as well as the use of so-called fact boxes to convey medical information in a concise and easily understandable format (Schwartz et al. 2009; Gigerenzer 2014b). Research also shows that training people to use the power of presentation formats is more sustainable than merely teaching them the application of Bayes’ rule (Sedlmeier and Gigerenzer 2001). Over the past decade, different ways have been explored for the intuitive and transparent communication of health information, as well as for the development of graphical presentation formats that help people make sense of health statistics (for reviews see Akl et al. 2011; Gigerenzer et al. 2007). The upshot is that the human mind is not necessarily doomed when it comes to probabilistic thinking. Whereas many researchers endorse the view that people inevitably fall prey to ‘cognitive illusions’, harnessing the power of presentation formats offers a means to help people make sound probabilistic inferences. This, in turn, can provide a foundation for helping people make better decisions without nudging them towards an externally specified goal.

CONCLUSION A functional match between mind and the task environment leads to successful decision making. Fast-and-frugal heuristics are ecologically rational when used under conditions that satisfy such functional matches. Thus, boundedly rational agents make smart decisions by exploiting the ecological rationality of heuristics. Heuristics can capitalize on learned and evolved capacities, or can be designed to create efficient and effective tools

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Heuristics: fast, frugal, and smart 115 for decision making. A heuristic is neither good nor bad per se. Rather, the effectiveness of a heuristic strategy can only be gauged with respect to the structure of information in the environment within which it is used. As such, errors can be scrutinized as informative where they are indicating a mismatch between the environment, strategies, or evolved and learned capacities. Specifying proper matches and teasing out the mismatches between heuristics and their task environment constitutes the study of ecological rationality of heuristics. Intelligent behavior, when appearing less than neoclassically rational, can be understood by breaking free of the restrictive benchmarks imposed by constructivist rationality. The answer is to be found in the ecological rationality of intelligent behavior, because in many real-world situations it is simply not rational to be rational. Through the complementary frame of ecological rationality, intelligence can be understood to be beyond the agents’ mind and without requiring complete comprehension of rules. Also, smart behavior emerges where proper evolved or learned capacities are triggered in reaction to the structure of the task environment. This chapter primarily focused on recent theoretical developments regarding the ecological nature of humans’ bounded rationality that ought to be of interest to behavioral economists. We invite economists and psychologists to join our dialogue and dig into less explored insights from psychology that promise informing behavioral economics on realworld, practical, and smart decision making. Beyond encouraging economists to adopt psychological insights into their models and theories, Simon (1959, p. 253) also challenges economists to communicate their ideas and findings to psychologists: the psychologist will also raise the converse question – whether there are developments in economic theory and observation that have implications for the central core of psychology . . . Influence will run both ways.

NOTES 1. Fast-and-frugal heuristics are interchangeably used in this text with simple and smart heuristics. 2. Savage (1954) drew significantly on the Theory of Games and Economic Behavior (von Neumann and Morgenstern 1947) and proposed a normative reading of the subjective expected utility theoretical framework. 3. McCloskey (1991) spells out the pragmatic significance of this point in ‘Economic science: a search through the hyperspace of assumptions?’ where she portrays the practice of axiomatic economics as a mathematical practice faithful to math departments’ ideal of consistency and coherence, but incapable of grasping and providing solutions to real-world problems. 4. A series of papers (Journal of Business Research, vol. 67, 2014) on the effectiveness of fast-and-frugal heuristics in business decision-making demonstrate this point.

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Akl, E.A., A.D. Oxman, J. Herrin, G.E. Vist, I. Terrenato, F. Sperati et al. (2011), ‘Using alternative statistical formats for presenting risks and risk reductions’, Cochrane Database of Systematic Reviews, 16 March, CD006776, doi:10.1002/14651858.CD006776.pub2. Altman, M. (2004), ‘The Nobel Prize in behavioral and experimental economics: a contextual and critical appraisal of the contributions of Daniel Kahneman and Vernon Smith’, Review of Political Economy, 16 (1) 3–41. Altman, M. (2012), ‘Implications of behavioural economics for financial literacy and public policy’, Journal of Socio-Economics, 41 (5), 677–90. Benartzi, S. and R.H. Thaler (2001), ‘Naive diversification strategies in defined contribution saving plans’, American Economic Review, 91 (1), 79–98. Berg, N. and G. Gigerenzer (2010), ‘As-if behavioral economics: neoclassical economics in disguise?’, History of Economic Ideas, 18 (1), 133–66. Brandstätter, E., G. Gigerenzer and R. Hertwig (2006), ‘Priority heuristic: making choices without trade-offs’, Psychological Review, 113 (2), 409–32. Daston, L.J. (1988), Classical Probability in the Enlightenment, Princeton, NJ: Princeton University Press. DeMiguel, V., L. Garlappi and R. Uppal (2009), ‘Optimal versus naive diversification: how inefficient is the 1/N portfolio strategy?’, Review of Financial Studies, 22 (5), 1915–53. Dewey, J. (1938), ‘Logic: the theory of inquiry’, reprinted in J.A. Boydston (ed.) (1986), John Dewey: The Later Works, vol. 12, Carbondale, IL: Southern Illinois University Press. Eddy, D.M. (1982), ‘Probabilistic reasoning in clinical medicine: problems and opportunities’, in D. Kahneman, P. Slovic and A. Tversky (eds), Judgment under Uncertainty: Heuristics and Biases, Cambridge: Cambridge University Press, pp. 249–67. Edwards, W. (1968), ‘Conservatism in human information processing’, in B. Kleinmuntz (ed.), Formal Representation of Human Judgment, New York: Wiley, pp. 17–52. Geman, S., E. Bienenstock and R. Doursat (1992), ‘Neural networks and the bias/variance dilemma’, Neural Computation, 4 (1), 1–58. Gigerenzer, G. (1991), ‘How to make cognitive illusions disappear: beyond “heuristics and biases”’, European Review of Social Psychology, 2 (1), 83–115. Gigerenzer, G. (1996), ‘On narrow norms and vague heuristics: a reply to Kahneman and Tversky (1996)’, Psychological Review, 103(3), 592–6. Gigerenzer, G. (2008), Rationality for Mortals: How People Cope with Uncertainty, New York: Oxford University Press. Gigerenzer, G. (2014a), ‘Breast cancer screening pamphlets mislead women’, BMJ, 348 (25 April), g2636–g2636, doi:10.1136/bmj.g2636. Gigerenzer, G. (2014b), Risk Savvy: How to Make Good Decisions, New York: Viking. Gigerenzer, G. and H.J. Brighton (2009), ‘Homo heuristicus: why biased minds make better inferences’, Topics in Cognitive Science, 1 (1), 107–43, doi:10.1111/j.1756-8765.2008.01006.x. Gigerenzer, G. and A. Edwards (2003), ‘Simple tools for understanding risks: from innumeracy to insight’, BMJ, 327 (7417), 741–4, doi:10.1136/bmj.327.7417.741. Gigerenzer, G. and U. Hoffrage (1995), ‘How to improve Bayesian reasoning without instruction: frequency formats’, Psychological Review, 102 (4), 684–704, doi:10.1037/0033-295X.102.4.684. Gigerenzer, G. and K. Hug (1992), ‘Domain-specific reasoning: social contracts, cheating, and perspective change’, Cognition, 43 (2), 127–71. Gigerenzer, G. and J.A. Muir Gray (eds) (2011), Better Doctors, Better Patients, Better Decisions: Envisioning Health Care 2020, Cambridge, MA: MIT Press. Gigerenzer, G., W. Gaissmaier, E. Kurz-Milcke, L.M. Schwartz and S. Woloshin (2007), ‘Helping doctors and patients make sense of health statistics’, Psychological Science in the Public Interest, 8 (2), 53–96, doi:10.1111/j.1539-6053.2008.00033.x. Gigerenzer, G., P.M. Todd and the ABC Research Group (1999), Simple Heuristics that Make Us Smart, New York: Oxford University Press. Goldman, A. (1988), Empirical Knowledge, Berkeley, CA: University of California Press. Gøtzsche, P.C. and K.J. Jørgensen (2011), ‘The breast screening programme and misinforming the public’, Journal of the Royal Society of Medicine, 104 (9), 361–9, doi:10.1258/jrsm.2011.110078. Gøtzsche, P.C. and K.J. Jørgensen (2013), ‘Screening for breast cancer with mammography’, Cochrane Database of Systematic Reviews, (6), 4 June, CD001877, doi:10.1002/14651858.CD001877.pub5. Green, R.F. (1984), ‘Stopping rules for optimal foragers’, American Naturalist, 123 (1), 30–43. Green, L. and D.R. Mehr (1997), ‘What alters physicians’ decisions to admit to the coronary care unit?’, Journal of Family Practice, 45 (3), 219–26. Grüne-Yanoff, T. and R. Hertwig (2015), ‘Nudge versus boost: how coherent are policy and theory?’, Minds and Machines, 25 (1–2), 1–35, doi:10.1007/s11023-015-9367-9.

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The beauty of simplicity? (Simple) heuristics and the opportunities yet to be realized* Andreas Ortmann and Leonidas Spiliopoulos

INTRODUCTION In this chapter we focus on the history of fast and frugal heuristics, as sketched out comprehensively in Gigerenzer et al. (1999) and scores of follow-up books (for example, Gigerenzer et al. 2011; Todd et al. 2012; Hertwig et al. 2013) and articles. What we consider must-read papers are listed in the further reading section at the end of the chapter. A recurring theme of this edited volume is that individuals can be smart and procedurally rational despite displaying errors in decisions. Such an argument implicitly assumes that there is an effort–accuracy tradeoff. Consider the feasible set of combinations of effort and accuracy as being constrained by the decision maker’s cognitive processes and features of the decision environment – this is the basis of prominent theories of bounded rationality. In this view, decision errors can be rationalized by arguing that regardless of which specific combination of effort and accuracy is chosen, as long as it is on the efficient frontier, a choice resulting in said decision error cannot automatically be classified as irrational per se. Errors are thus uncoupled from the notion of rationality, in contrast to neoclassical economics where errors are synonymous with irrational behavior. The notion of fast and frugal heuristics goes a step further than this argument, and its proponents contend that there exist decision environments – found with sufficiently high frequency in the real world – which can be exploited by appropriately adapted heuristics in a way that transcends the effort–accuracy tradeoff. Under such circumstances, normative models such as expected utility may be dominated by simple heuristics in both the accuracy and effort dimensions. We contextualize the emergence of this so-called ‘Ecological-Rationality’ (ER from here on) program as an explicit counterpoint to the ‘Heuristics-and-Biases’ (HandB from here on) program initiated by Kahneman and Tversky (for example, Tversky and Kahneman 1974; Kahneman and Tversky 1979; Kahneman 2003a, 2003b, 2011) that informed and inspired scores of early behavioral economists. Simple heuristics are here understood to be fast and frugal rules of thumb because they ignore information that is available and hence can shorten decision-making time. Also, they ought to reflect cognitive processes (and hence be able to predict) rather than be as-if modelling exercises that explain ex post. Our focus seems warranted by the fact that the HandB program has invaded economics, and other social sciences, to the extent that it is now by many measures thoroughly mainstream (for example, Camerer et al. 2004, 2011; Heukelom 2015; Thaler 2015). While in the past few years increasingly critical questions have been asked about the HandB program (for example, Ortmann 2015a, 2015b and references therein), the predominance 119

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of it has largely overshadowed the ER program, in our view to the detriment of both economics and the ER program. It has not helped that those in favor of an ER program have not done as much out-reach to economics as might have been desirable. Sketching with a very broad brush, we argue that the HandB program suggested that various bounds on rationality, and the make-up of human judgment- and decisionmaking facilities, induced humans to make rash decisions that produced systematic biases, or cognitive illusions. Cognitive illusions were rationalized with reference to optical illusions whose reality was well established. The heuristics that people were said to use, such as representativeness, availability, and anchoring and adjustment, were motivated by appeal to the principles also underlying optical illusions. An implicit – and increasingly explicit claim (for example, Thaler 1980, p. 40) – was that cognitive illusions were as robust as optical illusions (see also Kahneman 2003a, 2003b). Heuristics were considered to be problematic and decision makers as fallible, even gullible, and in dire need of all the help that they could get to improve on their decision-making skills. As Cochrane (2015) has noted, not inappropriately, this view represents for the HandB program proponents a considerable moral hazard problem. It is worth noting that the assessment of people’s performance as being severely wanting was quite a departure from the prevailing view in the 1950s, 1960s and early 1970s (for example, Edwards 1956; Peterson and Beach 1967; see also Ortmann 2015a). Even Tversky and Kahneman (1974), in the article that started it all, did not make the kind of sweeping claims that were made in the following decades. Drawing on arguments by Herb Simon (1947, 1955, 1956) and his insight that rationality cannot be defined through cognitive and emotional processes alone, Gigerenzer and the ABC Research group showed that many of the demonstrations of the HandB program were highly problematic. The main criticism was directed at the design and implementation of the experiments used to produce supporting evidence (for example, prominently Gigerenzer 1991), and that indeed heuristics could have surprising performance properties, particularly so as environments became more uncertain (Gigerenzer and Gaissmaier 2011). We first review in more detail how this battle of programs unfolded, then lay out what we consider the considerable accomplishments of the ER program and point out some overlooked connections between the ER program and economics, and finally, enumerate what we consider to be open questions and challenges. In the interest of full disclosure, we note that both authors spent time at the Max Planck Institute for Human Development, which now houses the ABC and ARC groups (both of which contribute to the ER program; more about this below), Ortmann for one year each in 1996–97 and 1999–2000 with the ABC group, and Spiliopoulos having visited the ABC group twice (for a couple of weeks each) and since mid-2014 being first a Humboldt Experienced Research Fellow with the ARC group and then a senior researcher.

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The beauty of simplicity? 121

HOW THE BATTLE OF THE HANDB PROGRAM AND ER PROGRAM UNFOLDED First, the Heuristics and Biases Program (HandBP) Calling Richard Cyert, James March, Herbert Simon the ‘old behavioral economists, who focused on bounded rationality, satisficing, and simulations’ (Sent 2004, p. 740), historian of economics Esther-Mirjam Sent explained the transition from old to new behavioral economics (ibid., pp. 742–7), thus: ‘The roots of new behavioral economics may be traced to the 1970s and the work of especially Amos Tversky and Daniel Kahneman’ (ibid., p. 742). She identifies the ‘Behavioral foundations of economic theory’ conference held at the University of Chicago in October 1985 as a key event. In the preface to their book that drew on the conference, Hogarth and Reder (1987, p. vii) argued that there was ‘a growing body of evidence – mainly of an experimental nature – that has documented systematic departures from the dictates of rational economic behaviour.’ In his review of the book, Smith (1991, p. 878) dismissed such a claim: ‘(experimental economics) documents a growing body of evidence that is consistent with the implications of rational models’. Acknowledging that Simon’s work on bounded rationality had influenced them, too, Kahneman (2003a, p. 1449) identified three separate lines of research. The first explored the heuristics that people use and the biases to which they are prone in various tasks of judgment under uncertainty, including predictions and evaluations of evidence . . . The second was concerned with prospect theory, a model of choice under risk . . . and with loss aversion in riskless choice . . . The third line of research dealt with framing effects and with their implications for rational-agent models . . .

and Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models. The rational-agent model was our starting point and the main source of our null hypotheses, but Tversky and I viewed our research primarily as a contribution to psychology, with a possible contribution to economics as a secondary benefit. We were drawn into the interdisciplinary conversation by economists who hoped that psychology could be a useful source of assumptions for economic theorizing, and indirectly a source of hypotheses for economic research (Richard H. Thaler, 1980). (Kahneman 2003a, p. 1449)

Kahneman and Tversky’s HandBP was based on the idea that thinking was typically fast and rarely slow, and very fundamentally about accessibility or intuition. The argument was that, since our thinking was typically fast, it had to rely on rules of thumb (heuristics) which led to systematic divergences (biases) from normative behavior as described by standard economic theories (Tversky and Kahneman 1974; Kahneman and Tversky 1996; Kahneman 2003a, 2003b). People were increasingly conceptualized as bumbling fools and this theme was the general drift taken up by those starting the movement that later became behavioral economics. Thaler (1980, p. 40), for example, exclaimed that ‘Research on judgment and decision making under uncertainty, especially by Daniel Kahneman and Amos Tversky (1974; Tversky and Kahneman 1979) has shown that . . .

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mental illusions should be considered the rule rather than the exception. Systematic, predictable differences between normative models of behavior and actual behavior occur . . .’. Importantly, the cognitive illusions were explicitly constructed (for example, Kahneman 2003a, 2003b) in parallel to optical illusions whose reality and robustness had been reasonably well established. It is striking that the optical-illusion analogy was not taken to its logical conclusion, namely, that the documented illusions either never occur in the environment or, in the few instances when they do, they rarely impose any real cost on the organism. We have argued elsewhere (Spiliopoulos and Ortmann 2014) that specific diagnostic tasks, that is, specific parameterizations of tasks where competing models make starkly different predictions, should not be used to infer the rationality of agents. Rationality can only be assessed on a wide range of parameterizations that must include those found in the real environment (on this, see Hogarth and Karelaia 2005, 2006, 2007; Erev et al. 2017). There were some obvious problems with the HandB approach, and two decades ago they were the subject of a highly visible dispute between Kahneman and Tversky (1996) and Gigerenzer (1996) about the reality of cognitive illusions. From the critics’, and the present authors’, view the HandBP was characterized by a lack of process models (key concepts such representativeness, anchoring and adjustment, and availability being hardly more than labels), too much story-telling, un-incentivized scenario studies, polysemy, often deception, and experimenter demand effects, to name a few. There was, in Nobel Prize laureate Vernon L. Smith’s sarcastic but brilliant observation, too much fishing in the tail ends of human behavior (Smith 2003, p. 467, fn. 8). No surprise then that many anomalies were found that were taken as proof of people’s limited rationality. The interpretation of that evidence as being indicative of humans’ typically underwhelming performance has been contested ever since it was proposed, by the ER program and many others working in the neoclassical tradition (for example, Smith 1991). Second, the Ecological-Rationality Program (ERP) The ABC research program (see also Lopes 1992) was constructed in contrast to the HandBP. Gigerenzer (1991), for example, successfully deconstructed some key findings of Kahneman and Tversky who eventually found themselves prompted to respond to Gigerenzer’s critique (Kahneman and Tversky 1996; Gigerenzer 1996). ABC also developed a fundamentally different view of heuristics and did so by formulating cognitive process models that could be tested. It is interesting to note that many of the process models were also based on a frequentist view of the world, with ABC researchers taking broadly an evolutionary-psychology perspective, which conceptualized humans as intuitive statisticians that were almost naturally good at navigating environs that were familiar to them. It was also demonstrated persuasively that an important moderator of these findings is the way statistical information is presented (Sedlmeier and Gigerenzer 2001; see Hertwig and Ortmann 2004 for a summary). To the extent that the HandBP was gobbled up entirely by the initial waves of behavioral economics/finance, ABC remained an outsider of sorts although its influence has grown, as recently evidenced by a 20-year celebration that was attended by more than 100 participants. Part of the problem is that ABC rarely engaged with modern economics and focused its critiques on normative economic models of deductive reasoning. We

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The beauty of simplicity? 123 argue below that important work assuming inductive reasoning in economics can serve as a bridge with the ERP, although important differences remain, and considerable opportunities have yet to be realized.

THE ACCOMPLISHMENTS OF THE ECOLOGICALRATIONALITY PROGRAM The ERP is characterized by a heavy reliance on cognitive process models (which require some serious theorizing), empirical and experimental testing of these models, and an important methodological innovation: the preferred mode of testing relies on ‘out-ofsample’ prediction, or ‘cross-validation’ (Gigerenzer and Gaissmaier 2011). That is, performance is not measured by the best fit on an existing dataset but by the performance of a model on not yet known datasets, also done in Ericson et al. (2015) and Erev et al. (2017). There is no data-fitting after the fact. Cross-validation addresses the important bias–variance tradeoff (Gigerenzer and Brighton 2009). Simple models exhibit higher bias but typically less variance than complex models – it is the relative strength that determines which type of model outperforms the other in prediction. The key finding is that heuristics often exhibit little or no bias vis-à-vis more complex models, therefore the variance effect tends to dominate; we return to this below. Among ERP’s key successful demonstrations is that, when cross-validation is used, the performance of simple heuristics such as the recognition heuristic or the ‘take-the-best’ is better than that of complicated, computationally slow and greedy models such as multiple regression favored by economists (for example, Gigerenzer et al. 1999; Gigerenzer and Brighton 2009; Todd et al. 2012; Gigerenzer and Gaissmaier 2011). The simple, and rather intuitive, reason is that multiple regression essentially over-fits, looking backwards, without taking into account the noisiness that is inherent in datasets. An important implication is that the widely believed effort–accuracy trade-off (Payne et al. 1993) is often not something we need to worry about. Those using simple heuristics can have both. Less can be more. Much work has been done to understand these remarkable results; what drives the success of heuristics such as recognition and take-the-best is now much better understood (Baucells et al. 2008; Luan et al. 2011, 2014; Katsikopoulos 2013; Drechsler et al. 2014). There are three important environmental characteristics that are sufficient, but not necessarily necessary, that induce these striking results: non-compensatoriness of cues, dominance, and cumulative dominance.1 If at least one of these three is true, a lexicographic heuristic exhibits no bias vis-à-vis a linear rule, and is computationally less demanding. These theoretical results would not be important if these conditions were not found regularly in real environments. Şimşek (2013), however, found that these conditions are very common in 51 real-world datasets; consequently, a lexicographic heuristic performed as well as multiple linear regressions in the median dataset for approximately 90 percent of cases. Recent work has analyzed fast-and-frugal trees and successfully connected them to signal-detection theory (Luan et al. 2011, 2014); new heuristics such as the fluency and priority heuristics have been proposed (Hertwig et al. 2008; Brandstätter et al. 2006, 2008; Drechsler et al. 2014; but see also Johnson et al. 2008 on the priority heuristic); and a persuasive rationalization has been provided for the tendency of many

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economists and psychologists to overlook the benefits of simplicity (Brighton and Gigerenzer 2015). Another important contribution of the ERP is the distinction between ‘decisionsfrom-description’ (DfD) and ‘decisions-from-experience’ (DfE) and the empirical validation of a robust gap between the two (for example, Barron and Erev 2003; Hertwig and Erev 2009). It is indeed intuitive that risk maps into, or maybe better invokes, DfD, and that uncertainty maps into, DfE. Furthermore, these map into Savage’s (1954) distinction between small (DfD) and large (DfE) world decision making (see Gigerenzer and Gaissmaier 2011 for a discussion). We also note parenthetically that in strategic environments DfD and DfE also map into eductive and evolutive (deductive and inductive) game theory (Binmore 1990; Friedman 1991). Hopefully, researchers both at ABC and ARC continue recent attempts at theory integration (for example, Schooler and Hertwig 2005, Luan et al. 2011, 2014) and related attempts to break down disciplinary boundaries (for example, Hutchinson and Gigerenzer 2005). This was, to some extent, also reflected in the make-up of the ABC Research Group but perhaps not as much as would have been desirable ex post in particular regarding the group’s engagement with economists. An increasing number of economists and researchers from management and organization (no wonder here, given where it all started: Simon 1955, 1956) have been attracted by the ER paradigm. For example, Åstebro and Elhedli (2006) have empirically demonstrated the usefulness of simple heuristics in forecasting commercial success for earlystage ventures. Eisenhardt and some of her colleagues (see, for a self-centered primer, Bingham and Eisenhardt 2014) have argued that successful repeated product innovation is best implemented through ‘simple rules’, or ‘semi-structures’, which define a path between too much and too little structure. Maitland and Sammartino (2014) have empirically demonstrated the use of simple decision rules for location choice by multinational companies when environments are politically hazardous. Indeed, Artinger et al. (2014) have provided a useful primer of heuristics as adaptive decision strategies in management but it seems clear that the use of heuristics in management and organization is understudied and remains a fruitful area of research. To see how understudied the topic is academically, Google strings such as ‘rules of thumbs to determine when projects pay off’ find more than 14 million hits and scores of lists of simple decision rules for everything from cash flow, real-estate investments, to other financial investments. While there can be no doubt that progress towards a science of heuristics has been tremendous and that the ABC group’s influence is increasing, there remain important blind spots though in our view.

THE ERP AND ECONOMICS – A MISSED OPPORTUNITY (SO FAR) The incompatibility of the ERP with economics has been emphasized by a number of ERP researchers. To a large extent, the ERP is positioned as an antithesis both to the HandBP and the neoclassical-economics program, including behavioral economics, which some view as a disguised extension of the neoclassical program (Berg and Gigerenzer 2010). We are sympathetic to the claims made, as far as they pertain to the overwhelming mass of research often dubbed behavioral economics. Exceptions to this exist, this book

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The beauty of simplicity? 125 serving as a case in point. For example, we ourselves have argued about the advantages of process models compared to as-if models (Spiliopoulos and Ortmann 2015). However, we fear that a purely antagonistic approach of emphasizing the divide has the unfortunate consequence of deepening the schism rather than fostering an exchange between these programs. The differences in opinions are well known; here we will attempt to highlight (perhaps surprising) similarities between these research programs; indeed in some cases we will find parallel, independent emergence of similar ideas. This suggests that there is significant scope for future exchange of ideas and productive collaboration between researchers from the two fields. Heuristics Extremely interesting work from economists like Manzini and Mariotti (for example, 2007, 2012a, 2012b, 2014; see also Mandler et al. 2012) seems to have developed in parallel to the work of the ABC Research Group. Parallel, yet mostly independent, work can be scientifically counterproductive in the sense that closer collaboration could have afforded increasing returns to research and the avoidance of duplication (for example, see Arkes and Ayton 1999 on the Concorde fallacy and related work in economics on sunk cost effects such as Friedman et al. 2007 and McAffee et al. 2010). Broadly inspired by the work of Gigerenzer and associates, the well-cited Manzini and Mariotti (2007) formalizes and axiomatizes a type of sequential eliminative heuristic demonstrating that boundedly rational choice procedures can be tested with observable choice (‘revealed preference’) data favored by more traditional economists. The more recent Manzini and Mariotti (2012a, 2014) builds on this earlier two-stage deterministic model of choice by providing models of stochastic choice when consideration sets are present (that is, agents fail to consider all feasible alternatives), a popular but typically less formalized approach in management science and marketing science that is related to random utility models that have been around for decades in economics. Mandler et al. (2012) provides procedural foundations for utility maximization, with the checklists in the title of their paper being the equivalent of the – preferably noncompensatory – cues central to the fast and frugal heuristics extensively analyzed by the ABC Research Group. The authors show that under specific conditions procedural utility maximization matches that of substantive utility. In Manzini and Mariotti (2012b), the authors extend and formalize a choice procedure introduced by Tversky (1969) that has recently also prominently featured in the work of Luan and colleagues (Luan et al. 2011, 2014). How to Choose Heuristics from the Adaptive Toolbox? Initial criticisms that the ERP had not adequately specified the heuristic selection method of the adaptive toolbox has prompted work directed at strategic selection. The most prominent response to this critique was to postulate a reinforcement learning mechanism over heuristics (Rieskamp and Otto 2006) – see also the RELACS model by Erev and Baron (2005). This is essentially the same solution proposed for strategic decision making by economists. For example, Aumann (1997, pp. 7–8) writes: ‘Ordinary people do not behave in a consciously rational way in their day-to-day activities. Rather, they

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evolve “rules of thumb” that work in general, by an evolutionary process . . . or a learning process with similar properties.’ In the El Farol bar problem (Arthur 1994), agents hold a heterogeneous set of simple predictive models and learn to use the more effective rules (given their individual experience) over time; interestingly, such a learning process converges to the Nash equilibrium solution. Empirical work in repeated games by Stahl (1996, 1999, 2000) and Haruvy and Stahl (2012) find evidence that subjects learn to use relatively simple rules based on their prior performance – they refer to their model as rule-learning. These are concepts strikingly similar to those proposed by the ERP; however, the ERP studies were in the domain of individual decision making, whereas the economic studies are in strategic decision making. Clearly, there is potential here for both disciplines to interact and advance our knowledge of the strategy selection problem. What is the Appropriate Performance Metric for Model Comparisons? The ERP has promoted, rightly in our view, the use of cross-validation to compare the performance of heuristics to other more complex models, hence shifting the focus from explanation to prediction. This is a consequence of the effects of the bias–variance dilemma. More complex models will tend to fit better in-sample than simpler models (such as heuristics), but may perform worse on out-of-sample predictions. Friedman (1953) was an early proponent of the notion that theories should be evaluated on the basis of their predictive power; of course, ERP researchers would take issue with his contention that the processes (and underlying assumptions) are irrelevant – see, for example, the billiard player example in Friedman and Savage (1948). Studies published in prominent economics journals as far back as Camerer and Ho (1999), and including more recent work such as Wilcox (2011) and Spiliopoulos (2012, 2013), have also argued for, and used, cross-validation. See also Erev et al. (2017) and literature therein. What is the Appropriate Space for the Calculation of Deviations from Rationality? A further issue concerns how we measure deviations from rationality, if they exist at all. The ERP focuses on deviations in the consequence space, that is, comparing the actual loss in terms of the consequences of a behavior. Consequences can be actual payoffs, if they are well defined for a problem, or a metric based on the percentage of correct/wrong responses often used in binary tasks. Using deviations in the consequence space instead of the choice space is important, as seemingly large differences in choice may not translate into large deviations in the consequence space, particularly when computational costs are included. In the early history of Behavioral Economics, deviations from rationality were typically measured in the choice space, and this still occurs to a considerable extent. However, experimental economists have taken issue with experiments that have a flat payoff function around the normative solution, culminating in the payoff-dominance critique (Harrison 1989) that prompted a large debate in the field (see the comments and replies to this paper in the American Economic Review, 82 (5) in 1992). While originally intended as a critique of the design of many experiments in economics, implicit in the payoff-dominance critique is the notion that non-optimal behavior can only be identified when it is accompanied by large costs in the consequence space. A large deviation in the

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The beauty of simplicity? 127 choice but not consequence space can be thought of as suboptimal behavior with a low opportunity cost. The Interaction between Simple Decision Rules and the Environment The ERP is based on the premise that rationality should be assessed in the context of the environment, that is, Simon’s ‘scissors’ metaphor. In strategic settings, the definition of the environment must be extended to include institutions, market characteristics and the interactions between agents. Perhaps surprisingly, to ERP researchers, an early example of such interactions was given by Becker (1962) who analyzed a model of markets in which participants behaved irrationally or randomly. He found that seemingly rational behavior at the macro level (not only in the consequence space, but also in the choice space) could arise even from random behavior at the micro level. In this spirit, more recent developments in economics include the zero-intelligence program initiated by Gode and Sunder (1993) who examined the effects of the structure of continuous double-auctions on market outcomes. They found that simple agents, who made random bids with the only constraint that they do not make offers that would lead to a loss, converged and achieved near perfect allocative efficiency. The lesson to be learned from this research is that rationality cannot be ascribed to individual decision makers without explicit consideration of the environment. Cognitive Bounds and Behavior The premise that less is more with respect to the amount of information that decision makers use can be linked to bounds on cognition such as limitations in the amount of information that can be held in working memory (Cowan 2000) or the long-term memory retrieval system (Schooler and Anderson 1997). Economists have similarly been concerned with simple strategies that do not use all available historical information, dating back to the Axelrod (1984) tournament. Tit-for-tat and the win–stay/lose–shift strategies are examples of relatively simple heuristics that perform well in repeated games and are robust to the exact composition of types in the population and to noise. Explicit modeling of forgetting has been common in economic studies of learning in repeated games since Roth and Erev (1995) and Cheung and Friedman (1997). Finite-state automata are another methodological tool explicitly aimed at examining the effects of limiting the prior (in a temporal sense) information that a player conditions his/her strategies on (for example, Rubinstein 1986). Furthermore, it is well known in game theory that more information does not necessarily lead to better outcomes. Procedural Modeling An important characteristic of most ERP studies is the insistence that models should be procedural (or process based) in contrast to the majority of models in economics that are as-if models. The advantage of procedural models is that they make more specific predictions (choices and processes) than as-if models and are more falsifiable in the Popperian sense. For example, see Johnson et al. (2008) who argue that the process data is incompatible with that implied by the priority heuristic; this, of course, would not have been pos-

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sible for an as-if model. It is perhaps here that cognitive psychologists have already exerted a unidirectional influence on economists. Early work in psychology employing processtracing techniques such as Mouselab (Johnson et al. 1989) and eye-tracking have spilled over to economics; see Crawford (2008) for an excellent overview. Providing process-level foundations to existing as-if models in economics, and highlighting the value-added of this, is another way of engaging economists with the ERP. For example, Fischbacher et al. (2013) modify economic theories of social preferences by imposing a decision tree structure to the order in which these variables are examined. Similarly, Spiliopoulos (2013) transforms a process-free model of pattern recognition in games (Spiliopoulos 2012) into a process-model encompassing both exemplar- and prototype-based categorization grounded in the ACT-R architecture. Reasoning by Similarity and Cases Reasoning by similarity can be a useful tool when confronted with the uncertainty of a new situation of which an agent has not had experience. Important theoretical contributions have been made by economists to case-based and analogy-based reasoning; see, for example, early work by Rubinstein (1988) and Leland (1994) on decision under risk and the extensive work of Gilboa and Schmeidler (1995, 2001). Other work by economists exploiting similarity in inductive inference involves the question of how agents play a new game (that they have not seen before); specifically, how prior experience from other games may spill over to new (unseen) games on the basis of similarity between games (for example, Mengel and Sciubba 2014). Also, Spiliopoulos (2013) shows that subjects learn from the similarity, not between games, but between patterns in the history of play during a single repeated game.

OPEN QUESTIONS AND CHALLENGES While the success of the ERP cannot be disputed, there remain many open questions in need of answers. We enumerate and discuss them next. First, what is the complete set of heuristics out there? This question may be unanswerable for the simple reason that, as illustrated by Ericson et al. (2015), there are probably as many definitions as there are researchers. Also, researchers very often have vested interests to differentiate their product (for example, Bingham and Eisenhardt 2014, or the already mentioned Ericson et al. 2015, who do not reference Gigerenzer et al. 1999). In other words, there will not be agreement on what is in the adaptive toolbox of heuristics any time soon. An answer to this question will become even harder as heuristics – which so far have been studied predominantly in non-strategic decision settings – will be addressed in strategic decision settings; see Vuori and Vuori (2014) for an excellent primer. An alternative approach is to first ask what is the set of building blocks that make up heuristics? A broad, but by no means complete, characterization is that these are comprised of search rules, stopping rules and decision rules. Second, how to choose the appropriate tool from that adaptive toolbox remains a prominent question in search of better answers – see Marewski and Link (2013) for a review. ERP researchers have made considerable progress on this issue, generating inter-

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The beauty of simplicity? 129 esting results about strategy selection (for example, Marewski and Schooler 2011). A predictable argument has it that strategy selection is the result of evolutionary pressure or strategy selection using a reinforcement learning mechanism over heuristics (Rieskamp and Otto 2006). We find that argument only partially persuasive. Our skepticism goes back to old debates about to what extent people take into account structural changes in the environment. There is some evidence that people, possibly moderated by market institutions, have in many circumstances surprisingly rational expectations but, of course, it is dependent on many things even without market institutions moderating. We do know that the use of heuristics changes when environmental conditions change (for example, the work of Hogarth and Karelaia 2005, see also Rieskamp and Otto 2006, Spiliopoulos et al. 2015, Spiliopoulos and Ortmann 2015) but we are far from understanding the issue of matching in their totality in a satisfactory manner. Ultimately, the complexity of the environment will determine the tools in the box. Third, while it is an interesting question to understand how changing environments can affect choice of heuristics, to what extent the use of heuristics can shape the environment is a question that brings about important issues of causality (for example, Hertwig et al. 2002 on parental investment) that strike us as under-researched. Fourth, ERP researchers have recently argued that the two programs of rationality not only have very different assessments of human rationality but also have very different policy implications identified as nudging and boosting (Katsikopoulos 2014; GrueneYanoff and Hertwig 2016). These issues strike us also as under-studied. We are also not certain that the real issue is that of nudging versus boosting. We do appreciate the fact that nudging might have some undesirable intertemporal consequences (for example, Carroll et al. 2009 and the literature that followed it) but submit that boosting is often an unavailable option. Despite the difficulties, this opens up important avenues for the ERP to have a significant impact at the policy level. Fifth, the ERP, it seems fair to say, has not managed to have much practical impact on management science and organization science. This is surprising given the intellectual origin of the key parts of the ABC agenda (Simon, anyone?). Despite the fact that many publications on the theoretical properties of heuristics have made their way into prominent management/organization science journals (for example, Hogarth and Karelaia 2005; Katsikopoulos 2013), we are unaware of any significant impact on this literature on organizations directly, or applied/empirical work on heuristics in organizations. This is particularly surprising given that bounded rationality has become an influential concept in management science and organization science and economics. An exception is the hiatus heuristic that predicts whether a customer is active or not, that is, will make future purchases. Wübben and Wangenheim (2013) not only find evidence of its use by executives, but also show using real-world data that simple heuristics can out-predict more complex models. Sixth, as (simple) heuristics are being discovered by management and organization sciences (for example, Loock and Hinnen 2015), the movement away from non-strategic decision making (the core of early ER research) to strategic settings brings in new complexities arising from strategic interactions. It is not that ABC has not started to struggle with these issues but the work in this area seems pedestrian compared with the rather more sophisticated work on non-strategic decision making. Promising examples include the collaboration between economists and psychologists in Fischbacher et al. (2013) mentioned

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earlier, which we hope to see more of in the future. Another example is Stevens et al. (2011), who examine the effects of forgetting on the emergence of cooperative strategies in repeated interactions. Further bridging the different concepts of bounded rationality that psychologists and economists would be a fruitful endeavor. There are important differences across disciplines that we cannot fully discuss here – Katsikopoulos (2014) and Grüne-Yanoff et al. (2014) are excellent primers. Seventh, the topic of learning has not been broached successfully by the ERP; however, the potential exists for important work on simple heuristics of learning. A starting point is Selten’s learning direction theory (LDT), which is ultimately a simple story of ex post rather than ex ante rationality using minimal information – note again that this is an inductive model of reasoning. For example, LDT requires information only about the direction that would have led to an improvement in the outcome; reinforcement learning would also require the magnitude and regret-based learning would require information about counterfactual outcomes. As an aside, we draw the reader’s attention to the edited volume by Gigerenzer and Selten (2002). An excellent example of work along these lines is Bonawitz et al. (2014) who show that a simple heuristic (win–stay, lose–sample) can approximate computationally demanding Bayesian inference in non-strategic settings. Strategic interactions entail additional uncertainty – how often is the assumption of perfect information fulfilled in the real world? Do we know what the action space is, what the payoffs are, and the type/motives of our opponent? With so much uncertainty is strategic ignorance or bounded sophistication necessarily irrational? Ecological-rationality program researchers should note that economists have not ignored these important questions, such as uncertainty, as the literature is literally full of extensions and concepts specifically addressing them. On the other hand, ERP researchers can and should critique the characteristics of the solutions proposed by economists. For example, in many cases the extensions or refinements to equilibrium solution concepts that deal with these types of uncertainty may be orders of magnitude more complicated than those under perfect information. Again, however, we note that these solutions belong to the deductive strand of game theory, not the inductive strand; the latter should be far more palatable to psychologists. Eighth, and relatedly, some celebrated heuristics can easily be exploited (for example, default settings in a situation where the choice architect has vested interests: credit card companies, and so on). In general, it is necessary to assert to what extent the interests of the default-setter and the people that default are meant to nudge coincide. It would be a mistake to assume that it is always the case. Ninth, Goldberg (2005; see also Goldberg and Podell 1999) have argued that studying lotteries does not capture decision making in the real world in reasonable ways. The real issue is what to do with other problems that cannot be represented by lotteries with two or three outcomes? Tenth, the fast and frugal heuristics literature, in its insistence on avoiding the calibration of heuristics to empirical data, has glossed over the issue of behavioral heterogeneity.

CONCLUDING DISCUSSION We set out to sketch established facts and open questions about simple heuristics, while also pointing out some areas of similar thinking with the economics discipline that could

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The beauty of simplicity? 131 serve as a bridge for future work. As it turns out there is an increasing number of authors that lay claim to the term ‘simple heuristics’ which seems to originate with Gigerenzer et al. (1999). While sketching the history and different premises of the two big programs in the heuristics space, the ‘Heuristics-and-Biases’ program and the ‘Ecological-Rationality’ program, we have focused on the latter and discussed its undoubtable accomplishments and prospects. Among its considerable accomplishments are the successful demonstration that, when cross-validation is used, the performance of simple heuristics such as the recognition heuristic or the ‘take-the-best’ tends to be better than that of complicated, computationally slow and greedy models such as multiple regression favored by economists (for example, Gigerenzer et al. 1999, Todd et al. 2012; Brighton and Gigerenzer 2009; Gigerenzer and Gaissmaier 2011). The simple, and rather intuitive, reason is that multiple regression is prone to over-fitting to the noise in the data-generating process by only looking backwards. Another important implication is that the widely believed effortaccuracy trade-off is often not something to worry about. It has also been demonstrated persuasively that an important moderator of these findings is the way statistical information is presented. There remain many open questions and interesting research topics which we have tried to enumerate. We have tried hard to draw attention to work in economics that seems closely related to the ERP and to highlight where common ground exists for the two disciplines to initiate a dialogue and collaborate despite their differences. The reader will notice that the majority of research that we have cited in economics is firmly grounded in inductive (learning from experience) rather than deductive models. We believe that much of the criticism of economics by ERP researchers has been directed at normative solutions involving deductive reasoning. This, however, is a straw man of sorts, and does not acknowledge the richness of contemporary economics. We further draw attention to the fact that many of the studies in economics that we have cited are published in mainstream, highly ranked journals such as the American Economic Review, Quarterly Journal of Economics, Econometrica, and Games and Economic Behavior. Therefore, we believe that sufficient interest exists for work that can be related to the ERP, and for the ERP to make significant headway into the economics discipline. This attempt will be most successful by connecting new research to prior work in economics and simultaneously pointing out the similarities and differences. Economists would also be well advised to seek out common ground with psychologists beyond the (now) orthodox heuristics-and-biases program.

NOTES *

The authors are grateful for critical and helpful commentary on earlier versions from Morris Altman, Nathan Berg, Gerd Gigerenzer, Ralph Hertwig, Konstantinos Katsikopoulos, Elizabeth Maitland (whose suggestion inspired the title of our chapter), and Ben Newell. All errors in judgment and tone are ours. 1. Non-compensatoriness of cues is satisfied if the weight of a higher ranked cue is greater than the sum of all lower ranked cues. Consequently, lower-ranked cues can be ignored as regardless of the cue values, it is impossible for them to reverse a decision made using the higher ranked cue. Dominance is satisfied if the cue values of one object are all greater than those of the other object. Cumulative dominance is satisfied if the cue values of one object cumulatively dominate those of the other object. Further discussions and mathematical definitions of these concepts can be found in Şimşek (2013).

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FURTHER READING Brandstätter, E., G. Gigerenzer and R. Hertwig (2006), ‘The priority heuristic: making choices without tradeoffs’, Psychological Review, 113 (2), 409–32. Brighton, H. and G. Gigerenzer (2015), ‘The bias bias’, Journal of Business Research, 68 (8), 1772–84. Dhami, M.K., R. Hertwig and U. Hoffrage (2004), ‘The role of representative design in an ecological approach to cognition’, Psychological Bulletin, 130 (6), 959–88. Gigerenzer, G. and H. Brighton (2009), ‘Homo heuristicus: why biased minds make better inferences’, Topics in Cognitive Science, 1 (1), 107–43. Gigerenzer, G. and W. Gaissmaier (2011), ‘Heuristic decision making’, Annual Review of Psychology, 62 (1), 451–82. Goldberg, E. and K. Podell (1999), ‘Adaptive versus veridical decision making and the frontal lobes’, Consciousness and Cognition, 8 (3), 364–77. Goldstein, D.G. and G. Gigerenzer (2011), ‘The beauty of simple models: themes in recognition heuristic research’, Judgment and Decision Making, 6 (5), 392–95. Grüne-Yanoff, T. and R. Hertwig (2016), ‘Nudge versus boost: how coherent are policy and theory?’, Minds and Machines, 26 (1), 149–83. Grüne-Yanoff, T., C. Marchionni and I. Moscati (2014), ‘Introduction: methodologies of bounded rationality’, Journal of Economic Methodology, 21 (4), 325–42. Hertwig, R. and I. Erev (2009), ‘The description-experience gap in risky choice’, Trends in Cognitive Sciences, 13 (12), 517–23. Hogarth, R.M. and N. Karelaia (2007), ‘Heuristic and linear models of judgment: matching rules and environments’, Psychological Review, 114 (3), 733–58. Katsikopoulos, K.V. and G. Gigerenzer (2008), ‘One-reason decision-making: modeling violations of expected utility theory’, Journal of Risk and Uncertainty, 37 (1), 35–56. Katsikopoulos, K.V., L.J. Schooler and R. Hertwig (2010), ‘The robust beauty of ordinary information’, Psychological Review, 117 (4), 1259–66. Luan, S., L.J. Schooler and G. Gigerenzer (2014), ‘From perception to preference and on to inference: an approach–avoidance analysis of thresholds’, Psychological Review, 121 (3), 501–25. Mandler, M., P. Manzini and M. Mariotti (2012), ‘A million answers to twenty questions: choosing by checklist’, Journal of Economic Theory, 147 (1), 71–92. Manzini, P. and M. Mariotti (2014), ‘Stochastic choice and consideration sets’, Econometrica, 82 (3), 1153–76. Marewski, J.N. and L.J. Schooler (2011), ‘Cognitive niches: an ecological model of strategy selection’, Psychological Review, 118 (3), 393–437. Pleskac, T.J. and R. Hertwig (2014), ‘Ecologically rational choice and the structure of the environment’, Journal of Experimental Psychology: General, 143 (5), 2000–2019. Schooler, L.J. and R. Hertwig (2005), ‘How forgetting aids heuristic inference’, Psychological Review, 112 (3), 610–28. Volz, K.G. and G. Gigerenzer (2012), ‘Cognitive processes in decisions under risk are not the same as in decisions under uncertainty’, Frontiers in Neuroscience, 6 (July), 1–6.

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Smart persons and human development: the missing ingredient in behavioral economics* John F. Tomer

INTRODUCTION There’s a growing sense among economists, especially behavioral economists, that the human actor in economics is not portrayed well by the economic man stereotype nor by the irrational, error-plagued person who is the stereotype deriving from psychological economics. The purpose of this chapter is, first, to explain about the inadequacy of these two stereotypical economic actors and, second, to develop an alternative, a more satisfactory stereotype known as the smart person. In the process, this chapter points the way to a better behavioral economics, a behavioral economics with smart people, a behavioral economics that is more realistic and more human. What is missing from the existing stereotypical actors, but present in the smart person actor, is the human who develops in stages along a number of developmental pathways over a lifetime. In contrast to the two existing stereotypes, the smart person’s character and capabilities are neither simply assumed nor inferred from the outcomes of narrow psychological laboratory experiments. The smart person’s character and behavior derive in good measure from the research of a variety of non-economist scientists and careful observers of human behavior. There is a tremendous need for a behavioral economics with smart persons in which the human actor, while far from perfect, develops, and all too often fails to develop, character and capabilities in a realistic way. The plan of the chapter is as follows. The next section explains what is missing from mainstream economics and psychological economics. The missing ingredient is the concept of human development. The third section carefully considers the characteristics of economic man, the human in mainstream economics. The following section carefully considers the character and capabilities of the human in psychological economics, particularly his or her lack of economic rationality. The fifth section develops a conception of an alternative human economic actor, an actor whose character and capabilities are much closer to the humans we know. This section explains how a behavioral economics with smart people has the potential to be a great improvement over the psychological economics version of behavioral economics with its error-prone stereotype.

THE MISSING INGREDIENT: THE CONCEPT OF HUMAN DEVELOPMENT The ingredient missing from economics is the conception of a human being as an individual who develops in many different ways along a sequence of stages, a maturational path. As wise thinkers through the ages have recognized, humans are capable of 137

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attaining a very high level of development, involving a full flourishing of all their human capabilities in the broadest and highest sense over their entire life cycle. Clearly, the high human development (HD) envisioned by these thinkers involves much more than the acquisition of cognitive capability or workplace skill. High HD certainly involves social, psychological, emotional, and biological dimensions, among others, but the ideal or potential HD often fails to occur. Generally, only when the environment is favorable do humans have a chance of developing a high degree of their potential. Therefore, a key question is, what has to happen for individuals to develop to, or near to, their full potential? What kind of environment is necessary for favorable development? Among the necessary environmental conditions commonly recognized as necessary for reasonably high HD are a good education and the kind of early life nurturing usually provided by two loving parents. A supportive community and society are also important. For many, of course, the environment may not be favorable in some important respects, and as a consequence individuals may fail to negotiate significant stages of development. Thus, an individual may get stuck or partially stuck at a certain developmental stage and may fail to develop further without special developmental interventions. Without such help, it is likely that the individual will remain stuck at a level of HD that does not allow the full development of their talents. Conventional economic thinking provides little or no recognition of how individuals can advance along important developmental pathways and how they can overcome the types of difficulties that would otherwise prevent or inhibit their development. The concept of HD used here draws from a number of different traditions. First, it incorporates the perspective of developmental scientists whose field of study broadly encompasses HD in physical or biological, cognitive, and psychosocial domains or behaviors (see, for example, two HD texts: Kail and Cavanaugh 2007; Papalia et al. 2009). Second, the HD concept is inspired by the humanistic psychological perspective of Abraham Maslow (1943), notably his hierarchy of needs. Third, it is informed by research on neurodevelopment (see, for example, Perry 2002), particularly Perry’s work related to the developmental difficulties occurring in early childhood. Fourth, the HD conception here has been influenced by Ken Wilber’s (see, for example, Wilbur 2001, pp. 5–16) conception of how humans develop in an unfolding series of stages and levels from lower order to higher order along many dimensions or lines. The HD concept used here is related to, but distinctly different from, the HD concept pioneered by Amartya Sen, Martha Nussbaum and others. The latter concept which has been much used by international agencies (for example, the World Bank and the United Nations) concerned with economic development emphasizes a great number and variety of human functioning and capabilities. The Sen or Nussbaum HD concept is very useful for thinking about national and world economic development and how its progress can be measured. A good overview of this concept and its uses can be found in Alkire (2010). What this concept lacks is a conception of the stages of development in a human’s life and how human capacities and orientations change in predictable ways and sometimes fail to change. That is, there is no conception of the multidimensional developmental process that humans experience and the challenges a human typically encounters along the developmental pathways. To better understand the HD concept, it is important to illustrate graphically its main pathways and the sequence of development along each. For the purposes of this chapter,

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Acquiring overall life direction, interests, outlooks, & motivation

4

3

Developing skills & talents: physical, academic, arts, technology Learning/appreciating many types of knowledge and acquiring academic discipline

2 Learning the basics: reading, writing, arithmetic 1 Figure 8.1

Educational and cognitive development

HD is represented as a three-sided pyramid. Each side represents a major developmental pathway. The three developmental pathways are (1) educational and cognitive development, (2) psychosocial, biological development, and (3) brain development (or neurodevelopment). In each case, the triangles representing the pathways start from very fundamental, early development and proceed stepwise to the highest level of development. The sequence of steps resembles in some respects Maslow’s (1943) hierarchy of needs in that, with some exceptions, earlier stages must precede later stages. Also, note that there is considerable interdependence among the three pathways. For economists, and presumably many academics, the easiest triangle or pathway to appreciate is the educational and cognitive development pathway. The side of the pyramid representing this pathway is shown in Figure 8.1. It starts at the bottom with ‘Learning the basics: reading, writing, arithmetic’. The second step is ‘Learning/appreciating many types of knowledge and acquiring academic discipline’. The third step is ‘Developing skills and talents: physical, academic, arts, technology’. The fourth and final step is ‘Acquiring overall life direction, interests, outlooks, and motivation’. The second pathway, psychosocial, biological development, is shown as the triangle in Figure 8.2. It starts with ‘Foundational neurodevelopment’ and proceeds to ‘Early learning, relating, and doing’ and then to ‘Becoming safe, secure, and satisfying physical needs’. The fourth step is ‘Finding oneself: competencies, motivations, values, and emotional intelligence’. The fifth step is ‘Finding oneself: friends, lovers, and loving family relations’. The sixth and final step is ‘Connecting to one’s highest values, spirituality, creativity, and aesthetics’.

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Connecting to one’s highest values, spirituality, creativity, aesthetics

6

5

4

3 2 1 Figure 8.2

Finding oneself: friends, lovers, loving family relations

Finding oneself: competencies, motivations, values, emotional intelligence

Becoming safe, secure, and satisfying physical needs Early learning, relating, doing Foundational neurodevelopment

Psychosocial and biological development

The third pathway, brain development, is shown as the triangle in Figure 8.3. It starts with ‘Foundational neurodevelopment’ and proceeds to ‘Neurodevelopment associated with doing, achieving, relating, and learning’. The third step is ‘Overcoming brain development deficiencies and problems’. The fourth and final step is ‘Developing creativity and peak performance brain functioning’. Figure 8.4 shows how the three triangles described above combine to form the HD pyramid. No doubt a much more careful and micro elaboration of the pathways by a developmentally oriented behavioral scientist would include many more steps in each pathway than the number included here. The benefit of using the HD pyramid is that it focuses attention on three main ways that important human capabilities change, have the potential to change, or fail to realize their change potential. In Wilber’s (2001, pp. 5–6) view, human development involves an unfolding, emergent process marked by progressive subordination of older, lower-order behavior and capabilities to new higher-order behavior and capabilities along different pathways or lines. Using the HD pyramid helps us understand how change along one pathway may facilitate change along another pathway and how barriers to change in a pathway may result in lack of desired change along another pathway. It is important to note that society has a strong effect on an individual’s development. The society’s ethics, norms, rules, and basic institutions are integrated and have a cohesion that affects how far individuals develop (Wilber 1996, pp. 138–41). The society’s ‘cultural center of gravity acts like a magnet on individual development. If you are below the average level, it tends to pull you up. If you try to go above it, it tends to pull you down’

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Developing creativity and peak performance brain functioning

4

3

Overcoming brain development deficiencies, problems Neurodevelopment associated with doing, achieving relating, learning

2 Foundational neurodevelopment 1 Figure 8.3

Brain development

Figure 8.3

Figure 8.2

Figure 8.1

Note that the three pathways are interdependent

Figure 8.4

Human development pyramid

(Wilber 1996, p. 139). The society’s developmental magnet helps you reach the expected level of development, but likely retards your earnest attempts to develop beyond the societal norm. As a consequence, relatively few people reach the highest developmental stages but many reach average levels.1

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ECONOMIC MAN: THE HUMAN IN MAINSTREAM ECONOMICS Economic man, or Homo economicus, is the well-known human economic actor in mainstream economics. Because economic man’s behavior reflects the rational choice theory at the heart of mainstream economics, his or her behavior is machine-like in its perfect rationality (see Simon 1983, pp. 12–17). Economic man chooses in a narrowly self-interested way, using perfect logic and a complete knowledge of alternatives, and thus, selects the alternative that best enables attainment of his or her subjectively defined ends. If economic man is a consumer, the end is utility; if a producer, the end is profit. If economic man were human, we could say that he or she possesses infinite, or at least extremely high, cognitive capacity. In contrast, it seems to be implied that economic man has no or low non-cognitive capacity, that is, capacity relating to psychological, emotional, and social functioning. This further implies that economic man has zero capacity for pure empathetic (or other interest) motivation, the motivation opposite to self-interest. Economic man is also unreflective in the sense that he or she cannot stop to consider the appropriateness or rightness of his or her choices. It is fairly obvious to many, including a number of leading economic thinkers such as John Stuart Mill (1836), that economics does not consider the whole of man’s nature. Accordingly, economic man is a one-dimensional being who merely compares alternative ways to achieve his or her economic ends. The economic man concept continues to be widely used in economic modeling and analysis despite the fact that there are many economists who understand (1) that humans do not generally know the consequence of their actions for their long-term physical and mental health, and (2) that humans cannot be relied on to make decisions in their strict self and selfish interest. In their models, economists often use an economic actor who is a representative agent, a typical decision maker of a certain type. In mainstream economics, such agents are economic men who perform a particular role; they are, for example, consumers or decision makers in a firm. These agents presumably have made significant investments in human capital in order for them to carry out their economic role. Regardless of their human capital, the agents in these models behave in a perfectly rational manner, albeit in a particular context. Economic man’s behavior is in accord with the formal model of rational choice known as subjective expected utility (SEU) theory in which economic man chooses the alternative that maximizes his or her expected value of utility. As Herbert Simon (1983, p. 13) points out, SEU ‘is a beautiful object deserving a prominent place in Plato’s heaven of ideas’. Unfortunately, according to Simon, the ‘SEU theory has never been applied and can never be applied . . . in the real world’ (1983, p. 14). This is because ‘human beings have neither the facts nor the consistent structure of values nor the reasoning power at their disposal that would be required, even in . . . relatively simple [lab] situations to apply SEU principles’ (Simon 1983, p. 17). That is, the economic man concept is simplistic and unrealistic. Consider economic man from a developmental perspective. Economic man is unchanging; he or she has no history and no future. That is, the qualities possessed by economic man did not come about through a process of human development, and there is no prospect of future development that will cause these qualities to change. Economic man’s character is simply assumed; it is not an object of theoretical or empirical study. If

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Smart persons and human development 143 economic man’s character (perfect rationality) had come about through a developmental process, we might say that economic man had reached an egoistic stage of development in which he or she is aware of having many wants and is perfectly logical and persistent in the endeavor to satisfy those wants. Such a hypothetical development stage resembles Wilber’s (2001, p. 9) third stage of human development in which the individual is powerful, egocentric, and has a self that is distinct from his or her ‘tribe’. According to Wilber’s (2001, pp. 9–11) estimates, the great majority of people in the twenty-first century world develop beyond this egocentric stage during the course of their lives.

PSYCHOLOGICAL ECONOMIC MAN: THE HUMAN IN PSYCHOLOGICAL ECONOMICS Psychological economics is the prominent strand of behavioral economics that borrows from psychology, especially cognitive psychology, in order to achieve a more realistic understanding of human economic behavior than is possible with mainstream economics. Psychological economic man is the human in psychological economics (PE). Psychological economic man’s character, in sharp contrast to economic man, is very much an object of study, especially empirical study. Psychological economics is oriented to investigating human cognitive performance in relatively narrow and well-defined situations in order to isolate humans’ precise decision making and judgment behavior. Psychological economics researchers have focused to a large extent on exploring the degree to which human behavior systematically departs from economic rationality, that is, the extent to which psychological economic man is different from economic man. Overall, the findings of PE research are that humans are much less rational than mainstream economics assumes. That is, we humans are systematically and predictably irrational in all phases of our lives; we make many different kinds of errors in a great variety of particular situations (Ariely 2009, pp. 239–40). These errors derive from, among other things, the anchoring effect, judgment by representativeness, overconfidence, theoryinduced blindness, loss aversion, salience, use of mental accounts, framing, inconsistent preferences, defective affective forecasting, difficulties dealing with probabilities and time, the narrative fallacy, hindsight bias, confirmation bias, overestimating rare events, status quo bias, planning fallacy, and the availability and affect heuristics (Kahneman 2011). In light of these findings, it is not surprising that the psychological economic man stereotype is very much one of an irrational and error-prone being. In comparison to economic man, psychological economic man is decidedly not smart. This characterization of psychological economic man’s judgment and decision making is more realistic than that of the economic man stereotype precisely because it is based on a great amount of research. An important aspect of PE involves understanding two systems in the mind, system 1 and system 2. System 1, associated with intuition, is the aspect of our mind that ‘operates automatically and quickly, with little or no effort and no sense of voluntary control’ (Kahneman 2011, p. 20). Many of the predictable human errors which PE focuses on occur when our minds are in system 1 mode. If, in the face of a difficult question or issue, no easy system 1 solution comes to mind, that is when we typically switch to system 2. System 2 refers to effortful mental activities requiring concentration and self-control

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(Kahneman 2011, p. 22). System 2 is slower; it may involve computation, deliberation, and constructing thoughts in an orderly series of steps. Psychological economics’ emphasis is less on explaining the reasons why humans commit cognitive errors and more on accurately characterizing humans’ cognitive performance. Nevertheless, a number of the leading researchers have offered explanations for humans’ error-proneness. According to Ariely (2009, p. 243), our senses and brain filter the information that comes to us so that the input to our decision making is not a fully accurate reflection of the reality of the situations we confront. In other words, the problem stems from ‘the basic wiring of our brains’ (Ariely 2009, p. 239). In Kahneman’s (2011, p. 51) view, errors of judgment and decision making often stem from ‘a self-reinforcing pattern of cognitive, emotional, and physical responses that [are] . . . associatively coherent’ (original emphasis). The errors often arise because our perception and cognition involve our body, not just our brain. Psychological economic man can learn and acquire the skill necessary to reduce the errors that typically occur when humans are operating in system 1 mode. When these error-causing difficulties are recognized, humans may switch to system 2 mode and may try harder in order to avoid significant mistakes, especially when the stakes are high (Kahneman 2011, pp. 25–8). To acquire these error reducing, decision-making skills ‘requires a regular environment, an adequate opportunity to practice, and rapid and unequivocal feedback’ on decision-making results (Kahneman 2011, p. 416). It should be noted that this learning does not amount to a move to a higher stage of HD. It is just psychological economic man’s regular mental mode of operation. It is also important to note that with respect to decision making and judgment, PE is largely concerned with humans’ cognitive functioning, not the non-cognitive functioning that would be part of humans’ move to a higher or lower stage of development. It is interesting to note that PE researchers, although they do not usually state it explicitly in their writings, strongly suggest that the systematic human departures from rationality that they find in their empirical research are ‘hardwired’ in the human brain and/or body. The term hardwired is understood to mean ‘pertaining to or being an intrinsic and relatively unmodifiable pattern’ (Etzioni 2014, p. 394). It is possible, though, that these cognitive errors are merely strong predispositions rather than the determinative attributes that PE researchers imply.2 If the errors and biases are not hardwired, it may be that these departures from rationality can be ‘corrected’ (Etzioni 2014, p. 397), possibly by virtue of education and training, by making a bigger effort, or via other interventions that take advantage of the human brain’s plasticity. The upshot of the above comparisons is that psychological economic man is more realistic than economic man, less rational than economic man, and is no better than economic man insofar as neither experiences human developmental stages.

NEEDED: A SMARTER PERSON IN A BETTER ECONOMICS Essence of the Smart Person Based on our analysis of the economic man of mainstream economics and the psychological economic man of psychological economics, there is clearly a need for a better

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Smart persons and human development 145 economics, a behavioral economics in which the actor is more human and less irrational. The desired economic actor should behave more realistically than economic man, be less error-prone than psychological economic man, and be more human in a developmental sense than either economic man or psychological economic man. Thus, the needed human actor should not only be a ‘smart person’, a person, who while far from being perfectly rational, is less fallible than psychological economic man, but should be a person whose capabilities and character develop in stages over his or her lifetime. The smart person (SP) is the boundedly rational decision maker whose decision-making behavior is generally in line with Herbert Simon’s understanding of how humans behave when making significant decisions. Therefore, in evaluating decision alternatives, SPs will generally consider a selected set of alternatives, evaluate each alternative sequentially, and then select the first satisfactory alternative, an alternative meeting the SP’s aspiration level (Simon 1955, pp. 110–12). This ‘satisficing’ decision-making procedure is boundedly rational in that it is intendedly rational. However, the SP’s rationality is limited by the human brain’s cognitive capacity and the complexity of the decision environment. It is only in the most simple and transparent situations that SPs can be perfectly rational in the utility maximizing sense (Simon 1959, p. 258). Thus, in the great majority of life decisions, SPs will be boundedly rational, reasonably competent decision makers.3 It is important to note that SP’s decision making can still be expected to manifest many of the errors and biases identified by psychological economics researchers, but these decision-making and judgment deficiencies will not be the defining characteristics of SPs’ decision making. With regard to HD, the SP actor is one who has the ability to develop to his or her potential, progressing along the three developmental pathways mentioned earlier (see Figures 8.1–8.4) as well as to develop along other unspecified paths, advancing stage by stage. The SP’s development may, however, fail to occur sometimes because the person’s developmental environment (parenting, community, society, and so on) has been unfavorable or for other reasons. As a consequence, in the absence of a helpful developmental intervention (for example, educational or therapeutic), the SP’s development along one or more pathways may become stuck. Further, owing to the interdependence of the pathways, progress or lack of progress along one pathway may affect progress or lack of progress along another pathway. Important Features of Human Development To appreciate the human development aspect of the SP, there are some aspects of HD that need further examination, particularly the non-educational aspects. In this regard, it is useful to give more attention to the neurodevelopment pathway. Neurodevelopment success and failure Bruce Perry’s (see, for example, 2002) work makes clear that we can only develop to our human potential if our brains develop to their potential. ‘Development [especially the neurodevelopment part] is a breathtaking orchestration of precision micro-construction that results in a human being’ (2002, p. 82). Eight key processes are involved in creating a mature, functional human brain: neurogenesis, differentiation, apoptosis, arborization, synaptogenesis, synaptic sculpting, and myelination (Perry 2002, pp. 82–5). It is not necessary here to consider each of these processes in detail. Suffice it to say that these processes

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relate to neurons: their birth, movement, specialization, death, formation into dendritic trees, the formation of connections among neurons (synapses), the structuring of the synapsis, and the creation of efficient electrochemical functioning in the neural networks. These neurodevelopment processes occur in response to experience and are most responsive to experience in positive and negative ways during infancy and childhood (Perry 2002, p. 82). All of these processes must go well; otherwise, abnormal neurodevelopment occurs, causing profound brain dysfunction (Perry 2002, p. 85). ‘In order to develop properly, each [brain] area requires appropriately timed, patterned, repetitive experience’ (Perry and Szalavitz 2006, p. 248). For optimal neurodevelopment, it is crucially important that the lower brain systems develop first in a healthy fashion; otherwise, development of higher, more complex parts of the brain will not be able to occur satisfactorily. Full, healthy brain development may fail to occur for many reasons, most notably, because of adverse early childhood experiences that often involve toxic stress or trauma. Owing to such neurodevelopment deficits, both children and adults can get stuck or partially stuck at a relatively low stage of brain development with serious consequences for their later behavior and functioning.4 Other human development failures In addition to and often coexisting with adverse childhood experiences, three other important non-educational kinds of situations in which humans fail to develop satisfactorily deserve note (Tomer 2014): 1.

2.

3.

The molecules of emotion (different types of receptors and ligands in the brain and body) may fail to flow freely such as when emotions are repressed or denied. As a consequence, body and brain network pathways get blocked, and people get stuck in unhealthy patterns of behavior and experience negative emotional states (Pert 1997). People may fail to develop important emotional competencies (for example, inability to handle one’s distressing emotions) deriving from a lack of coordination between a person’s thinking brain (neocortex) and their lower brain areas (Goleman 2011). People may fail to develop the personality traits that are needed for their educational success, labor market success, health, and positive personal outcomes (Almlund et al. 2011).

The Time Pattern of Human Development There are several noteworthy features of the time pattern of human development. Non-cognitive versus cognitive development In early childhood just after birth, a child is not ready to develop cognitively. The development that is taking place is non-cognitive development, mainly occurring in the lower brain areas (Perry 2002, pp. 86–8; Perry and Szalavitz 2006, pp. 247–8). During very early child development, children are acquiring basic brain organization, a stable emotional basis, a secure attachment to their primary caregiver(s), and the basis for good social relationships. Inevitably, as the child grows older and non-cognitive development progresses, the relative amount of time devoted to non-cognitive development will decline. In other words, as the child matures and becomes more secure, independent, and confident, the

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Smart persons and human development 147 child’s need for the nurture and care of a parent will become less and less. Also, as the child’s higher brain develops, a greater proportion of the child’s development will be cognitive. More and more of the child’s development will involve learning and acquiring skills. It is useful to view expenditure of efforts and resources to aid both the non-cognitive and cognitive development of children as investments in human capital. After all, both kinds of developmental efforts involve investments of resources that enable humans to function at a higher level whether at home, in the workplace, in the community, or in relationships. Development in transitional periods It should be noted that in addition to early childhood, there are certain other important times during an individual’s lifespan when people typically make transitions from one stage of development to the next. One important example is the transition from middle childhood to adolescence (see, for example, Papalia et al. 2009, ch. 11). Although some young people may experience this transition favorably as an important growth opportunity, it is not unusual for others to experience this transition as difficult and stressful. In many cases, people, often with a great amount of effort and some distress, successfully make these transitions, moving on to the next stage of their life. However, in other cases, people may get stuck or partially stuck at their present developmental stage, and as a consequence of this developmental failure, certain later life opportunities may be precluded. It is useful to think of people who are dealing with transitions as making substantial investments in non-cognitive human capital, investments that sometimes require professional help, such as from social workers or psychologists.

ADULT DEVELOPMENTAL STAGES The developmental stages of children and adolescents have long been recognized, but adult developmental stages have only gained wide recognition in recent decades. Levinson’s (1978) study of adult development is arguably the single most important contribution to understanding the progression of adult lives over the years.5 To understand adult life stages, Levinson studied the life stories of a relatively small number of adults (40 men in four occupations in his 1978 study).6 His findings led him to conclude that an adult’s life has a universal pattern, an underlying systematic, non-genetic progression. According to Levinson (1978), the life course from age 17 to old age consists of a combination of stable periods and transitional periods. During stable periods, a person makes decisions and commits to building a life structure. During transitional periods, a person tends to review and evaluate the present structure of his or her life in order to decide what aspects of their life to keep and what aspects to reject. As Sheehy (2006, p. xvii) explains, humans have a resemblance to lobsters in that during parts of their lives they develop a series of hard protective shells, and during other life segments they shed the shell when it has become too small and confining. Similarly, humans at certain ages tend to find their life structure (a relatively fixed, stable life agenda) coming undone and deteriorating. This may evoke a sense of ‘crisis,’ or at least unsettling feelings, that provide them the impetus and opportunity to change their present life structure in order to incorporate life elements that were not previously part of their life’s agenda.

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Based on his 1978 research on the pattern of adult development from age 17 to 60, Levinson has identified a number of developmental periods. First are two eras, early adulthood and middle adulthood. Early adulthood consists of two transition periods, early adult transition (age 17 to 22) and the age 30 transition (age 28 to 33), as well as two stable periods, entering the adult world (age 22 to 28) and settling down (age 33 to 40) (Levinson 1978, pp. 56–62). Middle adulthood consists of two transition periods, midlife transition (age 40 to 45) and the age 50 transition (age 50 to 55), as well as two stable periods, entering middle adulthood (age 45 to 50) and culmination of middle adulthood (age 55 to 60). Levinson mentions but did not study late adulthood (roughly 60 to 80) and late, late adulthood. During the stable periods, an adult develops a life structure which has important life components such as occupation, marriage-family, and friends. Adults seeks to create a structure that is simultaneously ‘viable in society and suitable for the self’ (Levinson 1978, pp. 53–4). Ideally, persons will decide on and build a life structure that will enable them to make their greatest contribution to society while enabling them to realize their dreams and values (Levinson 1978, pp. 51, 53–4, 324, 331). If the developmental tasks do not go well and a viable, motivating life structure is not created, the individual likely becomes stuck or partially stuck at an earlier stage of development (Levinson 1978, pp. 321–2). This generally is associated with decline, loss of vitality, imbalance, and stagnation. Erik Erikson’s (1982) writings on human development preceded Levinson’s, and they provide an interesting contrast with those of Levinson. Erikson (1982, pp. 32–3, 56–61, 69, 75) identified eight life (not just adulthood) stages: infancy, early childhood, play age, school age, adolescence, young adulthood, adulthood, and old age. Each stage is concerned with developing a basic strength and avoiding or fending off a core pathology or vulnerability. For example, in Erikson’s fourth stage, school age, children are developing competence and trying to avoid inertia and feelings of inferiority. In his eighth and final stage (old age), individuals are developing wisdom and integrity and avoiding despair and disdain. In the seventh stage (adulthood), individuals are developing generativity and care and avoiding stagnation and rejectivity. As Erikson (1982, p. 59) points out, ‘each [developmental] step is grounded in all the previous ones’. When any developmental step fails, the individual may not only realize the vulnerability or weakness associated with that stage but may regress to an earlier stage (Erikson 1982, p. 67). According to Levinson’s (1978, pp. 319–20) theory, the sequential developmental periods do not imply that adult development follows an ascending or hierarchical order. His view is that ‘the [developmental] tasks of one period are not better or more advanced than those of another, except in the general sense that each period builds upon the work of the earlier ones and represents a later phase in the cycle’ (Levinson 1978, p. 320). Thus, Levinson’s view is that the developmental periods are like seasons in that summer must follow spring, but that summer is not more developmentally advanced than spring. Consistent with this, when Levinson (1986, p. 12) refers to adolescence, he uses the term, adolescing, to mean ‘moving toward adulthood’ and, referring to adulthood, he uses the term senescing to mean ‘moving toward old age’ and death. In other words, when an adult grows older and thereby moves into a later developmental period, it does not imply that the individual’s capabilities have grown. I agree with Levinson to the extent that later developmental periods might simply allow a person to develop a greater range of abilities and interests.7 Levinson’s view, however, is contradicted by his findings indicating that

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Smart persons and human development 149 certain high-level abilities that middle and older aged people were able to develop could not have been developed unless they had developed certain prerequisite abilities in an earlier period of adulthood. It seems quite clear to me that adults in later life stages are in many cases acquiring capabilities that advance them to higher levels of the HD pyramid than would be possible for individuals in early adulthood. It is certainly true, though, that some older adults are only broadening their range of abilities and interests, not developing higher-level capabilities, and still others’ capabilities may unfortunately be declining as they age. Nevertheless, it is important to note that a significant number of Levinson’s findings seem to support the view that advancing to a later developmental period makes possible the development of certain types of higher capabilities. This viewpoint of ascending capabilities over the life course is more obvious in Erikson’s (for example, 1982) work. He makes clear that the full development of generativity and care must wait until middle to late adulthood even though it is based on seeds planted earlier. The full development of wisdom, integrity, and a number of other virtues must wait until relatively old age despite their basis in strengths developed earlier. From the research of Levinson, Sheehy and others, it is clear that adult human development is generally not a smooth process; stressful episodes and periodic crises are not uncommon. To a certain extent, this is inevitable, and adults need to figure out their developmental paths for themselves. However, as Levinson (1978, pp. 336–40) recognizes, it might make a lot of sense for society to try to smooth people’s developmental paths and to help developmentally failing adults. If a man’s early adulthood is dominated by poverty, recurrent unemployment, and the lack of a reasonably satisfactory niche in society, his adult development will be undermined. His energies will [then] go to simple survival rather than the pursuit of a Dream or the creation of a life structure that has value for himself and others. (Levinson 1978, p. 337)

If it were high on a nation’s priority list, much could be done to help improve adult developmental experiences especially in workplaces. In this regard, Levinson notes that ‘for large numbers of men, the conditions of work in early adulthood are oppressive, alienating and inimical to development’ (1978, p. 338). Levinson also notes that much could be done to provide ‘some degree of emotional support, guidance and sponsorship’ that would permit better development outcomes in early and middle adulthood. A society that does more along these lines is making the kind of investments in human capital that are likely to yield a high payoff for both individuals and society.

SMART PERSONS AND VIRTUE In addition to the various types of human growth that we customarily think of as elements of human development, humans may develop virtues. Smart persons can develop important virtues such as prudence, love of knowledge, courage, firmness, generosity, temperance, and justice. Virtues are acquired capacities or dispositions that enable persons to contribute in some generic way with a high degree of excellence to activities that are challenging and important (McCloskey 2006, p. 64; Roberts and Wood 2007, pp. 60–64). Virtues are not specific, technical skills and do not involve performing specific roles (for example, managing a business or playing basketball). Virtues are habits of the heart

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(McCloskey 2006, p. 64), and they are deep, enduring, settled character qualities that are formed by education in the broadest sense (Roberts and Wood 2007, p. 69). Virtues may be perfections in the sense of perfecting our natural qualities. Or they may be correctives in the sense of correcting our natural human defects (Roberts and Wood 2007, pp. 68–69). Virtues generally enable us to achieve excellence in some sphere of activity such as the interpersonal, the political or civic, the intellectual, or the moral (Roberts and Wood 2007, pp. 60, 215). Virtues may also enable us to achieve the kind of excellence sought in a certain type of society. The predominant virtues people develop in a socialist or communist society are likely to be quite different from those developed in a capitalist society. In general, the virtues people develop will depend on political ideologies, religious ideals, and the prevailing vision of the good society, etc. As Deirdre McCloskey (2006) explains, capitalist societies, particularly Christian ones, tend to thrive when their citizens manifest the seven ‘bourgeois virtues’ (love, faith, hope, courage, temperance, prudence, and justice). Prudence is the central ethical virtue of the bourgeoisie. However, settling for prudence alone, as all too many economists recommend, is a recipe for societal disaster. A good, stable capitalism can only occur when prudence is conditioned by and integrated with the other six virtues. In other words, in a healthy capitalistic society, it is important that prudence, the profane (P) virtue, be sufficiently balanced by the sacred and social (S) virtues, the other six (see Klamer and Yalcintas 2004; Khachaturyan and Lynne 2010). Virtues, rather than being a product of activities or institutions in which the intended goal is to develop certain virtues, are, generally speaking, developed as a by-product of activities and institutions whose main purpose is something else. For example, in the home, parents’ values, teaching, and example contribute to their children’s later development of virtue. Similarly, school teachers’ values, teaching, and example are an important influence on children’s ultimate virtue development. Another important influence is children’s learning about admirable leaders in political, religious, business, military, entertainment, and athletic spheres. Young people’s virtue development is also influenced by their learning about important events in which the actions of persons in the news have demonstrated out-of-the-ordinary, inspiring qualities. These different experiences of young people may be instrumental in planting the seeds (values, ideals, and so on) that only later when opportunities present themselves develop (with much intentional practice) into full-fledged virtues. Note that with respect to intellectual virtues what is needed is ‘training that nurtures people in the right intellectual dispositions’ in order that they develop the ‘habits of mind of the epistemically rational person’ (Roberts and Wood 2007, p. 22, original emphases). This ‘regulatory’ activity would ‘provide procedural directions for acquiring knowledge, avoiding error, and conducting oneself rationally’ (Roberts and Wood 2007, p. 21). Also note that developing human virtues is an activity that is consistent with progressing to the highest level of development along all three developmental pathways. In other words, developing virtue(s) is consistent with: (1) acquiring overall life direction (pathway 1), (2) connecting to our highest values (pathway 2), and (3) developing creativity and peak performance (pathway 3). Moreover, it is consistent with the idea that virtues represent uncommon, extraordinary development of character (Roberts and Wood 2007, ch. 3). No doubt, the person who has developed a high degree of virtue is a wise person whose thinking and decision making reflect his or her wisdom. This wisdom is not the same as having a high intelligence quotient (IQ), knowing a lot, or having a good technique.

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Smart persons and human development 151 Wisdom is ‘the moral quality of knowing how to handle your own limitations,’ notably, ‘the ability to go against our lesser impulses [vanity, laziness, cowardice, and so on] for the sake of our higher ones’ (Brooks 2014). A wise person with many virtues is a person who has reached a very high level of HD. Arguably, a behavioral economics for smart people can help us to appreciate the possibility of a wise human actor, but such a high level of HD is not a conceptual possibility in mainstream economics or PE.

THE SMART PERSON RECONSIDERED It is more difficult to specify the qualities and character of the SP than it is for economic man or even for psychological economic man. This is because the qualities and character of the SP are determined by multi-stage developmental processes that do not have well-defined outcomes, even though much can be confidently said about the developmental processes themselves. For example, we now know a great deal about the process of neurodevelopment. However, for a specific person, the childhood neurodevelopment outcome will depend on factors such as the quality of the person’s early childhood environment and the person’s genetic endowment. A different set of factors will determine a person’s developmental progress in later life stages. In general, a person’s development will be determined by the kinds of life challenges the person encounters and how they respond to those challenges. Persons who both experience relatively favorable life situations and who rise to the challenges they face will no doubt develop much farther along the pathways than those for which this has not been the case. Also, a person’s development can go further if he or she has benefited from an intervention (an investment in intangible human capital) designed to help him or her overcome the difficulties that he or she has experienced in the transition from one life stage to the next (see Tomer 2008). In the absence of such an intervention, the person might have become stuck, unable to move on to the next stage.8 How does SP compare to economic man and psychological economic man from the standpoint of the stage of HD they resemble? As suggested earlier, economic man resembles Wilber’s third stage in the development of human consciousness, egocentrism. What stage do the other two stereotypical men resemble? First, psychological economic man’s characteristics cannot be said to resemble any of Wilber’s (2001, pp. 5–13) eight HD stages. This is because psychological economic man does not have a single characteristic way of relating to other humans. Second, SP’s character cannot be definitively specified, and, accordingly, cannot be said to have a close correspondence to the characteristic behavior associated with any of the particular stages of HD identified by Wilber. However, it is possible that the character of the SP economic actor could resemble one of Wilber’s five stages of HD above egocentrism. For example, SP’s character could be strongly conventional and conformist (level 4) or scientific, materialist, and achievement oriented (level 5) or any of the other stages up to level 8, integrative (uniting feeling with knowledge) (Wilber 2001, pp. 9–13). The actual position of a particular SP’s character on this HD hierarchy will depend on the developmental progress that the SP has made. Since very few people reach HD levels 7 and 8 and many reach levels 4, 5, and 6, SP’s development is likely to be in the latter range. More generally, SP’s character, because it is a developmental outcome, is determined by the quality and duration of his or her developmental

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experience. In light of the above, perhaps we need to think more about the character of the people our societies are developing.

THE PROSPECT FOR A BEHAVIORAL ECONOMICS FOR SMART PEOPLE The development of a behavioral economics for smart people arguably could end up being very important. It could represent a significant step forward, not so much because it will replace earlier economic thought, but because it will strongly suggest both new thinking about what is possible with respect to developing human capabilities (a broadening of the human capital concept) and new thinking about the goals and prospects for economic policy. It could help economists and the public understand how humans, while not having super rational abilities, do have greater potential than previously understood. An important implication deriving from SP behavioral economics is that there is a great deal of human potential that has heretofore not been realized because of the blinders imposed on economic decision makers by prevailing economic thought. There is reason to believe that research in the SP behavioral economics vein will help to remove the blinders and point toward many of the ways in which individual human potential can be realized, and thereby, the potential of economies around the world can be realized.

CONCLUSIONS What economics needs is a behavioral economics with smart people. Unlike the economic man of mainstream economics and psychological economic man of psychological economics, the smart person develops capabilities and character in the course of advancing from stage to stage along a number of major developmental pathways during his or her lifetime. Because some persons will experience unfavorable environments without helpful interventions, they may get stuck and fail to develop very far. On the other hand, other people will advance to high levels of HD along a number of pathways. While smart people are far from being perfectly rational, they can improve their capabilities and character, learning to overcome many of their tendencies to error, thereby becoming competent, boundedly rational, virtuous, even wise, decision makers who make big and small decisions in their own best interests and in the best interests of their societies. A behavioral economics with smart people would presumably be a more optimistic economics. This is because it does not embrace the unrealistic rationality ideal of mainstream economics nor the entirely predictable irrationality of psychological economics. This is also because it embodies an understanding of how humans can in important ways improve themselves and their societies even though they may sometimes fail in the process.

NOTES *

I am indebted to both Leonard Marowitz and Betty Devine who read an early version of the manuscript and made comments and suggestions that have led to significant improvements.

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Smart persons and human development 153 1. Wilber (2001, pp. 9–13) has estimated the percentages of the population that reach eight major consciousness development levels. He estimates that less than 2 percent of the population reach the highest two levels of development. 2. The language used by PE researchers further implies that the cognitive errors they identify are universalistic, applying to all humans, not just particular groups of people (Etzioni 2014, pp. 394–5). There is, however, some evidence supporting the view that some groups of people do not behave in a predictably irrational manner. 3. Gerd Gigerenzer has explained in his research how human decision makers with limited computational ability and knowledge in the face of limited time and resources may make reasonably good decisions using heuristics, especially when they have had an opportunity for learning how to use them (see, for example, Gigerenzer and Goldstein 1996). 4. See, for example, the important empirical research of Anda et al (2006) and Felitti et al. (1998). 5. Levinson’s research builds to some degree on the research of Carl Yung and Erik Erikson (Levinson 1978, pp. 4–5); see, for example, Erikson (1982). 6. See also Levinson’s (1997) research on the adult developmental patterns of women. 7. It should be noted that there are certain types of human capabilities (athletic, mathematic, music, and so on) for which peak performance typically occurs prior to early middle age. For example, there is evidence that mathematicians generally make their greatest contributions prior to age 40. 8. Note that any society will have many characteristic developmental patterns, which include the typical developmental challenges faced and the typical levels of development reached by their citizens. In a particular society, the term SP presumably would refer to a person whose development outcome is somewhat typical for the society. Of course, in any society, there is considerable inequality in developmental outcomes. Thus, it can be useful to distinguish among groups with broadly different levels of development and competence. Recognizing this inequality may help us think more clearly about the kind of human capital strategy that would be best to achieve a country’s HD goals as well as its economic inequality goals.

REFERENCES Alkire, S. (2010), ‘Human development: definitions, critiques and related concepts’, OPHI Working Paper No. 36, 1 May, Oxford Poverty and Human Development Initiative, University of Oxford. Almlund, M., A.L. Duckworth, J.J. Heckman and T.D. Kautz (2011), ‘Personality psychology and economics’, NBER Working Paper No. 16822, March, National Bureau of Economic Research, Cambridge, MA. Anda, R.F., V.J. Felitti, J.D. Bremner, J.D. Walker, C. Whitfield, B.D. Perry et al. (2006), ‘The enduring effects of abuse and related adverse experiences in childhood: a convergence of evidence from neurobiology and epidemiology’, European Archives of Psychiatry and Clinical Neuroscience, 256 (3), 174–86. Ariely, D. (2009), Predictably Irrational: The Hidden Forces That Shape Our Decisions, revd edn, New York: HarperCollins. Brooks, D. (2014), ‘The mental virtues’, New York Times, 28 August, op-ed page. Erikson, E.H. (1982), The Life Cycle Completed: A Review, New York: W.W. Norton. Etzioni, A. (2014), ‘Treating rationality as a continuous variable’, Society, 51 (4), 393–400. Felitti, V.J., R.F. Anda, D. Nordenberg, D.F. Williamson, A.M. Spitz, V. Edwards et al. (1998), ‘Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults’, American Journal of Preventive Medicine, 14 (4), 245–58. Gigerenzer, G. and D.G. Goldstein (1996), ‘Reasoning the fast and frugal way: models of bounded rationality’, Psychological Review, 103 (4), 650–69. Goleman, D. (2011), The Brain and Emotional Intelligence: New Insights, Northampton, MA: More Than Sound. Kahneman, D. (2011), Thinking Fast and Slow, New York: Farrar, Straus, Giroux. Kail, R.V. and J.C. Cavanaugh (2007), Human Development: A Life-Span View, 4th edn, Belmont, CA: Thomson. Khachaturyan, M. and G.D. Lynne (2010), ‘Review of Deirdre N. McCloskey, The Bourgeois Virtues: Ethics for an Age of Commerce’, Journal of Socio-Economics, 39 (5), 610–12. Klamer, A. and A. Yalcintas (2004), ‘When being virtuous makes sense’, Aelementair, 3 (4), 1–4. Levinson, D.J. (1978), The Seasons of a Man’s Life, New York: Ballantine Books. Levinson, D.J. (1986), ‘A conception of adult development’, American Psychologist, 41 (1), 3–13. Levinson, D.J. (1997), The Seasons of a Woman’s Life, New York: Ballantine Books. Maslow, A. (1943), ‘A theory of human motivation’, Psychological Review, 50 (4), 370–96. McCloskey, D.N. (2006), The Bourgeois Virtues: Ethics for an Age of Commerce, Chicago, IL: University of Chicago Press.

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Mill, J.S. (1836), ‘On the definition of political economy, and on the method of investigation proper to it’, London and Westminster Review, October. Papalia, D.E., S.W. Olds and R.D. Feldman (2009), Human Development, 11th edn, New York: McGraw Hill. Perry, B.D. (2002), ‘Childhood experience and the expression of genetic potential: what childhood neglect tells us about nature and nurture’, Brain and Mind, 3 (1), 79–100. Perry, B.D. and M. Szalavitz (2006), The Boy Who Was Raised as a Dog and Other Stories from a Child Psychiatrist’s Notebook, New York: Basic Books. Pert, C.B. (1997), Molecules of Emotion: Why You Feel the Way You Feel, New York: Scribner. Roberts, R.C. and W.J. Wood (2007), Intellectual Virtues: An Essay in Regulative Epistemology, Oxford: Oxford University Press. Sheehy, G. (2006), Passages: Predictable Crises of Adult Life, New York: Ballantine Books. Simon, H.A. (1955), ‘A behavioral model of rational choice’, Quarterly Journal of Economics, 69 (February), 99–118. Simon, H.A. (1959), ‘Theories of decision-making in economics and behavioral science’, American Economic Review, 49 (3), 253–83. Simon, H.A. (1983), Reason in Human Affairs, Stanford, CA: Stanford University Press. Tomer, J.F. (2008), Intangible Capital: Its Contribution to Economic Growth, Well-being and Rationality, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Tomer, J.F. (2014), ‘Integrating human capital with human development: toward a broader and more human conception of human development’, unpublished manuscript. Wilber, K. (1996), A Brief History of Everything, Boston, MA: Shambhala. Wilber, K. (2001), A Theory of Everything: An Integral Vision for Business, Politics, Science and Spirituality, Boston, MA: Shambhala.

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PART II ASPECTS OF SMART DECISION-MAKING

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Behavioral strategy at the frontline: insights and inspirations from the US Marine Corps Mie Augier

1

INTRODUCTION

This chapter discusses and applies a few little ideas from the literature in behavioral organizational theory and strategy and discusses some implications for strategic managers of the behavioral strategy framework.1 In particular, it argues that behavioral strategy can help shed light on important decision making and organizational behaviors, illuminating but also helping to address key biases; and helping to contribute to the strategic design of the organizational and psychological architecture of organizations. This may help managers to understand the influences of key behaviors and improve the strategic management of them. The field of behavioral strategy has recently become successful as a scholarly framework (in particular within the strategic management literature); a framework which also can serve as an important lens to understand and address management issues such as behavioral biases. Behavioral strategy as an academic field is more recent than its practice, just as the field of organizations and management existed as practices well before the scholarly studies of them emerged. Indeed, it has been argued that ‘strategy’ as a practice is inherently ‘behavioral’ (Levinthal 2011; Fang 2013). In addition, strategy (and strategic management) is also inherently organizational; the strategic management of business firms and other organizations is the art and science of creating and sustaining competitive advantages in a world of competing organizations. The organizational and behavioral nature of strategy and strategic management combined make the nature of the manager’s task complex and challenging, dealing with complex environment, limited rationalities, and uncertainties and ambiguities; but it is also what makes it possible. There are traits, behaviors, norms, cultures, practices, and organizational mechanisms that alone and together make it possible to understand empirical behavior in organizations and, therefore, improve the management of them. The field of behavioral strategy, explicitly emerging within the organizational and behavioral realm, is therefore a promising field for scholars as well as practitioners; and, as it has evolved, it has contributed to the explication of several key dynamics of decision making and behaviors in organization (around themes such as learning, biases, and the interaction between individual psychologies and organizational characteristics), central to the domain of the strategic management of firms (Levinthal 2011; Powell et al. 2011). Behavioral organization studies and strategy holds valuable lessons for managers on several fronts. For instance, it views the organization as being shaped by its own history (as well as the interaction with others), but not entirely so, as there is room for proactively shaping the strategic environment and one’s performance in it. Behavioral strategy also provides important tools for implementing behavioral insights in practice (Lovallo and 157

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Sibony 2010). For example, understanding organizational behaviors and decision biases are central to making strategic decisions in a proactive way and shaping outcomes without being trapped by biases and earlier decisions (including investment decisions). Also, (strategic) managers must be able to successfully identify strategic asymmetries in the competitive environment, and translate those into the building and maintaining of competitive advantages (preferably in a sustainable way). Moreover, managers must be able to embrace essential and unavoidable uncertainties in the competitive battlefield – while skillfully adapting their own organizations (with the inertias and competency traps that entails). Strategic management and leadership of organizations is not easy, but behavioral organizational strategy as a framework has valuable tools for understanding the strategic environment, for understanding individual and organizational traps and biases, for understanding strategic asymmetries which can be useful in building organizational capabilities and competitive advantages, and for adapting and implementing the steps as part of the process of organizational adaptation. The potential of the newly emerged perspective includes not only developing scholarship or deriving managerial implications, but also facilitating the interaction and mutual learning between the scholarship and practice, thus enabling further development of empirically realistic scholarship as well as real world management strategies. That is, behavioral ideas are useful not only for our theories about strategy and our management of them; but also for the further (strategic) development of scholarly concepts and ideas (Simon 1986, 1997). In particular, behavioral strategy can help (re)connect behavioral perspectives to the organizational context, relevant as most decisions take place in organizational contexts (March and Simon 1958; Simon 1991). To explicate the importance of behavioral strategy and behavioral ideas in strategy, the chapter looks at a few behavioral concepts and ideas (organizational identification, the exploration–exploitation balance and the importance of organizational ambidexterity, and the nurturing of organizational innovation). Each of these dimensions are central to organizational performance; yet difficult to manage. By using behavioral ideas about organizations, decision making, and strategy to understand examples of such behavioral issues in action, the chapter also uses the perspective as a lens to understand a few aspects of an organization which is seemingly very successful in recognizing and managing the behavioral and organizational aspects of strategy in practice. The rest of this chapter elaborates some of these tools as relevant to understanding the managerial implications of behavioral strategy (section 2), uses those concepts and tools to understand the dynamics of a particular organization, the United States Marine Corps (USMC) (section 3), before discussing insights from the Marine Corps for both management and behavioral strategy as a field (section 4). The closing summarizes the chapter and provides a few suggestions for further research. Throughout the chapter, behavioral strategy ideas are used to explicate three ideas/ concepts/mechanisms useful for understanding aspects of ‘behavioral strategy in action’ in the Marine Corps, and with important managerial implications (which, in turn, may also help develop further the field of behavioral strategy). First, organizational identification and loyalty are powerful altruistic forces which can help minimize costs as well as key biases in organizations (Simon 1991). How can organizations encourage and develop organizational identification and loyalty, and how can strategic managers help shape human motivation in organizations? Using behavioral

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Behavioral strategy at the frontline 159 strategy and examining how Marines cultivate – in part through design of organizational and psychological architecture of their organizations – a devotion to the organization, and the importance of this to their ability to adapt, have important implications for how organizations can cultivate organizational identification and counter interest biases. The second theme concerns balancing organizational routinization and innovation, and managing inertias. Important research has led to greater understanding of competency traps, stability biases, social biases; and the organizational tendency to, in particular as they grow in age and size, repress variation, creativity and other forces which could lead to innovation. Behavioral research has also shown that it is essential to try and generate and manage a balance between the two. Using behavioral ideas, this chapter looks at how the Marine Corps has tried to pursue both organizational rigidity/stability, and innovativeness (through their educational and organizational structures as well as their approach to leadership). Lessons for management include the importance of intellectual outliers; and a decentralized leadership and management structure allowing for new ideas to be heard, regardless of where in the organization it comes from. The third theme is the pursuit of organizational transformation through a strategy of evolution with design. Creating even small changes in organizations is very difficult and initiating organizational transformation even more so, but is also necessary in order to adapt to the changes in competitor behavior and structure, and other external (and internal) events. The Marines used behavioral ideas in action to guide their most comprehensive transformation in recent (if not all) history, which helped rebuild the organization with ambidextrous elements in the design, enabled learning from failures and from hypothetical futures, and focused on changing how the organization ‘think’, not just what it could do. Lessons for managers include the importance of understanding the future competitive environment (for example, minimize competitor neglect and other actionoriented biases), and to design the organization to embrace competencies and routines that improve efficiency but also with a key role for experiments and learning. All three aspects or themes are examples of behavioral organization theory and strategy ‘in action’, and its managerial implications, but can also be useful for the future development of the field of behavioral organization theory and strategy itself.

2

BEHAVIORAL STRATEGY, A WHIRLWIND OVERVIEW OF A FEW KEY IDEAS

‘Behavioral strategy’, although only recently emerging as a distinct perspective within strategic management, is in many ways embedded in a significant part of the foundation for organizational decision making and strategic management, as well as understanding (and improving) management and strategy making in practice. Behavioral ideas about organizations have been part of the foundation for strategic management theory as it has evolved, both in diagnosing and framing the field’s central questions and in shaping some of the main scholarly perspectives such as evolutionary perspectives and (dynamic) capabilities theory (see, for example, Rumelt et al. 1994; Winter 2000; Augier and Teece, 2007, 2008). Building on the earlier foundational work in organizational behavior and combining behavioral perspectives on organizations, research on behavioral and psychological decision making, behavioral strategy focuses on themes such as learning, attention, satisficing

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and cognition. Recent behavioral strategy work also has called for a ‘new beginning’ to reconnect with behavioral economics and finance; to explicate the psychological grounding; and to integrate further psychology and strategy (Powell et al. 2011).2 Identified by merging ‘cognitive and social psychology . . . with strategic management theory and practice’ and aiming to ‘bring realistic assumptions about human cognition, emotions, and social behavior to the strategic management of organizations’ (Huy 2012, p. 240), the ‘new’ behavioral strategy is often largely rooted in individual behavior, but behavioral strategy as a whole also has significant organizational aspects, including insights into the development of organizational competency traps which may be beneficial for the organization in the short run, but inhibit ability to adapt to change in the longer run (March 1991). Implications for management and real-world strategy include illuminating certain key biases and bringing to light unconscious processes in organizational decision-making processes and asking key questions regarding the ongoing strategic processes in organizations, including if the strategic direction has to change, how to adapt and to what, and what key trends are important for shaping the strategic context for the firm. Research into the nature of organizational competency developments and the nature and need for innovation and change (as well as the psychological and organizational barriers to change) also include work on the mechanisms enabling and discouraging innovation and the possibilities and barriers to organization’s ability to adapt to disruptive changes (including technologies) (March 1991; Christensen 1997). Methodologically, behavioral strategy embraces multiple methods and methodologies in order to better understand real behavior and decision making in organizations.3 Using multiple methods, and disciplines, is important since understanding real-world behavior does not fit one or two lenses very neatly; yet it is also quite difficult for many reasons, including institutional and intellectual homophily which inhibits variation at the organizational as well as the ideas level. The mechanisms of homophily (operating in organizations as well as in communities of scholars) includes the inclination of organizations to recruit and retain people who are similar to each other in beliefs and competencies, as well as individuals within organizations often seeking to work with those individuals who are most like themselves. Over time, organizations forming groups of similar individuals are unlikely to create and encourage new and innovative thinking, so central to the healthy development of organizations and academic fields in the long run. Behavioral strategy, by embracing an empirically relevant perspective and using multiple methods, may be less likely (but not immune) to get stuck in such traps. Recent contributions relate behavioral strategy to related perspectives from behavioral neuroscience and cognitive neuroscience and reference point theory (Powell et al. 2011). Lovallo and Sibony (2010) emphasize also managerial implications, including biases which management can understand and discuss using the language of behavioral strategy. The biases are often manifestations of behavioral and organizational dynamics, and are shaped by such dynamics often in co-evolutionary and self-reinforcing ways. For example, ‘stability’ biases and organizational inertias are products of both individual psychologies and resistance to change (such as loss aversion and biases for status quo), as well as the way in which organizational routines and competencies develop, often leading to under-sampling of trying new things, and oversampling of doing more of the same, although for the organization to survive in the long run, a balance is central (March 1991).

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Behavioral strategy at the frontline 161 Another example around human motivation and conflicting interests in organizations opens the door for issues such as misaligned incentives (people pursuing individual goals at the expense of the organization’s goals) or misunderstanding (and misperceptions) of organizational goals (Cyert and March 1963). Understanding and awareness alone does not eliminate biases, but it is a central first step. For example, ideas from organizational behavior can help managers understand and design organizations that are more ambidextrous and that have mechanisms to cultivate creativity and ‘hot groups’ within organizations; groups that explore new ideas, despite stability, competency traps, and social biases for the status quo (March 1991; Leavitt 1995). Moreover, behavioral strategy can offer insight into the ‘strategic organizational design’ of organizational structures as well as the psychological and social architecture of the organization to help create a great sense of organizational identity and loyalty which can help curb issues of misaligned incentives and possibly help de-bias interest biases (Simon 1991). In both examples, integrating insights from both the older and more recent work in behavioral strategy into the behavioral and organizational nature of decision making can help us to better understand and address the biases. 2.1

The Importance of Organizational Identification

Herbert Simon’s contributions to behavioral organization studies and strategy include providing (with co-authors) many of the concepts which underlie behavioral economics and strategy (such as bounded rationality and satisficing) (March and Simon 1958). There is also in his work much untapped potential for strategy theory and management (Simon 1993; Augier and Sarasvarthy 2004). One source of insight and inspiration comes from his ideas on organizational identification, loyalty, and altruism; all ideas and mechanisms which counter self-interest seeking behavior and ‘interest biases’.4 A few decades ago, Simon pointed to the irony that despite the ubiquity of organizations, many of our scholarly theories of organizations were based on individual behavior and analysis of markets, rather than organizations (Simon 1991). He also emphasized the fact that real-world behavior in organizations often included motivational aspects that many theories could not explain, yet they were important to organizational dynamics and performance. Also, as Powell et al. pointed out, in behavioral strategy, ‘the whole question is how particular forms of behavior arise in and among organizations. If we do not show the mechanism, we do not explain the phenomenon’ (2011, p. 1375). A key in Simon’s ideas on identification is that while putting an organization’s goals ahead of our own may not be beneficial for individual organizational or societal members, it contributes to the overall adaptive and strategic fitness of the organization: ‘[S]ocial evolution often induces altruistic behavior in individuals that has net advantage for average fitness in society’ (Simon 1993, p. 160). He also finds that most economic and strategic theories ignore the powerful role that organizational identification can have in shaping both organizational member’s goals as well as their perceptions and cognition. Thus organizations with a higher degree of organizational identification and loyalty are more likely to be able to sustain elements of altruistic behaviors and trust, reducing further costs (for example, those associated with contracts and monitoring of possible agency problems), as well as strengthening the organization’s sense of identity and ‘culture’. Organizational identification can also help improve organizational coordination, as evolved shared goals

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and norms serve as ‘focal points’, reducing the costs of coordination and also making it more adaptive (Simon 1991). Also, as organizational members develop a greater sense of loyalty, they become more inclined to identity with the organization’s goals rather than their own, thus reducing ‘interest biases’. How can managers cultivate, foster and sustain organizational identification? Most management literature gives little clues – building explicitly or implicitly on notions of self-interest seeking individuals (embedded in concepts of ‘opportunism’ or ‘guile’; Dosi 2004; Augier and March 2008). The difficulty of building organizational identification, given its importance, suggests that studying organizations which have a strong culture of organizational identification might be useful. 2.2

Nurturing Innovation, Creativity and ‘Hot Groups’ within Organizations to Help Develop Entrepreneurial and Dynamic Capabilities

Even though many organizations have evolved towards being smaller, flatter, and more decomposed over the past decades, still many organizations are quite large. Big organizations are great for many things, but they also have some weaknesses, at least from the point of view of contributing to innovation and innovative capabilities in the long run. Behavioral strategy is a useful lens for understanding and addressing key challenges of big organizations, including: ●

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Stability biases and homophily dynamics. The tendency of organizations towards institutional and intellectual homophily, and towards reinforcing stability and social biases, and a tendency towards inertia rather than innovation. Inertia and competency traps. One of the most important concepts to capture the tendency for organizations to get ‘stuck’ doing more of the same is March’s idea of competency traps, and the need for organizations to balance exploring and exploiting activities (March 1991). March has argued how organizations, in order to be adaptive, must nurture and cultivate also the generation of new ideas, of creativity and other sources of innovation. The balancing between stability and change, exploration and exploitation is one of the most difficult things to do, and organizations such as Kodak are examples of failure to adapt owing to a fundamental imbalance between exploration and exploitation. Organizations as ‘unhealthy’ environments. Hal Leavitt adds a dimension to the competency trap argument by arguing that big organizations are ‘unhealthy’ environments for human beings, in part because their hierarchical structure undermines intellectual freedom and creativity which can lead to the generation of new ideas and innovations at the organizational and industry level (Leavitt 2007).

Other reasons for the stifling of entrepreneurial activities include social biases and norms leading to the elimination of outliers, and discouraging creative and ‘out-of-thebox’ thinking. Repressing creativity and the generation of new ideas is detrimental not only to the development of innovations in the longer run (and on the industry level), but also to the generation of organizational capabilities in the individual firms. Creative thinking and entrepreneurial behaviors are needed for managers too, and if not encouraged at all levels of the organization it is less likely that managers will exhibit entrepreneurial qualities.

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Behavioral strategy at the frontline 163 Managers must think outside of the box, set new goals, visions, aspirations for the organization and create new paths forward (Simon 1991). Thus, understanding better how some organizations cultivate individual outliers as well as hot groups is essential to the management of organizations (as well as giving inspiration to current managers as to how to generate such entrepreneurial dynamics within the firm) (Leavitt 2007; Augier et al. 2015). Carefully understanding the biases and dynamics of one’s organization (as well as others) can help management counter some of the biases and help determine and set clear and realistic strategies. The combined insights into behavioral and organizational aspects of decision making improve management’s ability to learn from and adjust its organizational and psychological architecture in order to develop robust capabilities for competing in the long run. The next sections take a look at some of the mechanisms and concepts of behavioral strategy ‘in action’.

3

COMPETING ON WARRIOR CAPABILITIES: THE ART AND SCIENCE OF BEHAVIORAL STRATEGY IN THE USMC

One organization with great potential for strategy/management/leadership insights is the USMC, a well-established organization, older and larger than most (business organizations at least) – yet it has received surprisingly little attention from strategy, organizations and management scholars, although it is an organization which can give a lot of insight and inspiration for practicing managers (as well as possibly for strategic management scholars), regarding the art and science of strategy. The Marine Corps provides useful examples of ‘behavioral strategy in action’, having both behavioral ideas about individual and organizational decision making (such as embracing uncertainty and being highly adaptive), as well as having mechanisms in place to counter some important biases.5 In particular, the Marine Corps’ ability to cultivate organizational identification and loyalty, its ability to maintain innovativeness, ambidexterity, and balancing of exploration and exploitation (even within a large organizational and seemingly hierarchical structure), and its adaptiveness through evolution with design might provide useful insights for managers and management scholars alike. The Marine Corps has a rich history of competing in several different environments (including peacetime) since its birth in 1775. The issue of ‘What makes marines a marine’ has been the source of puzzlement for decades, including the strong sense of organizational identification and unity – despite seemingly strong hierarchy – as manifested, for instance, in General Gray’s statement, ‘Every Marine is, first and foremost, a rifleman’ – putting functional specialties and individual interests aside. Indeed, a lesson from Gray is ‘always put your organization and your people ahead of yourself’ (personal conversation with General A. Gray), an interesting contrast to much of management theory’s emphasis on self-interest seeking. There are many components to what makes Marines seemingly more agile and able to adapt, including: issues of the organizational structure of the organization; how they cultivate their particular organizational culture; and if and how they attract those with more broader and curious minds. Here the focus is on only a subset of components that can help us see the relevance of behavioral strategy – for understanding organizational dynamics but also for learning from them to better manage our organizations.

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At its most basic level, organizational capabilities include resources (physical, intellectual and intangible), organizational aspects facilitating and shaping motivation, structure and processes. As all organizational capabilities, the Marine Corps organizational capabilities are more than the sum of its parts; more than the resources, training, and organizational structures, it is the synergy between them that creates uniqueness. Elements of Marine Corps ‘warrior capabilities’ include the following. 3.1

Cultivating Learned Selflessness, Organizational Identification and Loyalty

Human motives change over time, responding to experience and the surprises of history. (Simon 1993, p. 160)

As mentioned above, behavioral strategy suggests that certain kinds of human motivation, such as loyalty, can increase organizational identification, which in turn can help minimize interest biases and improve alignment with organizational goals. How organizations train, mentor and educate people helps provide a better understanding of the organization’s goals, values and culture. The Marines have an exceptional sense of organizational loyalty and dedication to the organization’s goal. Part of that may be explained due to the purpose and mission of the Marine Corps (as it might attract people dedicated to higher causes), but there is also an important element of strategic organizational design, including design of the psycho-organizational mechanisms to build and cultivate team spirit, organizational identity and loyalty. The Marines cultivate and build organizational identification and loyalty that encourages a kind of ‘learned selflessness’ and concern beyond oneself, and an identification with the organization’s goals, rather than individual goals, thereby contributing to one of the most powerful altruistic forces in Simon’s discussion (Augier and Guo 2016). The Marines do that at several (interrelated and overlapping psychological, psycho-cultural, sociological, physical, intellectual) levels, including: ●

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Sematic level: entering boot-camp, young Marines are no longer identified by their individual name but by reference to ‘this recruit’, ‘that recruit’, and so on; so the loss of ‘self’ relative to the identification with the organization is embedded even in the language used. Symbolic level: a first symbol of losing individual identity and giving up self is yellow footprints at the entrance to boot-camp, symbolizing a new path for the young Marines, stepping towards the disappearance of the individual and into a tradition and culture of selflessness and sense of duty. Stripping away individual characteristics include also the haircuts; sometimes making the young marines unrecognizable to themselves (which helps them shed their individualism and build team and organizational identity). At the physical level, Marine boot-camp is well known for its grueling exercises and tough physical standards. That too helps build team identity. For instance, by punishing recruits as a group for individual behavior (for example, being slow or otherwise not living up to the organization’s standards) helps instill the sense that any organization or team is only as strong as its weakest link (and discourages agency problem behavior). Several exercises are also designed to only be able to be

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successfully finished as teams. They also are taught to embrace uncertainty and fear, and to run towards the sound of the gun, not away from it. The fact that the Marine Corps builds a new identity instills in the young men an attitude that being a Marine is not a job; it is a calling. Such values help encourage an organizational identity and loyalty, and a pursuit of a logic of identity rather than one of consequences.

Thus the building of team identity, organizational identification and loyalty in the Marines has several levels and layers; including aspects of ‘strategic organizational design’ (for example, of exercises) as well as design of the psychological architecture and mental characteristics of marine training and education. This involves knowledge of what motivates humans and how to train and change psychology and behavior, as well as how individuals interact and are shaped by others and by the organizations they are in, in a coevolutionary way. The facilitation of organizational identification and loyalty can reduce possible conflict of interests as well as ambiguities and misperceptions about goals and interests. It also helps Marines to be highly adaptive on the battlefield. 3.2

Competing on Warrior Capabilities: Designing Ambidextrous Organizations to Counter Biases and Competency Traps

Marine training also signifies another interesting aspect of the organization; it exemplifies behavioral strategy in action and illustrates one way of managing the dynamics of inertia and innovativeness – not by balancing them (as in ‘either-or’), but by integrating them (both-and) into the heart of the organization’s capabilities. As the Marine Corps’ training guide states: ‘Training must be challenging. If training is a challenge, it builds competence and confidence by developing new skills. The pride and satisfaction gained by meeting training challenges instills loyalty and dedication. It inspires excellence by fostering initiative, enthusiasm, and eagerness to learn’ (USMC 1996, p. 4). It is not that marine training does not emphasize routines and rigidity; boot-camp is, after all, about the transmission of basic routines needed for successful military operations; marine training in history, martial arts, swimming, land combat – and training 37 000 recruits annually – requires much standardization, routinization and rigidity (Guo and Augier 2015). However, along with a reputation for rigorous training, the Marine Corps has also developed an instinct and inclination for innovativeness and a capacity for out-of-the-box thinking (for instance, the Marines have implemented one of the most visible and effective energy programs, the Expeditionary Energy Office). How can one organization simultaneously pursue rigidity and innovativeness? The routines underlying Marine training are focused enough to provide direction, yet flexible enough to accommodate the needs to adapt to changing conditions and commanders at all levels of the organization. Moreover, Marines are encouraged to think outside the box and ‘think strategically’ at all levels, despite a seemingly very hierarchical organization. The ability to listen to ideas from all levels is key to other intellectually innovative organizations in the past (Augier et al. 2015). The uniqueness of the Marine Corps from an organizational and capabilities perspective includes not only the important element of identification and altruism mentioned above, but also the fact that the Marine Corps is, effectively, ambidextrous by nature, mixing and integrating ‘core competencies’ of other services. While the Army is known for

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and trains for land combat, the Air Force for its flying, and the Navy for sea capabilities, the Marine Corps has important land as well as sea and air components, so it has a built-in need for flexible capabilities as well as for a mindset that is both able to learn from the past (and from other organizations) as well as creating the future (and combining capabilities and ideas). The embracing and simultaneous pursuit of exploration and exploitation in the Marine Corps is made possible in part by the leadership style. Far from being about micro management, a key to how Marines operate is the shared understanding of the commander’s intent (again, made possible by organizational loyalty and identification). In particular when in combat, the ability to adapt and be innovative at the front line comes from the ability of junior leaders to make in-real-time decisions – based on their understanding of their leader’s intent.6 The shared understanding of the organization’s goals (minimizing interest biases through training and the building of loyalty) is also made possible through organizational communication; not formal communication channels but largely informal and implicit channels; almost shared mental and cognitive models or frames: We believe that implicit communication – to communicate through mutual understanding, using a minimum of key, well-understood phrases or even anticipating each other’s thoughts – is a faster, more effective way to communicate than through the use of detailed, explicit instructions. We develop this ability through familiarity and trust, which are based on a shared philosophy and shared experience. (USMC 1997, p. 72)

3.3

Organizational Transformation is Difficult but Not Impossible

A third lesson from the Marine Corps exemplifying behavioral ideas in action relates to the difficulties in creating strategic change and transformation in organizations. Organizational change and transformation is so central to organizational adaptation, yet also very difficult. Powerful mechanisms of individual and organizational inertia include stability biases (such as preference for status quo, anchoring and loss and risk aversion); social norms and biases; and individual and organizational and bureaucratic inertia which alone and together make organizational change exceptionally difficult. The Marine Corps has successfully changed and adapted to the changing strategic environment over the past centuries. One of the most recent comprehensive transformation of the Marine Corps into a more adaptive organization was in the late 1980s and 1990s, led by legendary Marine General Alfred Gray, which manifested behavioral strategy at organizational design level, living strategy and the strategic management of organizational change as a process of evolution with design. A core insight is the limited rationalities and psychologies (and dynamics of the changing strategic landscape) not only of the strategic competition (who might opponents be 30–40 years from now?), but also the limited rationalities and psychologies of our own organizations and people. Gray knew that in order to really improve the Marine Corps, he had to change ‘how the organization thought’. The transformation needed to be intellectual as well as organizational and, starting with a hard diagnostic look, needed to be about how to think, not what to think. Gray outlined a framework for ‘war-fighting’, which evolved as a symbol of adaptive and strategic thinking. A key text which is read by all Marines at all levels, ‘War-fighting’ was first described as a ‘doctrine’ but was much more than that; it is a philosophy and a strategic way of thinking instilled in all Marines regardless of rank and age, and to be

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Behavioral strategy at the frontline 167 applied in peacetime as well as in wartime. In Gray’s words, it is a ‘philosophy for action that, in war, in crisis and in peace, dictates the Marine Corps approach to duty’ (personal conversation with General A. Gray). The overall war-fighting framework also became a foundation and framework for the Marine Corps’ central initiatives, such as maneuver warfare and decentralized leadership.7 Central ingredients in the philosophy were to be able to understand competitor weaknesses (a strategic asymmetry) and to use agility, decentralized decision making and speed to their own advantage. In effect, maneuver warfare emerged almost as a behavioral and evolutionary alternative to previous static approaches. The broader context for war-fighting and maneuver warfare as paradigms for enabling strategic change within the Marine Corps had a lot to do with the strategic context in which the Marine Corps found itself after Vietnam. The concept and philosophy did not come out suddenly, but resulted from organizational adaptation and careful strategic organizational design in order for the organization to adapt to the changes in the external strategic environment. Gray had encountered maneuver warfare earlier (having spent a lot of his youth overseas and having done a lot of reading). He had read a lot of Clausewitz – but also developed an affinity for Sun Tzu, studied strategic deception, and was interested in learning from mistakes made in past conflicts (in particular, Vietnam and Korea). The end of Vietnam War was a time of crisis for the country but also for the Marine Corps as an organization. They had lost some of their traditional core competencies and had big moral problems and knew they needed to upgrade the quality and capability of the organization, starting with education; broad reading lists also became an integral part of the Marine Corp’s educational experience, helping young marines to be intellectually agile as well, and cultivating broad and curious minds and lifelong learning. Another aspect of the upgrading of Marine Corps capabilities had to do with learning from experience and from experiments including failures. To buffer innovation within the organization (and to protect it from mechanisms that would kill it), Gray set up a maneuver warfare board in the second marine division in order to show the rest of the organization that using maneuver warfare could be successful and was central for organizational adaptation. At the training level, Gray also set up a combined arms operations exercise to test ideas. This was a free-play type exercise in order to inspire and facilitate learning across all levels and to encourage creative thinking and after-action discussions in environments where subordinates could speak freely and contradict superiors without fear and commanders could learn to receive criticism. While the maneuver warfare board as an organizational experiment did not last long, it illustrates the kind of experimentation with ideas and organizations which behavioral strategy scholars have argued can lead to improved adaptation (March 1991). It also illustrates Gray’s profound belief in people and in ideas; when asked about how he managed to change organizations (despite all the reasons organizations resist changed), he said ‘It’s easy! Unleash the people with ideas; and protect them from bureaucrats, admin and paperwork’ (personal conversation with General A. Gray); a refreshing bottom-up approach to change, very much in keeping with the emphasis from March and others on the need for nurturing novel and new ideas. In keeping with this, the current Marine Corps commandant issued a call just last year for more disruptive thinkers in the organization.

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MANAGING ORGANIZATIONS WITH THE TOOLS OF BEHAVIORAL STRATEGY AND WITH INSPIRATION FROM BEHAVIORAL STRATEGY IN ACTION

Building on the foundations and concepts of (old and new) behavioral strategy, and using some insights and inspirations from the USMC, we can find several ideas useful for strategic managers in organizations today. These include the following. 4.1

Building Organizational Loyalty

Leaders should think more about others than themselves . . . Being in the [organization] is not a job. We don’t work . . . We serve. (General Alfred Gray, 1991)

Recognizing the importance of mechanisms and behavioral ideas such as organizational loyalty, identification and altruism, how can managers help encourage and cultivate such behaviors? While business organizations probably will not develop ‘boot-camps’ for their people anytime soon, looking further into the psychological mechanisms of how the Marine Corps builds identification through boot-camps might provide insights that managers can be inspired to try to cultivate organizational identification.8 4.2

Embracing Uncertainty and the Simultaneous Pursuit of Exploration and Exploitation Can Help Counter Pressures towards Competency Traps

In the ‘fog of war’ there is chaos, and in that chaos opportunities present themselves. (Gray 1987, p. 18)

Behavioral strategy embraces uncertainty and ambiguity rather than trying to repress it. The competition organizations face involves uncertainty but if embraced, rather than assumed away, can also help shape the competition in the future. This involves understanding the psychologies of competitors, trying to create and utilize asymmetries in the competition to create and sustain competitive advantages. At the heart of this is a behavioral conception of decision making with individuals being limited in their rationalities and computational powers (March and Simon 1958; Cyert and March 1963).9 Thus the basis of the management of organizations in behavioral strategy is the ambiguity and uncertainty inherent in all decision making, giving rise to particular behaviors and necessitating the facilitation of shared perceptions and beliefs, starting with the leader’s vision and an understanding of the nature of the organization and its strategic environment. Technology has a place in uncertain situations, but not trying to reduce it. Marines use ‘technological advances to facilitate the human interface, not to chase after certainty in an inherently uncertain environment’, thus living the essential ‘ambiguities of experience’ (Mattis 2006, p. 16). Another insight from the Marine Corps is its ability to be adaptive in the battlefield owing to decentralized decision making and, essentially, satisficing (not maximizing) decisions. This is consistent with insights from behavioral decision making and strategy that have emerged with implications for both individual decision making and organizational behavior. The insight is that, in addition to perception, the human brain has other modes of thought – intuition and reasoning – each with its own characteristics. Intuition is

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Behavioral strategy at the frontline 169 particularly relevant to on-the-spot decision making in competition where there is no time for careful analysis of alternative options – much less any attempt to optimize. Instead, behavioral strategy and the Marines’ approach show that there is an evolutionary and adaptive value to being able to make on-the-spot ‘good enough’ solutions (Simon 1955). The research into and the importance of how Marines, chess players, fighter pilots, intensive care medical personnel and others use intuition and knowledge of past experiences without much analysis also invites further research on behavioral strategy into these processes and the synergies between the art and the science of strategic management. 4.3

Pursuing Organizational and Strategic Transformation through ‘Evolution with Design’

One of the paradoxes of organizations is that the more they build capabilities to do one thing, the less inclined they are to do others. Management scholars have pointed to the importance of ambidextrous organizations; those that can manage and balance both exploration and exploitation. Embracing a metaphor of organizational strategic management as one of evolution with design puts emphasis on the continuing strategic transformation and renewal of organizational capabilities as well as using and refining existing ones. Managers can help create better environments for this through strategic organizational design of the organizational and psychological architectures to facilitate learning, including from failures and counterfactual histories. Also central is an environment where ideas matter at least as much as rank; new ideas often do not come from the top of the organization, and if organizational members do not feel free to discuss them, these ideas will never reach the top. Google and others are famous for having setups for experimental thinking, and RAND, many years before, had carefully thought of this as well. It is essential to have an environment that encourages creative thinking. A former commandant of the Marine Corps University noted that he wanted a place where ‘freedom of thought was not only encouraged but rewarded. The idea is that the experimentation should be taken to the failure point . . . that only by reaching that point would we understand the unknowns’ (personal conversation with General A. Gray). Finally, at the level of the strategy making and the strategic leadership of such evolutionary and behavioral management processes, an important implication is captured in Henry Mintzberg’s image of a potter modeling clay into a piece of pottery being a better metaphor for how organizations actually develop strategies (rather than the planning metaphor in which senior decision makers formulate courses of action based on systematic, rational analysis of oneself and competitors, one organization’s strengths and weaknesses, and so one) (Mintzberg 1987). Such lines of thoughts suggest that we can generate implications for the generation of management strategies, including: learning about (and trying to cultivate) organizational identification and loyalty (to minimize interest biases; encouraging innovative and outof-the-box thinking (countering stability biases and competency traps); and embracing a vision of strategy as one of evolution with design, with decentralized leadership with emphasis on shared visions.

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CLOSING

The early work by behavioral organization scholars on the limitations of rational decision making was central to the development of ‘behavioral economics’ and its applications to investment decisions (and the subfield of behavioral finance); and behavioral organization theory also was significant as a foundation stone for the very field of strategic management as it began to take off in the 1990s. Work in the field of strategic management has continually built on and integrated these ideas as it has also evolved concepts (such as core competencies and dynamic capabilities) that are behavioral in origin and with implications for management, and this has helped the field of behavioral strategy to take off. Discussing the new developments in the field of behavioral strategy, Powell et al. (2011, p. 1370) noted that practitioners are skeptical, ‘doubting whether the field can go beyond cognitive biases to produce useful framework that integrate psychology and strategy practice’. They then attribute a large part of the problem to ‘inadequate paradigm development’ within the behavioral strategy camp itself, partly due to terminological confusion and not being sufficiently embedded in the existing intellectual and institutional structure of the larger research community.10 However, such a ‘pre-paradigmatic’ state also offers opportunities. For example, as fields become more ‘mature’ and professionalized, centripetal forces often lead to an increase of conversations within itself, thus inhibiting interdisciplinary learning. Moreover, it is also interesting to note that other fields and subfields developed some of their most successful – and empirically relevant and operationalizable – contributions before they became too self-aware of their development as a ‘field’. This is not an argument against developing behavioral strategy further; that is key too. However, doing it in a way that is less concerned about its relative status within the academic fields, and more concerned about developing behavioral strategy in a ‘behavioral’ (and empirically realistic) way, means that the field is less likely to get too far trapped in the usual competency traps of specialization, and more likely to be able to retain core behavioral elements, such as using different research methodologies, as well as being able to help organizational and behavioral strategy remain empirically relevant in Simon’s sense (1997). Several decades of work in behavioral strategy ideas and perspectives have brought about important concepts and ideas on several fronts. For example, at the level of the development of ideas on management, the areas of management, organizations, economics, leadership and strategy have become enriched with behavioral ideas and concepts (such as bounded rationality, routines, slack, and learning), helping to motivate new subfields such as evolutionary and capability reasoning, which, in turn, are key inspirations for the ‘new’ behavioral strategy framework. Such a framework, especially when combining old and new behavioral ideas, invites research on organizational altruism, innovation, ‘hot groups’, intellectual outliers, and creativity – in addition to already semi-established sub-fields such as entrepreneurship and learning. In addition to being a useful lens for developing the field(s) of strategy organization, and understanding issues such as biases in organizations, the behavioral strategy framework is also possibly useful for examining practices not usually well understood in the strategy literature – including issues of organizational identification and loyalty. Although many of these go against much of the scholarly work on strategy, they are important parts of the real-world psychological and social processes in organizations; and also can deliver important value (contributing to issues such as retention, trust, and

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Behavioral strategy at the frontline 171 networks). By explicating such processes in real-world organizations, we may also, in time, help shape the future development of behavioral strategy as a field.

NOTES 1. 2. 3. 4.

5.

6.

7.

8. 9.

10.

The use of the term ‘little ideas’ is inspired by Jim March’s style and approach to research (Augier 2015; Maslach et al. 2015). We can further argue that since strategy is essentially organizational in nature, it might be relevant to reconnect behavioral strategy (again) with organizations (without downplaying the work on individual decision making), and to try and develop behavioral strategy in a ‘behavioral’ way (Simon 1986). This is an argument in both behavioral strategy and some of its intellectual foundational roots. See, for example, March (1965), Simon (1954) and Powell et al. (2011). Understanding the mechanisms of organizational identification and how it influences organizational performance and strategy can also help address a call from the ‘new’ behavioral strategy school: ‘Behavioral strategy has a long way to go in linking individual psychology with organizational strategies. One of the distinctive features of strategic management is its emphasis on collective behavior, and behavioral strategy must explain the psychological or social mechanisms by which mental processes affect organizations’ (Powell et al. 2011, p. 1374). Using empirical behavior in organizations as inspiration for understanding certain managerial behaviors (as well as for theory development) is consistent also with the ‘old’ behavioral organizational perspective underlying the field of strategic management as the ‘old’ field of behavioral organization studies started from (1) using different disciplinary perspective and ideas to get insights into real organizations and (2) using empirical studies of mechanisms in real-world organizations, to inform the further development of those theories as well as facilitating a better understanding of practice, thus embracing both an inter-disciplinarity in theories and methodologies as well as two-way street learning between scholars and practice (Simon 1986). ‘[S]ubordinate commanders must make decisions on their own initiative, based on their understanding of their senior’s intent, rather than passing information up the chain of command and waiting for the decision to be passed down. Further, a competent subordinate commander who is at the point of decision will naturally better appreciate the true situation than the senior commander some distance removed’ (USMC 1997, p. 71). We elaborate on the leadership aspect in Augier and Guo (2016). Inspired by Sun Tzu, and in keeping with the Marines’ embrace of uncertainty, maneuver warfare is about embracing and even creating ambiguity and uncertainty: ‘Maneuver warfare seeks to shatter the enemy’s cohesion through rapid, focus and unexpected actions which create a chaotic situation with which the enemy can not cope’ (USMC 1997, p. 73). This also appeals to a logic of identity rather than a logic of consequences, and doing things not because of their consequences, but because it is a calling and a duty; a sense of ‘doing what must be done’ (Gray 1991a). This has been at the heart of behavioral ideas since Simon’s landmark articles in the 1950s explicating the dynamics of the limits to rationality and satisficing. Also embraced by Marines: ‘A military decision is not merely a mathematical computation. Decision making requires both the situational awareness to recognize the essence of a given problem and the creative ability to devise a practical solution. These abilities are the products of experience, education, and intelligence’ (USMC 1997, p. 86). ‘The term “behavioral strategy” is not widely used and means different things to different people. Behavioral strategy does not have an agreed statement of purpose, conceptual framework, core research problems, methodological standards, communities of scholarship, or supporting institutions’ (Powell et al. 2011, p. 1370).

REFERENCES Augier, M. (2015), ‘The power of “little ideas”’, Journal of Management Inquiry, 24 (3), 322–3. Augier, M. and J. Guo (2016), ‘Overcoming negative leadership challenges through we-leadership: building organizational commitment with inspirations from the United States Marine Corps’, in D. Watola and D. Woycheshin (eds), Negative Leadership: International Perspectives, Kingston, Ontario: Canadian Defence University Press.

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Augier, M. and J.G. March (2008), ‘Realism and comprehension in economics’, Journal of Economic Behavior and Organization, 66 (1), 95–105. Augier, M. and S. Sarasvathy (2004), ‘Integrating evolution, cognition and design: extending Simonian perspectives to strategic organization’, Strategic Organization, 2 (2), 169–204. Augier, M. and D. Teece (2007), ‘Competencies, capabilities and the neo-Schumpeterian tradition’, in H. Hanusch and A Pyka (eds), The Elgar Companion to Neo-Schumpeterian Economics, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Augier, M. and D. Teece (2008), ‘Strategy as evolution with design’, Organization Studies, 29 (8–9), 1187–208. Augier, M., J.G. March and A. Marshall (2015), ‘The flaring of intellectual outliers’, Organization Science, 26 (4), 1140–61 Christensen, C. (1997), The Innovator’s Dilemma, Boston, MA: Harvard Business School Press. Cyert, R. and J.G. March (1963), A Behavioral Theory of the Firm, Englewood Cliffs, NJ: Prentice Hall. Dosi, G. (2004), ‘A very reasonable objective still beyond our reach: economics as an empirically disciplined social science’, in M. Augier and J.G. March (eds), Models of a Man: Essays in Memory of Herbert A. Simon, Cambridge, MA: MIT Press. Fang, C. (2013), ‘Behavioral strategy’, in M. Augier and D. Teece (eds), The Palgrave Encyclopedia of Strategic Management, Basingstoke: Palgrave. Gray, A. (1987), ‘The art of command’, Marine Corps Gazette, 71 (10). Gray, A. (1991a), ‘Doing what must be done’, Marine Corps Gazette, 75 (2). Gray, A. (1991b), ‘A message from the Commandant of the Marine Corps’, Gazette, April. Guo, J. and M. Augier (2015), ‘The dynamics of rules, learning and adaptive leadership: inspirations and insights from the United States Marine Corps’, in D. Lindsay and D. Woychenshin (eds), Overcoming Leadership Challenges: International Perspectives, Kingston, Ontario: Canadian Defence Academy Press. Huy, Q.N. (2012), ‘Emotions in strategic organization: opportunities for impactful research’, Strategic Organization, 10 (3), 240–47. Leavitt, H. (1995), ‘The old days, hot groups, and manager’s lib’, Administrative Science Quarterly, 41 (2), 288–300. Leavitt, H. (2007), ‘Big organizations are unhealthy environments for human beings’, Academy of Management Learning and Education, 62 (2), 253–63. Levinthal, D. (2011), ‘A behavioral approach to strategy: what’s the alternative?’, Strategic Management Journal, 32 (13), 1517–23. Lovallo, D. and O. Sibony (2010), ‘The case for behavioral strategy’, McKinsey Quarterly, March, accessed 14 December 2016 at http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/ourinsights/the-case-for-behavioral-strategy. March, J.G. (1965), ‘Introduction’, in J.G. March (ed.), Handbook in Organizations, Oxford: Blackwell. March, J.G. (1991), ‘Exploration and exploitation in organizational learning’, Organization Science, 2 (1), 71–87. March, J.G. and H.A. Simon (1958), Organizations, Oxford: Blackwell. Maslach, D., C. Liu, P. Madsen and V. Desai (2015), ‘The robust beauty of ‘little ideas’, the past and future of a behavioral theory of the firm’, Journal of Management Inquiry, 24 (3), 318–20. Mattis, J. (2006), ‘Commanding General’s command & control (C2) intend’, Marine Corps Gazette, 90 (8), p. 16. Mintzberg, H. (1987), ‘Crafting strategy’, Harvard Business Review, 65 (4), 66–75. Powell, T., D. Lovallo and C.R. Fox (2011), ‘Behavioral strategy’, Strategic Management Journal, 32 (13), 1369–86. Rumelt, R.P., D.E. Schendel and D.J. Teece (1994), ‘Introduction’, in R.P. Rumelt, D.E. Schendel and D.J. Teece (eds), Fundamental Issues in Strategy, Cambridge, MA: Harvard University Press, pp. 1–8. Simon, H.A. (1954), ‘Some strategic considerations in the construction of social science models’, in P. Lazarsfeld (ed.), Mathematical Thinking in the Social Sciences, Glencoe, IL: Free Press, pp. 388–415. Simon, H.A. (1955), ‘A behavioral model of rational choice’, Quarterly Journal of Economics, 69 (1), 99–118. Simon, H.A. (1986), ‘Some design and research methodologies in business administration’, in M. Audet and J.L. Maloutin (eds), La production des connaissances scientifique l’administration, Quebec: Les Presses de L’Universite, pp. 239–79. Simon, H.A. (1991), ‘Organizations and markets’, Journal of Economic Perspectives, 5 (2), 25–44. Simon, H.A. (1993), ‘Altruism and economics’, American Economic Review, 83 (2), 156–61. Simon, H.A. (1997), An Empirically Relevant Microeconomics, Cambridge, MA: MIT Press. US Marine Corps (USMC) (1996), ‘The Marine Corps’ philosophy and principles of training’, in Unit Training Management Guide MCRP 3-0A, November, Department of the Navy, Washington, DC, ch. 1. US Marine Corps (USMC) (1997), Warfighting, MCDP 1, June, Department of the Navy, Washington, DC. Winter, S. (2000), ‘The satisficing principle in capability learning’, Strategic Management Journal, 21 (10–11), 981–96.

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10 Feminist economics for smart behavioral economics Siobhan Austen

1

INTRODUCTION

Many of the key themes and concerns of feminist economics were summarized by Marianne Ferber and Julie Nelson in their introduction to the ten-year retrospective Feminist Economics Today: Beyond Economic Man. They noted that feminist economics is distinctive in the serious attention it gives to women, its challenging of the common confusion of gender and sex, and its challenging of the economics discipline in masculineonly terms (Ferber and Nelson 2003, pp. 1–2). They highlighted the social construction of both economic behavior and the contemporary discipline of economics. Several of the themes and concerns of feminist economics overlap those of smart behavioral economics. There is, for example, a shared critical perspective on mainstream economic models and a common concern with the particular issue of preference formation. In this chapter we elucidate key themes in feminist economics and highlight its relevance to smart behavioral economics. The discussion in this chapter is organized through the use of concepts drawn from the institutional analysis and design (IAD) framework developed by Elinor Ostrom and her colleagues. Although this framework is most commonly associated with new institutional, rather than feminist, economics, its concept of situated actors is relevant to one of feminist economics’ central themes: of the gendered nature of economic behavior. The IAD framework allows us to trace out the various ways that men’s and women’s preferences are shaped by socially learned expectations associated with being male or female. This is particularly useful for the analysis of observed sex-based differences in behavior, a field of research where the interests of many behavioral and feminist economists appear to intersect. Several additional aspects of the IAD framework, including the influence of mental models on individuals’ processing information, can be used to highlight other shared interests of feminist and smart behavioral economists. This chapter includes a discussion of how ideas about the structure and influence of mental models are in line with the feminist critique of the methods commonly used in studies of sex-based differences in behavior. In doing so, the chapter highlights a further important theme in feminist economics, that science is a socially constructed activity, with the social location, status and gender of scientists and scientific communities all playing a significant role in determining the methods and practices of science (Barker 1999, p. 325). As a meta-theoretical framework, the IAD also has the advantage of facilitating comparisons of different theories and models. This helps us identify some of the particular features of feminist and smart behavioral economics in comparison with other theoretical traditions in economics, including mainstream economics. Toward the end of this chapter 173

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the discussion focuses on the feminist economics’ critique of the separate/soluble dichotomy in mainstream economics, whereby individuals in market situations are assumed to be atomized, self-interested, with exogenously determined preferences, while individuals in family situations are characterized as connected to each other, altruistic and engaged in a process of shaping preferences. Feminist economists have identified several problems associated with this dichotomy, including barriers to the economic analysis of the unique aspects of women’s lives. Reflecting one of feminist economics’ basic aims, of addressing the realities of women’s lives and their economic and other contributions (Harding 1999, p. 131), an alternative concept is thus advanced; that of ‘individuals-in-relation’. The chapter argues that this concept has the potential to guide future empirical and theoretical studies of men’s and women’s economic behavior. Some prominent feminist economists have already identified the strategic potential to link new institutional economics, which is Ostrom’s field, with feminist theory. Paula England and Nancy Folbre (2003, p. 62), for example, note the relevance of concepts such as endogenous tastes and reciprocity, which feature in new institutional (and smart behavioral) analysis, to notions about the gendered nature of economic behavior. However, many feminist economists contest other core concepts of new institutional and smart behavioral economics, such as the notion of boundedly rational economic agents. Julie Nelson (2003a, 2003b), for example, emphasizes the emotional and subjective aspects of decision-making, albeit the latter is incorporated in behavioral economics. Acknowledging these tensions, this chapter aims to further explore the potential connections between feminist, smart behavioral and new institutional economics. The organization of the chapter reflects these aims. The following section provides a brief introduction to the IAD framework. This is followed with a summary of the key features of feminist economics. Section 4 turns to a key research topic where the interests of feminist and smart behavioral economics appear to intersect, namely, the presence (or otherwise) of differences in the preferences and behavior of men and women. Section 5 explores the issue of (possible) differences in risk aversion in some detail, while section 6 considers the issue of altruistic preferences. Section 7 brings the discussion to a close with a summary of the key themes of feminist economics and some recommendations for smart behavioral economic research.

2

THE INSTITUTIONAL ANALYSIS AND DEVELOPMENT FRAMEWORK

The IAD framework is closely linked to the life work of Elinor Ostrom, the first (and thus far the only) woman to be awarded the Nobel Prize in economics. Ostrom described the IAD as a multi-level taxonomy of the universal components (organized in many layers) that are relevant to regularized social behavior (including interactions in markets, hierarchies and other situations). The broad features of the IAD framework are summarized in Figure 10.1. Of prime importance is the idea of an action arena. This is a ‘social space’ within which ‘participants with diverse preferences interact, exchange good and services, solve problems, dominate one another, or fight’ (Ostrom 2005, p. 14). The focus of IAD analysis tends to be on how the interaction between participants in different action situations is affected by

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Feminist economics for smart behavioral economics 175 Exogenous variables

Action arena

Biophysical/ material conditions Action situations Interactions Evaluative criteria

Attributes of community Participants Rules Outcomes Source: Ostrom (2005, p. 15).

Figure 10.1

A framework for institutional analysis

the characteristics of the situation itself, including the characteristics of the participants and their positions, preferences, levels of information, approaches to information processing, possible actions and potential payoffs. As Figure 10.1 indicates, interactions within situations lead to particular outcomes, which may be desirable or undesirable. The framework incorporates feedback loops to account for the way in which participants may respond to these outcomes by engaging in efforts to either change or reinforce the structure of the arena (as indicated by the line at the bottom of the figure). An important feature of IAD framework is its emphasis of the context of each action situation. Each action situation is understood to be ‘located’ within an action arena that is affected by a range of exogenous variables, including the attributes of the bio-physical world, the structure of the more general community (including the values generally accepted and the prevailing gender norms within the community), and the current set of rules in use, which will reflect the arena’s historical context. Some aspects of Ostrom’s work address the role of culture in shaping the mental models used by boundedly rational participants in different action situations. Ostrom (2005, pp. 106–7) highlights how the cultural environment, including its prevailing gender norms, shapes participants’ perceptions of what actions are possible, legitimate and desirable (or preferred), and it coordinates the actions of groups of participants. She also asserts that, because mental models are affected by culture, they are likely to be transmitted across generations, producing stability in patterns of behavior and outcomes over time. However, in Ostrom’s analysis, mental models can change/are not constant. Factors such as vividness and salience can be relevant to the type of model that is adopted and can be a source of change or difference in participants’ perceptions and actions (Ostrom 2005, p. 108).

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FEMINIST ECONOMICS

The IAD can be used to explain key features of feminist economics. Feminist economics can be distinguished especially from mainstream economics by its concern for the influence of the contextual environment on the preferences, possible actions, payoffs and outcomes for men and women in market and family situations. The ‘situated’ nature of economic behavior is a fundamental concept in feminist economics. Informed by feminist philosophy of science, feminist economics considers how individuals’ (participants’) economic power, obligations, goals, interests and, ultimately, their economic outcomes are affected by their social roles and relationships, and how these, in turn, are affected by their ascribed social identities, including their gender, race, sexual orientation, and ethnicity. As its name suggests, feminist economics pays particular attention to the gendered nature of the contextual environment, and its implications for men’s and women’s economic roles, actions and outcomes. Gender is distinguished from sex, or the biological differences between males and females.1 It is understood that societies or communities assign different roles, norms, and meanings to men and women and their actions. For example, in most societies individuals are assigned to distinct social roles based on their gender (such as men to ‘breadwinner’ and women to ‘caregiver’ within the family). Men and women are also expected to comply with different norms of behavior (for example, men are expected to be brave, and women modest). Furthermore, psychological traits of masculinity and femininity are linked to gender norms (for example, women are considered virtuous if they comply with a norm of modesty but assertiveness can be considered a vice). Using the language of the IAD, a community’s gender norms affect various elements of the action arenas within which men and women participate. The norms influence the ability of men and women to participate in particular situations, the positions that they can take up within these situations, the range and nature of their possible actions, their access to information, and, potentially, the way they process this information, their payoffs from different actions, and, arguably most importantly, the quality of their outcomes. In turn, the gendered distribution of economic outcomes is likely to be reflected in patterns of action at various levels of the social hierarchy aimed at either entrenching existing norms or challenging them. The way in which these perspectives have influenced the feminist economic analysis of economic behavior and outcomes can be illustrated with examples relating to the labor market. Feminist economic analysis of occupational choice have focused on the impact of social structures and relationships on women and men’s work and career goals (Pujol 1997; Strassman 1997). Studies of the gender pay gap have explored the influence of social norms associated with providing care on the distribution of unpaid household work and, subsequently, on the gendered nature and configuration of work (Folbre 1994). Other studies have examined the failure of apparently gender-neutral market institutions to adequately value the commodities produced by women (Himmelweit 1995; Ironmonger 1996). Importantly, feminist economics’ emphasis on the social construction of behavior and outcomes has also influenced its relationship with the discipline of economics. Feminist economists have identified the influence of a range of gender norms and cultural biases on (using the language of the IAD) the action situations associated with the development and perpetuation of economic theory. These include the tendency for ‘culturally “mascu-

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Feminist economics for smart behavioral economics 177 line” topics, such as men and market behavior, and culturally “masculine” characteristics, such as autonomy, abstraction, and logic . . . [to] define the field’ (Ferber and Nelson 2003, p. 1). It is important to note that feminist economics challenges these definitions of economics, and devotes energy to exposing the biases in the discipline, in addition to focusing on ensuring that the lives and experiences of women feature in economic analysis, and attempting to remedy the common confusion of gender with sex.

4

FEMINIST ECONOMICS AND THE ANALYSIS OF OBSERVED DIFFERENCES IN THE PREFERENCES AND BEHAVIOR OF MEN AND WOMEN

As can be anticipated, given the description provided in the previous paragraphs, feminist economics’ analysis of observed differences in preferences and behaviors of men and women is distinguished by its focus on their social origins. Observed differences in the preferences and behavior between men and women are, thus, often the starting point of inquiry (into their origins), rather than the end point of an investigation of (apparent) differences in the ‘natures’ of men and women. Feminist economics’ focus on the social origins of observed differences in the preferences and behavior of men and women reflects an argument that preferences and behavior are gendered. For example, boys and girls in most communities are socialized into particular behavioral patterns; trained to different norms of bodily comportment from an early age. Gender norms in Western societies tend to emphasize physicality, aggression and indifference for boys and constraint for girls and, as a result, men and women are likely to find different types of behavior comfortable and achievable with a degree of fluidity. Performing the gendered actions might feel ‘natural’, be associated with positive ‘payoffs’, and result in positive ‘outcomes’. On the other hand, performing actions that are typically assigned to the opposite sex might illicit a sense of novelty, self-consciousness, and awkwardness (negative payoffs and outcomes that are evaluated as poor). There are also important feedback effects, with the experience of poor/good performance influencing the incentive to invest in gendered skills. Gendered socialization can also cause differences in the way men and women process information about a similar situation or arena. This is because representational schemes that are functional for different gender roles can make different kinds of information salient. For example, in traditional domestic settings, women may notice dirt that men do not, ‘not because women have a specially sensitive sensory apparatus . . . [but] because they have a role which designates the females of the household as the ones who have to clean up’ (Anderson 2009, n.p.). These processes may also result in cognitive styles that differ between men and women. For example, the tendency for men to be allocated positions associated with political and economic power that require detachment and control may encourage a cognitive style that is abstract, theoretical, disembodied, emotionally detached and analytical. The tendency for women to be assigned positions associated with the provision of care may encourage a cognitive style that is concrete, practical, embodied, relational and emotionally engaged. (England 2003, pp. 36–8). Patterns of value are also gendered. There is a cultural tendency in most communities

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to link psychological traits considered ‘masculine’ with virtue when demonstrated by men, and ‘feminine’ traits with virtue when women demonstrate them. This influences the payoffs from different actions that can be performed by men and women and creates incentives for individuals to comply with prevailing gender norms. In academic work situations, for example, the quest for ‘masculine’ prestige may encourage the continued use of ‘masculine’ methods by men, and a rejection of methods associated with femininity or female-dominated fields of enquiry. For example, Nelson (1992) notes how the term ‘hard’ is often metaphorically attached to mathematical and quantitative analysis, and seen as positive and masculine. In contrast, the term ‘soft’ is attached to qualitative methods, is used as a pejorative, and is associated with femininity (see also Nelson 1996, 2003c). More generally, the material and other payoffs associated with different jobs or career paths can vary depending on whether the tasks entailed in the occupational role align with the individual’s gender. For example, men might perceive costs associated with their participation in types of work regarded as ‘feminine’, such as childcare; and women might attach costs to their involvement in types of work regarded as masculine, such as mining. The gendered distribution of power between men and women in many action arenas is an additional important influence on behavior and outcomes. It can cause ‘masculine’ actions to be valorized in particular privileged situations and women’s ability to participate in these situations to be limited. In academic situations, for example, the historical dominance of men has resulted in several formal and informal institutions that value (and thus produce positive payoffs for) ‘masculine’ forms of work and contributions to knowledge. The formal institutions include promotion criteria that emphasize a track record of journal publications and research grants. Often these criteria can only be satisfied by academics who are able to commit long working hours and have uninterrupted tenure, especially in their thirties. Gender differences (and inequity) in outcomes arise if men who conform to a traditional breadwinner role have some ability to achieve success in these situations, while other men and the many women who take on direct care roles in their families find it difficult to achieve positive outcomes. Finally, commonly held ideas about gender affect our perceptions of others (and their actions). A number of studies have demonstrated that the gender of a person affects the costs, benefits and probabilities that others assign to their actions (see, for example, Kahneman 2003). Barbara Reskin (2003), for example, highlighted how in employment situations managers might unconsciously attach certain behaviors, such as reliability or competitiveness, to particular individuals because of their gender. While the managers might consciously reject discrimination, their tendency to rely on familiar social categories might still cause them to think and ultimately act in ways that privilege individuals with a particular gender and disadvantage others. Paula England, Michelle Budig and Nancy Folbre’s (2002) analysis of the labor market outcomes of care workers has similar themes, highlighting how women are commonly perceived to be ‘naturally’ able to accomplish the work involved in caring for children, and for sick and elderly people. This is consequential because it tends to result in judgments of care work as something that does not require skill or effort, contributing to the low-wage outcomes of the many women engaged in care work (see also Austen and Jefferson 2014).

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5

FEMINIST ECONOMICS AND STUDIES OF SEX-BASED DIFFERENCES IN ATTITUDES TO RISK

We can consider now how feminist economists engage with the growing body of literature on sex-based differences in preferences and behavior. An important part of this literature deals with differences in the risk aversion of men and women. Much of it has been motivated by concern about evidence of an over-representation of women in relatively low-risk forms of assets and in particular occupations. This section provides an overview of these studies before introducing a critical perspective – informed by feminist economics – on the common conclusion that women are more risk averse than men. Studies of sex-based differences in risk preference have featured both studies of investment and insurance decision-making in the presence of risk and lottery or gamble experiments of risk-taking. Studies in the first group have used pension fund data to study the allocation of assets between investment options associated with different levels of risk. Studies in the latter group have included gambling experiments with student participants. They have typically focused on whether (and to what degree) the willingness to take a gamble or invest in a lottery is affected by the level of risk involved. Reflecting the acknowledged importance of both risk and loss aversion, many of these experiments have include scenarios where the possible outcomes are framed in terms of gains, while others are framed in terms of losses. Understandably, the analysis of sex-based differences has focused on the magnitude and statistical significance of observed differences in the choices of male and female participants. Commonly the studies have incorporated controls for other factors that might be relevant to a person’s risk preference, such as age. Several contextual environment experiments have involved students participating in computer-based simulated currency trading and stock market games (where the decision to enter particular currency markets or purchase particular securities involves risk). Apart from testing for sex-based differences in risk preference, these studies also examine the effects of factors such as ambiguity about the game’s outcomes, knowledge of financial markets, and confidence in financial decision-making. A recent study by Alison Booth, Lina Cardona-Sosa and Patrick Nolan (2014, p. 128) compared risk preferences exhibited by participants in mixed versus same-sex groups. Cathrine Eckel’s and Philip Grossman’s (2008, pp. 6–11) assessment is that neither the experimental nor the other studies provide conclusive evidence on the nature or extent of sex-based differences in risk preferences. Apparently this is ‘consistent with results from psychology, which tend to show differences in risk attitudes across environments for a given subject’ (Eckel and Grossman 2008, p. 6). Booth et al. (2014) also conclude that attitudes to risk are influenced heavily by contextual factors. In their study, the female participants’ willingness to invest varied substantially across the same-sex and mixed group settings of their experiments. Despite the mixed evidence from studies of the issue, Eckel and Grossman’s (2008, p. 6) general summary of the results of the gamble experiments is that they ‘suggest greater risk aversion by women’. Rachel Croson and Uri Gneezy (2009, p. 448) are more strident, claiming from their review of the literature on gender differences in preferences that ‘women are indeed more risk averse than men’. This is the starting point for an important review by prominent feminist economist Julie Nelson, who challenges the assumptions, methods and conclusions of behavioral

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studies of sex-differences in risk preferences. In her 2012 paper ‘Are women really more risk-averse than men?’, Nelson reported the findings of a meta-analysis of published articles on the topic of sex and risk, including the studies canvassed by Croson and Gneezy (2009), Eckel and Grossman (2008), and Booth et al.’s more recent work. It examined the available evidence on the quantitative magnitudes of the differences between the average level of risk aversion observed for men and women, and the extent to which the observed distribution of risk aversion varies between male and female samples. In doing so, Nelson attempted to redress the tendency for behavioral studies to rely on measures of statistical significance in their judgments of the significance of observed differences in the risk aversion of men and women: ‘In the gender-and-risk literature, as in other literatures, however, judgments of “significant difference” are generally based on statistical significance alone. Discussions of the absolute size of the difference, much less its possible implications for society or policy, are rare’ (Nelson, 2012, p. 6). Nelson’s ‘alternative’ approach to assessing the evidence on gender differences in risk preference produced some revealing insights. Only 25 percent of the studies that she reviewed identified a difference favoring lower male risk aversion of more than half a standard deviation. Only two studies found a difference of more than one standard deviation of difference. Four studies identified differences that are statistically significant in the direction of greater female risk-taking. Thus, in Nelson’s assessment, an appropriate summary of the results of studies of sex differences in risk preference is that they point to ‘a statistically significant difference in mean risk aversion between men and women, with women on average being more risk averse’ (Nelson 2012, p. 2). This stands in important contrast to Croson’s and Gneezy’s (2009, p. 448) claim that ‘women are indeed more risk averse than men’. The latter statement implies that risk preference is a stable characteristic of people defined by their sex, and that a lower risk preference is universally true for every individual member of the class ‘women’ (as compared with ‘men’). As Nelson notes (2012, p. 3), ‘this exceedingly strong implication is not likely intended by those who write such statements’, noting that ‘just one example of a cautious man and a bold woman disproves it’. However, she goes on to draw our attention to how the more probable meaning of the statement (that women are, or are disposed to be more risk averse by virtue of being a woman) is also problematic. ‘In the current example, the statement would imply that greater risk aversion is an essential characteristic of womanliness – or, by parallel reasoning, that greater risk seeking is an essential characteristic of manliness’ (Nelson 2012, pp. 3–4). Reflecting themes in feminist economics introduced earlier, Nelson rejects the notion that risk aversion is a sex-linked ‘trait’ and, instead, locates the source of observed sexbased differences in risk preferences in patterns of gendered socialization and power. As such, observed sex-differences should not be the end-point of inquiries into risk preferences but, rather, the stimulus for further inquiry into the gendered norms and other institutions that influence men’s and women’s attitudes to risk. This potentially creates an important role for future studies of the issue in different cultural contexts. It is important to note that Nelson’s critique of the gender and risk literature also relates to another theme of feminist economics that was highlighted in earlier sections of this chapter, namely, its critical perspective on possible biases within the economics discipline. Nelson highlights the influence of mental models on the work of researchers; of how the inferences we derive from empirical data are likely to reflect ‘the structure of

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Feminist economics for smart behavioral economics 181 our inside worlds – that is, of evolved, developmental human cognition’ (Nelson 2012, p. 5). She notes that the models that we use are significantly influenced by our experiences of and beliefs about men and women and, thus, perhaps, it is not surprising that many studies ‘leap’ from evidence of a statistically significant difference in average levels of risk aversion to conclusions about men’s and women’s natures. That is, researchers are (as are others) prone to ‘confirmation bias’, whereby we tend to more readily absorb information that conforms to our pre-existing beliefs, including our beliefs about the ‘nature’ of men and women. This can be an important (and potentially dangerous) source of distortion in our work. For example, as Nelson points out, if we report a statistical significance in risk aversion that is not substantially significant we can reinforce common stereotypes about men’s and women’s ‘natures’. To the extent that this diverts attention from cultural and other institutional sources of differences in behavior, it can be an obstacle to the design of appropriate policy measures aimed at improved gender equity. That is, there is a risk that research into sex-based differences may contribute to the perpetuation of gender inequality, rather than help to reduce it.

6

FEMINIST ECONOMICS AND STUDIES OF ALTRUISM

Similar themes are apparent in feminist economic analyses of altruism. A number of studies of differences in altruism between men and women have been undertaken, motivated by a sense that they could lead to different patterns of charitable giving, bargaining, and household decision-making. As such, gender differences in altruism are potentially consequential for outcomes across a number of different market and family situations. In their 2008 paper ‘Altruism in individual and joint-giving decisions: what’s gender got to do with it?’, Linda Kamas, Anne Preston and Sandy Baum reviewed the experimental evidence on sex-based differences in altruism, and contributed the findings of their own study of the issue. The first part of this section draws heavily on their summary of the relevant literature. Broader feminist economic perspectives on the topic of altruism are considered in the latter part of this section. Here the focus of the discussion turns away from the question of whether women are more or less altruistic than men and toward the general importance attached to altruistic (and other other-regarding) preferences by feminist economists. As Kamas et al. (2008) describe, experimental studies of altruism typically assess participants’ willingness to sacrifice their own outcomes to improve the well-being of another either by using a dictator, ultimatum, public good or investment or trust game. The authors favor a dictator ‘game’, where the dictator decides how to allocate a sum of money between himself or herself and another player, on the grounds that it has the greatest ability to separate the effects of altruistic preferences on behavior from the effect of risk and competition. In their dictator games, the recipient of the money is a charity. Kamas et al. (2008) report findings from their own experiments that indicate significant gender differences in altruistic behavior, with women giving significantly more, on average, than men. Their finding was generally suggestive of a pattern of difference similar to that observed by James Andreoni and Lise Vesterland (2001, p. 293). However, several studies included in their review, such as those by Martin Dufwenberg and Astri Muren (2006), reported higher levels of generosity by men. Kamas et al. (2008, p. 25) conclude that in

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the experimental literature there is no consensus on the substantive significance of gender differences in altruistic behavior. The explanation offered for the mixed results on altruistic preferences by Kamas et al. (2008, p. 25) centers on the differences in the experimental settings of the various studies. These differences relate to both the type of game used as well as ‘the experimental design or context . . . the framing of the experiment, the degree of anonymity, the subject population, and/or the manner in which the participants are chosen’ (Kamas et al. 2008, p. 25). Whether men or women are identified as the ‘more generous sex’ apparently varies with the price of giving, the degree of anonymity, and the possibility of reciprocity (see also Cox and Deck 2006). Several studies conclude that the gender of the recipient of an altruistic act also affects gift-giving. The study conducted by Kamas et al. (2008) found that gift-giving increased in mixed-sex team situations, and especially when the participants were able to negotiate a common gift. Kamas et al. (2008: 44) acknowledge (albeit in a footnote) that they do not provide an in-depth explanation of the causes of observed sex-based differences in altruism. However, they do allude to a number of influences stemming from the social environment, and these are potentially reflective of the processes of gendered socialization that were noted in earlier sections of this chapter. For example, their explanation for the observation of higher levels of gift-giving by mixed-sex teams includes a role for social information (about the social norm for gift-giving) and social image (a desire to be considered favorably by others) (Kamas et al. 2008, p. 27). As a reviewer of their paper apparently observed, it may also be possible that women are socialized to be more giving than men, and women’s identification as mothers or caregivers may lead to altruistic acts (Kamas et al. 2008, p. 45). The authors also acknowledge the possibility that as experiments of this type are conducted beyond the confines of the current set of developed western countries, the impacts of cultural and sociological forces on gender differences in altruism will become more apparent. The interpretation of experimental evidence offered by Kamas et al. contrasts that provided by Andreoni and Vesterland (2001). The latter appear to succumb to the various pitfalls involved in assessing sex-based differences in behavior that were noted by Julie Nelson. They infer from their experimental evidence (of a statistically significant gender difference in the observed levels of gift giving across 142 students in eight experimental settings) that ‘when altruism is expensive, women are kinder, but when it is cheap, men are more altruistic’ (Andreoni and Vesterland 2001, p. 293). The focus of their results is on the statistical significance of observed differences in means, rather than on the magnitude of these differences or the distribution of results. This is problematic when the observed gender gap is relatively small. Generally, the Andreoni and Vesterland study ‘essentializes’ the nature of men and women, reinforces common stereotypes, and fails to acknowledge the preferences of men and women who do not conform to group averages. It provides no insights into the possible sources of observed gender differences in altruism, and its discussion of policy implications is limited to a consideration of the consequences of gender differences in charitable gift giving and restaurant tipping. A more important feminist economic discussion of altruism shifts the focus of attention away from possible sex-based differences and toward the general importance of altruistic preferences (for men and women). This discussion forms part of a broader critique of the theoretical structure of mainstream economics by feminist economics. The critique

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Feminist economics for smart behavioral economics 183 argues against a narrow specification of the sources of individual motivation and argues instead for specifications that take account of various sources of motivation, including altruistic preferences, and the social influence on these motives. The critique of mainstream economic theory, developed by Paula England (see, for example, England 2003) focuses, first, on its assumption that individuals in market situations are atomized, self-interested, and have preferences no one can change. This is contrasted against the assumption that individuals in family situations are connected to each other, with interdependent preferences and engaged in a process of shaping the preferences and values of the young. As England explains, while the theory’s analysis of market situations features a ‘separative’ view of the self that presumes, amongst other things, that individuals lack sufficient emotional connection to others to feel any empathy – or to be altruistic, a ‘soluble’ self is assumed in its analysis of family situations, allowing both empathy and altruism to influence behavior and outcomes. England (2003, pp. 36–40) highlights the various problems with this theoretical structure. These include problems caused by incorrectly assuming pure self-interest in market situations, and by over-emphasizing the extent of empathy and altruism in family situations. Additional problems arise from the separative or soluble dichotomy and its relationship to gender dichotomies in western thought. England notes that in simple (sexist) formulations of western thought, men are seen as naturally separative, autonomous and individuated, while women are seen as naturally soluble, connected and yielding. Separation has been valorized in western thought, at least for men, while connectedness has been devalued. As a consequence, writing in economics and other fields has ‘failed to recognize that men are not entirely autonomous . . . whilst women’s nurturing work was taken for granted and excluded from . . . theory’ (England 2003, p. 38, original emphasis). These observations link to several of the themes of the feminist critique of mainstream economics noted in earlier sections of this chapter. For example, the valorizing of separation – and market situations – has contributed to a failure to adequately recognize the experiences and contributions of women. The gendered nature of the separative–soluble dichotomy helps to explain common confusion of gender with sex; of the tendency to identify ‘essential’ differences between men and women. Feminist economic analysis suggests that it is appropriate to assume that both male and female participants in market and family situations will have both ‘separative’ and ‘connective’ qualities; and that these qualities will have both positive and negative aspects. Core concepts, therefore, are of ‘individuals-in-relation’ or ‘relational autonomy’ (England 2003, p. 39). These concepts have obvious relevance for the feminist economic analysis of altruism (and for the analysis of the related concepts of cooperation and strong reciprocity). Altruism is potentially relevant to the preferences and behaviors of men and women. It should be considered as a source of motivation in market and other situations. There is a need for more theoretical and empirical studies of men’s and women’s altruistic (and other other-regarding) preferences. We need additional insights to how these preferences interact with other preferences, including self-regarding preferences; how they are shaped; and how they are influenced by different aspects of the contextual environment, such as general levels of altruism in the surrounding community. Taking this approach, studies of gender difference in altruism would ideally focus on women’s traditional association with the family sphere; how, therefore, they have been traditionally assumed or expected to be

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altruistic; and the consequences of these assumptions or expectations for their observed behavior and their economic outcomes. This approach features in Nancy Folbre’s (1995) analysis of caring labor, a topic that calls into question the role and impact of altruistic preferences and which also has relevance for a range of important policy issues, including the future quality and cost of child and elder care, and pay equity. Folbre highlights that caring relies on a range of motivations, including reciprocity, altruism and responsibility. She also emphasizes that these motivations are constructed in a social environment. Folbre recognizes that caring labor is associated with tasks that women often specialize in, such as mothering. However, she also emphasizes that caring labor can (and is) undertaken by both men and women, and that it occurs in both family and market situations. Folbre is particularly concerned with the interactions between the different sources of motivation for caring labor. She acknowledges the role of altruism but notes that it interacts with long-run reciprocity and the fulfillment of obligation or responsibility. As such, she describes carers as being both ‘connected’ (through their altruistic preferences) and ‘separate’ (in their concern for their individual payoffs). In Folbre’s analysis, individuals may provide care out of a sense of affection or responsibility for others, but their motivation to care is likely to also be influenced by long-run expectations of reciprocity of either tangible or emotional services. Care motives are also described as being dependent, in part at least, on the level of altruism and reciprocity within the surrounding community. In turn, social norms are ascribed a potential role in helping prevent a coordination (or caring) failure. Folbre accounts for gender differences in caring labor in a variety ways. First, social norms, as well as notions of obligation, are gendered. As such, they result in a different structure of payoffs for men and women involved in caring and other roles. The historical context is also important, with women’s traditional roles in caring for others potentially affecting the nature and extent of their altruistic ‘preferences’ and, thus, their evaluation of caring and other roles. The outcomes from caring situations, described by Folbre, are often not positive for women. Caring labor is typically low paid and aspects of the work – including the responsibility, skill and effort involved are not generally reflected in wage and other outcomes. Given that at least part of the motivation for caring labor is self-interested, the low wages place at risk the ongoing supply of care. An appropriate policy response to this dilemma would be to improve the ‘rate of return’ from caring labor, regulating wage outcomes to ensure that low wages do not crowd out care motives. The contrast between Folbre’s analysis of caring labor and that offered by mainstream economists is stark. The latter tend to rely on the notion of non-pecuniary preferences, which are typically ‘lumped together’ and modeled as exogenously (and, presumably, biologically) determined. The independent determination of motivation in these models results in a prediction that if an individual gains positive utility from caring he or she will be willing to trade-off lower wages to ‘indulge’ this preference. Wage regulation is rejected on the assumption of the absence of social or other barriers to mobility. Indeed, in some analyses, higher wages for carers are viewed as a threat to caring labor – based on a belief that higher wages would encourage the participation of individuals with less altruistic preferences (Heyes 2005).

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7

CONCLUSIONS

This chapter has attempted to convey key themes in feminist economics of relevance to smart behavioral economists. It takes a novel approach to this task by structuring the discussion using concepts and terms drawn from the institutional analysis and design framework, developed by Elinor Ostrom. The framework was used to identify the distinctive features of feminist economics, including, perhaps most importantly, the emphasis it places on understanding the social influences on individual preferences, actions and outcomes. The ‘situated’ nature of economic behavior is a fundamental concept in feminist economics. Feminist economics pays particular attention to how individuals’ economic power, obligations, goals, interests and, ultimately, their economic outcomes are affected by their social roles and relationships, and how these, in turn, are affected by their ascribed social identities, including their gender. Gender is distinguished from sex, or an individual’s biological identity of being male or female. It refers to socially learned expectations and behaviors associated with being male or female. The chapter has highlighted the various ways in which the concept of a ‘situated actor’ influences feminist economists’ engagement with topics in smart behavioral economics. It has demonstrated that feminist economists tend to take a cautious approach to the analysis of observed differences in behavior between men and women. While feminist economists do not deny that these differences exist, they emphasize the need to explore their sources in the social environment, and they sound a strong warning about the dangers of drawing inferences about the essential ‘natures’ of men and women from these differences. The concept of a situated actor is also apparent in feminist economists’ perspectives on the theories and methods used in the analysis of economic behavior. The chapter highlighted the feminist perspective that academic inquiry itself is a fundamentally social process. As such, participants in academic work situations are subject to biases that arise from their own (essentially limited) set of experiences, including their experiences of and beliefs about men and women. This can be an important source of error in academic work, potentially contributing to a reinforcement of stereotypes about men and women, rather than promoting greater gender equity. An important concern of feminist economists is to minimize these sources of error by training economists and promoting the adoption and enforcement of methodological principles designed to check the influence of gender bias. The chapter also emphasized the feminist economics’ critique of the separate–soluble dichotomy in mainstream economics. The mainstream assumption is that individuals in market situations are ‘separate’, that is, essentially atomized, self-interested, with preferences no one can change. In contrast, this theory assumes that individuals in family situations are ‘soluble’, that is, connected to each other, with interdependent preferences, and engaged in a process of shaping the preferences and values of the young. The critique observes that the separate–soluble dichotomy that characterizes mainstream economics has a strong gender dimension, with market situations commonly associated with the activities of men, while family situations are commonly linked to the activities of women. This has various negative impacts, including the tendency for the lives and experiences of many women to be excluded from economic analysis, and the ‘essentializing’ of men’s and women’s natures (‘men are self-interested and autonomous, while women are caring and dependent’). The approach has limited the analysis of the range of motivations

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(self-interested and other-regarding) affecting the behaviors of men and women in market and family situations. Feminist economics offers an alternative concept to guide future empirical and theoretical studies of behavior: that of ‘individuals-in-relation’. It conveys that men and women, in market and non-market situations, are likely to be influenced by self-interested and other-regarding preferences. The recommendation is for smart behavioral economics (smart decision-making) to continue to pursue studies of how different sources of motivation interact with each other; how preferences are shaped; and how they are influenced by different aspects of the contextual environment, such as general levels of altruism or reciprocity in the surrounding community. Further studies of gender differences in behavior are needed, but they should focus on how, for example, prevailing gender norms affect the positions men and women can participate in, the payoffs and value attached their alternative actions, and, ultimately, their economic outcomes.

NOTE 1. Increasingly, the analysis acknowledges multiple genders. This takes into account individuals whose gender identity differs from their biological sex.

REFERENCES Anderson, E. (2009), ‘Feminist epistemology and philosophy of science’, in E.N. Zalta (2012) (ed.), The Stanford Encyclopedia of Philosophy, Fall, accessed 17 December 2016 at https://plato.stanford.edu/archives/spr2009/ entries/feminism-epistemology/. Andreoni, J. and L. Vesterland (2001), ‘Which is the fair sex? Gender differences in altruism’, Quarterly Journal of Economics, 116 (1), 293–312. Austen, S. and T. Jefferson (2014), ‘Economic analysis, ideology and the public sphere: insights from Australia’s equal remuneration hearings’, Cambridge Journal of Economics, October, doi:10.1093/cje/beu042. Barker, D.K. (1999), ‘Feminist philosophy of science’, in P.A. O’Hara (ed.), Encyclopedia of Political Economy, London: Routledge, pp. 325–7. Booth, A., L. Cardona-Sosa and P. Nolan (2014), ‘Gender differences in risk aversion: do single-sex environments affect their development?’, Journal of Economic Behavior and Organization, 99 (March), 126–54. Cox, J. and C. Deck (2006), ‘When are women more generous than men?’, Economic Inquiry, 44 (4), 587–98. Croson, R. and U. Gneezy (2009), ‘Gender differences in preferences’, Journal of Economic Literature, 47 (2), 448–74. Dufwenberg, M. and A. Muren (2006), ‘Generosity, anonymity, gender’, Journal of Economic Behavior and Organization, 61 (1), 42–9. Eckel, C. and P. Grossman (2008), ‘Men, women and risk aversion: experimental evidence’, in C. Plott and V. Smith (eds), Handbook of Experimental Economics Results, vol. 1, New York: Elsevier, accessed 14 January 2015 at http://papers.ssrn.com/sol3/papers.cfm?abstract_id51883693. England, P. (2003), ‘Separative and soluble selves: dichotomous thinking in economics’, in M. Ferber and J, Nelson (eds), Feminist Economics Today: Beyond Economic Man, Chicago, IL: University of Chicago Press, pp. 33–60. England, P. and N. Folbre (2003), ‘Contracting for care’, in M.A. Ferber and J.A. Nelson (eds), Feminist Economics Today: Beyond Economic Man, Chicago, IL: University of Chicago Press, pp. 61–79. England, P., M. Budig and N. Folbre (2002), ‘Wages of virtue: the relative pay of care work’, Social Problems, 49 (4), 455–73. Ferber, M. and J. Nelson (2003), ‘Introduction: Beyond Economic Man, Ten Years Later’, in M. Ferber and J.  Nelson (eds), Feminist Economics Today: Beyond Economic Man, Chicago: The University of Chicago Press, pp. 1–33. Folbre, N. (1994), Who Pays for the Kids? Gender and the Structures of Constraint, Routledge: London.

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Feminist economics for smart behavioral economics 187 Folbre, N. (1995), ‘Holding hands at midnight: who pays for caring labour?’, Feminist Economics, 1 (1), 73–92. Harding, S. (1999), ‘The case for strategic realism: a response to Tony Lawson’, Feminist Economics, 5 (2), 127–33. Heyes, A. (2005), ‘The economics of vocation or “why is a badly paid nurse a good nurse?”’, Journal of Health Economics, 24 (3), 561–9. Himmelweit, S. (1995), ‘The discovery of unpaid work: the social consequences of the expansion of “work”’, Feminist Economics, 1 (2), 1–19. Ironmonger, D. (1996), ‘Counting outputs, capital inputs and caring labor: estimating gross household product’, Feminist Economics, 2 (3), 37–64. Kahneman, D. (2003), ‘A perspective on judgment and choice: mapping bounded rationality’, American Psychologist, 58 (9), 697–720. Kamas, L., A. Preston and S. Baum (2008), ‘Altruism in individual and joint-giving decisions: what’s gender got to do with it?’, Feminist Economics, 14 (3), 23–50. Nelson, J. (1992), ‘Gender, metaphor and the definition of economics’, Economics and Philosophy, 8 (1) 103–25. Nelson, J. (1996), Feminism, Objectivity, and Economics, New York: Routledge. Nelson, J. (2003a), ‘Confronting the science/value split: notes on feminist economics, institutionalism, pragmatism and process thought’, Cambridge Journal of Economics, 27 (1), 49–64 Nelson, J. (2003b), ‘Once more with feeling: feminist economics and the ontological question’, Feminist Economics, 9 (1), 109–18 Nelson, J. (2003c), ‘Separative and soluble firms: androcentric bias and business ethics’, in M. Ferber and J. Nelson (eds), Feminist Economics Today: Beyond Economic Man, Chicago, IL: University of Chicago Press, pp. 81–100. Nelson, J. (2012), ‘Are women really more risk-averse than men?’, Global and Development Institute Working Paper No. 12-05, Tufts University, Medford, MA. Ostrom, E. (2005), Understanding Institutional Diversity, Princeton, NJ: Princeton University Press. Pujol, M. (1997), ‘Broadening economic data and methods’, Feminist Economics, 3 (2), 119–20. Reskin, B. (2003), ‘Rethinking employment discrimination and its remedies’, in M. Guillen, R. Collins, P. England and M. Meyer (eds), The New Economic Sociology, New York: Russell Sage, pp. 218–44. Strassman, D. (1997), ‘Expanding the methodological boundaries of economics’, Feminist Economics, 3 (2), vii–ix.

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11 How regret moves individual and collective choices towards rationality Sacha Bourgeois-Gironde

1

IDENTIFYING REGRET

At first sight, regret, which is semantically very ambiguous and confined to psychological notions such as disappointment, remorse, or even repentance, is not the ‘smartest’ state of mind we can entertain in the course of our life. It is the feeling that we have been less than optimal in particular situations or longer stretches of time, where we could have acted on better lines, and we know it or realize it now, while in the throes of that negative emotion. It is ironical that the author most associated with a vindication of the virtuous role of emotions in decision-making in contemporary popular neurobiology, namely, Spinoza as interpreted by Damasio (Damasio 2004), emphasizes the irrationality of regret: ‘Repentance is not a virtue, that is, it does not arise from reason; instead, he who repents what he has done is twice wretched, that is, lacking in power’ (Spinoza 1677 [1996], p. 4). The individual not only has been practically suboptimal but a negative feeling accrues and prolongs her helplessness. Regret, thus viewed, does not represent the emotional side of a retrospective and corrective, cognitively driven process. To refute that view and make apparent how regret can help optimize decision-making processes rather than reinforce past failures, we need, first, to decompose between backward-looking and forwardlooking aspects of regret and, second, to better understand the articulation between its emotional and cognitive components. Taken altogether, these distinctions will permit us to define regret as a biologically anchored learning mechanism liable to direct decisions along an optimal path. In the absence of this psychological device, which apparently consists of dumbly lamenting over spilled milk, our decisions would really be dumber, as it is likely that we would not become aware, or at least sensitive, to our past mistakes. As for the emotional part, we are, by means of emotions such as regret, sensitized to suboptimality. However, the forward-looking and cognitive component is what makes regret a smart mechanism, and ourselves smarter by the same token, to the extent that the aversive aspect of felt pangs of regret drives up regret-avoidance behavior over the repetition of similar decision situations and, through the conscious anticipation of the emotional impact of comparatively bad or good consequences of a choice over several available options, it generalizes to a whole range of repeated or even one-off decisions. This pervasiveness of regret and its inherent cognitive nature triggered attempts at incorporating it in formal decision-theoretical frameworks. Regret, with its conceptual correlate disappointment, is one of the rare emotions which have been singularly identified and incorporated in formal decision-theory. Elster (1998) extends a long argument about how economists fail to account for specific emotional mechanisms. Most of the discussion about the relationship between emotions and rationality has been couched in general terms, falling short of phenomenological 188

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How regret moves individual and collective choices towards rationality 189 characterization and a specific analysis of emotional mechanisms potentially associated with behavioral optimization in choice contexts. This amounts to deliberate ignoring or downplaying of game-theoretical models of guilt or envy and regret-based decisiontheory that were already developed in the 1980s and 1990s. However, this also triggers attention and effort towards the possibility to more finely integrate the psychological description of emotional mechanisms – including phenomenological, behavioral and neurobiological levels – and decision-theoretical normative issues. At the same time, this reveals the tension between descriptive and normative relative imports in accounting for human rationality. One obvious way to alleviate this tension is to uncover some normative aspects of emotions. What has distinguished regret among other emotions is that it definitely bears a cognitive component. It is an articulate, sensitive state of mind. Regret consists in feeling negatively the comparison between two states of affairs. This involves a series of subperformances, alternatively pointing to cognitive processes and hedonic states and, on the whole, their integration into a unified affective state. Imagine you have forgone an optimal option and now find yourself in a position to compare what you have got with what you could have got; this implies your ability to: ● ● ●

engage in counterfactual thinking; ascribe a value to a present state of affairs and to a counterfactual one; and compare actual and counterfactual values.

This becomes even more intricate when we envision anticipated regret. Anticipated regret is the heuristic we need to incorporate into smart regret-based behavioral decision-theoretical models, as, on its basis, individuals will tend to avoid negative future consequences. From a cognitive standpoint, the smartness then required amounts to the following: ● ● ● ●

representation of possible future states of affairs; hedonic simulation of the future (also known as mental time travel); ascription of value to future alternative states under different courses of actions taken; and comparison of utilities derived from possible alternative states.

The integration of these cognitive sub-processes functionally yields a future utilityweight in present decisions. Specific cerebral mechanisms underpinning each of these processes and their functional integration have been described (Gilbert and Wilson 2007; Boyer 2008; Schacter et al. 2008). It is worth noting from the outset that the way regret has entered decision-theory in the past three decades, as a rival to a standard model of expected utility theory, relies on a much more stylized version than that which cognitive and affective neuroscientists probe into our brains. However, we think there is enough overlap between the working definitions of regret respectively adopted in psychology and in economics (especially in experimental decision-theory) to allow a fruitful hybridization of the approaches and uncover regret as a smart mechanism both from a psychologically realistic and a formally tenable perspective. The main distinctions to be drawn in both contexts are in terms of available information, responsibility, and valence (in that order),

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Table 11.1

Varieties of decision-theoretical emotions

Information Responsibility Valence

+

−

Regret Regret or remorse Rejoicing

Disappointment Deploring Regret

rather than in terms of mental abilities to project ourselves into alternatives and experience feelings about them (Zeelenberg 1999). Under that most basic view, to experience regret, individual X needs to know that her action A has led to consequence C and to further know that, had she taken another action, A’, the consequence would have been C’. Now, the difference, in terms of utility, u(C) – u(C’), if negative, is what regret amounts to. If we weaken or modify an element of this sequence, a typology of alternative ‘decisiontheoretical emotions’ derived from this definition of regret is generated (see Table 11.1). More forms of regret and related emotions can be encompassed if we consider the timing of the emotions with respect to the unfolding of the action; in particular regret can be post hoc, ex ante or online (we sometimes regret what we are doing while doing it). What has interested decision-theorists is the learning process associated with regret which ensures the transition from post hoc regret to anticipated regret and bias decisions to a predicted error minimization procedure (also known as minimax-regret). Young (2004) excellently accounts for this transition from experienced regret to anticipated regret. That transition goes from a backward-looking emotional state to a cognitive anticipation of future consequences and is at the core of what makes regret a potential smart mechanism in decision-making. In a past-payoff decision-model based on the elimination of regret, a single agent who faces a t-times repeated decision problem will try to eliminate or minimize regret over the period [1 − t]. The agent makes her choice over a set of actions A and obtains a payoff following each action taken. We do not have to consider the time that elapses between the action and its feedback, even though of course in a richer regret-based model this parameter is likely to play a role. The feedback of the action is not in itself the parameter to elicit regret: it has to be contrasted with the expected outcome or, differently, with the information about what she could have obtained had she taken another course of action, say, a. Let us suppose that second comparison is made possible at the end of period [1 − t]. As of period t, then, the agent’s regret for not having chosen action A is defined as the difference between two terms: the average payoff she would have obtained had she chosen a in periods 1 through to t, and the average payoff she actually obtained during those periods. Young defines an optimal regret-based strategy for this repeated decision problem: it satisfies a no-regret criterion if it ensures that for any sequence of action feedbacks, the agent’s regret for each of his actions becomes non-positive as t approaches infinity, all other things being equal. Some notable contributions made this stylized typology of regret and related emotions fit the investigation of underlying neurobiological mechanisms. We discuss them in the next section. By borrowing experimental paradigms from experimental decision-theory, neuroscientists indeed uncover specific mechanisms associated with the way repeated emotions are optimally biased by regret aversion. One of our concerns is to assess to

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How regret moves individual and collective choices towards rationality 191 what extent these uncovered neurobiological mechanisms correspond to the more stylized notion of regret that has been integrated in some decision-theoretical frameworks in the view of formalizing how anticipated regret-based learning happens to be an optimal decisional heuristic.

2

THE NEUROBIOLOGICAL BASIS OF REGRET

Damasio (2008) has largely documented the effect of some bilateral lesion of the ventral medial prefrontal cortex on regret aversion driven decision-making. He and his colleagues have studied several cohorts of patients who, post-lesion, display deep difficulties in planning their life, establishing friendly associations, finding business partners, avoiding financial losses, keeping a stable occupation, refraining from certain impulsions and profanities. These behavioral patterns are in neat contrast with these subjects’ profiles before the lesion. However, the lesion has left unscathed the cognitive abilities: normal intelligence, linguistic understanding, and memory, normal visual, hearing and tactile acuity (Bechara et al. 2000). What essentially differs before and after the brain accident is the decision-making process which has become long and intricate, with all sorts of alternatives entertained and pondered, leading to choices which are found difficult to make and most often disadvantageous. When such choices are repeated, no learning apparently occurs, no lesson of bad past experiences is taken and consistency in suboptimal behavior is observed. This main difference affecting decision-making has been probed on a specific task, called the Iowa gambling task (IGT). Behavioral responses on this task were associated with a measure of variation of the physiological skin conductance response (SCR) and cardiac pace, considered as the main somatic markers of emotional states.1 In the IGT, participants face four decks of cards from which they can freely pick cards one after another. They know nothing about the relative value of the decks except that they are not equivalent. So, they must learn about this difference, which amounts clearly to a learning task. Unbeknown to them, the game will stop after 100 cards have been turned up. The decks differ in the variance between gains and long-term profit they present. Decks C and D present a small spread of value (small gains and small losses) to an extent that makes them profitable if the participant sticks to them. Decks A and B embed a few attractive large gains but also large losses such that sticking to them surely makes the participant lose his or her initial endowment. Control subjects, after having explored for a short while Decks A and B, turn and keep on playing on Decks C and D. Ventral medial patients stay on Decks A and B and ask for credit when their endowment happens to be exhausted in the middle of the game. Besides this behavioral particularity, patients exhibit a special physiological pattern compared with controls: they fail to exemplify the somatic markers that induce healthy subjects to avoid negative consequence choices in favor of optimal decision-making. This neurobiological grounding of corrective anticipatory behavior seems a key element of more explicit and cognitively driven regret-based learning processes. Ventral medial patients seem unable to relate together current actions, expectations and past failures. Only the internal building of this temporal binding between past, present and future is likely to sustain the process of regret-based learning guiding repeated decisions towards optimal payoffs. They also seem to be unable to evoke emotions and to make

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them efficient mechanisms in action. Indeed, they are not deprived of all emotions and can abstractly judge that a situation (or an image or a fantasy) with a horrid component should trigger in them some felt negative emotion, but this emotion does not occur. They present the converse dissociation that the neuropsychologist Brenda Milner (1962) had observed with dorsal-lateral and hippocampal patients, who do the right thing but cannot verbalize it. The generation of this signal and its incorporation into executive function is an efficient mediation in decision-making. This signal amounts to an internal bias unconsciously diverting decisions from bad outcomes before it reaches the consciousness threshold, although this point has been contradicted by several authors (see, in particular, Persaud et al. 2007). Whether or not this signal is efficient before or after it reaches the individual’s consciousness, it is instrumental in defining anticipated regret as a mechanism whose cognitive and behavioral role is not independent of an embodied emotional alert system. The underlying cause of the decisional deficiency of ventral-medial patients is therefore, according to Damasio, a failed activation of covert emotional signals which are supposed to bias decision in a favorable direction in the long run. Damasio has, through his implementation of the IGT and the implicit role of SCR, made popular the view that the conscious explicit knowledge of preferences and choice-criteria is not enough to generate optimal decision behavior. Decision processes in the brain are distinct from other cognitive abilities governed by frontal lobes (for example, work-memory and responses inhibition) in the sense that they directly plunge in visceral pre-executive mechanisms associated with emotional autonomous arousal. More has to be said on how a post hoc experience of disappointment can change smoothly into an ex ante regret-aversive optimal behavior. We find some possible explanation when considering Damasio’s distinction between two types of internal signals (Damasio 2008). First, a genuine somatic loop generating ‘primary’ signals is triggered when individuals face choices under uncertainty and ambiguity. Second, a para-somatic loop can be activated by a mental representation of somatic states (consisting in cognitive states in which the subject simulates his being in a primary emotional state). This loop is selected by choices which, through previous repetitions of the similar situation, looms as quasi-certain or inevitable consequences. The transition from one loop to the other is driven by regret learning and, as we see, amounts to a deep change in the perception of the decision-theoretical context, from uncertainty to quasi-certainty. The study of this transition has given rise to several studies of what brain mechanisms help us adapt to different decisional structures. The fact that the choices are under ambiguity, uncertainty or risk is differentially treated by the brain (Hsu et al. 2005); the fact that we tend to systematically violate decision-theoretical axioms that are supposed to prevail in these distinct decisional-structures is also a feature that can be explained by the study of our brain fabric. In that context, regret has been made a paradigmatic case. For the purpose of studying specifically regret-aversion based decision-making, rather than conceptually and phenomenologically generic somatic markers, the IGT has been modified into a regret gambling task and used in several seminal studies (for example, Camille et al. 2004; Coricelli et al. 2005). In that task participants are invited to choose between two ‘wheels of fortune’, one on the left, one on the right. They display colored zones corresponding to possible gains or losses. For instance, in one typical choice, if subjects choose the gamble on the left, they might win €200 with 20 percent probability

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How regret moves individual and collective choices towards rationality 193 or lose €50 with 80 percent probability; if they choose the gamble on the right, they might win or lose €50 with equal probabilities. The regret gambling task differs from the IGT in that it implements two contextual conditions in terms of feedback provided. Partial feedback shows only the outcome of the chosen gamble; to that extent it amounts to IGT and participants can be disappointed given their expectation that the needle stops on a positive zone of the wheel. Complete feedback allows for a comparison between the consequence of participants’ choice and that of the foregone option, eliciting possible regret or relief. Physiological responses (skin conductance responses), choice behavior and brain activity are influenced by these different levels of feedback. Coricelli et al. (2005) have shown that the same brain areas are activated when the brain faces a certain choice situation, before a decision is made, as when it processed the outcomes of similar choices over past repetitions. Precisely, the orbital frontal cortex and the amygdala mediate how past regret history biases subjects towards minimizing regret across similar choice situations in anticipation of its possible consequences. This result is consistent with Damasio’s result, according to which ventral medial structures support the integration between cognitive and emotional components of the entire process of decision-making. Besides the bottom-up mechanisms that make emotions inflect decisions in an optimal sense, the orbital frontal cortex specifically uses a top-down process in which cognitive components, such as counterfactual thinking, modulate emotion. This complex, double-looped, relation between cognition and emotion has been modeled by evolutionary and behavioral responses. However, such a lesson from neurobiology about the adaptive value of regret stresses the tension between the construal of regret in biology and in decision-theory. We proposed a study of the Allais paradox with ventral medial lesions to explore this tension. Allais (1953) showed that people tended to exhibit inconsistent choice patterns when presented with pairs of options that involved a contrast between quasi-certain and risky options (see Table 11.2). The presentation of the Allais paradox in Table 11.2 contrasts a pair of choices among pairs of lotteries. The individual is first invited to choose between Lottery A and Lottery B. He is then confronted with a list of probabilities of obtaining certain payoffs, as in every classical lottery. We can see, for instance, that in Lottery A and Lottery B, he has the same chance (89 percent) of obtaining €500 000. Likewise when confronted with the other choice between Lottery C and Lottery D, he has the same 89 percent chance of winning nothing across the two then concerned lotteries. If the subject rationally applied von Neumann and Morgenstern’s axiom of independence (von Neumann and Morgenstern 1944) which states that a choice between two options should be independent of what is Table 11.2

Matrix of the Allais paradox

Lotteries

Lottery A Lottery B Lottery C Lottery D

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Probability of alternative states of nature S1, S2, S3 S1: 0.01

S2: 0.10

S3: 0.89

€500 000 €0 €500 000 €0

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common (in terms of both payoffs and the probability of their realization, which is clearly the case for the fourth column of the table when we consider alternatively A and B and then C and D) between the options, he should realize that the pairs of lotteries A and C and B and D actually present the same contingencies. This lack of application of such an independence axiom is what makes the subject reverse his choice, without realizing most of the time that he does so, across the two pairs of choices. Most of the time, a same individual will tend to prefer A to B and at the same time, without realizing any reversal in her preferences, will tend to prefer D to C. In order to grasp the implicit violation of the independence axiom presented by this preference reversal, please delete the last column of the table, which presents common consequences to be bracketed, and now realize how A = C and B = D. Regret theory provides a simple explanation of Allais’s paradox. A person who has chosen option B has, if state of nature S1 materializes, strong reasons to regret his or her choice. A subject who has chosen option D would have much weaker reasons to regret his or her choice in the case of S1. When regret is taken into consideration, it seems quite reasonable to prefer A to B and D to C. Several psychological mechanisms have been hypothetically suggested to account for this type of preference reversal, among which the attractiveness of sure gains and the anticipation of regret if those sure gains would have been forgone and yet realized. The bottom line is that an emotional disposition towards possible future outcomes is involved in the Allais paradox. We tested this hypothesis on a population of patients suffering from behavioral variant frontotemporal dementia (bvFTD), a clinical population known to present ventromedial prefrontal cortex dysfunctions and deficits in experiencing emotional deficit in decision-tasks. We contrasted this group to matched controls and patients with Alzheimer’s disease (AD) who had no ventromedial prefrontal atrophy. Our results showed a drastic diminution of Allaisian behavior among bvFTD patients by contrast with controls and AD patients. We concluded that prefrontal regions are crucial in the production of a behavior that typically stands in contradiction with a basic axiom of rational decision. By contrast, impaired emotional mechanisms ironically produce hyperrational (non-Allaisian) behavior in bvFTD patients (see Bertoux et al. 2013). Decision-theory aims to provide an axiomatic – and thereby in principle intuitive – basis upon which we can assess whether actual choices and repeated decision patterns comply with norms of rationality these axioms are supposed to encapsulate. When these patterns deviate from what logically follows from axioms, we would be alternatively tempted to weaken the latter for the sake of psychological realism or discard evidence on behalf of a principled incommensurability between the descriptive and normative levels. Regret-based decision-theory is a unique attempt, in the recent history of decision-theory, to combine these opposed tendencies in a sort of reflexive equilibrium approach that would jointly increase the intuitiveness of the axiomatic basis and the cognitive adequacy of the proposed theory.

3

REGRET IN DECISION-THEORY

The Allais paradox gave rise to alternative decision-theoretic accounts. One explanation for the A-D pattern is that decision-makers anticipate regret if they choose B and find themselves in the state of nature S1. Note that at this juncture two comparisons with the

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How regret moves individual and collective choices towards rationality 195 realized outcome are possible which can give rise to two distinct emotions, two forms of regret if one wishes. Depending on information about counterfactual outcomes or states of nature, the unfortunate decision-maker can express two forms of regret. Most naturally, he can regret his own choice B, since the state of affairs S1 has been the case and he won nothing. Had he chosen choice A he would have won a certain €500 000. However, not regretting his choice B, he could deplore his lack of luck and the fact that neither S2 nor S3 had been the case. To our best knowledge these two possible attitudes and types of regret when a risky decision has been made have not been studied. However, if that choice were repeated, it would be contrary to a rational regret-based decision to avoid B, which presents higher than expected utility, because there would be no grounded reason to think that the ‘universe’ is fatally stuck to the actualization of S1 (if it were, why would we speak of S2 and S3 in the first place and why would we have attributed the probability 0.01 to S1?). Per absurdum, we would systematically be deterred from choosing B, in a repeated sequence of choices, on the basis of anticipated regret if that individual had either developed extreme pessimism or, which amounts to the same, developed a distorted view of probabilities. This remark on the contrast of the relevance of taking into account regret in a single-choice versus repeated-choices situation makes the picture of how regret should enter into decision-theory more complex than at first sight. Regret-theory has been formalized to account for comparisons between actual and counterfactual outcomes within a single state of nature or ‘world’. Regret is relevant in that single world, as expressed through these introductory terms by Suhonen (2007, p. 11, emphasis added): The central idea behind regret theory is that, when making decisions, individuals take into account not only the consequences they might experience as a result of the action chosen, but also how each consequence compares with what they would have experienced under the same state of the world had they chosen differently . . . Then the overall level of satisfaction derived is a combination of the basic utility of the consequence actually experienced, and some decrement or increment of utility due to ‘regret’ or ‘rejoicing’.

In standard expected utility theory a prospect is evaluated according to the utility of each outcome irrespective of what the other possible outcomes can be. This is strictly forward-looking and consequentialist. As soon as an individual looks to past decisions to base his present choice on them or, more complexly, anticipate a future backwardlooking state of mind in which he anticipates he will be regretting the present decision he is liable to make, he immediately ceases to be strictly deciding along consequentialist guidelines. To incorporate such backward-looking attitudes in decision-making leads us to adopt a non-standard expected utility theory. For instance, an individual may wish to avoid uncertainty, or an individual may not be able to evaluate single payoffs per se but only by comparison with other possible outcomes, his mind being fit to reference points and relative status rather than to processing absolute values. Under a narrow Savagean interpretation of the Allais paradox (Savage 1954 [1972], consequences are identified with monetary payoffs. Under this restriction, expected utility theory is violated by most individuals (including Savage, according to the legend). Under a broader interpretation of what consequences are, or, equivalently, of what a decision-process amounts to, such as that provided by regret-theory, no violation exists. This way out may appear less than satisfactory, though once we relax axiomatic constraints every type of decisional pattern

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can be interpreted as satisfying some revised state of the axiomatics, emptying the theory of all normative and descriptive content. As Tversky (1975) underlined, because we wish to maximize the predictive power of the theory, we are tempted to adopt a restricted interpretation of utility, such as the identification of outcomes with monetary payoffs. In this respect, we can consider regret-theory (Bell 1982; Loomes and Sugden 1982, 1987a) as an optimal trade-off between axiomatic revision and predictive power, a maximally conservative attempt at deviating from standard expected utility with a view to intuitively account for robust behavioral data. Loomes and Sugden (1987b) have built a model that generates testable prediction and compares with alternative theories, standard or not. Regret-theory consists of the intertwining of two factors in a single utility function that thereby incorporates two measures of satisfaction: utility of outcomes, as usual, and a quantitative measure of regret. The mixing of the two – which supposes their commensurability and, therefore, a conceptual assimilation of regret to a negative payoff – yields a moderately modified concept of utility. In Bell’s terms (1982, p. 963) regret is measured as: ‘the difference in value between the assets actually received and the highest level of assets produced by other alternatives’. Tracking this difference across choices is what regret amounts to. This is represented by a two-parameter function u(x, y) where x is the actually received payoff and y the difference just referred to. x and y cannot jointly increase and by construction x is maximal when y is null, which means that in this approach, the payoffs of options to be compared always sum to zero and there is always a dominant choice. Interestingly, this functional representation, thus interpreted, presents a potential contradiction with another heuristic supposed to make us smart, namely, Simon’s satisficing principle (Simon 1956, pp. 129, 136). If I reach a level xˉ of payoff above which I do not experience any utility increase, then y can freely increase, in the sense that I could have gotten more than xˉ , but without the difference Y − xˉ any longer generating any regret. The contradiction is swiftly spelled out, at the theoretical level first, if we consider that the use of a minimax-regret heuristic is compatible with the optimal determination of our satisficing threshold: given a certain difference between x and y, we can decide not to take it into account, either because it is too small or because it is too large. In that sense, it is the individual’s sensitivity to comparisons and regret they elicit that endogenously defines their satisfaction threshold. We can indeed assume that people are sensitive in a non-linear way to different payoff intervals between what they get and what they could have got. If the different is negligibly small or unrealistically too large, I can cease to be sensitive to the discrepancy between actual and forgone outcome. It is likely that the individual feels regret when the comparison falls between a certain perceivable and conceivable gap, beyond which it appears a vain feeling. In that sense minimization of anticipated regret and satisficing are not incompatible smart decision-making principles. This is consistent with some recent results about the behaviors of maximizers and satisficers with respect to their feeling regret about their decisions (Moyano-Diaz et al. 2014). As the authors of the study point out, a main difficulty in the study of decision-making is precisely the combination of the two coexisting partially incoherent aspects and dimensions that are maximization and satisfaction. Regret can be seen as playing a subtle mediational role between these two dimensions. Regret is more than an emotional reaction against bad consequences; it is also an internal transformation, as we have seen, of these bad consequences into an anticipatory process. Regret is then inherent to its future avoidance and is compatible with maximizing utility. As Schwartz et al. (2002) found and is reported by

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How regret moves individual and collective choices towards rationality 197 Moyano-Diaz and his colleagues, maximizers tend to engage more often than satisficers in social comparisons and are more affected by them. Regret is for them a driving force towards optimization. Maximizing is not blind forward-looking or full-fledged consequentialism, then, as past errors and all alternatives are scrutinized. However, this type of regret-based maximization decision-making style bears a high psychological opportunity cost and potentially generates a lot of continuing frustration. In contrast, satisficers proceed more easily and are satisfied with their good enough option. In that sense, satisficers can be said to minimize the counterproductive use of first-order regret minimization. We then equate Simon’s satisficing to second-order minimax-regret in what we consider a fuller account of the psychological cost associated with the presence of bad decisions and the correlative first-order regret they tend to provoke. We could thus envisage a Simonbased regret model in which the individual is likely to experience regret when the psychological cost to do so does not exceed the benefits of correcting his or her present decision in view of future benefits. Admissible regret is thereby endogenously defined because future benefits are themselves bound by the individual’s level of satisfaction. When the latter is attained, there is no more valuable motive to admit regret as a reasonable emotion. This Simonian perspective on the link between satisfaction and regret suggests a deeper analysis of how the different ways of incorporating regret into decision-theory also convey different views of human rationality. In summary, Loomes and Sugden’s way is still essentially consequentialist, in the sense that regret is both included in a maximization process and that the functional representation they propose remains fundamentally compatible with a forward-looking attitude. Differently, the short analysis we have provided of the compatibility of satisficing and second-order regret-minimization amounts to a non-consequentialist view, decision-makers deciding now to ignore certain information and consequences of their choice beyond a certain threshold; satisfaction, in the Simonian sense, meaning not only to cease to adopt a utility maximizing attitude but also to cease to make any counterfactual comparison once a certain level of utility is reached. This way of blocking potential feelings of regret emphasizes, by contrast, the role of signals and omens in standard decision-making. We can imagine individuals ready to pay not to receive information about outcomes of forgone decisions. Karlsson and colleagues (2005), in an unpublished study, have documented a very similar phenomenon on financial markets, which they label an ‘ostrich effect’, people deliberately discarding information about their investments portfolios when markets go down. Regret aversion is seemingly an ordinary feature of decision-making related to a human propensity to respectively seek or avoid positive and negative omens and base our decision at this symbolic level rather than strictly focus on the evaluation of our choices’ consequences. We have studied regret in connection with the Newcomb problem in that perspective. This problem tackles deep philosophical issues around the nature of rationality, along the dividing line we have discussed above: can we rationally take into account information about our choices that in fact does not change the way consequences will be realized. Is there a way to vindicate the fact that, in some cases, we are sensitive to consequentially irrelevant information? This dividing line among decision-theorists – only among those of a philosophical bent though – has been labelled in terms of a difference between causal decision-theory (individuals make their choices according to basic stochastic dominance and independence principles) and evidential decision-theory (individuals may legitimately be influenced in their decision-making by symbols or information present in the choice

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situation which, in principle, do not affect the consequences) (Joyce 1999). Nozick (1969) devised that paradox of rationality with the explicit purpose of contrasting consequentialist and symbolic forms of rationality. In this problem, two boxes, one opaque, one transparent, are presented to a decision-maker along with the following message: Imagine a being with great predictive powers. You are confronted with two boxes: B1 and B2. B1 is opaque and B2 is transparent, you can see that it contains €1. B2 contains €1; B1 contains either €10 or nothing. You may choose B1 alone or B1 and B2 together. If the being predicts that you choose both boxes, he does not put anything in B1; if he predicts that you choose B1 only, he puts C10 in B1. 5> What should you choose?

We had hypothesized that if this choice of alternatives, as Nozick thinks, coincides with different types of rationality, they could also elicit different levels of confidence in our choices. If I really believe in God, I might be inclined to accept a certain level of nonconsequentialism in my choices. However, disappointment can be greater in that case than if I had made a purely consequentialist choice (ignoring the omen) followed by a bad outcome. I therefore considered that regret, when I realized what I could have got had I made the other choice, is modulated by the type of rationality implied by our choices (Bourgeois-Gironde 2010). Let us label individuals one-boxers and two-boxers according to their decisions in the Newcomb problem (Nozick 1969). Two-boxers go against the prediction. The decisioncriteria they presumably follow have been characterized, as we did, as consequentialist, but also, in philosophical parlance, as we commented above, as ‘causalist’, by contrast with ‘evidentialist’. Two-boxers thus apparently exhibit a higher autonomy, that is, a higher level of decisiveness, in their choices than do one-boxers, although the latter’s faith in the omniscient predictor can also yield a high level of decisional confidence (think of Pascal’s wager; Pascal 1897). Integrating the decision-criteria predictions, signs and symbolic value may not be altogether irrational (Nozick 1994). It is pervasive enough, as, for example, in convincing ourselves of our good health or of the influence of our vote in national elections by going to vote, by accomplishing acts that amount to generate selfmanipulated positive signals (Quattrone and Tversky 1988). Shafir and Tversky (1992) ran the first empirical investigation of Newcomb problems. They submitted to their subjects a Newcomb problem. Their cover story was that ‘a program developed at MIT was applied during the entire experimental session to analyze the pattern of your preferences, and predict your choice (one or two boxes) with 85% accuracy’ (Shafir and Tversky 1992, p. 461). Although it was obvious that no deus ex machina intervened at the moment of choice, most experimental subjects opted for the single opaque box rather than for the dominant two-boxes strategy. It is as if they believed that by declining to take the money in box B2, they could change the amount of money already deposited in box B1. Adding on their test, we measured whether regret was different when negative outcomes are revealed to one-boxers and two-boxers. We observed – by means of a retrospective measure of satisfaction on a five-point Likert scale – that one-boxers, when facing negative outcomes, experience a significantly greater amount of regret than do two-boxers in the same situation. This is due, we speculate, to the lesser decisiveness or autonomy with which those choices are made, in spite of their greater faithfulness to the prediction. If a difference emerges between types of decision and amount of regret in the Newcomb problem, this can be considered as a step toward a better understanding of

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How regret moves individual and collective choices towards rationality 199 how regret taps into rational antecedents of choices and can be modulated by competing criteria of rationality.

4

REGRET AS A COORDINATION DEVICE AND A SOCIAL MECHANISM

Bob wants to go to the opera with Ann tonight. However, in their last discussion, they split on a doubt about what they would do; Ann definitely seemed to wish to please Bob and to agree to go to the opera, although she had declared her preference for the boxing match in another part of town. When they left, Bob said clumsily but audibly that he would like to please her too. In the confusion they omitted to give each other their mobile numbers. Now Bob goes to the boxing match, and does not find Ann. In another scenario he goes to the opera but learns the next day that Ann had opted to go to the boxing match, having understood that Bob would join her there. One could say that an ex ante post hoc regret-minimizing based decision would have induced Bob to go to the fight, as he would have had the moral comfort to have tried to please Ann retrospectively. However, if Ann follows a similar strategy, they miss each other and fail to go out together to their joint detriment. Imagine this situation is repeated every day, with the same level of conversational confusion and omission of electronic devices coordination, by Bob and Ann. It is as if they both lived in ‘groundhog day’ with the characteristic fact that some external and internal elements (such as their inability to correct their spontaneous lack of coordination) fatally befell them. However, they can change their decision every day. There are many ways for them to get out of their predicament. They still have available external and internal resorts. They can use a binary coordinative device, like tossing a coin, or look at the sky, whether it is sunny or overcast, and silently fit their behavior, after a few learning trials, on this conventional signal. Or they can use regret, which is internal, on the hypothesis that they tend to feel the same, which was in the premise of the argument. The use of the coin, or the sky, or a traffic-light for that matter, is a coordinative device that leads to a correlated equilibrium (Aumann 1974). A correlated equilibrium arises in a situation where mis-coordination is likely, owing to several present Nash equilibria leading to suboptimal effects, and when players resort to a set of received signals or instructions by a neutral referee. A correlated equilibrium, technically, is a probability distribution over the players’ space of strategies realized by the referee (or by nature) and from which no player has a unilateral interest to deviate. Hart and Mas-Collel (2000) have shown in what sense regret is an adaptive heuristic in reaching correlated equilibria. Basing a decision on the one that simply minimizes our regret, in the ‘battle of the sexes’ between Bob and Ann described previously, could apparently lead to persistent mis-coordination. If one pleases the other in the same way, their paths will continue to diverge, but this could be solved by using the following heuristic: ‘Switch next period to a different action with a probability that is proportional to the regret for that action, where regret is defined as the increase in payoff had such a change always been made in the past.’ (Hart 2005, p. 1405). This works because it dynamically removes the players from a single-handed strategy (that would consist, for example, of blindly pleasing the other). Players engage in a learning dynamics that will make sources of regret endogenously

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evolve and, thereby, by individually, but jointly, directing their effort to minimize this regret, happen to reach a correlated equilibrium in an efficient way. Regret owing to mere failure of coordination (not simply because of own fault or misplaced benevolence) will progressively guide the choice of strategies by pondering the probability of which of the strategies is played. It means that both players end up feeling the same type of regret based on their suboptimal payoffs, whatever the personal motives that previously led them to mis-coordinate. Interestingly, jointly minimized regret, although an internal endeavor and learning mechanism, plays the role of a public coordination device, avoiding any need for external communication or objective means of coordination, as is required in other ways of reaching correlated equilibrium. The use of regret represents a smart heuristic in the sense that it allows players to eschew the computation of highly complex objects such as repeated games strategies and beliefs. By means of Hart’s heuristics they can simply trade-off between past payoffs associated with a given course of action and match past frequency of success with the probability with which they will stick to this course of action. More precisely, regret is a ‘smart’ heuristic in the sense that it is cognitively parsimonious (to match past payoffs and future actions probabilities is relatively easy), but also in the sense that it is not dumb or fully blind either, as it requires a certain level of freedom of choice and self-modulation of the weight the individual wants to give to this signal. It then requires a certain degree of rationality. In evolutionary dynamics, by contrast with learning dynamics such as the use of a matching-regret heuristic, agents do not have to exhibit any level of rationality, as their phenotype (observable behavior) is deterministically dictated by their genotype entailing that they have no leeway to modulate their strategies and learning mechanism. They play relatively fixed actions that aggregate into group behavior. What can be called rational or irrational in that context is the collective dynamics of the population to the extent that it leads to optimal steady states. Our next question, then, is to ask to what extent assessments of collective rationality can be informed by regret-based decisional patterns at the individual level? Voting procedures are paramount social mechanisms that display this discrepancy between individual and collective rationality. It can go both ways. The paradox of voting is a typical instance of collective rationality (the possibility of an optimal social choice through aggregation of individual preferences) not being supported by individual rationality (in large groups, it is ‘irrational’, when we think in costs-benefits terms, to pay the cost of going to the voting booth). Arrow’s impossibility theorem (Arrow 1951 [1963]), on the other hand, is a deep illustration of how individual rationally structured preferences (with respect to their transitivity) do not necessarily aggregate into a transitive social preference. A solution of the paradox of voting has been proposed by Ferejohn and Fiorina (1974) in terms of the minimax regret criterion. Voters choose the action that yields a minimal regret in a worst case scenario. This implies a form of strategic voting which can seem contradictory with the fact that, if voters are aware that their vote is unlikely to be pivotal, they could still vote sincerely. The situation is far from being unrealistic. Also, collective regret is likely to arise in that situation. Regret being elicited by the possibility of comparing what one has got from what one could have got depends on the possibility of counterfactual learning at some point of the democratic process. The 2007 elimination of candidate Lionel Jospin in the first round of the 2007 French presidential

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How regret moves individual and collective choices towards rationality 201 election may have aroused such an emotion among the voters for the minor left candidate, Taubira, leading to the far-right candidate Le Pen’s presence in the second round. The use of alternative voting procedures may not only have led to another result, but perhaps also to lesser pangs of regret felt by some of Taubira’s supporters (Baujard et al. 2014). Uninominal majority voting, approval voting, and evaluative voting differ in three comparable respects: (1) they are more or less expressive, in the sense that voters can convey more or less fine information about their preference in selecting their options; (2) it is more or less polarized, in the sense that negative feeling towards a candidate can be strengthened in evaluative voting if negative grades are allowed; (3) it can be more or less easily strategized. The parameters inherent to the voting procedure can be associated with its varying susceptibility to regret. If we have the opportunity to express a fuller and finer choice without jeopardizing the election of a consensual candidate (which is the case by use of approval voting in particular), regret, in case of non-success, will presumably be minimized. Tideman (1985) extended the minimax regret model of voting by borrowing from the Sugden and Loomes’ framework we discussed in section 3. He adds the concept of remorse and elation to the model, which are ‘emotions that arise as a consequence of being responsible for one’s circumstances by one’s own actions’ (Todeman 1985, p. 103). The key, there, is this feeling of responsibility which is a constitutive element of regret: losses and gains are accentuated if we are or feel responsible for the feared result. Guilt drives regret, even in a context where there is no objective influence of the voter on the outcome. It disproportionately distorts it and might explain high levels of voter turnout. Can it be smart in this social and political context? Emotions are an important element of the political game and influence behavior (individual or collective) (Groenendyk 2011). However, what has been less studied is how emotions endogenously depend on certain features of given democratic structures, in terms of choice procedures, aggregative mechanisms and informational filters and feedbacks. Understanding how the mechanisms of social choice generate by themselves emotional states and flux is crucial in view of the democratic regulation of the political game: democratic leaders do not want their institutions to be undermined by extreme political affects nourished by their constituencies. Several levels of analysis are relevant for this still widely open set of issues: (1) mechanisms of choice – essentially how the procedure used for voting and the aggregative mechanism associated with those rules trigger positive or negative emotions; (2) information and feedbacks about the political process and its results (pools and so on), on the basis on which payoffs comparison can be made and an adaptive regret heuristic launched; and (3) timing of choice – frequency of elections, possibility of voicing popular opinion in regular moments and channels – determining fluctuating emotional states in the population of voters. At a more general level, we could wish for a society that maximizes consensus, by minimizing the distance between the social choice and each individual vote (see Kemeny 1959) and that also minimizes regret in terms of minimizing individual loss functions. This is not strictly equivalent, and some dynamic emotional fluctuations in democracy might be due to the interplay between those two minimization functions. More generally, the issue is the analysis of how emotional dynamics in democracy might depend on fine structural choices on aggregative and informational political mechanisms. Again, we point here at a possible discrepancy between social consensus thus defined and individuals’ psychological

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factors such as regret or satisfaction with respect to the effective social choice. Each individual feels more or less satisfied with the current social choice (for example, the result of the last election). Ignoring, for the sake of a simpler presentation, dynamic effects occurring in the course of office terms, these levels of regret or satisfaction can be envisioned, as a first approximation, as products of loss functions (minimax regret) for each individuals. From a computational point of view the aggregation of individual loss functions in the population and the measure of the distance between each individual preference of that population and the social choice are not identical. Incorporating individual levels of regret as a parameter of social satisfaction with respect to some social choice function is then an attempt at combining collective and individual rationality. It also potentially anchors back social choice mechanisms into adaptive heuristics upon which real individuals tend to make decisions, combining emotional and cognitive abilities, and fine-tuning our biological and social fabrics.

5

CONCLUSION

Anticipated regret is one of the most efficient heuristics that we can use in order to avoid suboptimal decision-making. It has been incorporated in decision-theory at an axiomatic level, both in individual decision-making and in interactive strategic situations. Moreover, it has been demonstrated an efficient learning mechanism, leading to optimal decision-making, and coordinative device in multiple equilibria game-theoretical contexts. Regret-theory is nevertheless compatible with bounded rationality paradigms such as Simon’s satisficing principle; the latter involving a form of second-order modulation of the amount of regret that an individual can reasonably experience in order to guide his or her decisions towards a satisfaction threshold. This view seems in agreement with what recent brain-imaging studies have taught us about the neurobiology of regret. The emotion of regret lies at the junction of the processing of aversive states and of the cognitive anticipation of future outcomes of the individual’s actions. For this set of reasons, we consider regret to be one of the best candidates with a view to unifying biological and decision-theoretical approaches to optimally bounded decision-making.

NOTE 1. Somatic markers such as perspiration, hence skin conductance of body parts such as fingertips, or heartbeats, pulsations, and so on form a particular class of measurable bodily states that Damasio and Bechara in a series of influential studies in the 1990s have shown to be correlated with so-called secondary emotions. The latter are feelings that have been associated, through past repeated experiences, to the learning and anticipation of future outcomes in certain typical choice situations. When a somatic marker is associated with a particular negative outcome it functions as an alarm bell and is a reliable signal. It is convenient to understand anticipated regret in the framework of this ‘somatic marker hypothesis’. Interestingly these predictive somatic markers can be effective without fully arising to consciousness, making them an automatic self-corrective mechanism. (For another type of experimental approach on feelings of errors and their predictive and corrective roles see Gangemi et al. 2015.)

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REFERENCES Allais, M. (1953), ‘Le comportement de l’homme rationnel devant le risque: critique des postulats et axiomes de l’école américaine’ (‘The attitude of the rational man to risk: critical assumptions and axioms of the American school’), Econometrica: Journal of the Econometric Society, 21 (4), 503–46. Arrow, K. (1951), Social Choice and Individual Values, 2nd edn 1963, New York: Wiley. Aumann, R.J. (1974), ‘Subjectivity and correlation in randomized strategies’, Journal of Mathematical Economics, 1 (1), 67–96. Baujard, A., H. Igersheim, I. Lebon, F. Gavrel and J.F. Laslier (2014), ‘Who’s favored by evaluative voting? An experiment conducted during the 2012 French Presidential Election’, Electoral Studies, 34 (June), 131–45. Bechara, A., D. Tranel and H. Damasio (2000), ‘Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions’, Brain, 123 (11), 2189–202. Bell, D.E. (1982), ‘Regret in decision making under uncertainty’, Operations Research, 30 (5), 961–81. Bertoux, M., F. Cova, M. Pessiglione, M. Hsu, B. Dubois and S. Bourgeois-Gironde (2014), ‘Behavioral variant frontotemporal dementia patients do not succumb to the Allais paradox’, Frontiers in Neuroscience, 8 (September), 287. Bourgeois-Gironde, S. (2010), ‘Regret and the rationality of choices’, Philosophical Transactions of the Royal Society B: Biological Sciences, 365 (1538), 249–57. Boyer, P. (2008), ‘Evolutionary economics of mental time travel?’, Trends in Cognitive Sciences, 12 (6), 219–24. Camille, N., G. Coricelli, J. Sallet, P. Pradat-Diehl, J.R. Duhamel and A. Sirigu (2004), ‘The involvement of the orbitofrontal cortex in the experience of regret’, Science, 304 (5674), 1167–70. Coricelli, G., H.D. Critchley, M. Joffily, J.P. O’Doherty, A. Sirigu and R.J. Dolan (2005), ‘Regret and its avoidance: a neuroimaging study of choice behavior’, Nature Neuroscience, 8 (9), 1255–62. Damasio, A. (2008), Descartes’ Error: Emotion, Reason and the Human Brain, New York: Random House. Damasio, A.R. (2004), Looking for Spinoza: Joy, Sorrow, and the Feeling Brain, New York: Random House. Elster, J. (1998), ‘Emotions and economic theory’, Journal of Economic Literature, 36 (1), 47–74. Ferejohn, J.A. and M.P. Fiorina (1974), ‘The paradox of not voting: a decision theoretic analysis’, American Political Science Review, 68 (2), 525–36. Gangemi, A., S. Bourgeois-Gironde and F. Mancini (2015), ‘Feelings of error in reasoning – in search of a phenomenon’, Thinking & Reasoning, 21 (4), 383–96. Gilbert, D.T. and T.D. Wilson (2007), ‘Prospection: experiencing the future’, Science, 317 (5843), 1351–4. Groenendyk, E. (2011), ‘Current emotion research in political science: how emotions help democracy overcome its collective action problem’, Emotion Review, 3 (4), 455–63. Hart, S. (2005), ‘Adaptive heuristics’, Econometrica, 73 (5), 1401–30. Hart, S. and A. Mas-Colell (2000), ‘A simple adaptive procedure leading to correlated equilibrium’, Econometrica, 68 (5), 1127–50. Hsu, M., M. Bhatt, R. Adolphs, D. Tranel and C.F. Camerer (2005), ‘Neural systems responding to degrees of uncertainty in human decision-making’, Science, 310 (5754), 1680–83. Joyce, J.M. (1999), The Foundations of Causal Decision Theory, Cambridge: Cambridge University Press. Karlsson, N., D.J. Seppi and G. Loewenstein (2005), ‘The “ostrich effect”: selective attention to information about investments’, unpublished manuscript, available at SSRN 772125. Kemeny, J.G. (1959), ‘Mathematics without numbers’, Daedalus, 88 (4), 577–91. Loomes, G. and R. Sugden (1982), ‘Regret theory: an alternative theory of rational choice under uncertainty’, Economic Journal, 92 (December), 805–24. Loomes, G. and R. Sugden (1987a), ‘Some implications of a more general form of regret theory’, Journal of Economic Theory, 41 (2), 270–87. Loomes, G. and R. Sugden (1987b), ‘Testing for regret and disappointment in choice under uncertainty’, Economic Journal, 97 (388a), 118–29. Milner, B. (1962), ‘Laterality effects in audition’, in V.B. Mountcastle (ed.), Interhemispheric Relations and Cerebral Dominance, Baltimore, MD: Johns Hopkins University Press, pp. 177–95. Moyano-Díaz, E., A. Martínez-Molina and F.P. Ponce (2014), ‘The price of gaining: maximization in decisionmaking, regret and life satisfaction’, Judgment and Decision Making, 9 (5), 500–509. Neumann, J.V. and O. Morgenstern (1944), Theory of Games and Economic Behavior, vol. 60, Princeton, NJ: Princeton University Press. Nozick, R. (1969), ‘Newcomb’s problem and two principles of choice’, in N. Rescher (ed.), Essays in Honor of Carl G. Hempel, Dordrecht: Springer, pp. 114–46. Nozick, R. (1994), The Nature of Rationality, Princeton, NJ: Princeton University Press. Pascal, B. (1897), Pensées et Opuscules (Thoughts and Minor Works), Edition Brunschvicg, Paris: Hachette. Persaud, N., P. McLeod and A. Cowey (2007), ‘Post-decision wagering objectively measures awareness’, Nature Neuroscience, 10 (2), 257–61.

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Quattrone, G.A. and A. Tversky (1988), ‘Contrasting rational and psychological analyses of political choice’, American Political Science Review, 82 (3), 719–36. Savage, L.J. (1954), The Foundations of Statistics, New York: John Wiley & Sons, reprinted 1972, New York: Dover. Schacter, D.L., D.R. Addis and R.L. Buckner (2008), ‘Episodic simulation of future events’, Annals of the New York Academy of Sciences, 1124 (1), 39–60. Schwartz, B., A. Ward, J. Monterosso, S. Lyubomirsky, K. White and D.R. Lehman (2002), ‘Maximizing versus satisficing: happiness is a matter of choice’, Journal of Personality and Social Psychology, 83 (5), 1178–97. Shafir, E. and A. Tversky (1992), ‘Thinking through uncertainty: nonconsequential reasoning and choice’, Cognitive Psychology, 24 (4), 449–74. Simon, H.A. (1956), ‘Rational choice and the structure of the environment’, Psychological Review, 63 (2), 129–38. Spinoza, B. (1677), Ethica, ordine geometrico demonstrate, trans. E. Curley (1996), The Collected Works of Spinoza, vol. 1, Ethics, Princeton, NJ: Princeton University Press. Suhonen, N. (2007), Normative and Descriptive Theories of Decision Making Under Risk: A Short Review, Joensuu: University of Eastern Finland. Tideman, T.N. (1985), ‘Remorse, elation, and the paradox of voting’, Public Choice, 46 (1), 103–6. Tversky, A. (1975), ‘A critique of expected utility theory: descriptive and normative considerations’, Erkenntnis, 9 (2), 163–73. Young, H.P. (2004), Strategic Learning and Its Limits, Oxford: Oxford University Press. Zeelenberg, M. (1999), ‘Anticipated regret, expected feedback and behavioral decision making’, Journal of Behavioral Decision Making, 12 (2), 93–106.

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12 Is it rational to be in love? Paul Frijters and Gigi Foster

1

INTRODUCTION

Einstein reportedly once remarked that ‘only a life lived for others is a life worthwhile.’ Implicitly, Einstein thus viewed Rational Economic Man, who knows what he wants and only cares for himself, as not living a worthwhile life. To the degree that living a worthwhile life is rational, Einstein would have implicitly deemed ‘Rational’ Economic Man to be, well, not. The core process we aim to illuminate in this chapter consists of people – even very smart ones – being programmed, through economic and social means, to exhibit loyalty to both other people and abstractions. As members of families, nation states, religions, sports teams, professions, and friendships, most of us will have loyalties outside ourselves, deriving pleasure from seeing our loved ones thrive, but economists lack a tractable theory for how such loyalties come about. We therefore also lack an understanding of how groups employ institutions and strategies to make new generations of individuals adopt those loyalties that are useful to existing groups, as well as to successful functioning in their future lives, including as members of groups yet to emerge. In this chapter we try to fill that void, adding loyalty to the basic economic toolkit. Our theory of how people change their loyalties includes a large role for the unconscious mind: we will argue that changing loyalties is not a conscious choice. Based on introspection and simple observation of the human condition, we claim that people cannot consciously choose to increase their love to any level they want. They may consciously put themselves into circumstances that push them towards falling in love with something, but they cannot simply decide to love something and make this state of affairs come about instantaneously. In that sense, being in love is an unconscious process and thus rational in a limited way, on a par with other bodily processes that are unconsciously regulated, like maintaining blood sugar or testosterone levels. We start with a toy model of standard economic rationality wherein individuals never change their loyalties, which we then stretch and shape, via a series of intermediary models, into a model of fluid loyalties and rules of thumb as to how those changes come about. We illustrate how the fluid notion of loyalty throws light on group-mediated phenomena such as education, national symbols, group ideals, and adherence to the ideals of science. We use a simple public goods game to illustrate how particular loyalties to group abstractions held by a small minority help to coordinate a whole group of individuals on the optimal outcome for the group as a whole – a feature that has come in particularly handy during the course of human development. This then leads to a short discussion of the institutions via which group power is organized and maintained. The topics addressed in this chapter are thematically related to existing large economic literatures on household bargaining, parental investments, and reciprocity. However, as far as we know, these literatures have never discussed in mathematical terms how loyalties 205

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arise and change, which is the main focus of our chapter. In that sense, we know of no prior work that we directly build upon. When we speak of ‘mainstream economics’ as the counterpoint to our models incorporating loyalty, we have in mind the notion of rational economic man that is taught to undergraduate students of economics, rather than the many expansions in various sub-literatures that the few who proceed to higher studies will encounter. The literatures adjoining this chapter and the content of first-year textbooks that we take as indicative of ‘mainstream economics’ thinking are both discussed in Frijters and Foster (2013). What this chapter adds to that book is an extended set of mathematical models that describe how loyalty arises. We also contemplate here a meta-question: is it advisable to resist changes in loyalties, seeking to remain immutable and fixed over time, like the rigid figure of mainstream economic models? Or is it smarter to be what we call in later sections a Rhytonian rationalist, whose notion of self changes over time via the ebbing and flowing of his bonds with other people and entities? We argue that from the view of the initial self, changing loyalties is like submitting to a form of premature death: a betrayal of what was originally cared for. From the view of the rational ever-experiencing self, however, developing loyalties to abstractions and people as they are encountered is likely to be the more adaptive and happiness-maximizing strategy. In this sense, we explicitly embrace self-delusion, belief in non-existing entities, and the jettisoning of loyalty towards prior notions of self as potentially quite ‘rational’ and evolutionary adaptive choices.

2

THE ARGUMENT FOR LOYALTY

We start with the observation that people are capable of forming lasting bonds both between themselves and other humans, and between themselves and abstractions that they conjure in their minds. These bonds are all held in the mind, but have large behavioral implications. The clearest evidence for this claim is pure introspection: is there truly nothing outside yourself that you care about, and with which you feel yourself to have long-lasting bonds? Is every favor you bestow on others the result of selfish maximization, providing no ‘warm glow’ (Andreoni 1990) or other positive internal reward? We need the concept of loyalty to explain behavior. Without a mental adherence to gods and spirits, we should not see lucky charms or private prayer, or any other activity that others cannot see us doing but that involves a time investment towards unseen and arguably non-existing entities. Additional evidence comes from our emotional responses to how we think others view us, showing a mental adherence to abstract ideals of behavior against which we believe we are judged (for example, fairness, chivalry, integrity): loyalty to these ideals is implicit in self-loathing internal experiences like guilt and shame that in turn have been known to drive behaviors from listlessness (Hentschel 2007) to self-harm (Inbar et al. 2013). A great deal of human behavior seen throughout history, from the child sacrifice of ancient cultures to the Australian Aboriginals’ ritual of pointing the bone, is extremely difficult to explain without the existence of some unseen link people sense they have to things outside themselves. How do these mental bonds arise? How does a person come to start ‘believing’ in social institutions, such as democracy, human rights, or equal opportunity? How does

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Is it rational to be in love? 207 he develop bonds with gods, or for that matter with other humans? Only once we have a working model for the development of such bonds will we be able to think in an organized way about when they arise and what their behavioral implications might be. Throughout this chapter, we refer to such bonds as ‘love’ or ‘loyalty’. We will offer a precise mathematical definition in later sections, but intuitively, true loyalty will be defined as the inclusion of a mental depiction of an outside entity within the mental depiction of the Self.1 Love will push the one who loves to take actions that support the loved entity, because the loving individual receives internal rewards (‘utility’) from doing so and pain from perceiving that his ideal is suffering, just as he would experience pleasure or pain when other parts of his Self (such as his physical person, or his self-esteem) are stimulated in positive or negative ways.

3

FROM GREEDY RATIONALITY TO RHYTONIAN RATIONALITY

In this section, we sequentially work through a series of simple models of the objective function that individuals are trying to maximize, starting with a standard mainstream economic model. Our final model nests a classical vision of rational individuals who are loyal only to a limited notion of Self, but also accommodates rational individuals who have fluid loyalties to a much broader notion of Self. To experience the difficulty of deciding which among the alternative personas these models accommodate is the most ‘rational’, a reader might approach them from the point of view of a concerned grandparent: which persona would you wish your own grandchild to have? 3.1

Greedy Rationality

To set the scene, suppose there are N entities. At least some subset of N must be thought of as actual individuals throughout all of our models, although later the set N will also include entities that do not exist. We focus on human decision maker i, a member of set N, where i will throughout the exposition denote the experiencing individual (that is, ‘the entity known as i that experiences utility’). Final consumption in period t of individual i is denoted by the vector of consumption goods Xit. Apart from consuming goods, individuals also have a resource that we call ‘power’, which can be intuitively understood as the ability to influence the environment, most notably other individuals. Power is intimately tied to the social environment. The power of individual i is denoted by sit and can be at least partly expressed as a function of the elements of Xit, as when some amount of power derives from the purchasing power denoted by a weighted average of consumption goods. However, power is not fully reducible to a function of consumption, as it can also come from an individual’s physical strength and other socially recognized promises and rights – elements that do not have a straightforward relation to traded consumption goods. The Xit vector can also include sit as an element, to accommodate the possibility that power may provide direct utility. We think of consumption goods – that is, Xit excluding the sit element, for all i and t – as transferable among entities, with the pre-transfer allocation (called the ‘endowment’ or ‘production’) of a good x to entity i in time t denoted by | xit. By design, if an entity is a

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member of the subset of imagined but not real entities, its actual endowments of both consumption goods and power are zero. Each transfer of good x from i to j, which is part of a ‘trade’ as traditionally understood if j directly reciprocates, is denoted Txjit. Each use of the power of an individual i towards an entity j is denoted Tsjit. Transfers of goods can encompass trades between people, but also may include offerings to gods or ideals that do not exist but still appear in the set N of perceived market players. While we initially think of the transferable goods in this model as physical goods and readily observable services, we later allow for the possibility that some consumable goods do not exist at all: they can include imagined goods like salvation in the afterlife, the triumph of science over ignorance, and other such higher-order imaginary things that rely on the abstractive capacities of the real traders to be sustained as goods with consumption value. Individuals’ goods-transfer and power-application decisions at time t are based on expectations of the elements of Xi at time t and in future periods. This formulation allows for the possibility that people make investments (transfers) in order to have a higher Xi now or in the future that never in actuality materializes. The simplest form of ‘greedy rationality’ is then associated with an individual i who at time 0 (= today) makes choices based on his attempted maximization of EUi 5 E c a e2rtUit d 5 E c a e2rt (uc (Xit)) d t

t

xit 5 | xit 2 a Txijt 1 a Txjit j

(12.1)

j

E [ Txijt ] 5 Txijt; E [ Tsijt ] 5 Tsijt , where the final lines hold for all elements of X across all times periods, and for all i within the set N (meaning that expected and actual transfers of goods and power between any two entities are equivalent). r is a discount factor and uc(Xit) the utility enjoyed by individual i that is derived from his final consumption. In this model, the individual maximizes his utility subject to expectations (=E[.]) that conform to reality, so in that sense he is fully rational. The technology of exchange and power are inputs into a ‘reciprocation function’, Txjit, that can directly depend on both the first-move transfers from i to j (Txijt) and the power applied to j by i (Tsijt). Indeed, all the elements | xit, E[Txijt] and E[Tsijt] should be understood as functions of the other elements. Since we are not concerned here with finding analytical steady-state solutions for transfer levels, but rather with describing a theory of love and what it means to be a rational decision maker in a world with love, there is no ex ante restriction we must impose on these functions. The formulation above is a simplification of the types of utility set-ups suggested by the extensive literature on choice behavior (for example, Neumann and Morgenstern 1944; Fishburn and Rubinstein 1982; Prelec and Loewenstein 1991): it involves linear and intertemporal separability of particular utility items, exponential discounting, a lack of procedural irrationality (such as an inability to calculate or limited memory), no direct modelling of uncertainty, and no explicit utility role for procedural aspects of how the eventual allocation of goods comes about.2 In what follows, many of the mainstream extensions to this basic framework will emerge, but in a format different from that used by others, using this simple and flexible model as a starting point.

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Is it rational to be in love? 209 Within this formulation, the classic economic motivation of greed can be seen as a type of direct grab, whereby an individual can invest resources that beget an immediate payback in terms of Xit and/or sit. Such a ‘grab’ may involve a material quid pro quo, as is the case with classic voluntary trade, but may also involve threatened or actual physical domination, such as theft or rape, or other means of appropriation without (full market value) compensation. In its simplest form, a choice to be greedy is based on the expectation that an investment in an attempt to make a resource grab will have a direct payoff, that is, that a transfer of good a, Tajit, can be ‘paid for now’ by means of an investment in a good of recognized value b, Tbijt, or by the application of power: dE [ Tajit ] dTbijt

. 0,

dE [ Tajit ] dTsijt

.0

(12.2)

The expectation of payback from the ‘grab’ strategy can be rational because the individual expects a voluntary trade from entity j, or because he expects to get away with appropriating the desired good a from entity j. To make this model completely standard, the only adjustment required is to assume that power equals the market value of wealth (that is, that sit 5 g x | xit pxt , with pxt a ‘market’ price for good x that is identical and non-manipulable for everyone). However, in our use of the model we do not want to limit the concept of power to market income, since that would be equivalent to assuming that everything of value is for sale and that all individuals live in perfect markets as price-takers. Several subtle aspects of the representation above are of particular relevance to the topic of this chapter. First, the individual doing the maximizing is completely cognizant of his own feelings, as he is able to predict with perfection how he would feel about any possible state of the world. The decision-maker in the above model is hence a savant when it comes to himself, a paragon of self-knowledge that Socrates would have admired, allowing him to perform a quite incredible maximization routine. He has perfectly rational expectations about his future feelings when he is deciding to get married for the first time, have a child, buy his first car, or vote for the first time, and in that sense is not undergoing the excitement of ambiguity, nor is he discovering himself: he is calmly proceeding along the path that brings him greater expected utility than any other path he could have chosen. In reality, no one can be assumed to be that aware of himself or of the world that produces the final outcomes Xit and power quantity sit for each individual (in this sense, real individuals cannot avoid being only boundedly rational (Simon 1982)). This is unfortunate empirically, as there is no baseline population that truly behaves like the model, so the model’s use as a descriptive tool is exceedingly limited. To suggest the formulation above as possible even by approximation means viewing this incredible self-knowledge as an aspirational assumption – an obtainable goal for our decision-maker – rather than a reasonable descriptive assumption. There is an immediate and important corollary to the realization that the standard formulation is not a description of how any actual individual truly thinks. This corollary can be stated as the need to start with a different baseline model for any actual analysis of choices. Because this sounds (and is) a daunting task, it is tempting to see the baseline model as much more attainable than it really is and to present real-life behavior as, by

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contrast, ‘irrational’ or ‘anomalous’. Other authors, for example, speak of deviations from rationality caused by perceptive or mental limitations (Simon 1955; Tversky and Kahneman 1974). This gives far too much credit to the aspirational model, however, which we argue should not be treated as a positive model: it merely defines a particular notion of rationality and admirable behavior, of which vastly slimmed-down versions may be useful in formulating analytically tractable models and empirical estimation. From that perspective, it is a second-order question what kind of mental limitation we should try to accommodate in extensions. Much more important is what whole class of behavior we should try to incorporate that the standard model has assumed to be irrelevant or beyond-scope. We argue that the loyalty of humans to others or, in other words, their social nature offers the simplest, most holistic, and hence most sensible direction to look towards when considering how to expand this aspirational model of mainstream economics. Socialization can influence individuals’ decisions of ‘what to be and what to aim for’, and can hence radically alter our understanding of the individual’s maximization problem, if only through the influence of parents and other groups that constrain and guide individuals as they develop. If it is accepted that socialization occurs and can be partly responsible for the real-world choices of individuals, this raises the question of whether it is truly ‘smart’ to aspire to operate like a greedy ‘rational’ human, and hence to neither truly care for others nor indulge in self-delusion of the sort that causes the set N to include unreal entities. From a prima facie empirical point of view, it seems quite unlikely that in the real world, this type of rational economic man is what a smart person would want to be: there is pervasive evidence that very religious and overly optimistic people, that is, those who would count as ‘self-deluded’ in the standard model, are quite a bit happier than others (Leung et al. 2005; Lewis et al. 2005). This should already give us pause for thought. The supposed ‘smartness’ reflected in the canonical parts of the standard model, namely, exponential discounting and rational expectations, is usually defended by pointing to the fact that an individual would change his mind if he did not discount exponentially, and that he could achieve better final outcomes (that is, a higher Xit at the end of period t) if he knew better how those outcomes came about. Hence, or so the argument goes, other ways of thinking and deciding would not be evolutionarily adaptive in a repeated setting with learning. While these claims are in themselves not always true in the presence of strategic considerations wherein pre-commitment matters,3 the main problem with such a defense of ‘rational man’ is that it does not point to a means of horse-racing the standard model against clearly articulated alternatives: in which type of operating environment should we consider the alternatives? Implicitly, an adherent of this view must presume that Uit is predicted perfectly by Xit (that is, that preferences are fixed), so that under perfect-market circumstances (that is, the absence of strategic considerations that open a role for irrationality) we can make the best plans to maximize our utility through our choice of consumption levels. Yet, having a modus operandi that maximizes consumption in a perfect market environment will not work out so well in other environments, nor when consumption is not the thing one aims for, nor when utility functions are flexible. Another important hidden element in the standard formulation is that an individual’s notion of ‘Self’ is taken to be self-evident and fixed: it is an entity towards which a person displays absolute altruism. The Self in the standard formulation is taken as an unchanging

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Is it rational to be in love? 211 entity about which an individual does, and even should, care about in the future to an unchanging degree. As Ng (1992) and others have said, that kind of formulation presumes the existence of something like a soul that is unchanging and that provides solidity to the notion of Self that an individual cares about. In reality of course, this is an idealized abstraction: people change in a myriad of ways over time and there is no a priori reason why it should be rational or evolutionarily adaptive for them to care about their future selves rather than, for example, just caring about the momentary pleasure obtained from their current self’s experiences. How strange it is, on reflection, to assume that the self is fixed. Individuals continuously change form and even change the make-up of their bodies, as they experience a constant exchange of fluids, solids, and even genetics with the outside world. Our genome is more like a moving cloud than a fixed point, with our genes exchanging genetic material with microbes and constantly changing as a result of cellular processes. Even in terms of mental traits, individuals routinely change their minds, their attitudes, and their preferences over time, often quite dramatically over the whole of the life course as new ideologies and religions come into being. What is fixed in reality is less the mind and body of an individual, but more his social endowments, such as his possessions, his passport and associated ‘rights’, his kin relations with others (as father, son, and tribe member, for example), and so on. The idea that it is somehow smart or optimal to care for some discounted flow of benefits towards a fixed ‘Self’ is not grounded in any economic logic or underlying foundation of rationality: it is itself a convention, an advocated position, a choice to buy into a particular abstraction being offered by a group (in this case, mainstream economists). Finally, the treatment of time in the standard model is also not as ‘rational’ as it might seem at first glance. Not only does the model assert that an individual is wholly uninterested in and unresponsive to the past, but the future is also purportedly seen as fundamentally different from the present: the present is taken as known, and the future is taken as expected. ‘Rational’ economic man does not care about his history or his ancestors, observes everything about the present with total objectivity, and is completely detached when forming expectations about the future. Purely from a psychological and neuroscientific point of view, this is an odd proposal. There is no clear way for a human mind to think in different ways about the past, present, and future. All these windows of time can be experienced and reacted to in the same way, using the same neuronal hardware and pathways. Consider fear, which can arouse great emotions in the present moment even if the fear does not materialize in the future, or can arise in reaction to a remembered childhood fairytale: the feared event is an imagined outcome that creates utility effects in the present moment when it is held in the mind, independent of the supposed timing of the feared event. The past, too, can therefore arouse emotions, and the image of the past is subject to constant re-writing and re-interpretation, much like the present is only experienced through the filter of our senses, rather than being objectively observed. The standard model’s artificial distinctions between a future that is coolly expected, a present that is certain, and a past that is coolly and totally ignored, are neither realistic nor obviously desirable from a welfare or evolutionary perspective. In sum, the greedy, rational individual lives in a delusion of fixity, and has accepted that his own current and future consumption are the only things that matter, but otherwise sees the world as it truly is. From this starting point, our next step is to include a more dynamic notion of Self.

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3.2

Naïve Love

How can we conceptualize a changing Self, in a way that is both realistic and amenable to incorporation into the analytical framework of economics? Previous attempts to broaden the Self studied by economists (for example, Ainslie 1985) have been written in philosophical terms and/or focused on the formalization of only relatively small details of the arguably very complex problem. Our proposal by contrast is to consider a large addition – the possibility of being in love – and model this directly as an internal experience that drives change in the Self, leading to changed loyalties. We can incorporate both the process of change and the outcome into the economic toolkit by constructing individuals who have the capacity to start to care about a more expansive notion of Self that includes the feelings and experiences of others. An individual who has come to ‘love’ (or to ‘be loyal’) is someone who cares for another entity, j, to a degree bijt ≥ 0. Someone who loves in what we term a naïve way, but in all other ways conforms to the supposed rationality of the standard formulation, will then maximize EUi 5 E c a a bijt e2rtUjt d 5 E c a a bijt e2rt uj (Xjt) d 5 a a bij0 e2rt uj (Xjt) N

t

j51

E [ bijt ] 5 bij0

N

t

j51

N

t

j51

(12.3)

This formulation denotes a situation in which the individual anticipates at time 0 a fixed degree to which he cares for others in all periods of the remaining future (where this degree is denoted bij0), and this expectation of the fixity of love is a mark of his naïveté. He maximizes the streams of future utility towards those entities to which he is loyal, on the basis that his loyalties are unchanging from today onward, without wondering where those loyalties came from. The individual is now incredibly cognizant not only of his own psychology, but also of the psychology of those he presently loves, being able with perfect foresight to anticipate how they would feel under various circumstances. The naïvely loving individual is supremely loyal to a fixed notion of Self that now includes not merely his own ‘soul’ (via bii0, a term which is included in the summation above over all entities N), but also those of others. Despite the ignorance it assumes about how attachments change, this formulation allows for the same kind of rationality as before: the individual sees the world and himself as they truly are. 3.3

Being in Love

How then does bijt develop over time? This question essentially asks for a theory of love, not only for one’s own ‘experiencing self’ (for lack of a better word) but for any outside entity towards which one might plausibly develop love. We have proposed such a uniform theory of love elsewhere (Frijters and Foster 2013), which we call the love principle, and here extract its core implications for the mathematics and intuition of the present set of models. We simplify the argument by presuming throughout that individuals want the good a from the entity they will end up loving, just as we used that same good a as the desired acquisition target when describing greed. We argue that love increases when an individual believes unconsciously that increasing

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Is it rational to be in love? 213 his love for j will increase future transfers to him by j of the desired good a that cannot be obtained from j via direct grabbing. Because being in love involves the change of self, the individual is effectively investing part of his ‘soul’, in the expectation of reciprocity from the loved entity in the form of the transferral of the desired good. The simplest way to express mathematically what the loving mind perceives is as follows: dE [ Ta jit1s ] dbijt

. 0,

dE [ Ta jit1s ] dTbijt

0 bijt # 0,

dE [ Ta jit1s ] dTsijt

0 bijt # 0

(12.4)

Where the unconscious expects that the entity holding the desired good a will respond, in terms of goods transferral, to an investment of ‘self’ (dbijt > 0). The conscious belief that (additional units of) the desired good cannot be obtained by transferring goods or by using ] ] dE [ Ta dE [ Ta power without an investment of the self ( dTb 0 bijt and dTs 0 bijt) 4 is what makes  the investment of self optimal, as the usual ‘grabbing’ strategy of the conscious is thwarted. ] dE [ Ta Whether an individual consciously expects that d b .0 is ambiguous in our theory, but we argue the generic answer is likely to be ‘no’, meaning that the resulting shift in the Self comes as a surprise to most people. We view the period of change in loyalty thereby much like a unconsciously regulated bodily process such as our internal circadian rhythm: we can put ourselves in a situation where our internal clock gets reset, and can even take substances that help with that resetting, but we cannot consciously direct the dials of our internal clock. Purely on empirical grounds, it appears to be the same with love. The love principle presented in Frijters and Foster (2013) contends that the individual is not typically reflective about this expectation of the reciprocity initialized by love, and does not wonder exactly how the supposed reciprocal transfers will come about. Part of the evidence for this contention is the lack of interest that people show in questions about the mechanics of things like ‘Karma’ and ‘a good afterlife’. How are these things actually organized? How do ‘God’ and even ‘our partner’ actually come to care for us and give us transfers? This is a subject of surprisingly little critical thinking, which we argue is because it is not the conscious mind that does the expecting, but rather the unconscious. Frijters and Foster (2013) suggest that our expectation of reciprocity by the entities we love must be hidden from our critical thinking because our conscious mind would feel its self-esteem diminished by an open admission of weakness (embodied in the inability to ‘grab’ the outside entity’s resources). This then gives rise to rational self-delusion about the love mechanism itself: in order to submit but still feel good about it, we do not tell ourselves that we submit, but instead pretend that love arrives unexpectedly. Another aspect of reality that this formulation accommodates is that believed future transfers may never actually arise. Examples are transfers that supposedly take place after death, or that involve a cure for incurable illnesses. Actual transfers, such as when a nation rewards its war veterans, can of course also arise. Whether the expected transfer eventually occurs or not, our contention is that love is quintessentially about a potentially deluded unconscious mind that craves a transfer. We argue that it is important for the maintenance of love that the lover now and then sees believed confirmation of his expectation of reciprocity, such as signs from god or tokens of goodwill, but still, no actual transfers need ever occur in reality. ] dE [ Ta Looking more carefully at the process underlying the statement that d b . 0 we have jit 1 s

jit 1 s

ijt

ijt

jit 1 s

ijt

jit 1 s

ijt

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in mind that an individual believes the entity possessing the desired good a is interested in our loyalty as well as other goods: it wants power over us (directly via bijt) but also actual goods transfers from us that prove that power over us. While in love, a person will therefore transfer goods k at time t to the loved entity, that is, Tkijt > 0, where k includes goods believed to be valuable to the loved entity. The bundle of {bijt, Tkijt} is then transferred in the expectation of a reciprocal but uncertain transfer of a in the future. Transfers to the loved entity in our theory thereby come about both from believing that the loved entity receives utility from the gift, and as a means of quelling internal doubt about the function E[Tajit+s]. In the background, an individual will implicitly have some notion of the elements in the utility function of the loved entity, even if that entity does not exist. Relevant to this, Frijters and Baron (2012) use a laboratory experiment to examine gift-giving to an abstract entity they called ‘Theoi’, which in reality was a computer algorithm randomizing its decisions about transfers in the form of ‘market prices’. The authors hypothesized that participants believed Theoi’s utility function was of the following form: UTheoi,t 5 a [ 2 TaTheoi,i,t1s 1 h (TaTheoi,i,t1s 2 pk *Tki,Theoi,t) ] i

hr . 0, hs , 0

(12.5)

where Tki,Theoi,t took the form of an allocation of real money, and TaTheoi,i,t+s the later setting of market prices. This formulation has the key characteristic, via the function h(.), that Theoi’s marginal utility of rewarding i with a gift of good a (=TaTheoi,i,t+s) increases with more valuable transfers from i to Theoi (= Tki,Theoi,t), with pk just picking up relative price effects (the locally perceived relative worth of good a compared to good k).5 The belief system of individual i about the loved entity’s utility function is crucial in determining the behavioral ramifications of loyalty. For example, if individual i believes that the entity only wants particular goods (like certain Greek gods who were allegedly only concerned with burned meat), then that is what is transferred. If the individual i believes the entity wants loyalty (like most Greek gods purportedly did), then that is what is invested.6 We do not discuss where such beliefs come from, because a cursory glance at history tells us that it is highly context-dependent. We merely note the bewildering array of things that people can believe non-existent entities care about, including adherence to rules of behavior, high art, dance, sex, poetry, life, and on and on. As a general rule we suggest that non-existing entities are often believed to care about exactly the same things that are deemed valuable in the society from which the believer comes, and in that sense Tki,Theoi,t will often overlap with the socially-determined notion of power dTs (that is, dTk . 0), such that we must transfer something of our own (purchasing) power in order to make an impression on the non-existing entity. If the unconscious were choosing optimally according to this belief, the optimizing investment would then solve ] 0E [ Ta 0EU dEU ] 50 0 E[Ta 1 dTk which would thus nail down the transfer Tki,Theoi,t if 0Tk 0Ta we make particular choices about the underlying functions. i, Theoi, t

i, Theoi, t

Theoi, i, t 1 s i, Theoi, t

i

Theoi, i, t 1 s

i

Theoi, i, t 1 s

i, Theoi, t

3.3.1 The love bargain If we think of all potentially loved entities j as being like the artificial entity ‘Theoi’ above in terms of real transfers, then we can map the reaction function of these entities into the

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Is it rational to be in love? 215 utility-maximization program of the rational individual. We have two choices as to how to model this: we could focus on individuals’ belief structures surrounding the entities j and incorporate those explicitly into the model, or we could focus on how the result of those belief structures adjusts the Self over time, as transfers are made to the entities j, and add the elements that this process implies to the maximization problem for individuals. We opt here for that second approach. We thus propose a mathematical description of how love changes in response to transfers, themselves optimally chosen based on the unconsciously imagined reactions of outside entities that are believed to demand our loyalty in return for entertaining a bargain. We propose that love increases in those time periods when an individual transfers more power to an entity than he receives back, where power now certainly includes purchasing power (that is, goods) but also anything else that would be understood by the individual to influence his environment and people around him: bijt11 5 f ( bijt,pi,kTkijt 2 E [ Tajit ]) 0 2f f (0,0) 5 0, f r . 0, ,0 0bijt 0 ( pi,kTkijt 2 E [ Tajit ]) dTsijt ~ pi,k . 0 dTkijt

(12.6)

This formulation states that individual i’s love towards entity j is path-dependent to an extent, but also increases when i transfers good k to entity j at a moment when j is not perceived to transfer the desired good a back to i, with pi,k the relative price as perceived by i (which allows for individuals to want different things). The imagined current expected transfer is denoted as E[Tajit], again allowing individuals to believe that they are getting transfers from non-existent entities. The investment of power is made under the expectation that the power transfer in turn engenders a transfer towards oneself in the future (in periods s > t), but does not involve other expected trades (a form of ceteris paribus condition: there are no other benefits expected from the investment of Self).7 The dependence on the existing loyalty is such that the higher the existing loyalty, the higher the transfer dTs must be that maintains that loyalty: 0b 0 (p Tk 0 f2 E [ Ta ] ) , 0. The assumption dTk ~ pi,k denotes the idea that the perceived value of the transfer to the loved entity in the mind of the person giving it is proportional to its relation with (socially defined) power: the higher is pi,k and thus the more an individual is giving up part of his social power for an outside entity in return for an uncertain return transfer in future periods, the faster the increase in loyalty towards that entity. This law of motion will vary in quality from person to person, depending on what the person believes the outside entity will react to, which could be time, fervency, physical goods, and so on – which we view, through the lens of the economist, as information about the outside entity’s utility function. The actual good transferred by the loving person reveals what that loving person believes the loved entity to care about. For instance, if the outside entity only cares about loyalty and nothing else, then the good k would merely be equal to bijt, but generically the outside entity will be presumed to care about loyalty as evidenced by visible transfers. Love of j by i, bijt, is decided upon this period on the basis of expected future transfers 2

ijt

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and an individual acts upon his love in this period by making transfers towards the loved entity. The transfers can range from writing love poems for a wooed woman, to taking risks in war for a loved cause, to burning meat for a wooed god. In a way, love is an answer to the problem of missing markets, either for goods that do not exist at all, or among entities that have no power to force transfers otherwise. It is intimately tied to the notion of power, in that the individual must care about whatever it is that he is giving up to the wooed entity dTs (and hence dTk is proportional to the relative price). If we think of power as purchasing power, which is a highly culturally specific interpretation of how one can influence others, then increasing love involves giving up some purchasing power (time, money, or goods) for the supposed benefit of the loved entity. Our love principle pre-supposes that individuals are not aware of the production technology giving rise to these expected future transfers Tajit+ s, as it is the unconscious mind, rather than the conscious mind, that makes the love bargain. ijt

ijt

3.4

Types of Rationality

If we take this line of reasoning a step further, different types of ‘rationality’ and notions of Self come into view based on the degree to which an individual is aware of how loves may ebb and flow over time. Using a simple linear parametrization, where qi will denote a level of rhytonia (explained below), gi a level of self-rationality, and hi a level of worldrationality, the maximization problem for individual i at t = 0 can be re-stated as: N N | | | EUi 5 E c a a b ijte2rtUjt d 5 a a b ijte2rt auj aE c Xjt 1 a TXmjt 2 a TXjmt d b b t

t

j51

m

j51

m

| b ijt 5 qi bijt 1 (12qi)bij0
F

0.5061 2.4023 0.9281 0.8156 2.0202 0.0124 0.0058

3 2 1 4 1 3 1

0.1687 1.2012 0.9281 0.2039 2.0202 0.0041 0.0058

0.56 4.39 3.24 0.69 7.35 0.01 0.02

0.6396 0.0150 0.0750 0.6014 0.0079 0.9978 0.8891

Notes: a 1 5 not important in selection of seed, 2 5 important, 3 5 very important. b 0 5 chronic stressor, 1 5 catastrophic stressor. c 0 5 none, 1 5 little, 2 5 much. d 0 5 no WTP, 1 5 WTP is greater than 0 *** Significant at the .01 level; ** significant at the .05 level; * significant at the .10 level.

As some farmers reduced their ratings of seeds’ drought tolerance trait while others increased their ratings, we take the absolute value of ratings changes for drought tolerance to test for whether different factors are associated with any change in seed trait ratings. For comparison, Appendix 14.2 includes tables with our evaluations of drivers of seed trait changes for excess rain tolerance, as several villages had significant changes in their use of seeds with this trait. To test whether the correlations are significant, we first conducted one-way analyses of variance (ANOVA) for the absolute change in ratings of seeds’ drought tolerance and baseline measures of farmer attitudes (Table 14.7). The only non-categorical independent variables – average yield loss and willingness to pay (WTP) – were divided into quintiles using the egen command in Stata. The results of the ANOVA tests (Table 14.7) indicate that there is not a statistically significant relationship between changes in farmers’ selection of seed toward droughtresistant seed and baseline willingness to take risks, average yield loss from drought, or willingness to pay to reduce the likelihood of catastrophic losses from drought. Perceived control over losses from drought, however, is significant at the .01 level, and farmers’ stated importance of drought-tolerance in seed selection and perceptions of drought as a catastrophic stressor are also significantly associated with changes toward droughttolerant seed, at the .05 and .10 level, respectively. To further test these associations, we conducted simple ordinary least squares (OLS) regressions of the absolute change in ratings of seed drought-tolerance on farmers’ primary plots against potential drivers of seed change, retaining baseline measures of willingness to take risks and the three variables that appear to have significant associations with changes in seed traits (Table 14.8). We also include some control variables that may be expected to influence seed selection, including age, literacy, and number of plots planted with maize. We hypothesize that farmers with a greater number of plots would be

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Do changes in farmers’ seed traits align with climate change? 267 Table 14.8

Results of OLS regression for absolute change in mean rating of seed drought-tolerance on primary plot

Variable Willingness to take risks Importance of drought-tolerant seed traita Perceived importance of drought stressorb Perceived control over losses from droughtc Age Knowledge of reading and writing Number of plots planted with maize Melchor Ocampo Roblada Grande Queretaro Observations Adjusted-R2

Coeff. (std. err.)

P > |t|

−0.0263 (0.0608) 0.2196 (0.1302) 0.1662 (0.1736) 0.7486 (0.3245) −0.0045 (0.0041) −0.2844 (0.1478) 0.0038 (0.0538) 0.1489 (0.1663) −0.1643 (0.1709) −0.0813 (0.1874) 90 0.1155

0.667 0.096* 0.341 0.024** 0.276 0.058* 0.944 0.373 0.339 0.666

Notes: a 1 5 not important in selection of seed, 2 5 important, 3 5 very important. b 0 5 chronic stressor, 1 5 catastrophic stressor. c 0 5 none, 1 5 little, 2 5 much. ** Significant at the .05 level; * significant at the .10 level.

more willing to change their selection of seed traits on a given plot, as they would still be able to use previously tested seeds on their other plots. In addition, we also include variables for farmer villages, as we have seen that farmers in certain villages have had more significant changes in their seed trait selection. As with the ANOVA analysis, we find no statistically significant relationship between changes in farmers’ rating of seed traits and willingness to take risks. We also find that the perception of drought as a catastrophic stressor is no longer statistically significant. Farmers’ stated importance of drought tolerance in seed selection and perceived control over losses from drought, however, remain statistically significant and associated with larger changes in the drought tolerance of seeds on the primary plot. Perceived control over losses from drought appears to have the largest effect on selection of seed traits, with differences in control between ‘none and a little’, or ‘a little and much’, associated with three-quarters of a unit change in the four-point seed rating scale. We find that farmers’ age, village, and number of plots planted with maize are not significantly associated with changes in drought-tolerant traits of planted seed.

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Unexpectedly, both Roblada Grande and Queretaro have negative coefficients, indicating smaller absolute changes in seed drought tolerance from 2005–07, even though farmers in both villages increased their ratings of their seed’s drought tolerance, on average. This negative association may be because farmers in these villages had smaller absolute changes in drought tolerance ratings than farmers from Dolores Jaltenango, although these changes are not significant. Knowledge of reading and writing, however, is significantly but negatively associated with changes in drought tolerance ratings. To test the robustness of our findings, we also conducted regression analyses looking at relative rather than absolute changes in drought tolerance ratings and looking at the average change in drought tolerance ratings across all farmer plots as opposed to just on their primary plot (not presented). The significance of different variables varies somewhat across these models. When considering relative changes in drought tolerance ratings of seeds on the primary plot, only the dummy variable for the village of Melchor Ocampo is significantly associated with a change in ratings of seeds on the primary plot. For relative changes in ratings for seeds across all plots, the Melchor Ocampo dummy variable along with the stated importance of drought-tolerant traits and perceived control over losses from drought are all significant. For absolute differences in seed trait ratings across all plots, only perceived control over losses from drought is significant. We therefore observe that farmer’s stated importance of drought-tolerant seed traits is significantly associated with changes in selection of drought-resistant seed in two of the four models, while farmers’ perceived control over losses from drought is significant in three models. These findings suggest that farmers’ seed selection decisions are associated with their perceptions of climate change and of their ability to respond to climate change, though the association is not always clear. The survey does not ask farmers about their perceived importance of or control over excess rain as a stressor or about their willingness to pay to reduce losses from excess rain, but we conducted a similar analysis as for drought tolerance considering farmers’ selection of seeds with excess rain tolerance, without these variables (Appendix 14.2). While farmers’ stated importance of excess rain-tolerance in seeds is significant in the ANOVA analysis, none of the variables are significantly associated with absolute changes in excess rain tolerance ratings in the OLS regressions. In models using relative changes in excess rain tolerance ratings, the coefficient for Queretaro is significant when considering seed traits on all plots, while the coefficients for Roblada Grande and literacy are significant when considering seeds on the primary plot only. This finding suggests that village-level factors may play an important role in farmers’ seed selection decisions.

5

CONCLUSION

Our research shows that farmers in four villages of Chiapas, Mexico, changed their seed ratings of tolerance or resistance to four environmental stressors, most notably drought tolerance, although average changes differed by village. Changes in ratings of drought and excess rain tolerance are generally aligned with climate change predictions for temperature and precipitation in these villages, though the degree of alignment varies by village and depending on the climate model we use. Not unexpectedly, farmers’ changes in seed trait ratings do not perfectly correspond to climate change predictions, as climate variations

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Do changes in farmers’ seed traits align with climate change? 269 are uncertain, and as current seed trait choices may be based on more short-term climate change expectations than those in our models. While we cannot test whether changes in seed trait ratings are deliberate adaptions to climate change, we find that farmers’ baseline attitudes may partially motivate changes in seed trait ratings. Although willingness to take risks does not appear to affect farmer seed selection, farmers’ stated importance of drought tolerance in seed selection and their perception of control over losses from drought in 2005 are both associated with larger absolute changes in seed drought tolerance ratings between 2005 and 2007. On the other hand, literacy appears to decrease the likelihood of changes in ratings for this trait, though the possible reasons for this association are not clear. The concept of bounded rationality suggests that individual rationality in decisionmaking is constrained by information availability, individuals’ capacity to evaluate and process information, and time available to make decisions. Our results suggest that farmers’ selection of seed agronomic characteristics, whether knowingly or not, are aligned with long-term climatic fluctuations owing to climate change as predicted by climate models, and that baseline attitudes towards different stressors and farmers’ education may also play a role in selection of seed traits. Our findings are limited by the small sample size and by the relatively short timeframe of the study when compared with timelines for climate change, but are generally robust to several model specifications. This study lays a foundation for future investigation into what other variables may drive farmers’ climate adaptation behaviors given rational behavior under enormous uncertainty.

NOTES 1. Digital Climate Atlas of Mexico: http://uniatmos.atmosfera.unam.mx/ACDM/servmapas and National Meteorological Service of Mexico Weather Stations: http://smn.cna.gob.mx/index.php?option5com_cont ent&view5article&id542&Itemid575 (both accessed 11 January 2017). 2. Note that both sources had the same average for village of Queretaro in the HADGEM 1 temperature model (Figure 14.2). 3. The model at that time was the predecessor to the HADCEM 3 – the HADCM 2. Note that all HAD-rooted models stem from the Hadley Centre’s larger Unified Model, but vary depending on the necessary application (seasonal, decadal and centennial climate predictions). 4. The survey does not include questions on perceptions of or losses from excess rain. 5. Figures showing expected temperatures for the GFDL CM3 model are included in Appendix 14.1. 6. The baseline average temperatures for Queretaro are the same for both sources, hence only one line. 7. The grey parallel lines on the bar graphs represent the baseline averages from two different sources. 8. Note that we are suggesting a relationship between excess rain and rotting, as excess rain can lead to rotting of the maize crop, and as changes in seed ratings for these two traits appear to be associated with one another. 9. Farmers in Dolores Jaltenango did not significantly change their ratings of seed traits with the exception of wind tolerance, so we cannot evaluate whether changes align with climate change predictions.

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Kunzekweguta, K. Mazvimavi, P. Craufurd and P. Dorward (2012), ‘Farmer perceptions on climate change and variability in semi-arid Zimbabwe in relation to climatology evidence’, African Crop Science Journal, 20 (2), 317–335. Pressoir, G. and J. Berthaud (2004a), ‘Patterns of population structure in maize landraces from the Central Valleys of Oaxaca in Mexico’, Heredity, 92 (2), 88–94. Pressoir, G. and J. Berthaud (2004b), ‘Population structure and strong divergent selection shape phenotypic diversification in maize landraces’, Heredity, 92 (2), 95–101.

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Do changes in farmers’ seed traits align with climate change? 271 Seo, S.N. (2012), ‘Decision making under climate risks: an analysis of sub-Saharan farmers’ adaptation behaviors’, Weather, Climate, and Society, 4 (4), 285–99. Servicio Meteorológico Nacional (Meteorological Service of Mexico) (SMN) (2014), Weather Stations, accessed 5 August 2014 at http://smn.cna.gob.mx/index.php?option5com_content&view5article&id542& Itemid575, last updated 2014. Simon, H.A. (1982), Models of Bounded Rationality: Empirically Grounded Economic Reason, vol. 3, Cambridge, MA: MIT Press. Smit, B. and M.W. Skinner (2002), ‘Adaptation options in agriculture to climate change: a typology’, Mitigation and Adaptation Strategies for Global Change, 7 (1), 85–114. Smithers, J. and B. Smit (1997), ‘Human adaptation to climatic variability and change’, Global Environmental Change, 7 (2), 129–46. Stokes-Prindle, C., B. Smoliak, A. Cullen and C.L. Anderson (2010), ‘Crops & climate change: maize’, White Paper, Evans School Policy Analysis & Research Group (EPAR), University of Washington, Seattle. Sutherland, L.A., R.J. Burton, J. Ingram, K. Blackstock, B. Slee and N. Gotts (2012), ‘Triggering change: towards a conceptualisation of major change processes in farm decision-making’, Journal of Environmental Management, 104 (August), 142–51. Tambo, J.A. and T. Abdoulaye (2012), ‘Climate change and agricultural technology adoption: the case of drought tolerant maize in rural Nigeria’, Mitigation and Adaptation Strategies for Global Change, 17 (3), 277–92. Ureta, C., E. Martínez-Meyer, H.R. Perales and E.R. Álvarez-Buylla (2012), ‘Projecting the effects of climate change on the distribution of maize races and their wild relatives in Mexico’, Global Change Biology, 18 (3), 1073–82. US National Oceanic and Atmospheric Administration (NOAA) (2004), North American Drought Monitor Maps, September–December 2004, accessed 9 January 2014 at http://www.ncdc.noaa.gov/temp-and-precip/ drought/nadm/nadm-maps.php. Vigouroux, Y., C. Mariac, S. De Mita, J. Pham, B. Gérard, B. et al. (2011), ‘Selection for earlier flowering crop associated with climatic variations in the Sahel’, PLoS One, 6 (5), e19563. Waddington, S.R. (2014), Personal email correspondence with C. Leigh Anderson, 9 January.

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APPENDIX 14.1

GFDL CM3 MODELS Expected village temperature in April 2015–39

27.5 27.0

Temperature (C)

26.5 26.0 25.5 25.0 24.5 24.0 Queretaro

Dolores Jaltenango

Melchor Ocampo

Roblada Grande

Expected village temperature in July 2015–39 27.0 26.5

Temperature (C)

26.0 25.5 25.0 24.5 24.0 23.5 23.0 Queretaro

Figure 14A.1

Dolores Jaltenango

Melchor Ocampo

Roblada Grande

Expected village temperatures under GFDL CM3 scenario

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Do changes in farmers’ seed traits align with climate change? 273 Expected village precipitation in April 2015–39 60

Precipitation (mm)

50 40 30 20 10 0 Queretaro

Dolores Jaltenango

Melchor Ocampo

Roblada Grande

Expected village precipitation in July 2015–39 450 400

Precipitation (mm)

350 300 250 200 150 100 50 0 Queretaro

Figure 14A.2

Dolores Jaltenango

Melchor Ocampo

Roblada Grande

Expected village precipitation under GFDL CM3 scenario

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APPENDIX 14.2 Table 14A.1

EVALUATION OF DRIVERS OF SEED TRAIT CHANGES FOR EXCESS RAIN TOLERANCE1

Results of one-way ANOVA for absolute changes in selection of excess raintolerant seed traits on primary plot

Variable Willingness to take risks Importance of excess rain-tolerant seed traita **

SS

df

MS

F

Prob > F

1.0240 3.8637

3 2

0.3413 1.9319

0.68 4.27

0.5677 0.0168

Notes: a 1 5 not important in selection of seed, 2 5 important, 3 5 very important. ** Significant at the .05 level.

Table 14A.2

Results of OLS regression for absolute changes in rating of seed excess rain-tolerance on primary plot

Variable Willingness to take risks Importance of excess rain-tolerant seed traita Age Knowledge of reading and writing Number of plots planted with maize Melchor Ocampo Roblada Grande Queretaro

Note:

a

Coeff. (std err.)

P > |t|

0.0121 (0.0890) 0.1783 (0.1111) −0.0086 (0.0055) −0.2827 (0.2024) −0.0944 (0.0717) 0.1321 (0.2165) −0.0246 (0.2241) 0.1047 (0.2266)

0.892 0.112 0.121 0.166 0.192 0.543 0.913 0.645

1 5 not important in selection of seed, 2 5 important, 3 5 very important.

Note 1.

The survey does not ask farmers their perceptions of excess rain as a stressor. The three stressors that farmers are asked about are drought, pests, and root lodging.

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15 Rationality, globalization, and X-efficiency among financial institutions Roger Frantz

1

INTRODUCTION

Rationality – that is, rational decision making – and efficiency are considered to be good things. Rationality in orthodox theory means decision making consistent with the maximization of expected utility. Efficiency has traditionally meant (market) allocative efficiency, that is, P 5 MC. In 1966 Harvey Leibenstein introduced the concept of X-efficiency. X-efficiency is not about the market or P 5 MC. X-efficiency is about the firm’s costs. An X-efficient firm minimizes its costs, and produces on its cost frontier (or production frontier). An X-inefficient firm produces above its cost frontier (and/or below their production frontier). An X-inefficient firm is the result of human behavior which fails to keep costs to a minimum. A major issue is whether employees who contribute to X-inefficiency are irrational, stupid, and lazy. An argument will be made that people on average tend to be smart, given the opportunities and constraints they face. Herbert Simon might call their behavior constrained by bounded rationality; Leibenstein used the term selective rationality. Neither implies irrationality. Both may imply non-optimizing behavior. Errors in decision making are made, but not because people are irrational, stupid, and lazy. Beginning in 1967, empirical research on X-efficiency appeared in the literature. From 1967 to 1995 there were approximately 55 empirical studies published in journals. Between 1995 and 2013 there were at least 150 studies. Many of these studies are about the liberalization of the banking sector in many countries and the effects of the global economic crises of 2007–08. This chapter discusses only a small sample of the recent surge in the literature to ask the question whether changes in X-(in)efficiency imply anything about efficiency or rationality among members of firms? Again, are members of firms which are X-inefficient irrational and/or thoughtless (the opposite of smart)? First we ask how Leibenstein would answer that question. We also need to distinguish narrow from broad definitions of rationality, as well as allocative from X-efficiency. A summary of some empirical studies then illustrates the issues raised above.

2

WHAT IS EFFICIENCY? WHAT IS RATIONALITY?

It is necessary to consider the terms efficiency and rationality in the context of (the development of) X-efficiency (XE) theory. In 1966, the year Leibenstein’s first article on XE theory appeared in the American Economic Review, efficiency meant allocative efficiency, and rationality meant the complete rationality of ‘economic man’. Simon was speaking about bounded rationality and Hayek was speaking about the impossibility of complete 275

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rationality, to give only two examples, but the orthodoxy was efficiency meant allocative efficiency, and rationality meant the complete rationality of ‘economic man’. These are the two concepts which Leibenstein attacked with X-efficiency. For Leibenstein if the word efficiency had any meaning then there had to be at least the possibility of inefficiency, and if rationality had any meaning then there must be at least the possibility of irrationality. Allocative efficiency means P 5 MC, or MPl/Pl 5 MPk/Pk 5 . . . MPn/Pn. If either or both of these did not hold, then allocative inefficiency exists. No one took issue with this form of inefficiency. But this was an inefficiency of the market caused by market power. What about an inefficiency within the firm? Might firms not be cost minimizers, that is, might they not be internally efficient? If firms are internally efficient then it must be at least possible that they are not. Internal inefficiency is X-inefficiency. X-(in) efficiency does not focus on prices and outputs, but on costs. More than a few economists took exception to the possibility of this type of inefficiency. If it exists then it calls into question whether people are rational or economic man. An economic man would minimize the firm’s costs, but ‘X-inefficiency man’ does not. There was a lot at stake, or so it seemed. Critics would say that if firms are not minimizing costs then it is because some constraint is not being taken into account. These constraints have included leisure at work, incomplete property rights, risk aversion, and rent seeking behavior. Each constraint can increase costs above the technological minimum, but each can be defined as being a rational response to the environment. Hence there is no (X) inefficiency. What exists is efficiency within a more complete set of constraints. Economists practicing this orthodox approach believed that it was enough that there exists what sounds like a constraint and can be used in a sentence. Engaging in empirical research to identity the causes and effects of the constraints was not necessary. Many others have estimated a cost function for a group of firms in the same industry and investigated whether the firms are producing on the cost frontier. Orthodox theory at the time stated that they will be on the frontier. Look at an intermediate micro theory textbook; the entire emphasis is that firms are assumed to be on the frontier. However, if they are off the frontier then are they 1 percent, 2 percent, or 3 percent from the frontier, or 20 percent, 30 percent or more? One, 2 and 3 percent can be ignored, but 20 percent or 50 percent cannot. In the cases of 20 percent or 50 percent, X-inefficiency is a form of inefficiency which is a problem for the economy. These economists also investigated, empirically, why X-inefficiency differed among firms. Having argued for at least the possibility of the existence of (non-allocative) inefficiency and irrationality, Leibenstein then asked a central question for his research: what are the implications of (non-allocative) inefficiency and irrationality for economic theory? To summarize the academic environment within which Leibenstein was responding, allocative inefficiency occurs in a market when P ≠ MC. Non-allocative inefficiency occurs when a firm is not on the cost and production frontiers. Firms are assumed to be on their frontiers, minimizing their costs. That is what rational members of firms do: economic man makes rational decisions. However, the existence of x-inefficiency means that economic man is not ‘all there’. Members of firms are not completely rational (in the conventional sense), they are, in Leibenstein’s (1976, p. 72) words, ‘selectively rational’.

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3

NARROW AND BROAD RATIONALITY

We now move away from Leibenstein’s initial challenge. Let us say that a Chinese bank owned by the state in 1980 is producing with costs 50 percent above the cost frontier. The government starts a liberalization program privatizing banks and allows new banks, both foreign and domestic, to enter the market. The response is that by the year 2000 the once Chinese monopoly bank moves closer to their cost frontier. The bank is more X-efficient, but, are the employees of the bank more rational in 2000 than in 1980? Let us speculate a bit. The employees started to pay more attention to details about how to lower their costs and get closer to the cost frontier. Managerial efficiency improved. According to Leibenstein the answer would, therefore, be that they are being more rational. In Adam Smith’s (1994) view being rational means having reasons for your behavior. Maximizing utility or income or anything is not a criteria for rationality. Seeing a clear advantage of doing X and acting in a way consistent with X is rational behavior. The individual is acting consistently with what they believe to be their own self-interest. Hence being X-inefficient can be interpreted as consistent with rational behavior given the preferences of the economic agents Herbert Simon comments: ‘The rationality of The Wealth of Nations is the rationality of everyday common sense. It follows from the idea that people have reasons for what they do. It does not . . . assume any consistency in what factors are taken into consideration in moving from one choice situation to another’ (Simon 1997, p. 7). Or consider Ludwig von Mises’s use of the term rationality: ‘The fundamental thesis of rationalism is unassailable. Man is a rational being; that is, his actions are guided by reason’ (Mises 2005, p, 269). Or, ‘Action is, by definition, always rational. One is unwarranted in calling goals of action irrational simply because they are not worth striving for from the point of view of one’s own valuations’ (Mises 2003, p. 3). Similar to Smith, Mises uses a broad definition of rationality. At the other extreme is subjective expected utility (SEU). Rational behavior, according to SEU, first means that the person has a well-defined utility function which allows them to assign a cardinal number reflecting the utility of future events. Second, the person faces a clear set of alternatives they can choose from. Third, they can designate a joint probability distribution for all future events. Fourth, the person maximizes expected value or utility. The SEU-person is not unlike an adult Simba from The Lion King who, standing on the cliff overlooking all the land below, controls everything he sees. Alas, Simba is a fantasy created by the writers of the story. In The General Theory Keynes made a statement about classical theory reminiscent of Leibenstein’s reaction to neoclassical theory: ‘Our criticism of the accepted classical theory of economics has consisted not so much in finding logical flaws in its analysis as in pointing out that its tacit assumptions are seldom or never satisfied, with the result that it cannot solve the economic problems of the actual world’ (Keynes 1965, p. 178). Likewise, Leibenstein’s criticism of orthodox neoclassical micro theory was the assumption of complete rationality and the restriction of the definitions of (in)efficiency limited to allocative (in)efficiency. Keynes challenged the (full) rationality assumption and gave several examples: the money illusion, the formation of incorrect expectations by business people, the minimum rate of interest needed for investment, and the presence of ‘animal spirits’. These spirits are spontaneous urges which direct our behavior, and are contrasted

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with mathematical or rational expectations. Having asserted human lapses from full rationality Keynes then asked what the implications for economic theory are. His answer: deviations from full-employment. As Simon points out, a ‘cottage industry’ developed trying to show that less than full employment was consistent with full rationality (Simon 1997, p. 15). Likewise a cottage industry, albeit a smaller cottage, developed to show how what Leibenstein referred to as X-(in)efficiency is no in-efficiency at all; it is an efficient solution when all the constraints are identified. Smith’s use of the term rationality was a broad definition of rationality. To be rational means to have reasons for what you do. Therefore, Smith would not see irrationality everywhere in most actions. Simon refers to the broad definition as subjective or bounded rationality. He says that rationality ‘denotes a style of behavior that is appropriate to the achievement of given goals, within the limits imposed by certain conditions and constraints’ (Simon et al. 1992, p. 123). The conditions and constraints in the broad definition are subjective or perceived characteristics of the environment or the person himself. Keynes’s was a more narrow definition. For example, to be rational means to be free of money illusion, and a person is either free or not free of the money illusion. Leibenstein took a narrow definition of rationality and efficiency. Firms which are not on the cost frontier are inefficient, and the members of those firms are not fully rational. Taken in historical context, Leibenstein’s position is understandable because he was reacting to a neoclassical theory which asserted the impossibility of non-allocative (in)efficiency or irrationality. Leibenstein took the view that there are explicit criteria for both efficiency and rationality, and the firm and its members either meet those criteria or they do not. No excuses, no ‘because’s’ accepted. Once Leibenstein presented XE theory and empirical evidence began to pile up, the critics of XE theory decided that neoclassical micro theory had no need of the concept because the broad definition handles all human behaviors. Why Does X-(in)efficiency Exist? Some critics of X-efficiency theory insist that it does not exist. I believe that what they mean is that it cannot exist. Critics have engaged mostly in presenting arguments which are merely tautologies (Frantz 1997). Assuming that it does exist, what does it look like? It looks like a firm producing below its production frontier and/or above its cost frontier. X-(in) efficiency is not about deviations from P 5 MC, but about costs above minimum and output below maximum. Why does it exist? Leibenstein presents several interrelated reasons. First, the human personality contains two parts, a superego and an id. The superego wants to work hard, improve itself, do things correctly, and find solutions to problems using rational decision-making processes. The id wants to be free of work. The id employs lazy decision rules and only moderate work effort, both of which raise costs above the technological necessary minimum. Second, managers are only rarely the owners, creating an ‘agency problem’. Third, workers have discretion about levels of work effort. Fourth, monopoly power gives employees an environment in which they can pursue their own interests rather than the firm’s interests. In what sense does this create an inefficiency? In the sense that the firm is not doing as well as it can. Are the firm’s members less than economic-man rational? Yes in the sense that their behaviors create (X-)inefficiency. Again, this is rationality in a narrow sense; there are criteria for rationality and the firm’s

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Rationality, globalization, and X-efficiency among financial institutions 279 members are said not to meet those criteria. Had they met them then the assumption is that the firm would be producing on its frontiers. In the next section I present a few of the rapidly increasing studies on X-efficiency in order to illustrate differences between the narrow and broad definitions of efficiency and rationality.

4

ALLOCATIVE AND X-EFFICIENCY

Market power adversely affects allocative efficiency. Power gives a firm the ability to raise price above marginal cost and to restrict output in such a way that the industry’s output rate is below the competitive level. The price and output deviations are known as the deadweight welfare loss or the ‘Harberger triangle’, named after Arnold Harberger (Harberger, 1954). The market is inefficient because the optimum amount of resources has not been allocated to the production of the commodity. However, the firm with market power is efficient as long as it sets its output rate correctly, where MR 5 MC; sets its price correctly, the point on the demand curve when MR 5 MC; and minimizes its costs of production for a given rate of output. Since these three conditions are assumed, the firm is efficient even when the market is inefficient. Stated in different terms, the firm is always assumed to be efficient. Since firms are assumed efficient, the only definition of efficiency which has any economic implications is allocative – market – efficiency. These assumptions have allowed economic theory to focus on the efficiency of markets while placing much less attention on the internal efficiency of firms. X-efficiency is the name given to non-allocative efficiency by Harvey Leibenstein. Empirical estimates of the size of allocative inefficiency reveal that for the entire economy allocative inefficiency is less than 1 percent of gross domestic product (GDP). Some estimates have it between 0.001 and 0.0001 of GDP. For a $16 trillion GDP this is equal to between $16 billion and $1.6 billion. Each year Americans spend $18 billion on specialty coffee, and $7 billion on potato chips. Allocative inefficiency is, to use a line from the Godfather, ‘small potatoes’. It is, for all intents and purposes, insignificant. Robert Mundell (1962), the 1999 winner of the Nobel Prize in Economics, thus lamented that if inefficiency is insignificant then so are economists! Fortunately inefficiency is not limited to allocative inefficiency. X-inefficiency – internal inefficiency of the firm – costs that are higher than technologically necessary costs – has been estimated to be in the area of 3 percent of GDP. For a $16 trillion economy this is $480 billion. (An anonymous source reports that when Mundell heard this his blood pressure dropped 15 points.) One reason X-inefficiency is larger than allocative inefficiency is because X-inefficiency applies to every unit of output produced by a monopoly firm, whereas allocative inefficiency only applies to the change in output due to the higher price charged by the monopoly firm. The equation to calculate allocative inefficiency is: ((DQDP/2) (% national income in industries creating the misallocation)). Leibenstein challenged some of the core assumptions around which economics is built. He added a behavioral element to economics; the behaviors which result in efficient and rational behavior, and the conditions which are most conducive to both. Leibenstein, as I

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have said elsewhere (Frantz and Leeson, 2013), is part of the first generation of behavioral economists, together with Simon, Katona, Hayek, Nelson, and Winter.

5

STUDIES ON X-(IN)EFFICIENCY

The only studies included here are those in which the author(s) state that their research is about X-efficiency. There are thousands of other studies which are almost identical in terms of topics, research methods, and conclusions which do not mention X-efficiency. The sample here is a small sub-sample of the more than 150 articles on China, Taiwan, Korea, and Pakistan (Frantz 2007, 2016). China’s Banking System Until recently China’s financial sector was not a modern financial sector which converts saving into investment. The financial system was a tool of the central government’s political decisions. Transforming Chinese banks into modern commercial banks required modern corporate governance, for example: independent bank directors; effective operational mechanisms, risk management and transparency; clear working goals, market share and/or profits; and international competitiveness. One of the major institutions of the Chinese banking system has been the Big Four state-owned commercial banks (SOCBs). The SOCBs have been noticeably inefficient with negative profits and a high level of non-performing loans (NPL). One cause is that the SOCBs provided services to stateowned enterprises (SOEs) – one monopolistic institution servicing another monopolistic institution – and hence there was a general lack of incentives for efficient behavior. The banks’ behavior was more politically motivated than efficiency motivated. Mao died in 1978 and shortly thereafter China set out to increase economic efficiency. Beginning in 1979 China began creating a ‘two tier’ banking system: the People’s Bank of China (PBC), and four state-owned banks (SOBs). The latter included the Bank of China (BOC). The PBC remained China’s central bank, but commercial activities were the focus of the four state-owned banks. Beginning in 1985 the state-owned banks had nation-wide branches and were accepting deposits and making loans. By 1986 they were engaged in universal banking. Small and medium-sized commercial banks were introduced in 1985 in order to promote competition. The ‘Decision on Financial System Reform’ in 1993 promoted a competitive commercial banking sector. The PBC remained in charge of monetary policy, but in 2003 the China Banking Regulatory Commission (BRC) took over the regulatory function formerly in the hands of the PBC. The BRC allows foreign institutions to own as much as 25 percent of a Chinese financial institution. Three state-owned policy banks (PBs) were created in 1994 to make loans formerly done by the (SOB). In 1999 China created four Asset Management Companies (AMCs), one for each of the Big Four SOCBs. Formed as a way to compensate the Big Four for their service to the nation, the AMCs were created to take over the Big Four’s NPLs. In 1999, NPLs valued at about 1.5 trillion renminbi, an amount equal to about 20 percent of China’s GDP, were taken over. Eleven joint-stock owned banks were created in 2005. It is important that the primary stockholders of these ‘private enterprise’ banks were the state or SOEs. There were 111

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Rationality, globalization, and X-efficiency among financial institutions 281 city commercial banks owned by local public and private institutions, three rural commercial banks, and 35 544 rural credit coops. By 2004 there were about 204 foreign bank subsidiaries. Between 1992 and 2004 a significant change in both deposits and loans was the declining (but still large) share by state-owned banks and the increase in both by jointstock owned banks. The Commercial Bank Law in 1995 addressed the intent to create a modern financial system, meaning a set of institutions which would protect both the banks and the bank’s customers, having management motivated by efficiency considerations, while at the same time promoting a socialist market economy. In 1998 the government ended credit quotas. Thereafter banks could adjust interest rates within a ‘modest’ amount to take risk into account. In 2001 China joined the World Trade Organization (WTO) and had five years to open up their banking sector to international competition. Opening the economy to foreign interests was essential for China’s membership of the WTO. Foreign banks were required to have unrestricted access to the Chinese financial sector. In October 2005 the China Construction Bank initiated an initial public offering (IPO), China’s banking sector’s first IPO. The IPO was listed on the Hong Kong stock exchange. This was the first time that Chinese stock was listed in an overseas stock exchange. By 2007–09 Chinese banks were the largest commercial banks in the world. The various reforms have had positive effects. For example, among the largest state-owned banks the ratio of NPLs to total loans, which was about 30 percent in 1999, dropped to 10.5 percent in 2005, then to 6.7 percent in 2007, and further to 2.8 percent in 2008. Bank profits, measured as either return on assets (ROA) or return on equity (ROE) increased. From 2002 to 2006 the ROE and the ROA increased about three times, reaching levels of about 11 percent and 0.5 percent, respectively. The recession of 2007–09 had intense effects on the world’s financial sector. On the one hand, financial giants such as Citigroup and the Royal Bank of Scotland saw their stock prices fall by more than 95 percent. On the other hand, China’s three large SOCBs became the world’s three largest commercial banks. It was only two years earlier, 2005, that saw the first Chinese bank IPO, and four years earlier that China joined the WTO. The changes the Chinese economy went through would seem to provide a different set of incentives to managers and other employees. Did managers and other employees become smarter, more rational? X-efficiency among Chinese Banks Yao et al. (2008) look at Chinese commercial bank efficiency both before and after China joined the WTO in December 2001. Pressure for reform came not only from membership in the WTO, but also from foreign banks entering the Chinese market. Efficiency and productivity is measured using the data envelope analysis (DEA) and the Malmquist index. Data envelope analysis is a non-parametric method of measuring a firm’s distance from their production or cost frontier. The Malmquist index measures productivity differences between firms (or entire economies). The period of study is 1998–2005. Average X-efficiency for all banks over the entire period was 0.85. Thus, on average a Chinese bank produced 15 percent below their production frontier or 15 percent above their cost frontier. The range by individual banks was 0.96 (Bank of China) to 0.64 (Guangdong Development Bank, a joint equity bank). China entered the WTO in December 2001. Average XE score from 1998 thru 2001 was 0.82. From 2002 to 2005 the score was 0.90.

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Clearly, XE increased after China entered the WTO. The average score increased over the entire period, from 0.86 in 1998 to 0.92 in 2004. Total factor productivity of the entire sample banks increased significantly by 5.6 per cent per annum over the data period. Of this 5.60 percent figure efficiency growth was 2.88 percent per year and technical change growth of 2.64 percent per year. Efficiency growth measures the extent to which banks approach the frontier, while technological growth measures how fast the frontier is shifting out. The majority of productivity growth among state-owned (joint equity) banks was efficiency growth. After China joined the WTO total factor productivity grew at an annual rate of more than 10 percent. The WTO provided strong incentives for productivity (technological growth) and (X-)efficiency growth. The authors conclude that the Big Four need ‘minimum intervention’, managers with ‘professional qualifications’, and mergers and acquisitions (M&A) allowing them to take advantage of economies of scale and scope. Rezvanian et al. (2011) investigate how entry into the WTO affected the performance of Chinese banks. The sample, which covers the years 1998–2006, consists of 62 banks representing 66 percent of Chinese bank assets. For the entire period the average XE score was .94. This means that the average banks produced 6 percent above their cost frontier. The average scores for allocative efficiency (AE) – optimal factor ratio – and scale efficiency (SE) – optimal size – were 0.98, and 0.94, respectively. As reported in most studies XE is lower than either AE or SE. The pre-WTO period was designated as 1998–2001, while post-WTO was 2002–06. When Tobit regressions are run separately for each ownershipgroup – foreign, domestic, Big Four, joint stock commercial banks (JSCBs), and city commercial banks (CCBs) – XE increased for each group after China joined the WTO. One of the independent variables measures ‘regulatory quality’. Their definition is: ‘Regulatory quality measures the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development’ (Rezvanian et al. 2011, p. 449). In a regression with ‘overall efficiency’ being the dependent variable, although regulatory quality has a positive coefficient in two of three regressions, none are statistically significant, which can be a product of sampling or sample size issues. Overall technical efficiency (OTE) includes both X-efficiency and scale efficiency. The change in OTE has two components: technological change (T), and total factor productivity (TFP). The former represents a firm catching-up to the frontier, while the latter represents shifts in the frontier. During the pre-WTO period, banks of every ownership form experienced lower T and TFP. During the post-WTO period, banks of every ownership form experienced progress with respect to T and TFP, and XE. The WTO applied pressure and Chinese banks responded – as expected. Wu et al. (2009) estimate the effects of a bank issuing an IPO on ROA. Their sample is 14 commercial banks including the Big Four for the period 1996–2004. They find that ROA increases from the time the bank issues an IPO through to the period immediately following the issuance of the IPO. However, over time ROA falls. Why the absence of good long-run performance? The authors’ reasons include the following three reasons. First, banks tend to see the stock market as a way to raise short-term funds. Second, the relatively under-developed state of the Chinese stock market has led to decisions about capital allocation based more on speculation and less on ‘sound fundamentals’. Third, funds raised in the stock market are too often used to open new branches and hire more staff; new technologies are too often ignored. A poorly functioning stock market and mis-

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Rationality, globalization, and X-efficiency among financial institutions 283 guided management, even in the face of increased competitive pressures, have reduced the ability of banks to garner long-run benefits from the stock market, and this has reduced XE below what it would have been with a better functioning capital market and better management. Luo and Yao (2010) use data from 14 Chinese banks listed on a stock exchange to investigate the effects of being listed on bank efficiency. The authors use data on three bank ownership forms: SOBs, joint equity banks (JEBs), and CCBs. The data show that X-efficiency increased from the year before to their IPO year. Second, one year after the IPO, X-efficiency either remained constant or fell by a very small amount. Third, the overall effect of the IPO is to raise X-efficiency by about 4.1 percent. Fourth, X-efficiency among SOCBs is below CCBs and JEBs by about 2 percent. Two reasons given are overexpansion (too high fixed costs) and redundant staffing, implying that the marginal product of labor is very low. Jiang et al. (2009) estimated efficiency for all major commercial banks in China which control more than 85 percent of bank assets for the period 1995–2005. The average level of X-efficiency is about 0.70. Joint stock commercial banks are overall the most X-efficient with an average level of 81 percent, and were between 8 percent and 18 percent more X-efficient than state-owned banks. Joint stock commercial banks used their greater levels of XE to generate profit levels closer to the maximum possible level of profits. García-Herrero et al. (2009) investigated the causes of low profits among 87 Chinese banks accounting for more than 80 percent of total assets for the banking sector for the period 1997–2004. They find that banks which are less X-efficient are also less profitable; the quality of management decision making being an intangible bank-specific factor. The authors point out that the literature reports that state-owned banks are 8 percent to 18 percent less X-efficient than non-state-owned banks. Fu and Hefferman (2007) studied four state-owned and ten joint-stock owned banks between 1985 and 2002. They utilize a stochastic frontier approach (SFA). The SFA decomposes the error term into random errors and inefficiencies. X-efficiency is measured as the cost for the best practice bank/actual cost of an individual bank. Estimates of X-efficiency are separated for the two reform periods as they define them: 1979–92, and 1993–2002. Their overall results show that on average banks are operating 40–60 percent below the X-efficiency frontier; the average level of X-efficiency was 0.4 to 0.6. On average, privately owned joint-stock owned (privately owned) banks were about 20 percent more X-efficient than the state-owned commercial banks. Yao et al. (2007) used data from 22 banks over the period 1995 to 2001, and a stochastic frontier production function to measure the effects of ownership form and a hard budget constraint on X-efficiency. (A hard budget constraint means that a firm which does not at least break even will face consequences from stakeholders.) Their empirical results show that on average X-efficiency was 0.65. On average state-owned banks were 8–18 percent less X-efficient than private-sector banks. Not surprisingly, banks facing a looser budget constraint tended to be less efficient than banks facing a tighter budget constraint. Fu and Hefferman (2009) showed that SOCBs, especially the Big Four grew beyond optimal size and into the area of diseconomies of scale. The resulting higher costs reduced X-efficiency. State banks contributed to the decline in X-efficiency in another way. They continued to be pressured to make loans to inefficient SOEs, which during stage 1 were making profits. Joint stock commercial banks, whose directors/owners were SOCBs,

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were also pressured to lend money to failing SOEs. Although NPLs fell for the banking system as a whole to 15.5 percent by 2004, the NPL ratio for the Big Four was 50 percent to 60 percent. Politics rather than economics continued to take its toll and a loss of X-efficiency was the result. They find that JSCBs are about 7 percent more X-efficient than are SOCBs. Chen et al. (2005) examined allocative and X-efficiency among 43 Chinese banks during the period 1993–2000. The sample included four SOCBs, seven nation-wide JSCBs, 24 regional JSCBs, and eight international trust and investment trust banks. The authors define X-efficiency as including both technical (producing on the frontier), and allocative efficiency (using the right combination of inputs): XE 5 TE × AE. XE is the ratio of minimum to actual costs. Over the period 1993 to 2000 technical-efficiency among all banks averaged 0.77, allocative efficiency averaged 0.61, and X-efficiency averaged 0.469. The authors also present estimates for the three forms of efficiency both before (1993–94), and after (1996–2000) China’s 1995 financial deregulation via the Commercial Bank Law of 1995. Before deregulation, 1993–94, the average X-efficiency score was 0.47. Post-deregulation, 1996–97, average X-efficiency was 0.53. However, the increase was short lived. By 2000 XE had decreased from 0.53. Deregulation had the expected effect in the short run. Having made some initial changes which increased X-efficiency, the old behavior patterns apparently returned. Banks may have become more X-efficient in order to satisfy private sector lenders. Having done so, having received loans, managers apparently returned to old behavior patterns. Will all banks approach the same level of XE over time? If the market for corporate control is functioning well then the answer would seem to be yes. Fung and Chen (2010) investigate two major sources affecting productivity among banks, and two major theories of productivity convergence among banks. Their sample consists of 32 Hong Kong banks for the period 1993–2002. The two major theories of the determination of productivity is X-efficiency and scale efficiency. Fung and Chen (2010) focus on management’s ability to control cost, to be more X-efficient. There are two different types of productivity convergence: absolute and conditional convergence. Absolute convergence means that productivity differences among banks gets smaller over time. Conditional convergence means that an initial difference in productivity will create long-run differences in productivity, and a bank’s long-run productivity level depends on their level of X-efficiency. In other words, every bank approaches their own long run, steady-state, level of productivity. Their evidence is consistent with conditional convergence; X-efficiency is a key variable in the determination of long-run productivity. Kwan (2006) investigated causes of X-(in)efficiency among Hong Kong (HK) banks from 1992–99. Interest rates were regulated until 1994 and the regulation gave banks some protection from competition. Deregulation would be expected to increase pressure for a higher level of X-efficiency. At the same time Hong Kong had a large number of banks, and many of the medium-sized banks are private and controlled by the founding family. The publicly owned banks had agency problems also reducing pressure for X-efficiency. The net effect of these situations? For the full sample, average X-inefficiency declined by 36 percent (it fell from 45 percent in 1992 to 29 percent in 1999). For large banks, average X-inefficiency fell from 37 percent to 26 percent (30 percent), and among small banks it

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Rationality, globalization, and X-efficiency among financial institutions 285 fell by 26 percent (31 percent to 23 percent). Average X-inefficiency over the entire period ranged from 16 percent to 30 percent. The fact that smaller privately held banks were more X-efficient is consistent with the agency costs of the separation of ownership and control. Taiwan, Korea, ASEAN, Pakistan Chan et al. (2011) investigate the efficiency of foreign banks in four Association of South East Asian Nations (ASEAN) countries, Indonesia, Malaysia, the Philippines, and Thailand, for the period of 2001–08. Efficiency was measured using the stochastic frontier analysis. In each year from 2001 to 2007 Malaysia had the highest level of X-efficiency. In 2001 and 2007 their X-efficiency rating was 0.83 and 0.84, respectively. The nation with the lowest X-efficiency was Thailand, with an index of 0.73 in 2001 and 0.74 in 2007. Foreign banks in Malaysia were more X and profit efficient than foreign banks in the other countries; trade restrictions are lower in Malaysia than in the other countries. Malaysia also had the highest ranking of economic freedom. Chiu et al. (2010) study the effects of deregulation of the banking industry of Taiwan during the 1980s. The deregulation took place during the early 1980s under the aegis of the Commercial Bank Established Promotion Decree. Under the decree the number of banks increased significantly. The increased competition raised the issue of the efficiency of the banks. To answer how competition was affecting efficiency the government placed an emphasis on the bank’s credit rating, and looked at the relationship between a bank’s credit rating and their efficiency and productivity for the period 2001–03. The sample consists of 34 Taiwanese commercial banks. Productivity change is measured by the Malmquist total factor productivity (TFP) index, and is the product of efficiency change (X-efficiency) and technological change. X-efficiency is measured by the DEA. The results of their analysis shows that at a point in time banks with a higher credit rating have a higher level of X-efficiency on average. Second, after the bank’s credit is taken into account in the analysis of the bank’s level of X-efficiency, the bank’s average level of X-efficiency is higher. This is interpreted to mean that the bank’s credit rating is a measure of the bank’s overall (X) efficiency and the bank’s goodwill. For the entire period average X-efficiency increased by 4.2 percent, and technological change increased by 0.7 percent. Total factor productivity (TFP) increased throughout the period by 4.9 percent. Between increases in XE and increases in technological change, increases in XE had a larger effect on TFP. Chen et al. (2000) use data from 34 domestic commercial banks in Taiwan, in 1997, to test the efficiency of commercial banks. Efficiency was measured using data envelope analysis. Of the 34 banks, seven were publically-owned and 27 were privately owned. The average efficiency score, the authors use the term technical efficiency in an identical way as X-efficiency, was 0.93. Of the 11 highest scoring banks, ten were privately owned. The publicly owned banks had an average efficiency score of 0.88, while the privately owned banks’ average score was 0.94. Why the difference in average efficiency scores? According to the authors, the management of publicly owned banks do not make effective decisions. Part of this is due to the fact that they are expected to carry out political objectives rather than restrict themselves to pursuing economic efficiency. A second reason is that bad managers are too often kept on the job. A third reason is that privately-owned banks are more likely to use automated

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(less labor per unit of output) banking services, and this is correlated with higher bank efficiency. Choi and Hasan (2005) use a sample ranging from 14 to 21 Korean commercial banks for the period 1998–2002. This period is after Korea’s financial crises of 1997. In 1999 the Korean parliament passed the ‘Revised Bank Law’ which required more outside board members by Korean banks. The results of their analysis showed that after foreign ownership on the board of directors reaches a certain critical mass, foreign ownership and ROA are positively related with each other, and in a statistically significant manner. After the critical mass is met then either superior management strategies are imported (to Korea) or Korean management is pressured to do so on its own. The year 1999 was also the beginning of deregulation of the banking sector. The variables for both deregulation and foreign board members were both positive and generally statistically significant. Shin and Kim (2011) study the effects of bank restructuring of Korean banking beginning with the crises of 1997. Restructuring followed the beginning of the crises including mergers and acquisitions, and a reduction of commercial banks from 26 to 13. The economy recovered. From 1998 to 2007 rates of return on assets and equity, and the Bank of International Settlements (BIS) capital adequacy increased, while NPLs decreased. Market concentration measured by the Herfindahl–Hirschman Index (HHI) based on assets or deposits approximately doubled. Pure technical efficiency, or X-efficiency, averaged 0.64 for the entire period, with a range of 1.0 to 0.25. Scale efficiency averaged 0.85 (economies of scale) with a range of 1.0 to 0.22. Higher levels of X-efficiency and scale efficiency increase both rate of return on assets, market share and industry concentration. The effect of X-efficiency on profits and market share is taken as evidence for the efficient markets hypothesis; firms which survive and prosper are the more efficient firms. However, after 1997, bank profits were increased, not by higher levels of X-efficiency, but by mergers and acquisitions increasing scale efficiency. An et al. (2007) used a nationwide sample of multi-branch commercial banks in Korea. The period of study was 1987–97. The motivation was to estimate the effects of government control of a private enterprise bank on the bank’s performance. Private enterprise banks are still under government control since government appoints the bank’s chief executive officer (CEO). Banks strongly influenced by the government have a relatively high level of NPLs, and set aside a relatively small amount of capital to serve as loss provision. They also have higher cost levels, that is, lower levels of XE, and lower profits. The authors note that these results are consistent with a bank forced to make decisions based on political objectives rather than efficiency objectives. The authors conclude that higher XE requires respect for private property, minimum government domination, managers hired for their skill and not their political acumen, and free trade. Aftab et al. (2011) study the effect of banking liberalization of the early 1990s on the relationship between efficiency on stock performance among a sample of seven Pakistani banks for the period 2003–07. One result of bank liberalization is that owners and investors ‘have become increasingly interested in knowing determinants of bank performance and its relationship with share performance’ (Aftab et al. 2011, p. 3975). That is, they are becoming more attentive to X-efficiency. Stock performance is measured by the cumulative annual share returns (CASR), and X-efficiency by the DEA. Average XE scores assuming constant returns and variable returns to scale were 0.60 and 0.80, respectively.

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Rationality, globalization, and X-efficiency among financial institutions 287 Cumulative annual share returns ranged from 6 percent to 13 percent. The regression with CASR as the dependent variable shows that XE has a positive and statistically significant effect on CASR. About 11 percent of the change in CASR is explained by changes in X-efficiency. In other words, more efficient banks are more successful banks, providing support for the efficient markets hypothesis. Akhtar (2002) investigates bank performance in Pakistan using a sample of 40 banks for the year 1998. He defines X-efficiency as consisting of technical and allocative efficiency, ‘technical efficiency, which reflects the ability of a firm to obtain maximum output from a given set of inputs, and allocative efficiency, which indicates the ability of a firm to use the inputs in optimal proportions, given their respective prices’ (Akhtar 2002, p. 568). For all banks, average XE was 0.8. For publicly owned, privately owned, and foreign banks, the averages were 0.77, 0.80, and 0.75, respectively. In each case, allocative efficiency exceeded XE. Of course, since XE 5 AE times technical efficiency (TE). Therefore, AE would have to be larger than XE, the exceptions being if AE and TE were either 1.0, or 0.0. For all banks, TE and AE were 0.86 and 0.93, respectively. For publicly owned, privately owned, and foreign banks, TE and AE were, 0.85 and 0.90, 0.86 and 0.93, and 0.82 and 0.92, respectively. Allocative efficiency exceeds TE (XE), as it does in almost every study. However, the difference between estimates for public and private firms is quite small. Akhtar expected foreign banks to be more efficient when in fact they are less efficient. His explanation is that, ‘most of the foreign banks in Pakistan often target a niche market that is corporate sector which is more volatile and might make them inefficient (Akhtar 2002, p. 574).

6

SUMMARY AND CONCLUSIONS

The sample of articles reviewed here is consistent with the vast literature on X-efficiency over the past 15 years or so. What did we learn? Better managers, having better technology available, or being in an environment of deregulation produces higher levels of X-efficiency. Making decisions according to political decisions reduces X-efficiency. Are the politicians irrational? Definitely not, because they are relentlessly pursuing their goals as well as they can, and are often successful. Issuing an IPO increases X-efficiency, at least in the short run. Joining the WTO has the same effect: pressure for efficient behavior produces efficient behavior. Being in an environment of economic freedom creates more ‘positive’ pressure for efficiency. Were the managers inefficient/irrational before the IPO and/or joining the WTO? Not necessarily; they faced a different set of constraints and given those constraints they may very well have been acting rationally. From a narrow definition of rationality similar to the one used by Leibenstein, this is not correct. From a broad definition it is correct. In the environment of rapid change newer banks face tighter constraints than older banks and are more X-efficient. Managers of older banks can enjoy the ‘quiet life’, and they often do just that: nothing irrational about that. Stateowned commercial banks are less X-efficient; income guarantees can do that. Again, the managers/employees are pursuing their goals in a permissive environment. Their behavior may be a rational response to the permissive environment. Is there any inefficiency in their permissive-guided behavior? From the point of view of the firm they will be above their cost frontier producing with ‘excess’ costs, and hence

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there is X-inefficiency. Are the managers/employees rational? They could be. Are they thoughtless (the opposite of smart)? Not necessarily. Are they making errors in decision making? From the point of view of the firm, yes. From their personal point of view, maybe not. Leibenstein’s agenda was to question the assumptions that efficiency only means allocative efficiency and that humans are fully rational. He found it necessary to use a narrow definition of both efficiency and rationality. We can use broader definitions, and from broader definitions X-(in)efficiency can coexist with rational behavior. Also, behavioral economics need not be based on people being stupid. Herbert Simon told us decades ago that human rationality is bounded. Hayek wrote about why humans can hope for nothing more. Some are more bounded than others: size matters.

REFERENCES Aftab, M., S. Ahamad, W. Ullah and R. Sheikh (2011), ‘The impact of bank efficiency on share performance: evidence from Pakistan’, African Journal of Business Management, 5 (10), 3975–80. Akhtar, M. (2002), ‘X-Efficiency analysis of commercial banks in Pakistan: a preliminary investigation’, Pakistan Development Review, 41 (4), 567–80. An, J., S. Bae and R. Ratti (2007), ‘Political influence and the banking sector: evidence from Korea’, Oxford Bulletin of Economics and Statistics, 69 (1), 75–98. Chan, S. and M. Karim (2011), ‘Efficiency of foreign banks: evidence from selected (Association of Southeast Asian Nations) ASEAN countries’, African Journal of Business Management, 5 (14), 5617–26. Chen, T. and T. Yeh (2000), ‘A measurement of bank efficiency, ownership and productivity changes in Taiwan’, Service Industries Journal, 20 (1), 95–109. Chen, X., M. Skully and K. Brown (2005), ‘Banking efficiency in China: application of DEA to pre- and postderegulation eras: 1993–2000’, China Economic Review, 16 (3), 229–45. Chiu, Y., C. Ma and M. Sun (2010), ‘Efficiency and credit rating in Taiwan banking: data envelopment analysis estimation’, Applied Economics, 42 (20), 2587–600. Choi, S. and I. Hasan (2005), ‘Ownership, governance, and bank performance: Korean experience’, Financial Markets, Institutions & Instruments, 14 (4), 215–41. Frantz, R. (1997), X-Efficiency: Theory, Evidence and Applications, 2nd edn, Boston, MA: Kluwer Academic. Frantz, R. (2007), ‘Empirical evidence on X-efficiency, 1967–2004’, in R. Frantz (ed.), Renaissance in Behavioral Economics. Essays in Honor of Harvey Leibenstein, New York: Routledge, pp. 211–28. Frantz, R. (2016), ‘50 years of X-Efficiency research’, working paper, San Diego State University, San Diego, CA. Frantz, R. and R. Leeson (2013), Hayek and Behavioral Economics, New York: Palgrave Macmillan. Fu, X. and S. Hefferman (2007), ‘Cost X-efficiency in China’s banking sector’, China Economic Review, 18 (1), 35–53. Fu, X. and S. Hefferman (2009), ‘The effects of reform on China’s bank structure and performance’, Journal of Banking and Finance, 33 (1), 39–52. Fung, M. and A. Chen (2010), ‘Convergence of total factor productivity among banks: Hong Kong’s experience’, Global Finance Journal, 21 (2), 201–10. García-Herrero, A., S. Gavilá and D. Santabárbara (2009), ‘What explains the low profitability of Chinese banks?’, Journal of Banking & Finance, 33 (11), 2080–92. Harberger, A. (1954), ‘Monopoly and resource allocation’, American Economic Review, 44 (2), 77–87. Jiang, C., S. Yao and Z. Zhang (2009), ‘The effects of governance changes on bank efficiency in China: a stochastic distance function approach’, China Economic Review, 20 (4), 717–31. Keynes, J.M. (1965), The General Theory of Employment, Interest and Money, New York: Harcourt, Brace, and World. Kwan, S. (2006), ‘The X-efficiency of commercial banks in Hong Kong’, Journal of Banking & Finance, 30 (4), 1127–47. Leibenstein, H. (1966), ‘Allocative efficiency vs. “X-efficiency”’, American Economic Review, 56 (June), 392–415. Leibenstein, H. (1976), Beyond Economic Man, Cambridge, MA: Harvard University Press. Luo, D. and S. Yao (2010), ‘World financial crisis and the rise of Chinese commercial banks: an efficiency analysis using DEA’, Applied Financial Economics, 20 (19), 1515–30.

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Rationality, globalization, and X-efficiency among financial institutions 289 Mises, L. von (2003), Epistemological Problems in Economics, 3rd edn, Auburn, AL: Ludwig von Mises Institute. Mises, L. von (2005), Theory and History: An Interpretation of Social and Economic Evaluation, Indianapolis, IN: Liberty Fund. Mundell, R. (1962), ‘Review of L.H. Janssen, “Free Trade, Protection and Customs Union”’, American Economic Review, 52 (2), 622. Rezvanian, R., R. Ariss and S. Mehdian (2011), ‘Cost efficiency, technological progress and productivity growth of Chinese banking pre- and post-WTO accession’, Applied Financial Economics, 21 (7), 437–54. Shin, D. and B. Kim (2011), ‘Efficiency of the banking industry structure in Korea’, Asian Economic Journal, 25 (4), 355–73. Simon, H. (1997), An Empirically Based Microeconomics, Cambridge: Cambridge University Press. Simon, H., M. Egidi, R. Viale and R. Marris (1992), Economics, Bounded Rationality and the Cognitive Revolution, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Smith, A. (1994), The Wealth of Nations, New York. Modern Library. Wu, H., C. Chen and H. Lin (2009), ‘Can a stock market listing help to improve the operational performance of China’s banks?’, Journal of Economic Policy Reform, 12 (1), 13–28. Yao, S., G. Han and C. Feng (2008), ‘Ownership reform, foreign competition and efficiency of Chinese commercial banks: a non-parametric approach’, The World Economy, 31 (10), 1310–26. Yao, S., C. Jiang, G. Feng and D. Willenbockel (2007), ‘WTO challenges and efficiency of Chinese banks’, Applied Economics, 39 (5), 629–43.

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16 The evolution of governance structures in a polycentric system Edward McPhail and Vlad Tarko

INTRODUCTION A polycentric governance system is a system of several independent centers of authority and decision-making operating under an over-arching set of formal and informal rules (Ostrom et al. 1961; Ostrom 1991b, ch. 9, 1999; Ostrom 2005, ch. 9, 2010; Wagner 2005; McGinnis and Ostrom 2012; Aligica and Tarko 2012, 2013; Tarko 2015; McGinnis 2016). The overarching set of rules is necessary because the decisions of one center create positive and/or negative externalities upon others. Despite these externalities, the centers do not merge into a single unified and centralized decision-making unit for several reasons. First, the centers operate under heterogeneous beliefs and preferences which makes consensus-building too difficult (McGinnis 2016). In the language of the calculus of consent (Buchanan and Tullock 1962 [1999]), both the decision-making costs of centralized decision-making and the external costs of each central decision, imposed upon those who would disagree with the centralized decisions, would be too large. Second, the decision centers address a wide range of problems, and the solutions to each of these problems can have very different optimal scales (Ostrom et al. 1961). This means that decision centers have to find a way to face the problem that the scales of operation of administrative units are rigid, while problems are fluid and come at varied, and changing, scales. As Ostrom et al. (1961) first noted, and as Elinor Ostrom and her collaborators later documented across a wide range of examples (Bish 1971; Bish and Kirk 1974; Ostrom 1976; Ostrom et al. 1978; Bish and Ostrom 1979; Ostrom et al. 1988; McGinnis 1999), the solution to this administrative rigidity problem is to have smaller administrative units cooperate on a quasi-ad hoc basis to address larger-scale problems as they appear. The side effect of this solution is that, rather than having a hierarchical public administration organization, we are left, by necessity, with a polycentric one. Different decision-centers are constantly engaged in mutual adjustment, both in terms of competing with one another and in terms of cooperating to solve larger-scale problems (McGinnis 2016). Furthermore, in line with their heterogeneity of beliefs and preferences, the cooperation is conditional, involving a certain degree of free entry and free exit. A polycentric system usually does not have purely deliberate social-economic outcomes. Similar to a market, the outcome of the operation of a polycentric system is an emergent order which has certain unplanned features. These features may be desirable or less desirable. The way in which a polycentric system addresses undesirable outcomes is by altering the set of overarching rules, such that the emergent outcome will be improved, for example, by limiting some negative externalities or by making broader use of the knowledge discovered by one decision-center. Figure 16.1, adapted from the frameworks proposed by Aligica and Tarko (2012) and McGinnis (2016), illustrates the structural 290

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The evolution of governance structures in a polycentric system 291 Multiple centers of authority Externalities between centers Structure: Overarching system of rules Heterogenous beliefs and preferences

Mutual adjustment: Process:

Cooperation Competition

Certain degree of freedom to entry Certain degree of freedom to exit

Outcome: Emergent order

Nature of the problem Scale economies Re-evaluation of overarching rules:

Representation Collective choice mechanisms Information sharing

Figure 16.1

The structure and operation of a polycentric system

features of a polycentric system and the two processes involved: (1) the operational-level process, concerned with the actual production of public goods and the emergent systemlevel order; and (2) the collective-choice process, concerned with identifying problems, giving voice to different points of view about each problem, and reforming the overarching set of rules. The concept of polycentricity involves a generalization into the realm of public economics of the concept of markets. Markets are one special case of polycentricity (Ostrom 1991, pp. 229–31), but polycentricity aims to capture the characteristics of productive and sustainable emergent orders even outside the operation of the price system. Other examples of polycentric systems include the scientific community, competitive local public economies, common law, and international relations (Ostrom 1991, ch. 9). Generally speaking, the purpose of this concept is to provide insight into the conditions under which non-market emergent orders can be expected to lead to desirable outcomes. The concept of polycentricity was first developed by analogy to markets in the context of the metropolitan governance debate, when the mainstream of the public administration profession was arguing in favor of consolidating the metropolitan administrations into large centralized bodies that would take advantage of economies of scale (McGinnis

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1999; Aligica and Boettke 2009; Aligica and Tarko 2012; Boettke et al. 2013, 2016). The Ostroms dissented from this intuition, thanks to an analogy to markets, and highlighted the potential efficiency of local public economies: Duplication of functions is assumed to be wasteful and inefficient. Presumably efficiency can be increased by eliminating ‘duplication of services’ and ‘overlapping jurisdictions.’ Yet we know that efficiency can be realized in a market economy only if multiple firms serve the same market. Overlapping service areas and duplicate facilities are necessary conditions for the maintenance of competition in a market economy. Can we expect similar forces to operate in a public economy? (Ostrom and Ostrom 1977 [1991], pp. 163–97)

From their perspective, the most likely path to efficient public administration was not consolidation, but developing smart overarching rules that would allow productive ‘interorganizational arrangements’. Such arrangements ‘would manifest market-like characteristics and display both efficiency-inducing and error-correcting behavior. Coordination in the public sector need not, in those circumstances, rely exclusively upon bureaucratic command structures controlled by chief executives. Instead, the structure of interorganizational arrangements may create important economic opportunities and evoke selfregulating tendencies’ (Ostrom and Ostrom 1977 [1991], pp. 163–97). From this conceptual starting point, focused on facilitating the emergence of productive bottom-up orders in the realm of public economies, the Ostroms and their colleagues have gathered comprehensive empirical evidence regarding the advantages, in terms of efficiency, voice, and resilience, of such competitive public structures (Bish 1971; Bish and Kirk 1974; Ostrom 1976; Ostrom et al. 1978; Bish and Ostrom 1979; Ostrom et al. 1988; McGinnis 1999). Half a century later, we have the empirical evidence regarding the validity of the polycentric perspective, but a new need is felt for a deeper theoretical understanding of the competitive aspect of polycentricity. The Bloomington studies of local economies have shifted the debate towards much greater skepticism regarding centralization, while supporting the idea of institutional competition. However today, the metropolitan debate, with respect to police services in particular, has reignited, owing to the failure of ‘community policing’ (Boettke et al. 2013; 2016), and the idea that institutional competition leads to a ‘race to the bottom’ still persists (for example, Geradin and McCahery 2004). The simplest theoretical approach has been to import into public economics the model of perfect competition. Indeed, the Tiebout ‘voting with your feet’ model (Tiebout 1956; Ostrom et al. 1961; Donahue 1997; Caplan 2001; Howell-Moroney 2008; Boettke and Marciano 2016) is thought to generate efficient outcomes primarily thanks to an assumption of relatively low exit costs. However, as noted by authors like Buchanan and Goetz (1972), Donahue (1997) and Caplan (2001), such an assumption is often unwarranted (see also Boettke and Marciano 2016). For example, capital is often difficult to relocate. Moreover, public services and regulations are bundled into large packages and, hence, we can rarely choose the prefered bundle of public services in the same manner as we choose a prefered bundle of private goods. Furthermore, the payment for public services, in terms of taxes, also does not occur in the same straightforward manner as in private markets. Paying taxes is more similar to providing someone a grant in the hope of then receiving a particular bundle of services. To make matters worse, the rational ignorance of voters further diminishes their ability to constrain the public administration (Lyons

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The evolution of governance structures in a polycentric system 293 and Lowery 1989; Lowery 1998; Boettke et al. 2011). This makes it difficult to reveal accurately citizens’ preferences about public goods, and to establish optimal levels of expenditure. As noted by Vincent and Elinor Ostrom (1977 [1991], pp. 163–97), while ‘[a]n expression of demand in a market system always includes reference to what is forgone as well as what is purchased’, by contrast, ‘[t]he articulation of preferences in the public sector often fails to take account of forgone opportunities’. As they put it, ‘[w]hereas the income received for providing a private good conveys information about the demand for that good, payment of taxes under threat of coercion indicated only that taxpayers prefer paying taxes to going to jail. Little or no information is revealed about user preferences for goods procured with tax-supported expenditures’. These limitations do not imply that institutional competition is meaningless; they only lead us to the conclusion that we need to model competitive public economies using oligopoly and monopolistic competition models, rather than the perfect competition model. Depending on which model of oligopoly we use (Kreps 1990, ch. 10), such as Cournot, Stackelberg or Bertrand, and focusing on the quality of public goods rather than on quantity, we would get different predictions with respect to the ‘price’ (that is, tax rates) and quality of the services provided. With Cournot competition, when two (or few) competing providers simultaneously choose how much to provide, we obtain a higher price than the perfectly competitive model, but lower than the monopoly price. In the case of Stackelberg competition, when one of the providers acts as a leader and the other as a follower, the result is that the leader gets a higher market share. If capital costs are low, the price under Stackelberg competition is lower than under Cournot, while the opposite holds if capital costs are high. Under Bertrand competition, the providers compete in terms of price rather than quality. If each jurisdiction could in principle satisfy the entire demand (that is, no constraints of potential capacity), the outcome of Bertrand competition, even with just two providers (as long as collusion is avoided), is the same as under perfect competition. In case of capacity constraints, some of the customers end up paying more, while the rest of the customers pay the lowest price. It seems to us that most public economies are better described by the Cournot and Stackelberg models because of relatively weak constraints on tax collection – the public sector operates under a ‘soft budget constraint’ (Kornai 1986). Even so, tax competition among jurisdictions introduces a certain element of Bertrand competition into the picture. To the extent that Bertrand competition is present in public economies, it is usually under capacity constraints. Such capacity constraints are partly natural, as a result of people’s preferences against too high population densities, and partly artificial, owing to immigration restrictions and constraints created by zoning laws (for example, leading to higher housing prices and rents). This brief discussion of oligopoly models shows that, although imperfect Tiebout competition cannot be expected to lead to perfectly efficient results, we still, nonetheless, have reasons to believe that a certain tendency towards efficiency persists. In what follows, we build a model of (1) interjurisdictional competition and (2) interjurisdictional cooperation for providing larger-scale public goods, under very stringent assumptions about knowledge and benevolence. We assume that households have no knowledge about other jurisdictions, and decide purely on the basis of their satisfaction with their current jurisdiction (that is, leaving decisions are blind leaps into the unknown), and we assume that local governments operate without any inherent concern for the public interest, that

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is, as revenue-maximizing Leviathans. We can see this as a ‘robust political economy’ (Levy 2002; Farrant 2004; Leeson and Subrick 2006; Pennington 2011; Boettke and Leeson 2012) model of the operation of a polycentric governance system. In line with the Bloomington approach, we build a model of both interjurisdictional competition and interjurisdictional cooperation. This allows us to model the bottom-up emergence and evolution of governance structures, in line with the perspective first put forward by Ostrom et al. (1961) and later elaborated both theoretically and empirically by Elinor Ostrom and others. Our robust political economy assumptions about limited knowledge and benevolence allow us to avoid some of the main sources of skepticism regarding the original Tiebout model. The next section starts by building a simple model which assumes a given scale of jurisdictions. Section 3 relaxes this assumption describing a process by which the scale at which a public service is provided increases or decreases, hence describing the emergence and evolution of larger-scale governance structures. Section 4 shows that alternative mechanisms to exit, such as voice, emerge as a consequence of some households either not having the resources to move or still holding hope that the quality of public services will improve. We, thus, see voice as a second-best solution: only households that find it too costly to move (owing to a variety of costs ranging from simple moving costs all the way to the costs of leaving behind their social networks and social capital) use voice as an attempt to improve the public services they receive. While exit provides a direct, unconditional, benefit to the household as a result of its own action, voice provides a benefit only conditional on what other households also agree to do.

2 2.1

A MODEL OF ENDOGENOUS QUALITY JURISDICTIONS Assumptions

Some of the classic endogenous quality (EQ) models rely on public reputation as a quality assurance mechanism (Klein and Leffler 1981; Allen 1984; Shapiro 1982, 1983; Rogerson 1983, 1987). By contrast, we develop here a model focused on individual household exit. A public reputation mechanism assumes that households communicate with one another and share their experiences of various jurisdictions. However, some of the strongest critiques of the Tiebout model rest on the empirical observation that the amount of such information that people in fact have is very limited (Lowery and Lyons 1989; Lyons and Lowery 1989; Lowery 1998; Boettke et al. 2011). In the EQ model presented below, inspired by Gintis (1989) and McPhail (1997, 2001), households know only the quality history of their own consumption. They do not know the quality histories of other households, only their own. Furthermore, we analyze the situation for just one public service at a time, rather than for the bundle of all services simultaneously. At first, this may seem an odd choice, given that public services within a jurisdiction are a package deal, and local citizens and firms do not pay for each service individually, but only pay a unified tax. However, there are two strong reasons for developing the model this way. First, as long argued by the Bloomington school, from the classic Ostrom et al. (1961) paper onward, different services are optimally provided at different scales, and the administrative structure of governance has to adapt in various ways to this diversity. In this

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The evolution of governance structures in a polycentric system 295 chapter we are not interested in actually modeling the details of the negotiation process between jurisdictions and the exact political mechanisms by which a given service ends up more or less centralized. Instead, we provide only an equilibrium model highlighting the broad presumed tendency towards which the complex underlining negotiation process tends to lead. However, the point still holds that we need to analyze the situation issue by issue. The second reason is related to the issue of citizens’ limited knowledge. Boettke et al. (2011) cite several empirical studies demonstrating that citizens have very poor comparative knowledge about the public services offered by different jurisdictions and about the tax rates in different jurisdictions. This evidence provides a strong reason to dismiss Tiebout competition models that bundle all the services together, that is, which assume that households do a comprehensive comparative analysis of all services across all jurisdictions before moving. By contrast, we assume the opposite idealization, namely, that the moving decision of a given household depends only on one service, that is, the service they regard the most important. For example, a household that cares primarily about the quality of the public schools will probably be informed about the public schools, but not about many other public services. As such, the results of the surveys mentioned by Boettke et al. (2011) are neither surprising nor relevant. Because of this more realistic assumption about knowledge, we address the Tiebout competition with respect to each public service separately. Each public service will have a different set of ‘marginal citizens’ who are sufficiently unsatisfied about the quality history of the public service they have received to explore their moving options. Each such set of ‘marginal citizens’ is much smaller than the entire population of a jurisdiction. As this approach is based on ‘marginal citizens’, the model may not apply to all public services, because the least important public services may not have any marginal citizens. That is, perhaps no one’s decision to move would rest on the quality of some of these least important services. As such, our model only covers the most important issues. The model can be extended by assuming that individual households’ decision to move depends on more than just a single issue, but for simplicity’s sake we do not cover such extensions here. 2.2

The Model

Suppose a jurisdiction produces an indivisible public good. Households enjoy one unit of the good per period. For the sake of expositional clarity, we assume that households behave according to a specific functional form. Production occurs under constant returns to scale at a per unit cost of c(q; w), where q denotes the quality of the good, and w is the vector of factor prices. We also assume that producing a higher quality service is more expensive, that is, ∂c/∂q, ∂2c/∂q2 > 0 for q > 0, c(0) 5 ∂c/∂q(0) 5 0, and that there is a finite qmax such that limq→qmax c(q) = ∞. Let T > 0 be the tax or fee paid by the household to the jurisdiction for the good, and we denote jurisdiction’s per unit profit (rent) as p 5 T − c(q; w). In practice, a jurisdiction collects taxes, and then has a separate fiscal decision-making process, determining how much to allocate to fund the production of each public good. For our purposes, it is sufficient to note that a household pays a de facto sum T for a given service. This T is an imperfect equivalent of the price citizens pay for the service, although, as we have mentioned in the introduction, this ‘price’ is unlikely to properly reflect the opportunity costs of providing a particular quality of the public good.

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We define the endogenous quality model as consisting of households that behave according to the moving-out algorithm described below and of jurisdictions that behave as revenue-maximizing Leviathans. 2.2.1 Households moving algorithm In what follows we focus on households ‘voting with their feet’. However, the exact same logic applies to the movement of capital; indeed, some of the criticism of the Tiebout model has focused on capital more than on households (for example, Caplan 2001). Although, for simplicity, we focus on households, the results should, thus, be interpreted in a more general fashion as referring to both households and firms. We consider a set of households who stay with a jurisdiction for a number of periods, and then contemplate moving when dissatisfied. These are the ‘marginal households’ for the public good under consideration, that is, those households who care about this public good above all others and whose moving decision is influenced by the quality of this public good. We assume that each household chooses a level of service quality qcrit and treats the service in each period as a success if, and only if, the observed quality is greater than qcrit. As mentioned, the critiques of Tiebout competition focus mainly on the existence of prohibitive moving costs. In our model, moving costs simply imply that a higher rate of failure is required before the household decides to move, that is, moving costs cause a decline of qcrit. The household aspires to attain a certain quality level, qasp, and faces moving costs, cmove, so that qcrit 5 qasp − cmove where qasp, cmove Î [0,1]. The moving costs may include a wide variety of factors, from simple transportation costs and search costs, to the psychological costs due to severing local social connections. If cmove 5 1 then even for very high aspiration quality such as qasp 5 1, the household would find the costs of moving to be prohibitively high and would never move. We assume that when qcrit ≤ 0, the district still provides some minimum level of quality qmin > 0. A household leaves the jurisdiction when the average number of failures exceeds the average number of successes. Feller (1968) demonstrates that this behavior follows a Bernoulli random walk, which becomes relevant in the jurisdictions’ calculus below. Furthermore, in line with the empirical evidence that people have limited knowledge about the quality of services and the tax rates in other jurisdictions, we make the weakest possible assumption about household information: when a household decides to move from one jurisdiction, it chooses another jurisdiction at random. That is, for people who have very poor information about the quality of services and tax rates in other jurisdictions, it is as if they are randomly choosing where to move. 2.2.2 Jurisdictions as revenue-maximizing Leviathans Let Ft(q) be the probability that a household remains with a jurisdiction supplying quality q for t periods. If r > 0 denotes the discount rate for the jurisdiction, so that d5

1 [ (0,1) 11r

(16.1)

denotes the discount factor, then the present value of profits from a household who stays with the jurisdiction for exactly t periods is given by the following expression:

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The evolution of governance structures in a polycentric system 297 p (d 1 d2 1 ... 1 dt) 5 p

1 2 dt r

(16.2)

The expected profit stream from a household that moves out after a finite number of periods t is `

ap t51

1 2 dt ␾t r

(16.3)

while that from a household that never switches is ` p a1 2 a ␾t b r t51

(16.4)

Thus, total expected profit is the sum of the two `

E [p ] 5 a p t51

` ` 1 2 dt p p ␾t 1 a1 2 a ␾t b 5 a1 2 a dt␾t b r r r t51 t51

(16.5)

In accordance with the revenue-maximizing Leviathan concept (Brennan and Buchanan 1977; Buchanan and Brennan 1980, 1985; Engineer 1990; McGuire and Olson 1996), we assume that each jurisdiction is trying to maximize its profits by collecting as much taxes as possible and providing the cheapest possible services. However, even under such a harsh assumption, the possibility of exit provides a check on exploitation. If there is an interior solution for q, maximum expected profits occur when q satisfies the jurisdiction’s first-order condition: r

` ` 0␾t dE [ p ] 0c 5 2 (T 2 c (q; w)) a dt 2 a1 2 a dt␾t b 5 0 dq 0q 0q t51 t51

(16.6)

Based on equations (16.5) and (16.6) we can now derive a series of consequences. 2.3

Consequences of the EQ Model

Theorem 1: Every positive tax yields an interior jurisdiction optimum at which the tax exceeds marginal cost and the jurisdiction earns a positive rent per unit. Proof:

Rearranging equation (16.6) and solving for T gives: T 5 c (q,w) 2

` ` 0␾t 21 0c a1 2 a dt␾t b a a dt b 0q 0q t51 t51

(16.7)

` t ) To sign the rent note that ∂c/∂q > 0 and that (1 2 g t51 d ␾t . 0. Hence for ` ` 0␾t 21 0c a1 2 a dt ␾t b a a dt b . 0, 0q 0q t51 t51

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we need to show that 0␾t , 0. 0q

(16.9)

Recall that q denotes the probability that a unit of the good is a success. By Feller (1968) the number of successes over failures follows a Bernoulli random walk where the transition probabilities are (1 − q) up and q down. Feller shows that the probability of stopping at period t is the probability of a first zero crossing of a random walk at period t, which has a generating function satisfying the following quadratic equation: f(d) 5 (1 − q)d + qdf2(d)

(16.10)

Additionally, Feller provides the solution for f(d), giving the following: ␾ (d) 5 (1 2 "1 2 4q (1 2 q) d2) /2qd

(16.11)

d (1 2 ␾2) 0␾ 52 0q 1 2 2qd␾

(16.12)

We calculate that:

0␾t

To show 0q , 0, we need only show that 2qdf < 1 for d, q P (0,1). However, 2qdf ≥ 1 implies that 2qdf2 ≥ f. In turn, this and f(d) 5 (1 − q)d + qdf2(d) implies that 2(f − (1 − q)d) > f, which gives f > 2(1 − q)d. Since q $ 12 in this case, this implies f > d. But this contradicts d − f 5 (1 − 2f)qd ≥ 0. Hence the rent is positive and T ≥ c(q; w). Discussion of theorem 1: In equilibrium, the tax equals marginal cost plus a rent. Even with zero moving costs, the rent is still positive. The limited knowledge of households ensures this outcome. The rent is the ratio of the expected marginal cost of quality incurred over a household’s tenure with the jurisdiction, and the discounted marginal change in the length of stay owing to a change in quality. It represents the tradeoff the jurisdiction faces as it raises quality. Increasing quality means that, on the one hand, costs rise per unit of output, but on the other hand, the expected length of time a household stays in a jurisdiction rises as well. Because of our assumption of zero-interjurisdictional knowledge on the part of the households, jurisdictions cannot attract more households by raising quality, they can only retain them longer. It is instructive to rearrange equation (16.6) as follows: (T 2 c (q;w)) a a dt `

p

t51

` 0␾t 0c b 5 2 a1 2 a dt␾t b 0q 0q t51

(16.13)

The left-hand side is the discounted expected marginal rental stream from one household due to an increase in quality. The right-hand side is the expected discounted marginal cost

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The evolution of governance structures in a polycentric system 299 of quality for one household. Thus, the jurisdiction chooses quality such that the marginal cost of quality equals the marginal profit from keeping more taxpaying citizens, ` ` 0␾t 0c 5 (T 2 c (q;w)) a 2 a dt b a1 2 a dt␾t b 0q 0q t51 t51

p

16.14

h(q)

or, 0c 5 p (T,q,w) h (q) 0q

(16.15)

The right-hand side is the additional profit the jurisdiction receives from an increase in quality owing to the increased expected length of stay by a given household. The left-hand side is the additional cost incurred by the jurisdiction from this increase in quality. Here, h(q) denotes the quality elasticity of sales. It shows how responsive the length of stay of a household is to changes in quality. Thus, it tells us how many more citizens are retained by the jurisdiction and therefore how much the demand for public services rises when quality rises. As mentioned, these citizens are from the ‘marginal group’ with respect to the public service under analysis, that is, citizens who primarily care about this service. Theorem 2: Quality is an increasing function of the tax, and a decreasing function of factor prices.

Proof: Taking the total differential of equation (16.6), treating r as a constant, and q and T as variables, we find that r

0 2E [ p ] 0 ` t dq 2 r a a d ␾t bdT 5 0 0q 2 0q t51

(16.16)

0 ` a a t51dt ␾t b dq 0q 5 .0 dT 0 2E [ p ] 0q 2

(16.17)

and solving for dq/dT gives

by the firm’s second order condition and the fact that ` 0 a a dt ␾t b , 0, 0q t51

(16.18)

as shown in the proof to theorem 1. Costs are a function of factor prices, w, as well as quality, c(q; w). Differentiating equation (16.6) with respect to factor price k, wk, yields

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(16.19)

Discussion of theorem 2: Despite the fact that we are using the ultra-pessimistic revenuemaximizing Leviathan concept, and despite the minimalistic assumptions about households’ knowledge, we still obtain the conclusion that jurisdictions are forced to provide an increased benefit when raising taxes. These results also show that if we know the tax rate or, more precisely, the fraction from the taxes collected in the jurisdiction that go to paying for the public service we are analyzing, and the factor prices, the model determines the quality of the service. This is why we refer to this model as an endogenous quality model. Different jurisdictions may differ either in terms of their implicit tax rates for each service (which is a combination of the overall level of taxes and budget allocation decisions), or in terms of the factor prices. Geographical and other differences between jurisdictions are captured by these factor prices. Various federal-level policies, such as subsidizing land development in certain areas or establishing differential policy regimes in different geographical areas, are also included here in the factor prices. As we would expect, if factors of production become more expensive, the quality of services using those factors declines – unless a corresponding increase in taxes occurs. This can have an interesting dynamic in terms of local economic development. For example, public services such as quality roads contribute to development by reducing transaction costs. If labor costs increase, this will have a negative effect on road maintenance. The decrease in road quality could be prevented by an increase in taxes; however, increasing taxes itself can have a negative impact on development. So, shocks to factor prices can have wide-reaching consequences via a chain of effects. Similarly, the model readily accommodates shocks to demand. Consider an especially large outflow of households from a jurisdiction. As a result, the remaining households are more valuable to the local government. We model this via a decrease in r or, equivalently, an increase in d. Theorem 3: For a given tax, a sudden outflow of households will lead, ceteris paribus, to a rise in quality and a fall in the jurisdictional rent. Proof: The outflow of households is modeled as an increase in d. Taking the total differential of equation (16.2), noting that r 5 (1 − d)/d, and treating q and d as variables, we find that

r

0 2E [ p ] 0 2E [ p ] 1 0E [ p ] dq 1 2 2 ar bdd 5 0 2 0q 0q0d d 0q

(16.20)

and solving for dq/dd gives

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The evolution of governance structures in a polycentric system 301 0 2E [ p ] 1 0E [ p ] 2 2 dq 0q0d d 0q 52 .0 2 [ ] dd 0E p r 0q 2

(16.21)

0 2E [ p ] 0E [ p ] . 0, 50 0q0d 0q

(16.22)

0 2E [ p ] ,0 0q 2

(16.23)

r

where

by the first order condition, and

by the second order condition. The rent, T − c(q; w), decreases because, as 0c/0q . 0, a higher quality service implies a higher cost. Discussion of theorem 3: Consider a jurisdiction such as Detroit which has suffered large outflows of households. One response is for the city to attempt to provide greater quality since each household relationship is now more valuable but, as in the case of labor cost shocks that we discussed above, this ‘negative demand shock’ has additional effects that can undermine the attempt to raise quality. Urban flight may well undermine the web of social connections that made old Detroit a more attractive place to live, so, as the number of households falls, the social capital is also destroyed, further reducing the value of remaining in Detroit. Interestingly, such considerations about ‘social capital’ are already implicitly included in our model thanks to the mathematical properties of the Bernoulli random walk. We, thus, do not need to include them as special, additional factors. The Bernoulli random walk stipulates that the longer a household resides in a jurisdiction, the more successes it has, and, therefore, the more failures it will take to cause the household to contemplate moving. We can interpret this as a kind of reservoir of goodwill that the jurisdiction has built up. The greater the number of successes, the greater the length of time a household resides in a jurisdiction, and the more ‘forgiving’ the household is bound to be, in the sense that it will take more failures to fully erode that goodwill. Recall that the household considers switching only when the average number of failures exceeds the average number of successes. So, for a household that has resided in a jurisdiction for t periods, as t grows large, the number of failures required to prompt them to consider switching grows as well. This stickiness or reluctance to move comes not just from the successes themselves but also from the duration of time. We may imagine that the longer the time a household resides in a jurisdiction the more rooted it becomes; the greater the connections – social and economic – it has made. The Bernoulli random walk catches the flavor of this intuition, although we have not included an explicit mechanism in our model deliberately for this purpose. In section 4 we consider further elaborations of this idea based on the concepts of co-production and voice. The EQ model developed so far does not allow for declines in quality to coexist with declines in population; but, empirically, such things arguably occur.

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It is the concept of co-production, introduced in section 4, that allows us to account for such situations. While more work remains to be done investigating the complex interaction of these effects, our sketch above describes some of the intuition provided by the EQ model. If critical quality is a function of the time a household has spent in a jurisdiction, then more structure is needed. With a more fully specified treatment we can investigate how age composition of households, income differences, preference heterogeneity, and others have on the provision of quality.

3

THE SCALE OF PUBLIC SERVICES

The previous section has endogenized the quality of the public services, but has not addressed the issue of scale. Different services are best provided at different scales. To account for the scale at which the public service is provided, we start by considering the smallest, most local, jurisdictions as our basic unit of analysis. For example, in Buchanan’s (1987) social contract account, the basic units are individuals. He noted that we should not assume ‘without inquiry, that the individual [is] locked into membership in a political community and that the range and the scope of the collective’s activities [are] beyond the control of the individual’ (Buchanan 1987, p. 306). We need to understand ‘the conditions that must be present for the individual to find it advantageous to enter into a political entity with constitutionally delineated ranges of activity or to acquiesce in membership in a historically existent polity’ (Buchanan 1987, p. 306). In our account, we use the smallest local jurisdictions as the basic institutional unit, rather than individuals, but Buchanan’s point still stands, that we need to have a mechanism by which the scale at which a public service is provided increases or decreases. The previous section simply assumed that the scale at which a public service is provided is given, and focused on endogenizing the quality of services and the rents each jurisdiction provides. We now endogenize scale as well. We define an endogenous quality polycentric system as consisting of EQ jurisdictions and households, and having a structure emerging by the process described in this section. 3.1

Assumptions

We assume that larger scale provision results from smaller scale jurisdictions collaborating to create a larger scale organization for the provision of particular services which will be available to households in all the participating jurisdictions. We refer to the number, n, of these smallest local jurisdictions, combining into a larger administrative unit to provide the public service that we are analyzing. This number measures the degree to which services are centralized. If the service is completely centralized, n 5 100 percent, that is, all local jurisdictions have merged into a single collaborative unit, while lower n indicate various levels of decentralization. This way of thinking about scale fits a wide range of historical examples of both increased centralization and increased decentralization. For example, the formation of the United States or of the European Union (EU) involved a negotiation process among smaller administrative units for the creation of a larger-scale association. In line with our

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The evolution of governance structures in a polycentric system 303 model that uses jurisdictions as the unit of institutional analysis, such examples involved the association of pre-existing jurisdictions, rather than of individuals. Similarly, examples of associations breaking up, such as the collapse of the Western Roman Empire, the gradual reduction of the Byzantine Empire, or more recently, the break-up of the Soviet Union or the exit of Singapore from Malaysia, also involve local jurisdictions exiting previous associations. The same phenomenon also happens at much more local levels. Consider a typical example of common pool resources studied by Elinor Ostrom (1990). Following a severe overfishing issue in Maine lobster fisheries in the 1920s, the local communities were able to address the free-riding problem by appealing to a larger-scale organization, the state of Maine, which ‘supported informal local enforcement efforts’ (Ostrom 1999a, p. 40). More recently, the informal local fishing organizations were transformed into formalized councils with democratic local elections and formalized authority over specified geographical areas. This facilitated the cooperation between local communities in regard to large-scale problems and the bottom-up emergence of larger-scale associations: ‘the formalization of local zones was followed, almost immediately, by the creation of an informal council of councils to address problems at a greater than-local scale’ (Ostrom 1999a, p. 40). A similar process occurred with respect to Washington state Pacific salmon fisheries, where salmon over-fishing was solved by means of ‘a “co-management” system that involves both the state of Washington and the 21 Indian tribes in diverse policy roles related to salmon’ (Ostrom 1999a, p. 40). These examples show that cooperation between local communities sometimes rests on using pre-existing larger-scale administrative units. In our simplified model, however, we consider only the bottom-up process. 3.2

Organization Costs

In line with the above polycentric way of thinking about how larger-scale public services are provided by an emergent collaborative agreement between smaller-scale administrative units, let us define the transaction cost, F(n), involved in setting up the larger-scale collaborative agreement. This cost is the same as the ‘decision-making cost’ from the calculus of consent model, and, as argued by Buchanan and Tullock (1962 [1999], ch. 6), F(n) is an increasing function, F’ > 0. The larger the coalition of jurisdictions, that is, the more centralized the provision of the public service, the greater the decision-making cost for setting up the association. F(n) can also be understood as an entry fee that each jurisdiction pays as a cost of becoming part of the larger collaborative association. For example, in the case of the EU’s expansion, F is the cost of negotiating during the accession process and the costs of pre-accession reforms requested by the EU. Because of the need for consensus in accepting a new country into the union, the larger n is, the more difficult the entry of new countries becomes. To develop a model of scale and equilibrium, more structure must be added to the cost side of our model. If the only costs that a jurisdiction faces are variable, then positive per-unit rents combined with constant returns to scale always imply positive economic profits ex ante entry and, consequently, an indeterminate scale. As is well known in the product quality literature, there exist various ways by which profits can be dissipated in equilibrium.1 In our model, this is owing to the fact that as more jurisdictions join the association, the decision-making costs increase.

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Apart from the joining fee F, we also assume that the jurisdiction incurs a per period fixed cost, f, which we can see as an ongoing tax for being part of the larger association. This fee accounts for things like the continued monitoring and enforcement of the rules of the larger association. 3.3

Centralization

Our model depends on jurisdictions forming expectations about how many households they are going to have, but does not depend on specific assumptions about how exactly they form these expectations. This includes anything from rational expectations, to adaptive expectations, to utterly irrational expectations. Let h0 be the actual number of households that a jurisdiction has upon entry, and ht as the number of households a jurisdiction has in period t. Then, for our purposes, all we need to know here is that E0(ht|h0) denotes the jurisdiction’s expectation, formed before entry, of the number of households in period t, given the actual number of households that a jurisdiction has upon entry. For example, the public service may have certain economies of scale or there may be positive interjurisdictional externalities that would be facilitated by the association (as in the common-pool examples given above). As such, individual jurisdictions can only provide by themselves a relatively low-quality service, and, hence, satisfy few households. Once they form the association, the quality of the services increases, each of them keeping more households (while jurisdictions that have not entered the association would lose households at a higher rate). Thus, by entering the association, the jurisdiction receives E [ p (n) ] 5 a dt c (T 2 c (q,w)) E0 (ht |h0) 2 f d t51 p `

(16.24)

as the expected discounted profit stream for the life of the inter-jurisdictional association. Theorem 4:

In an EQ polycentric system there exists a unique equilibrium (F*, n*).

Proof: If the present value of entry exceeds the entry fee, then the service becomes more centralized as more jurisdictions enter. Jurisdictions join the association as long as E[p(n)] ≥ F(n). The expansion of the association ends when t a d [ pE0 (ht 0 h0) 2 f ] 5 F (n) `

(16.25)

t51

Thus, in equilibrium, jurisdictions are indifferent between entering and staying out of the association. This provides the equilibrium level of centralization, n*, and, implicitly, the equilibrium entry fee, F* 5 F(n*). Discussion of theorem 4: Perhaps the most surprising aspect of this model is that the entry fee is endogenized as well. That is, the jurisdictions that are already part of the association cannot require an arbitrarily large entry fee, nor can the new entrant require an arbitrarily low entry fee. What happens is that the existing jurisdictions estimate a certain increase in their individual rents as a result of accepting the new member, which

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The evolution of governance structures in a polycentric system 305 E[π] – F(n)

10

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Figure 16.2

Equilibrium level of centralization

incentivizes them to keep the entry fee low. The costs of allowing a new jurisdiction in depend on whether the existing jurisdictions think they are at ‘full capacity’ of households already. If they are not, they want more immigrant households (such that tax revenues increase). However, if joining the association improves the public services within the new jurisdictions, fewer households would leave. This may be more than compensated by the fact that joining the association may significantly reduce the moving costs. If the existing jurisdictions think they are already at ‘full capacity’, they would perceive the arrival of further immigrant households as a cost, rather than as a benefit. This, for instance, seems to fit the perceptions driving the anti-EU campaign in Britain. We can also have a graphical representation of theorem 3, by plotting the difference E[p(n)] − F(n) as a function of n. The intersection with the horizontal zero axis provides the equilibrium scale of the public service, n* (Figure 16.2). 3.4

Decentralization

Every period, a jurisdiction must decide whether to remain part of the association or exit (Buchanan and Faith 1987). Taking the number of households as given in period t, a jurisdiction decides to continue to be part of the association into period t + 1 by comparing the expected future rents from staying in to the future stream of costs. Jurisdictions exit when their expected profits are negative. Let h0 denote now the number of households a jurisdiction has upon entry. Successive per period household numbers are denoted h1, h2,. . ., and so on. A jurisdiction decides to exit the association when p a dtE0 (ht 0 h0) , `

t51

f r

(16.26)

Note the role that f plays. As long as the jurisdiction is not yet at full capacity, and as long as there remains a positive probability of attracting another household, no matter how far off into the future this may occur, without a fixed per period cost, the jurisdiction

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would never choose to exit the association. With a fixed per period cost, a jurisdiction will choose to exit the association if its current number of households gets below a critical minimum2 or above the perceived full capacity level. If the membership fee, f, increases for some reason, then the association is in danger of breaking up. This is, for example, a common explanation of the fall of the Western Roman Empire (focused more on internal rather than external factors) – when the costs paid by different provinces of being part of the empire increased, they no longer had a vested interest in continuing to be part of the empire, and the center did not have the military capacity to force them to stay (Jones 1986; MacMullen 1990). Similarly, the recent difficulties that the EU has faced because of the refugee crisis have suddenly increased the costs of the union, leading to moves towards dissolution, such as suspending the open borders within the Schengen region (Alderman and Kanter 2016; Rankin 2016). Consider also another different example. As we have seen earlier, according to theorem 2, if the price of factors increases, the quality of the services decreases. Consequently, a price shock to some factor markets may lead to a decline in the quality of service that the association of jurisdictions can provide, leading to a decline in the number of households. This may push some of the member jurisdictions below their critical number of households, leading them to exit the association, despite the fact that this will cause an even greater reduction in quality. Thus, revenue-maximizing Leviathans can spiral into vicious cycles. Conversely, we can also understand the possibility of the opposite type of vicious cycle, namely, to an over-centralization process. When centralization increases, the moving costs increase, and, hence, the probability of moving out gets lower. As a result, once households moving costs are increased, further centralization may become profitable for the jurisdictions. Centralization eliminates the variety across jurisdictions and, hence, benefits the local governments providing lower quality services. That is, failing local jurisdictions, on certain margins, are more likely to call for centralization on those margins, even if this comes at the expense of higher quality jurisdictions. 3.5

Comparison with the Calculus of Consent Optimum

The model we developed above, from the perspective of local jurisdictions that act as revenue-maximizing Leviathans, does not lead to the same conclusion as the calculus of consent optimum. Buchanan and Tullock described the centralization/decentralization optimum in the following way: ‘The group should be expanded so long as the expected costs of the spillover effects from excluded jurisdictions exceed the expected incremental costs of decision-making resulting from adding the excluded jurisdictions’ (Buchanan and Tullock 1962 [1999], p. 113). As mentioned previously, F(n) corresponds to the calculus of consent decision-making costs. Let us denote the costs of interjurisdictional spillover externalities as S(n). Mathematically, the calculus of consent optimum level of centralization is given by ds 5 −dF or, equivalently, d (S (n) 1 F (n)) 5 0 dn

(16.27)

Buchanan and Tullock (1962 [1999], pp. 44–8) refer to the total costs, S(n) + F(n), as ‘the costs of social interdependence’.

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The evolution of governance structures in a polycentric system 307 Costs Calculus of consent optimum

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EQ polycentric system equilibrium

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By contrast, in our model, jurisdictions decide to enter or exit a larger association based not on interjurisdictional externalities but on their expectation of profits. That is, the collective association that provides the public service gradually expands up until the entry fee becomes higher than the expected profit, which may happen either before the calculus of consent is reached, in which case the polycentric system remains too decentralized, or after, in which case the polycentric system becomes too centralized (Figure 16.3). Therefore, an important question for future research becomes: under what conditions does the profit seeking mechanism of our Tiebout model converge with the calculus of consent optimum? Going back to the framework of the calculus of consent, the question is whether we can have a viable ‘invisible hand’ mechanism operating in the political realm in the same way as we have one in the market realm: Adam Smith and those associated with the movement he represented were partially successful in convincing the public at large that, within the limits of certain general rules of action, the selfseeking activities of the merchant and the moneylender tend to further the interests of everyone in the community. An acceptable theory of collective choice can perhaps do something similar in pointing the way toward those rules for collective choice-making, the constitution, under which the activities of political tradesmen can be similarly reconciled with the interests of all members of the social group. (Buchanan and Tullock 1962 [1999], p. 22 emphasis added)

We have no answer to this question here, but we highlight it as an important concern for future research and point out that, at least prima facie, our imperfect Tiebout competition model does not seem to be bounded towards the calculus of consent optimum. As highlighted by the above quote, this may be because we have not discussed possible

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constitutional rules for constraining the Tiebout competition towards the social optimum. Our model opens the door for conceptualizing such constitutional rules more rigorously.

4

FURTHER IMPLICATIONS

Our framework is compatible with the view that exit and voice are complementary (Hirschman 1970; Oakerson and Parks 1988; Lyons and Lowery 1989), but, nonetheless, exit takes precedence. This is because, in the case of exit, individual action has an immediate effect, while, in the case of voice, individual action only has an effect contingent on the actions of others. Let us give a brief overview of how voice emerges as a strategy for the case when the moving condition is not satisfied – either because there is still hope that the jurisdiction will perform better or because the household does not have the resources to move. 4.1

Voice and Co-production

According to the co-production model (Parks et al. 1981; Brandsen and Pestoff 2006; Aligica and Boettke 2009; Aligica and Tarko 2013), the quality of the public service depends not only on the local government, but also on the involvement of the households. We can model this as a Cobb–Douglas production function (Aligica and Tarko 2013): q 5 kHaGb

(16.28)

where G is the contribution of the local government, and H is the average involvement of all households, H5

1 N a hi N i51

(16.29)

with N the total number of households in the jurisdiction and hi the level of involvement of household i.3 We now have the following implication. When one household observes that q < qcrit, but the moving condition is not yet satisfied, that is, average number of failures is still smaller than the average number of successes, the household is going to increase their involvement, that is, their hi increases causing a slight increase in H. This may lead to q < qcrit in the next period, especially if many households engage in the same kind of action, or it may prove insufficient. If it proves insufficient for a sufficient number of periods, the average number of failures eventually gets higher than the average number of successes, and the household moves. For example, suppose that the quality of education decreases below the critical level. As a result, if they still do not move out of the jurisdiction, parents will get more involved with education. The parents may get involved with after-school tutoring, may have fundraisers for the school, and they may pressure the school principal. As another example, suppose that the quality of policing decreases below the critical level. The households may dedicate more time to the neighborhood watch. As we have already seen with theorem 3, as a result of the Bernoulli distribution, a long history of past successes will make households more reluctant to leave. We can now add

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The evolution of governance structures in a polycentric system 309 the implication that it will also determine the households to engage in more co-production. This reflects a more specific mechanism for creating social capital. The households that have been in the jurisdiction for longer will have greater social capital, that is, they are more likely to engage in co-production. They act as if they have a lot more at stake in the quality of the jurisdiction. We may further consider possible modifications of the model that incorporate the concept of loyalty (Hirschman 1970). One way would be through the effects that loyalty might have on the critical quality parameter. The longer a household resides in the jurisdiction, the lower the critical quality. This is perhaps the simplest way to capture the effect lengthening the expected stay of the household. Another way which, we believe, better captures the intuition would leave the critical quality unchanged. Instead, as noted above, the longer a household resides in a jurisdiction, the more likely the household is to engage in co-production and voice to try to make the quality in the jurisdiction higher than it was before. The household will desire to provide resources towards the provision of a public good or goods. The longer the household has been in the community and the greater the number of connections the household maintains with other people in the community, the more likely they are to engage in community activism and community activities. The longer a household resides in a particular jurisdiction, the greater the social network, the greater the social connections, the more it feels like home. To return to our previous discussion of Detroit, we can imagine that the large exodus of households would adversely affect the web of social connections, making it feel less like home, reducing social capital and causing loyalty to fall. Hence, a negative demand shock is not the only adverse event. There are negative knock-on effects as well leading to an additional outflow of households that will affect co-production and voice. 4.2

Income Effects

There are two types of income effects that affect moving decisions. Recall that the critical minimal quality was defined as qcrit 5 qasp − cmove, the difference between the minimum aspirational level and a factor that accounted for moving costs. On the one hand, income may lower the minimum aspirational level of quality. On the other hand, households with lower income, but high minimum aspirational quality, will be dissatisfied but will be less able to move. Poorer households have fewer resources to contribute to the co-production of public services. For example, a single mother that has more than one job will have less time to spare to contribute to her child’s education, to pay for tutoring, or to get involved to pressure the public school. As a result, poorer households may have realistic expectations of lower-quality services, which will lead them to have a lower minimum aspirational quality. As such, low-income agents are less likely to move from their current jurisdictions even if they could afford the moving costs. By contrast, high income agents would be more likely to move, further reducing the quality of services because of the decline in overall co-production participation. Consequently, those districts that initially were populated more with low-income households would tend to keep low-income residents longer and the higher income residents would tend to move out. We see that it is the co-production aspect of the public goods production that is generating this sorting effect. This is a very different mechanism from the Schelling discrimination model, and other homophily

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models, which rest on preferences about neighbors (Kossinets and Watts 2009). In our case, households only have preferences about public services, but, nonetheless, can end up sorting by income. Some low-income households may nonetheless maintain a high aspirational quality. Some may have had a very high baseline to begin with. Also, a low-income household can have a low minimum critical quality for the package of many services, but still have a preference for high quality of some of the services, for example, schooling. The fact that such low-income households may find it difficult to move may counterbalance to some extent the negative effect discussed in the previous paragraph. Unable to move away, they will tend to use voice and the ballot box as a second best option, and they will also be willing to engage in more co-production. These households will have a desire to pursue co-production, community-level service provision, voice via the political system, and other such measures whose production makes economic sense because of the lower opportunity cost of the households. Those households do not exercise the exit option, but instead they substitute into co-production and voice with respect to the services that they find wanting. This second effect is probably relatively low for most local governance issues, which explains why lower-income jurisdictions tend to have lower-quality services, but it is a much more significant factor for large-scale issues. When centralization is very large, the moving costs are prohibitive for a very large segment of the population. While above we were assuming that only the poorest households are trapped, with highly centralized issues almost everybody is. Therefore, many will have resources to spare for using voice as an alternative strategy for trying to improve public services. We can see this as the origins of voice. This relatively unintuitive prediction follows: we are more likely to see people engage in voice with respect to highly centralized issues than with respect to local matters. This is unintuitive because collective action is easier within smaller groups (Olson 1971). However, exit is also easier for highly local issues.

5

CONCLUSION

We have laid out an endogenous quality polycentric system model of citizens’ behavior and local governments’ behavior that combines: ●

●

interjurisdictional imperfect competition, determining the quality of public goods and tax rates within each jurisdiction as a function of citizens’ moving costs and governments’ factor prices; and interjurisdictional cooperation, determining the emergent scale of public goods as a function of organization costs.

This model is based on very weak assumptions about the knowledge of citizens and the benevolence of governments. Citizens are assumed to know nothing except their own personal experience living within a given jurisdiction – hence, dissatisfied citizens move out to random new jurisdictions. Local governments are assumed to be revenue-maximizing Leviathans, taking decisions about increasing the quality of public goods or of cooper-

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The evolution of governance structures in a polycentric system 311 ating with other jurisdictions solely on the basis of increasing their own rents (that is, difference between tax revenues and the cost of providing public services). We have shown that, even under such harsh assumptions, jurisdictions will not increase taxes without increasing the quality of services, and they will respond to outflows of citizens by increasing the quality of services. Because moving costs are not zero, citizens will also respond to declines of quality, first by increasing their own participation in coproduction activities and by means of voice, and only as a last resort by moving out. We have also explored various inefficiencies that can emerge within the model. Shocks to factor prices can lead to downward spirals of quality and outflow migration. Both under-centralization and over-centralization vicious cycles are possible under certain conditions. Moreover, even without such vicious cycles, our Leviathan model does not necessarily lead to the level of centralization that the calculus of consent describes as the social contract optimum. This opens the door for further research into analyzing what types of constitutional constraints may force the Leviathan model to converge on the calculus of consent optimum.

NOTES 1. For example, in their seminal contribution to this literature, Klein and Leffler (1981) dissipate profits through non-price competition: firms invest in non-salvageable firm-specific assets, such as firm logos, expensive signs, and elaborate storefronts promoting the firm’s name, that provide the greatest direct service value to consumers. These brand-name capital investments not only help alert consumers to the presence of the firm’s product, but such investments also serve an important signaling role: they demonstrate the size of the sunk capital selling costs incurred by the firm and signal to the buyer the presence of price premia. 2. To solve for critical minimum of households, the manner in which jurisdictions form their expectations must be specified. Although we do not provide the details here, for a number of common-sense assumptions, we obtain the intuitive conclusion that the minimum number of households a jurisdiction must expect is a decreasing function of their probability of them moving out (see McPhail 2001 for proofs). 3. A more complex model of social capital would involve a nonlinear expression for H as a function of the individual his. The exact non-linear expression would describe the social structure of the jurisdiction, for example, if households i and j have a shared experience and relationship, the term hihj will appear in the expression of H. The simple linear form in the equation above does not include any social structure, that is, households act independently of one another.

REFERENCES Alderman, L. and J. Kanter (2016), ‘Europe’s border checks become economic choke points’, New York Times, 1 March. Aligica, P.D. and P.J. Boettke (2009), Challenging Institutional Analysis and Development: The Bloomington School, London and New York: Routledge. Aligica, P.D. and V. Tarko (2012), ‘Polycentricity: from Polanyi to Ostrom, and beyond’, Governance, 25 (2), 237–62. Aligica, P.D. and V. Tarko (2013), ‘Co-production, polycentricity, and value heterogeneity: the Ostroms’ public choice institutionalism revisited’, American Political Science Review, 107 (4), 726–41. Allen, F. (1984), ‘Reputation and product quality’, RAND Journal of Economics, 15 (3), 311–27. Bish, R.L. (1971), The Public Economy of the Metropolitan Areas, Wisbech: Markham. Bish, R.L. and R.J Kirk (1974), Economic Principles and Urban Problems, Englewood Cliffs, NJ: Prentice-Hall. Bish, R.L. and V. Ostrom (1979), Understanding Urban Government: Metropolitan Reform Reconsidered, Washington, DC: American Enterprise Institute for Public Policy Research. Boettke, P.J. and P.T. Leeson (2012), ‘Liberalism, socialism, and robust political economy’, Journal of Markets & Morality, 7 (1), 99–111.

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Boettke, P.J. and A. Marciano (2016), ‘The distance between Buchanan’s “An Economic Theory of Clubs” and Tiebout’s “A Pure Theory of Local Public Expenditures”. New Insights based on an unpublished manuscript’, GMU Working Paper in Economics No. 16-10, George Mason University, Fairfax, VA, accessed 2 March 2016 at http://papersssrncom/abstract52729670. Boettke, P.J., C.J. Coyne and P.T. Leeson (2011), ‘Quasimarket failure’, Public Choice, 149 (1–2), 209–24. Boettke, P.J., J.S. Lemke and L. Palagashvili (2016), ‘Re-evaluating community policing in a polycentric system’, Journal of Institutional Economics, 12 (2), 305–25. Boettke, P.J., L. Palagashvili and J. Lemke (2013), ‘Riding in cars with boys: Elinor Ostrom’s adventures with the police’, Journal of Institutional Economics, 9 (4), 407–25. Brandsen, T. and V. Pestoff (2006), ‘Co-production, the third sector and the delivery of public services’, Public Management Review, 8 (4), 493–501. Brennan, G. and J.M. Buchanan (1977), ‘Towards a tax constitution for Leviathan’, Journal of Public Economics, 8 (3), 255–73. Buchanan, J.M. (1987), ‘Justification of the compound republic: the calculus in retrospect’, Cato Journal, 7 (2), 305–12. Buchanan, J.M. and G. Brennan (1980), The Power to Tax, Cambridge and New York: Cambridge University Press. Buchanan, J.M. and G. Brennan (1985), The Reason of Rules, Cambridge and New York: Cambridge University Press. Buchanan, J.M. and R.L. Faith (1987), ‘Secession and the limits of taxation: toward a theory of internal exit’, American Economic Review, 77 (5), 1023–31. Buchanan, J.M. and C.J. Goetz (1972), ‘Efficiency Limits of fiscal mobility: an assessment of the Tiebout Model’, Journal of Public Economics, 1 (1), 25–43. Buchanan, J.M. and G. Tullock (1962), The Calculus of Consent, reprinted 1999, Indianapolis, IN: Liberty Fund. Caplan, B. (2001), ‘Standing Tiebout on his head: tax capitalization and the monopoly power of local governments’, Public Choice, 108 (1–2), 101–22. Donahue, J.D. (1997), ‘Tiebout? Or not Tiebout? The Market metaphor and America’s devolution debate’, Journal of Economic Perspectives, 11 (4), 73–82. Engineer, M. (1990), ‘Brennan and Buchanan’s Leviathan models’, Social Science Journal, 27 (4), 419–33. Farrant, A. (2004), ‘Robust institutions: the logic of Levy?’, Review of Austrian Economics, 17 (4), 447–51. Feller, W. (1968), An Introduction to Probability Theory and Its Applications, vol. 1, 3rd edn, New York: Wiley. Geradin, D. and J.A. McCahery (2004), ‘Regulatory co-opetition: transcending the regulatory competition debate’, in J. Jordana and D. Levi-Faur (eds), The Politics of Regulation, Cheltenham, UK and Northampton, MA, USA: Edward Elgar, pp. 90–123. Gintis, H. (1989), ‘Power to switch: on the political economy of consumer sovereignty’, in S. Bowles, R.C. Edwards and W.G. Shepherd (eds), Unconventional Wisdom: Essays in Honor of John Kenneth Galbraith, New York: Houghton-Mifflin, pp. 65–80. Hirschman, A.O. (1970), Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States, Cambridge, MA: Harvard University Press. Howell-Moroney, M. (2008), ‘The Tiebout Hypothesis 50 years later: lessons and lingering challenges for metropolitan governance in the 21st century’, Public Administration Review, 68 (1), 97–109. Jones, A.H.M. (1986), The Later Roman Empire, 284–602: A Social, Economic, and Administrative Survey, 2 vols, Baltimore, MD: Johns Hopkins University Press. Klein, B. and K.B. Leffler (1981), ‘The role of market forces in assuring contractual performance’, Journal of Political Economy, 89 (4), 615–41. Kornai, J. (1986), ‘The Soft budget constraint’, Kyklos, 39 (1), 3–30. Kossinets, G. and D.J. Watts (2009), ‘Origins of homophily in an evolving social network’, American Journal of Sociology, 115 (2), 405–50. Kreps, D.M. (1990), A Course in Microeconomic Theory, Princeton, NJ: Princeton University Press. Leeson, P.T. and J.R. Subrick (2006), ‘Robust political economy’, Review of Austrian Economics, 19 (2–3), 107–11. Levy, D.M. (2002), ‘Robust institutions’, Review of Austrian Economics, 15 (2–3), 131–42. Lowery, D. (1998), ‘Consumer sovereignty and quasi-market failure’, Journal of Public Administration Research and Theory: J-PART, 8 (2), 137–72. Lowery, D. and W.E. Lyons (1989), ‘The impact of jurisdictional boundaries: an individual-level test of the Tiebout model’, Journal of Politics, 51 (1), 73–97. Lyons, W.E. and D. Lowery (1989), ‘Citizen responses to dissatisfaction in urban communities: a partial test of a general model’, Journal of Politics, 51 (4), 841–68. MacMullen, R. (1990), Corruption and the Decline of Rome, reprinted edn, New Haven, CT: Yale University Press.

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The evolution of governance structures in a polycentric system 313 McGinnis, M.D. (ed.) (1999), Polycentricity and Local Public Economies: Readings from the Workshop in Political Theory and Policy Analysis, Ann Arbor, MI: University of Michigan Press. McGinnis, M.D. (ed.) (2016), ‘Polycentric governance in theory and practice: dimensions of aspirations and practical limitations’, paper presented at the Vincent and Elinor Ostrom Workshop in Political Theory and Policy Analysis, 14–17 December 2015, Indiana University, Bloomington, IN. McGinnis, M.D. and E. Ostrom (2012), ‘Reflections on Vincent Ostrom, public administration, and polycentricity’, Public Administration Review, 72 (1), 15–25. McGuire, M.C. and M. Olson (1996), ‘The economics of autocracy and majority rule: the invisible hand and the use of force’, Journal of Economic Literature, 34 (1), 72–96. McPhail, E. (1997), ‘Endogenous quality and intra-industry trade’, working paper presented at the University of California, Riverside, CA, February 1998. McPhail, E.A. (2001), ‘Endogenous quality and intra-industry trade’, PhD dissertation, University of Massachusetts, Amherst, MA. Oakerson, R.J. and R.B. Parks (1988), ‘Citizen voice and public entrepreneurship: the organizational dynamic of a complex metropolitan county’, Publius, 18 (4), 91–112. Olson, M. (1971), The Logic of Collective Action: Public Goods and the Theory of Groups, Second Printing with New Preface and Appendix, revised edn, Cambridge, MA: Harvard University Press. Ostrom, E. (1976), The Delivery of Urban Services: Outcomes of Change, Beverly Hills, CA: Sage. Ostrom, E. (1990), Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge: Cambridge University Press. Ostrom, E. (1999a), ‘Polycentricity, complexity, and the commons’, The Good Society, 9 (2), 37–41. Ostrom, E. (2005), Understanding Institutional Diversity, Princeton, NJ: Princeton University Press. Ostrom, E. (2010), ‘Beyond markets and states: polycentric governance of complex economic systems’, American Economic Review, 100 (3), 641–72. Ostrom, E., R.B. Parks and G.P. Whitaker (1978), Patterns of Metropolitan Policing, Pensacola, FL: Ballinger Ostrom, V. (1991), The Meaning of American Federalism, San Francisco, CA: ICS Press. Ostrom, V. (1999b), ‘Polycentricity (Part 1 and 2)’, in M.D. McGinnis (ed.), Polycentricity and Local Public Economies, Ann Arbor, MI: University of Michigan Press, pp. 52–74 and 119–38. Ostrom, V. and E. Ostrom (1977), ‘Public goods and public choices: the emergence of public economies and industry structures’, reprinted in V. Ostrom (1991), The Meaning of American Federalism: Constituting a SelfGoverning Society, San Francisco, CA: ICS Press, pp. 163–97. Ostrom, V., R.L. Bish and E. Ostrom (1988), Local Government in the United States, San Francisco, CA: ICS Press. Ostrom, V., C.M. Tiebout and R. Warren (1961), ‘The organization of government in metropolitan areas: a theoretical inquiry’, American Political Science Review, 55 (4), 831–42. Parks, R.B., P.C. Baker, L. Kiser, R. Oakerson, E. Ostrom, V. Ostrom et al. (1981), ‘Consumers as coproducers of public services: some economic and institutional considerations’, Policy Studies Journal, 9 (7), 1001–11. Pennington, M. (2011), Robust Political Economy: Classical Liberalism and the Future of Public Policy, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Rankin, J. (2016), ‘EU’s Schengen members urged to lift border checks to save passport-free zone’, Guardian, 2 March. Rogerson, W.P. (1983), ‘Reputation and product quality’, Bell Journal of Economics, 14 (2), 508–16. Rogerson, W.P. (1987), ‘The dissipation of profits by brand name investment and entry when price guarantees quality’, Journal of Political Economy, 95 (4), 797–809. Shapiro, C. (1982), ‘Consumer information, product quality, and seller reputation’, Bell Journal of Economics, 13 (1), 20–35. Shapiro, C. (1983), ‘Premiums for high quality products as returns to reputations’, Quarterly Journal of Economics, 98 (4), 659–79. Tarko, V. (2015), ‘Polycentric structure and informal norms: competition and coordination within the scientific community’, Innovation: The European Journal of Social Science Research, 28 (1), 63–80. Tiebout, C.M. (1956), ‘A pure theory of local expenditures’, Journal of Political Economy, 64 (5), 416–24. Wagner, R.E. (2005), ‘Self-governance, polycentrism, and federalism: recurring themes in Vincent Ostrom’s scholarly oeuvre’, Journal of Economic Behavior & Organization, 57 (2), 173–88.

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PART IV TAX BEHAVIOUR

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17 Taxation and nudging Simon James

1

INTRODUCTION

‘The art of taxation consists in so plucking the goose as to obtain the largest possible amount of feathers with the smallest possible amount of hissing’ – a view attributed to Jean Baptiste Colbert who served as Louis XIV’s Finance Minister for the period 1665 to 1683. The ‘art of taxation’ may be thought of in more modern terms as smart behaviour – incorporating a comprehensive understanding of the motivation behind individuals’ decisions regarding tax compliance. This includes not only rational decisionmaking narrowly defined, which tends to focus on pecuniary rewards and punishments, but also other factors influencing the decision-making process. Since Colbert’s time both the economic goose and the tax feathers have grown enormously around the world, as indicated in section 2, and perhaps it is remarkable that the hissing is not greater than it actually is. Nevertheless tax compliance is often not as good as governments would wish and considerable effort and resources are put into tax administration and enforcement. The purpose of this chapter is to look beyond a tax compliance policy based on penalties for non-compliance and to examine whether and how taxpayers may be persuaded to cooperate by using a wider behavioural approach to tax compliance and in particular by ‘nudging’ them in the right direction drawing on the work of Thaler and Sunstein (2008) Nudge: Improving Decisions about Health, Wealth and Happiness. The Organisation for Economic Co-operation and Development’s Compliance Risk Management (OECD 2004, p. 37) refers to the contribution of James et al. (2001) in outlining two main approaches to encourage taxpayers to comply with the requirements of the tax system. The first of these might be referred to as the ‘rational’ economic approach where taxpayers are assumed to respond primarily, if not exclusively, to the economic costs and benefits of different actions. Such an approach therefore tends to focus on the detection of tax evasion and penalties for those who are caught. The second approach involves a wider understanding of factors influencing taxpayers’ behaviour and this includes the application of such insights to ‘nudging’ individuals in the right direction. This is not a completely new approach as behavioural factors have been used to encourage tax compliance many times in the past. For instance, the practice of withholding tax at source rather than trying to extract it later from taxpayers can be traced back to the sixteenth century in England (Soos 1997) and this arrangement took advantage of phenomena now described in the modern behavioural literature as the endowment effect, loss aversion and status quo bias. Both approaches are necessary to the development of successful tax compliance strategies. Unlike resource allocation through markets, where goods and services are generally received only in return for payment, individuals might be able to receive the benefits of public expenditure even if they avoid their tax obligations. Taxpayers may also collectively express their unwillingness to pay tax, and there are many examples over the centuries. In 317

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England, King John’s demands for more taxation precipitated the crisis of 1215 that led to the King’s submission and the issue of Magna Carta, and subsequently taxation was a major influence on the development of Parliament. Its role in the American Revolution – ‘no taxation without representation’ – is well known, although one might agree with Callender (1909, p. 23): ‘That a great reluctance to pay taxes existed in all the colonies, there can be no doubt. It was one of the marked characteristics of the American people long after their separation from England.’ However, many taxpayers are willing to pay their allocated share of taxation without being coerced by heavy-handed legal and administrative action. As Posner (2000, p. 1782) put it, the penalty for ordinary tax convictions is usually modest, the chance of detection often trivial and yet most individuals pay their taxes. Hence some further explanation is required. One of the basic reasons for compliance with tax obligations is, of course, that many taxpayers are willing to support public expenditure, even at the historically high levels of modern times. Such basic willingness to cooperate may be reinforced using behavioural insights to encourage or ‘nudge’ taxpayers to meet their obligations in full and without recourse to generally less pleasant and often much more costly methods of enforcement. A distinction is often made between tax avoidance and evasion. Tax avoidance refers to behaviour of individuals or businesses designed to reduce tax liability by legal means, for example, by taking advantage of tax concessions for particular activities. Tax evasion refers to action to reduce tax paid by illegal means, for example, by failing to disclose taxable income. Tax evasion is the primary concern of policies to improve tax compliance but developing and applying understanding of taxpayer behaviour to tax compliance policy may also reduce the acceptability of ‘aggressive’ and ‘artificial’ forms of tax avoidance. To examine the role of behavioural factors in tax compliance the chapter begins with section 2 on taxation to describe how large and pervasive taxation is in modern economies. Section 3 presents a summary of the contribution of behavioural economics to improving tax compliance and indicates areas where developing nudges may have considerable potential. Section 4 then specifically examines nudging and section 5 outlines some further applications to tax issues. Finally, some conclusions are drawn.

2

THE SIZE AND EXTENT OF MODERN TAXATION

The Taxes The relatively smooth operation of modern tax systems, and the rather limited amount of audible hissing involved in removing the tax feathers from Colbert’s goose, may mean many are not aware of the full extent of taxation in modern economies. It is therefore worth examining briefly the scale of modern taxation. The figures for total tax revenue as a percentage of gross domestic product (GDP) for members of the OECD are presented in Table 17.1. There is some scope for variations in the way tax revenue is measured and an important issue is whether or not social security contributions should be classed as taxes. A useful definition of a tax is ‘a compulsory levy made by public authorities for which nothing is received directly in return’ (James 2012, p. 247, original emphasis) and

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Total tax revenue as a percentage of GDP in OECD countries 2011 Including social security contributions

Denmark Sweden Belgium France Finland Italy Norway Austria Netherlands Hungary Slovenia Luxembourg Germany Iceland United Kingdom Czech Republic Portugal Israel Estonia Poland Spain Greece New Zealand Canada Slovak Republic Japan Switzerland Ireland Turkey Australia Korea United States Chile Mexico

47.7 44.2 44.1 44.1 43.7 43.0 42.5 42.3 38.6 37.1 37.1 37.0 36.9 36.0 35.7 34.9 33.0 32.6 32.3 32.3 32.2 32.2 31.5 30.4 28.7 28.6 28.6 27.9 27.8 26.5 25.9 24.0 21.2 19.7

(1) (2) (35) (35) (5) (6) (7) (8) (9) (105) (105) (12) (13) (14) (15) (16) (17) (18) (195) (195) (215) (215) (23) (24) (25) (265) (265) (28) (29) (30) (31) (32) (33) (34)

Excluding social security contributions 46.7 34.1 29.9 27.4 31.1 29.6 33.0 27.8 23.7 24.1 22.1 26.0 22.7 31.9 29.1 19.5 23.7 27.0 20.4 20.9 20.1 21.6 31.5 25.8 16.5 16.8 21.6 23.3 20.1 26.5 19.8 18.5 19.9 16.9

(1) (2) (7) (11) (6) (8) (3) (10) (175) (16) (21) (14) (20) (4) (9) (30) (175) (12) (25) (24) (265) (225) (5) (15) (34) (33) (225) (19) (265) (13) (29) (31) (28) (32)

Source: OECD (2013b, Pt. II Tax levels and tax structures, table 1, p. 90).

this covers many social security contributions as they are usually compulsory and the link between paying contributions and entitlement to benefits is often a loose one at best. If social security contributions are included, then tax revenue as a percentage of GDP is as high as 47.7 per cent for Denmark and over 40 per cent for Austria, Belgium, Italy, Finland, France, Norway and Sweden. The figures for most of the remaining OECD members are over 30 per cent. Although some countries have significantly lower tax ratio figures they still represent enormous amounts of tax revenue. Even if social security

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contributions are excluded from the figures, six OECD countries still have tax revenues over 30 per cent and most of the rest over 20 per cent of GDP. In addition to the enormous amount of taxation in modern economies, the effects of taxation throughout the economy may be more extensive than is commonly realized. For example, even those with incomes too low to bring them into personal income tax are still likely to pay significant amounts of taxes on goods and services. This means, of course, that almost everyone in modern economies bears some part of the tax burden. Furthermore, the study of the incidence of taxation shows that even the prices of goods and services that are not directly subject to taxation may be affected by taxation on other things. An obvious example is ‘duty free’ products at airports which are rarely priced so as to pass on the full benefit of their tax free status to the final consumer. More generally, the effects of taxation work their way through an economy by changes in prices, outputs, incomes and government expenditure. The Tax Authorities Not surprisingly revenue authorities around the world have spent a great deal of effort in researching factors affecting compliance. For example, in her 2014 Annual Report to Congress, the National Tax Advocate Nina Olson commented on tax compliance research relating to sole proprietors – a group Internal Revenue Service data show are responsible for the greatest portion of the shortfall in tax revenue. For sole proprietors it was found that trust in the government, the Internal Revenue Service and in the fairness of the tax system is ‘the greatest corollary to tax compliance behaviour’. She continued: ‘Specifically, the factors that appear to have the greatest influence on whether a taxpayer is compliant or noncompliant are the norms of the taxpayer’s community and the provision of taxpayer service’ (National Tax Advocate 2014, p. xi). More generally, taxpayer cooperation will be influenced by a range of factors and taxpayer attitudes. Tax authorities have long recognized differences in taxpayers in these respects and the importance of developing an appropriate compliance strategy to address them. For example, both Australia (Australian Tax Office 2002) and New Zealand (2003) developed a ‘Compliance Model’ that links different types of taxpayer motivation and the appropriate official response. The model incorporates a range of taxpayer attitudes at four levels – from a definite decision not to comply to a willingness to do the right thing and may be summarized with the appropriate compliance strategy as follows: Attitude to compliance Have decided not to comply Don’t want to comply Try to, but don’t always succeed Willing to do the right thing

Compliance strategy Use full force of the law Deter by detection Help to comply Make it easy

Tax authorities have used many ways to increase compliance in addition to possible legal sanctions. Although it has proved very difficult to simplify tax systems, it has often been possible to simplify tax administration as far as many taxpayers with straightforward affairs are concerned, for example, with the one-page US return the 1040EZ. Furthermore there has been general recognition that many taxpayers need assistance with their tax

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affairs. There have also been initiatives drawing on insights from behavioural economics to ‘nudge’ taxpayers in the right direction and behavioural economics is therefore the subject of the following section.

3

BEHAVIOURAL ECONOMICS

Behavioural economics has recognized applications to taxation (see, for example, James 2006; Congdon et al. 2009; Leicester et al. 2012) and the potential for many more. Behavioural economics has been described by Camerer and Loewenstein (2004, p. 3) as increasing the explanatory power of economics by basing it on ‘more realistic psychological foundations’ though it should be added it also draws on other academic disciplines. Putting it another way, Camerer and Malmendier (2007, p. 235) suggested that behavioural economics modified ‘the standard economic model to account for psychophysical properties of preference and judgement, which create limits on rational calculation, willpower and greed’. However, it has become a very wide field of study and the Cabinet Office and Institute for Government (2010, p. 16) stated one ‘weakness of the literature around behavioural economics is there are now literally hundreds of different claimed effects and influences’. To identify the most important topics, the present author (James 2015) surveyed three behavioural economic texts, Camerer et al. (2004), Schwartz (2008) and Wilkinson (2008) adding the number of pages referenced for each behavioural topic. This was not intended to be a precise exercise since many of the concepts overlap and there are significant differences in the way each book is referenced. Nevertheless, this exercise gave a clear indication that some behavioural topics attract far more attention than others. Three such topics – fairness, prospect theory and emotional factors – were referenced on over 100 pages of the three books together, and all those referenced on more than 50 pages are shown in Box 17.1. The issue referenced to the largest number of pages was fairness and, if ‘inequality aversion’ is included as well, fairness stands out as easily the most prominent topic. Fairness is one of the key aspects of a successful tax compliance policy. There have been many cases of collective non-compliance when taxpayers’ perceptions of fairness have not been taken into account, from the ‘no taxation without representation’ view and the

BOX 17.1

FREQUENTLY REFERENCED BEHAVIOURAL TOPICS

Fairness – opinions on the relative position of different individuals. Prospect theory – examines decision-making involving risk and probabilities. Emotional factors – rather than carefully thought through views. Mental accounting – individuals may have separate accounts mentally for psychologically separate outcomes. Loss aversion – Losses cause more pain than equivalent gains cause pleasure. Time preference, including myopia – preference for utility to be received sooner rather than later. Reciprocity – responses to the behaviour of others. Framing – the way choices are presented may influence decision-making. Source: James (2015).

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Boston Tea Party of 1773 (see, for example, Labree 1964) to the UK’s failed community charge or ‘poll tax’ which was a factor in the resignation of the Prime Minister Margaret Thatcher in 1990 (Gibson 1990). Behavioural economics has much to contribute to understanding the importance of fairness in tax policy as it does to many other issues. All the behavioural topics in Box 17.1 have been shown to have effects on tax compliance and there is only space for some illustrations from the substantial literature. Evidence indicating the importance of fairness in tax policy is presented by James (2014). Dhami and al-Nowaihi (2007) argue that tax evasion is more satisfactorily explained by prospect theory than it is by expected utility theory. Murphy and Tyler (2008) found that even emotions can play an important part in tax compliance behaviour. Ashby and Webley (2008) presented evidence that mental accounting was relevant to tax compliance since it might affect whether individuals thought particular forms of income should be taxed or not and therefore whether they were reported. ReesJones (2014) showed how loss aversion is an explanation for tax avoidance. In terms of time preference, Chorvat (2007) argued that tax compliance might be improved if tax did not have to be paid at the same time as tax returns had to be filed. Luttmer and Singhal (2014, p. 157) took reciprocity to mean situations where willingness to comply with the tax system depends on the person’s relationship with the state, which includes views that taxes are part of a social contract where taxes pay for public expenditure as well as perceptions of legitimacy and fairness. Finally, the way issues are framed may affect public perceptions relevant to the tax system and this may have implications for compliance (McCaffery and Baron 2004). To summarize, each of the behavioural issues found to be most prominent in the behavioural texts surveyed, namely, fairness, prospect theory, emotional factors, mental accounting, loss aversion, time preference, reciprocity and framing, have implications for tax compliance and, therefore, at least a potential role in developing nudges that might encourage taxpayers even further in meeting their fiscal obligations. A modern concept incorporating behavioural effects and providing a useful framework for understanding tax compliance is ‘tax morale’. This has been defined as the ‘intrinsic motivation to pay taxes’ (James 2012, p. 262). A thorough account appears in Torgler (2007) and an analysis of public opinion surveys relevant to tax morale is provided by the OECD (2013c). Specific issues relating to tax morale are addressed in particular publications such as cultural differences (Alm and Torgler 2006), direct democratic rights (Torgler 2005) and religious faith and activity (Torgler 2006). Frey and Torgler (2007) found a high correlation between tax morale and perceived tax evasion and there is other evidence that a higher level of tax morale and institutional quality, such as government effectiveness and regulatory quality, can lead to a smaller shadow economy (Torgler and Schneider 2007). An important point made by Alm et al. (2004) is that taxpayers’ sense of civic duty and therefore tax morale are more likely to be encouraged if they are treated like clients rather than offenders. One way of doing this might be the greater use of nudging techniques rather than legal coercion.

4

NUDGING

‘Nudging’ might be taken to mean anything designed to change individual behaviour short of a legal obligation. In that sense tax systems are already used extensively through

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the use of tax concessions to encourage ‘good behaviour’ – such as saving in a pension plan – and extra taxation for ‘undesirable’ behaviour – such as consuming alcohol and tobacco or causing pollution. Individuals may not behave in the ways intended but there would be financial consequences to their actions. However, in their influential book Nudge, Thaler and Sunstein (2008, p. 6) defined ‘nudge’ in a more precise way: ‘A nudge . . . is any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives.’ They suggest that to be considered a nudge a measure should be easy and cheap to avoid. They give an example of a nudge as encouraging the consumption of fruit by putting it at eye level. In contrast banning junk food does not count as a nudge because it removes choice. They also go on to differentiate between homo economicus or ‘Econs’ from homo sapiens or ‘Humans’. An Econ can ‘think like Albert Einstein, store as much memory as IBM’s Big Blue, and exercise the willpower of Mahatma Gandhi’ (Thaler and Sunstein 2008, pp. 6–7). A Human cannot behave in such a way and can be expected to make mistakes. A nudge is anything ‘that significantly alters the behaviour of Humans, even though it would be ignored by Econs’ (Thaler and Sunstein 2008, p. 8). Econs primarily respond to financial incentives – such as tax concessions for pension saving – and humans respond to both incentives and nudges. Thaler and Sunstein sketch out six principles of good choice architecture. They include for this purpose financial incentives of the sort not included in their definition of nudge but the other five principles are consistent with that definition. These are defaults (that people will take the line of least resistance), expect errors, provide feedback, ‘map’ the relationship between choice and the ultimate outcome and structure complex choices to assist decision-making. By rearranging the six key points as iNcentives, Understanding mappings, Defaults, Give feedback, Expect errors and Structure complex choices, the authors offer the mnemonic NUDGES illustrated in Box 17.2. Measures based on nudging have been adopted extensively with respect to such issues as health, pensions, personal finance and charitable giving where it is considered individuals might make better decisions but statutory regulation is not considered appropriate (see, for example, James 2015). In such areas, there has been much discussion about nudging as a policy instrument concerning such matters as liberty, paternalism, fairness and practical issues – see, for example Amir and Lobel (2008), Goodwin (2012), Hausman and Welch (2010), Sunstein and Thaler (2003), Vallgårda (2012) and Whyte et al. (2012, p. 32). However, with regard to such issues, unlike health and so on where individual choice is

BOX 17.2

NUDGES

iNcentives – this refers to financial incentives. Understanding mappings – the relationship between choices and outcomes. Defaults – people tend to take the option that requires the least effort. Give feedback – the best way to help Humans improve. Expect error – a system should be designed to expect mistakes. Structure complex choices – to help Humans with numerous alternatives. Source: Based on Thaler and Sunstein (2008).

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BOX 17.3

MINDSPACE

Messenger – who delivers the message may be important. Incentives – affect behaviour in different ways. Norms – individuals try to conform to group norms. Defaults – inertia is an important factor in behaviour. Salience – the more specific and salient the more influence. Priming – previous awareness may increase acceptability. Affect – the emotional dimension of a response. Commitments – trying to stick to promises and reciprocate. Ego – behaviour towards promoting image of self. Source: Based on Cabinet Office and Institute for Government (2010).

important, taxation is not normally voluntary but a legal obligation which is based on collective rather than individual decision-making in each country. In taxation the issue is not whether the state should influence individual decision-making about whether or not to pay tax but how best to encourage individuals to do so. Nudging is not, of course, a new phenomenon. Marketing fits the Thaler and Sunstein (2008) definition if it does not include financial incentives for consumers. Marketing professionals have been far ahead of academic commentators in understanding and using behavioural factors to influence consumer choice. Governments have also used such techniques to support policies designed to influence behaviour and it is not surprising that some governments have done so with enthusiasm. For example, in the UK, the Behavioural Insights Team (BIT) was established in 2010 in the Cabinet Office at the centre of government specifically with the aim of helping the government develop and apply lessons from behavioural economics and behavioural science to public policy (Cabinet Office 2010). Perhaps it is not surprising that the BIT became widely known as the ‘Nudge Unit’ (James 2015). This initiative led to a number of developments including the Cabinet Office and Institute for Government (2010, p. 6) publication which focuses on ‘nine robust influences on human behaviour’ which ‘have been repeatedly found to have strong impacts on behaviour’ (Dolan et al. 2012, p. 265). This was condensed into a manageable ‘checklist’ which took the form of the mnemonic MINDSPACE shown in Box 17.3. The Nudge and Mindspace contributions cover similar ground and both explicitly incorporate incentives defaults. However, the NUDGE mnemonic also gives prominence to important aspects of process – such as expecting errors and structuring complex choices – which are particularly relevant to taxation. MINDSPACE explicitly includes norms which have a particular role in tax nudges as well as the delivery of the message, salience and priming which also have explicit tax applications.

5

TAX NUDGES

NUDGE and MINDSPACE both offer incentives and defaults as key headings and MINDSPACE also highlights norms. All three have particular relevance for taxation so this section will concentrate on incentives, defaults and norms in turn.

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Incentives Incentives cover the first of the two main approaches to tax compliance summarized in the introduction: that taxpayers are ‘rational’ and make decisions on the basis of monetary outcomes. Such taxpayers fit Thaler and Sunstein’s (2008) concept of an Econ – a person motivated only by financial incentives and disincentives and capable of precise calculations of advantage and disadvantage. A tax compliance policy based on such assumptions would concentrate on such factors in the form of detection and penalties for non-compliance. However, most, if not all, taxpayers at least partly fit Thaler and Sunstein’s model of Humans who are motivated by a wider range of factors. Such taxpayers are not necessarily motivated by monetary considerations alone, are not very good at complex calculations and make mistakes. The main difficulty with the first approach to compliance is that too much reliance on the assumption that taxpayers are Econs may have adverse effects on Humans. In principle at least, the purpose of taxation is to improve well-being, and the benefits of public expenditure should therefore exceed the costs of taxation. If a system based on penalties is implemented too zealously it can lead to additional costs in terms of ‘pain and suffering’ as described by Payne (1993) who presents evidence that anxiety has been deliberately used to promote tax compliance. Among other things, Payne (1993, p. 130) quotes a former official of the US Internal Revenue Service (IRS) who begins his book (Strassels 1981, p. 3) by claiming that the IRS has worked hard to create ‘a state of fear and loathing’ among taxpayers and that nothing ‘is more central to the IRS strategy of tax collection than scaring you, the taxpayer, and keeping you that way’. Furthermore, if taxpayers are Humans rather than Econs, an unduly harsh policy of enforcing tax compliance may produce undesirable and unnecessary side effects such as taxpayer resistance. For instance, both Schmölders (1970) and Strümpel (1969) described German taxation at the time as being very rigid in its assessment procedures which led to an effective but expensive and confrontational system. A notable outcome ‘of the relatively coercive tax-enforcement techniques is the high degree of alienation from the state . . . [which] negatively influences the willingness to cooperate’ (Strümpel 1969, p. 29). Furthermore, there may be an unwillingness to undertake legitimate commercial activities if there is a risk of heavy-handed treatment by the tax authorities. If this leads to a reduction in the level of economic enterprise it might not only reduce the amount of taxation paid, so directly undermining such a harsh policy, but there might also be wider implications such as making a country less productive and competitive in all sorts of ways – an increasingly important consideration in a global economy. Instead of depending too much on such an approach, it might be better to develop the behavioural approach which might improve compliance without the undesirable effects. Defaults Thaler and Sunstein (2008, p. 83) describe defaults as ubiquitous and powerful and this is certainly true in tax administration. One of the main defaults is withholding tax at source so the tax does not even pass through the hands of taxpayers but instead goes directly to the tax authority. As pointed out in the introduction, withholding can be traced back to sixteenth-century England (Soos 1997) and is a standard feature of many tax systems. The

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OECD (2013a), in compiling information on OECD and some other selected countries, found that withholding tax at source is very widespread for personal income tax and, in most countries where applicable, for social security contributions as well. Many countries also have withholding arrangements for interest and dividend income. It is possible that withholding tax might encourage an underground economy but for most employees and other taxpayers this is not a realistic option. A further nudge to comply is that there are widespread mandatory reporting arrangements for a wide range of payments. Such mandatory reporting together with technological developments has led to an even bigger nudge with pre-filled or ‘pre-populated’ tax returns. These are returns incorporating information received from third parties about the taxpayer’s income and other details, and the development of pre-filled returns has been examined by Highfield (2006). The role of the taxpayer in this process is to confirm that the information on the return is correct or amend it and to supply any other information required. Pre-populated returns have been used for some time. Denmark first introduced such arrangements in 1988 though originally the system was quite primitive since the amount of information that could be collected and processed was limited. However, the arrangements were progressively enhanced during the 1990s and similar arrangements were introduced in Sweden in 1994 and Norway in 1998. As the application of technology to tax administration progressed, other countries have also introduced pre-filled returns, for example, Australia in 2006 (Evans and Tran-Nam 2010) and these developments are being considered elsewhere. Such returns can contain details of most major sources of income together with the tax withheld, asset purchases and sales, specific deductions that are obtained from third party sources or calculated according to a formula, personal tax allowances, tax credits and, even, calculations of tax payable or overpayments of tax to be refunded. Norms The observation that individuals conform to social or group norms was one of the headings in MINDSPACE. Although it was not included in the acronym NUDGE, Thaler and Sunstein (2008) not only discuss a social norms approach but actually give a tax example. This consisted of experiments conducted in 1995 by the Minnesota Department of Revenue to assess different strategies for increasing voluntary compliance with personal income tax. Four strategies were tested. Three of these were the examination or audited tax returns with prior notice to the taxpayers, improved customer service and the redesign of the tax form but these strategies were found to lead to modest or no improvement in tax compliance. The fourth strategy involved sending two information letters. One of the letters described the public expenditure funded by taxation – health, education and so on – but it did not lead to any improvement in compliance. The other letter pointed out that perceptions of widespread tax evasion were incorrect and, according to the available data, there was a high level of compliance. This letter reinforced social norms about tax compliance and had a significant and positive effect (Coleman 1996). This had not been anticipated but clearly could be an effective improvement in the decision-making environment for compliance so the Minnesota Department of Revenue undertook a second randomized controlled experiment with the letter the following year which confirmed the original findings (Coleman 2007). The letter also attracted much wider attention and became known as the ‘social norms letter’. Further evidence that perceptions of social

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norms influence tax compliance behaviour has come from a range studies including Bobek et al. (2007, 2013), Posner (2000) and Wenzel (2005). A particular study by Hallsworth et al. (2014) used two large natural field experiments involving over 200 000 individuals in the UK to demonstrate that tax compliance was considerably improved by including social norms and public goods messages in standard reminder letters about tax payments. An important dimension is whether taxpayers prepare their tax returns themselves or use a professional tax preparer. A study by Hasseldine et al. (2007) in the UK used actual taxpayer returns of sole proprietors who, as indicated in section 2 above, have been identified as a group associated with particular compliance risks. This study distinguished between sole proprietors who prepared their own returns and those who used paid preparers and examined the comparative effects of letters containing a range of normative and sanctions based messages. Of the two normative letters, the first offered taxpayers assistance and the second indicated that the norm is to comply and reminded individuals that their taxes are spent on ‘things like hospitals, schools and pensions’. The three sanctions-based letters mentioned in turn increased risk of audit, possible penalties and, the third letter, that the taxpayer had already been selected for audit. When compared with a control group who did not receive any of these letters, all the letters had significant effects but they differed between the self-preparers and those using paid preparers; for example, the letter based on norms and public spending was more effective with the self-preparers. This seems to lead to the reasonable conclusion that nudges adapted to the circumstances of particular taxpayers are likely to achieve the most success.

6

CONCLUSIONS

The size and pervasiveness of taxation in modern economies makes tax compliance an important part of government policy. There are two main approaches to promoting compliance, one based on the assumption that taxpayers make ‘rational’ economic decisions and the other taking into account a wider range of behavioural factors. The economic approach to compliance will always be necessary as there is a legal obligation to pay taxes which ultimately has to be supported by a system of penalties for non-compliance. However, too much reliance on such an approach and over-zealous enforcement can result in unduly heavy costs of compliance, the possibility of alienating individuals from the state and, perhaps, a reluctance to undertake economic activity if there is perceived to be a risk of inappropriate and severe responses from the tax authorities. Therefore there is a strong case to supplement the economic approach to compliance with a more comprehensive approach, and it is particularly helpful to draw on the insights available from behavioural economics in general and the literature on ‘nudging’ in particular. A whole range of possible ways in which a behavioural approach may support tax compliance are discussed in the chapter but, given Thaler and Sunstein (2008, p. 100) came up with the mnemonic NUDGE and the Cabinet Office and Institute for Government (2010) with MINDSPACE, this chapter offers a third mnemonic to summarize the contribution to tax in the form of COMPLIANCE shown in Box 17.4. Box 17.4 indicates how choice architecture can make significant improvements to the lives of others through the design of ‘user-friendly environments’ (Thaler and Sunstein 2008, p. 11). ‘Opt-out’ rather than ‘opt-in’ reflects default arrangements which are widely

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BOX 17.4

COMPLIANCE

Choice architecture – creating a user-friendly environment. Opt-out rather than ‘opt-in’ policy – or defaults. Mental accounting. Preference (time). Loss aversion. Incentives – financial. Assistance for taxpayers. Norms. Cultural factors which affect tax morale. Equity – fairness. Source: Author.

used in tax administration particularly by withholding tax at source. The roles of mental accounting, time preference and loss aversion were among the topics included in the summary of the contribution of behavioural economics in section 3. Incentives represent the financial factors involved in the economic approach to compliance. Assistance to taxpayers was included in the discussion of tax authorities in section 2. Norms turn out to be an important factor in taxpayer decision-making, as is tax morale, and probably the single most important factor is equity or fairness. As mentioned above, there are hundreds of behavioural effects and influences and it is clear that many of them have at least the potential to play a useful role in successful tax compliance strategies. A narrow definition of rationality leading to a focus on penalties to enforce compliance is not sufficient to develop successful arrangements regarding tax compliance policy. Instead, a wider definition of rationality is required involving smart behaviour incorporating a more comprehensive understanding of the motivation behind individuals’ decisions regarding tax compliance.

REFERENCES Alm, J. and B. Torgler (2006), ‘Culture differences and tax morale in the United States and in Europe’, Journal of Economic Psychology, 27 (2), 224–46. Alm, J., F. Martinez-Vazquez and F. Schneider (2004), ‘“Sizing” the problem of the hard-to-tax’, in J. Alm, J. Martinez-Vazquez and S. Wallace (eds), Taxing the Hard-to-Tax: Lessons from Theory and Practice, Amsterdam: Elsevier/North Holland, pp. 11–75. Amir, O. and O. Lobel (2008), ‘Stumble, predict, nudge: how behavioral economics informs law and policy’, Columbia Law Review, 108 (8), 2098–137. Ashby, J.S. and P. Webley (2008), ‘“The trick is to stop thinking of it as ‘your’ money”: mental accounting and taxpaying’, paper presented at the IAREP/SABE World Meeting 2008, ‘Economics and Psychology: Methods and Synergies’, October, Rome. Australian Taxation Office (2002), Compliance Program 2002–03, Canberra: Australian Taxation Office. Bobek, D.D., A.M. Hageman and C.F. Kelliher (2013), ‘Analyzing the role of social norms in tax compliance behavior’, Journal of Business Ethics, 115 (3), 451–68. Bobek, D.D., R.W. Roberts and J.T. Sweeney (2007), ‘The social norms of tax compliance: evidence from Australia, Singapore and the United States’, Journal of Business Ethics, 74 (1), 49–64. Cabinet Office (2010), Applying Behavioural Insight to Health, London: Cabinet Office, Behavioural Insights Team.

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Cabinet Office and Institute for Government (2010), MINDSPACE, Influencing Behaviour through Public Policy, London: Cabinet Office. Callender, G.S. (1909), Selections from the Economic History of the United States 1765–1860, Boston, MA: Ginn & Co. Camerer, C.F. and G. Loewenstein (2004), ‘Behavioral economics: past, present, future’, in C.F. Camerer, G. Loewenstein and M. Rabin (eds), Advances in Behavioral Economics, Princeton, NJ: Princeton University Press, pp. 3–51. Camerer, C.F and U. Malmendier (2007), ‘Behavioral economics of organizations’, in P. Diamond and H. Vartiainen (eds), Behavioral Economics and Its Applications, Princeton, NJ: Princeton University Press, pp. 235–90. Camerer, C.F., G. Loewenstein and M. Rabin (eds) (2004), Advances in Behavioral Economics, Princeton, NJ: Princeton University Press. Chorvat, T. (2007), ‘Tax compliance and the neuroeconomics of intertemporal substitution’, National Tax Journal, 60 (3), 577–88. Coleman, S. (1996), The Minnesota Income Tax Compliance Experimental Tax Results, St Paul, MN: Minnesota Department of Revenue. Coleman, S. (2007), ‘The Minnesota Income Tax Compliance Experiment: replication of the social norms experiment’, 1 November, accessed 1 March 2015 at http://ssrn.com/abstract51393292. Congdon, W.J., J.R. Kling and S. Mullainathan (2009), ‘Behavioural economics and tax policy’, National Tax Journal, 62 (3), 375–86. Dhami, S. and A. al-Nowaihi (2007), ‘Why do people pay taxes? Prospect theory versus expected utility theory’, Journal of Economic Behavior & Organization, 64 (1), 171–92. Dolan, P., M. Hallsworth, D. Halpern, D. King, R. Metcalfe and I. Vlaev (2012), ‘Influencing behaviour: the mindspace way’, Journal of Economic Psychology, 33 (1), 264–77. Evans, C. and B. Tran-Nam (2010), ‘Managing tax system complexity: building bridges through pre-filled tax returns’, Australian Tax Forum, 25 (2), 247–76. Frey, B.S. and B. Torgler (2007), ‘Tax morale and conditional cooperation’, Journal of Comparative Economics, 35 (1), 136–59. Gibson, J. (1990), The Politics and Economics of the Poll Tax: Mrs Thatcher’s Downfall, Cradley Heath, Warley: EMAS. Goodwin, T. (2012), ‘Why we should reject “nudge”’, Politics, 32 (2), 85–92. Hallsworth, V., J.A. List, R.D. Metcalfe and I. Vlaev (2014), ‘The behavioralist as tax collector: using natural field experiments to enhance tax compliance’, NBER Working Paper No. 20007, National Bureau of Economic Research, Cambridge, MA, accessed 2 March 2015 at http://www.nber.org/papers/ w20007. Hasseldine, J., P. Hite, S. James and M. Toumi (2007), ‘Persuasive communications: tax compliance strategies for sole proprietors’, Contemporary Accounting Research, 24 (1), 171–94. Hausman, D.M. and B. Welch (2010), ‘Debate: to nudge or not to nudge’, Journal of Political Philosophy, 18 (1), 123–36 Highfield, R. (2006), ‘Pre-populated income tax returns’, in M. McKerchar and M. Walpole (eds), Further Global Challenges in Tax Administration, Birmingham: Fiscal Publications, pp. 331–58. James, S. (2006), ‘Taxation and the contribution of behavioral economics’, in M. Altman (ed.), Foundations and Extensions of Behavioral Economics: A Handbook, New York: M.E. Sharpe, pp. 589–601. James, S. (2012), A Dictionary of Taxation, 2nd edn, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. James, S. (2014), ‘The importance of fairness in tax policy: behavioral economics and the UK experience’, International Journal of Applied Behavioral Economics, 3 (1), 1–12. James, S. (2015), ‘The contribution of the UK’s Behavioural Insights Team’, International Journal of Applied Behavioral Economics, 4 (2), 54–70. James, S., J. Hasseldine, P. Hite and M. Toumi (2001), ‘Developing a tax compliance strategy for revenue services’, Bulletin for International Fiscal Documentation, 55 (4), 158–64. Labree, B. (1964), The Boston Tea Party, New York: Oxford University Press. Leicester, A., P. Levell and I. Rasul (2012), Tax and Benefit Policy: Insights from Behavioural Economics, London: Institute for Fiscal Studies. Luttmer, E.F.P. and M. Singhal (2014), ‘Tax morale’, Journal of Economic Perspectives, 28 (4), 149–68. McCaffery, E.J. and J. Baron (2004), ‘Framing and taxation: evaluation of tax policies involving household composition, Journal of Economic Psychology, 25 (6), 679–705. Murphy, K. and T. Tyler (2008), ‘Procedural justice and compliance behaviour: the mediating role of emotions’, European Journal of Social Psychology, 38 (4), 652–68. National Tax Advocate (2014), Annual Report to Congress, Taxpayer Advocate Service, accessed 1 March 2015 at www.TaxpayerAdvocate.irs.gov/2014AnnualReport.

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New Zealand (2003), Report of the Inland Revenue Department for the Year Ended 30 June 2003, Wellington, New Zealand: Inland Revenue Department. Organisation for Economic Co-operation and Development (OECD) (2004), ‘Compliance risk management: managing and improving tax compliance’, Centre for Tax Policy and Administration, OECD, Paris, accessed 2 March 2015 at http://www.oecd.org/dataoecd/44/19/33818656.pdf. Organisation for Economic Co-operation and Development (OECD) (2013a), Comparative Information on OECD and other Advanced and Emerging Economics, Paris: OECD Publishing, doi:10.1787/9789264200814-en. Organisation for Economic Co-operation and Development (OECD) (2013b), Revenue Statistics: 1965–2012, Paris: OECD Publishing, doi:10.1787/rev stats-2013-4-en-fr. Organisation for Economic Co-operation and Development (OECD) (2013c), Tax and Development: What Drives Tax Morale?, Tax and Development Programme, OECD, accessed 1 March 2015 at http://www.oecd. org/ctp/tax-global/TaxMorale_march13.pdf. Payne, J.L. (1993), Costly Returns: The Burdens of the U.S. Tax System, San Francisco, CA: ICS Press. Posner, E.A. (2000), ‘Law and social norms: the case of tax compliance’, Virginia Law Review, 86 (8), 1781–819. Rees-Jones, A. (2014), ‘Loss aversion motivates tax sheltering: evidence from U.S. tax returns’, 8 December, Operations and Information Management Department, University of Pennsylvania, accessed 1 March 2015 at http://ssrn.com/abstract52330980. Schmölders, G. (1970), ‘Survey research in public finance: a behavioural approach to fiscal theory’, Public Finance, 25 (2), 300–306. Schwartz, H. (2008), A Guide to Behavioral Economics, Falls Church, VA: Higher Education Publications. Soos, P.E. (1997), The Origins of Taxation at Source in England, Amsterdam: IBFD Publications. Strassels, P.N. (1981), All You Need to Know about the IRS: A Taxpayer’s Guide, New York: Random House. Strümpel, B. (1969), ‘The contribution of survey research to public finance’, in A.T. Peacock (ed.), Quantitative Analysis in Public Finance, New York: Praeger, pp. 13–32. Sunstein, C.R. and R.H. Thaler (2003), ‘Libertarian paternalism is not an oxymoron’, University of Chicago Law Review, 70 (4), 1159–202. Thaler, R.H. and C.R. Sunstein (2008), Nudge: Improving Decisions about Health, Wealth and Happiness, New Haven, CT: Yale University Press. Torgler, B. (2005), ‘Tax morale and direct democracy’, European Journal of Political Economy, 21 (2), 525–31. Torgler, B. (2006), ‘The importance of faith: Tax morale and religiosity’, Journal of Economic Behavior & Organization, 61 (1), 81–109. Torgler, B. (2007), Tax Compliance and Tax Morale: A Theoretical and Empirical Analysis, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Torgler, B. and F. Schneider (2007), The Impact of Tax Morale and Institutional Quality on the Shadow Economy, IZA Discussion Paper No. 2541 and CESifo Working Paper Series No. 1899, accessed 3 March 2015 at http:// ssrn.com/abstract5958248. Vallgårda, S. (2012), ‘Nudge – a new and better way to improve health?’, Health Policy, 104 (2), 200–203. Wenzel, M. (2005), ‘Misperceptions of social norms about tax compliance: from theory to intervention’, Journal of Economic Psychology, 26 (6), 862–83. Whyte, K.P., F. Selinger, A.L. Caplan and J. Sadowski (2012), ‘Nudge, nudge or shove, shove: the right way for nudges to increase the supply of donated cadaver organs’, American Journal of Bioethics, 12 (2), 32–9. Wilkinson, N. (2008), An Introduction to Behavioral Economics, Basingstoke: Palgrave Macmillan.

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18 Income tax compliance* Erich Kirchler, Barbara Hartl and Katharina Gangl

INTRODUCTION Tax evasion and aggressive tax avoidance are significant problems in most societies and represent an important source of a potential loss of revenue for governments. Understanding taxpayers’ behavior and fostering tax compliance are therefore important challenges for the state. The traditional economic approach to tax behavior assumes that taxpayers are rational decision makers trying to maximize their utility while filling in their tax returns. From the individual perspective, the smartest thing to do would be to evade taxes as long as the probability of getting caught is low and the expected fine is small. This assumption is also reflected in the public opinion that tax evasion is a game won by the clever and the intelligent (Kirchler 1998). Honest taxpayers are perceived to be hard working, but not as intelligent as tax evaders. This is puzzling, as in many countries in the world tax honesty is rather high compared with the low audit rates (Kirchler 2007). Citizens value public goods and comprehend the necessity of tax collection to finance them, but are at the same time often reluctant to accept the full burden of their taxes. Indeed, since the beginning of tax collection, taxpayers have complained, and their complaints survive in the hieroglyphics of ancient Egypt and in the work of scholars from medieval times until today. For instance, Thomas Aquinas (1225–74) is said to have coined the description of taxes as ‘legal theft’; and Peter Sloterdijk (2010) wonders why the public has not engaged in a civil rebellion against the prodigious enlargement of the tax base, which he perceives as the equivalent of socialist expropriation. In a recent survey in Germany, taxpayers show an ambivalent attitude towards taxation and taxes: although the majority of respondents maintain that paying taxes is a duty which must be respected, a vast majority complain that the tax burden is much too high, the legal and bureaucratic procedures of filing taxes are complex and too time-consuming, and politicians spend tax money wastefully (Deutsche Wirtschafts-Nachrichten 2014). Tax attitudes vary among taxpayers: the self-employed appear to hold more negative attitudes than white collar workers or civil servants (Kirchler 1998), and citizens expressing preferences for liberal and right-wing political parties also express preferences for ‘less state’ and lower taxes as compared to those who typically vote for left-wing parties (Lozza et al. 2013; Sussman and Oliviola, 2011). The desire to reduce taxes can be even stronger than rational maximization of one’s own income, leading to the selection of suboptimal options. Sussman and Oliviola (2011) presented scenarios describing two stores selling television sets which the participants were to imagine purchasing. They were invited to choose either buying the television at full price in a store nearby or to take a 30-minute drive to a store offering a discount. In one scenario the discount was related to taxes (tax-free discount equivalent to 8 percent discount); in the other scenario, taxes were not mentioned but the discount was slightly larger (9 percent discount). Willingness to take the 30-minute drive was significantly 331

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higher if the discount was related to taxes, even if the discount was smaller. If taxes can be avoided, people are also willing to wait longer for a commodity, to invest in riskier assets, and express higher acceptance when confronted with moving to a country with a longer daily commute (Sussman and Oliviola 2011). People perceive tax evasion as illegal and unacceptable, whereas they endorse tax avoidance, defined as the legal reduction of taxes (Kirchler et al. 2003). Tax avoidance refers to the reduction of the tax burden within the limits of the law, for instance, through the exploitation of tax loopholes. Tax avoidance can be perceived as a positive and clever act (Kirchler et al. 2003). In contrast, tax evasion is seen as immoral, illegal and as a form of fraud associated with the shadow economy (Kirchler et al. 2003). Also, tax avoidance in the form of aggressive tax planning against the spirit of the tax law, such as the crossborder money shifting of international companies, is seen as unfair by many taxpayers and provokes protests by the media and the citizens. For example, in 2011, the information technology company Apple made a profit of US$22 billion, but owing to aggressive tax planning, the company paid less than 1 percent of its profit in taxes (Szigetvari 2014). When Starbucks was accused of aggressive tax planning in the United Kingdom by the media, consumers took to the streets in protest of this non-cooperative behavior and boycotted Starbucks stores (Cambell 2012). In this chapter, we start with the economic perspective on tax behavior which has traditionally dominated tax research. Tax-paying decisions are conceptualized as decisions under uncertainty, shaped by the probability of audit and the severity of punishment in case of detected wrongdoing. We then present the results of psychological tax behavior research and consider different segments of taxpayers and their respective attitudes towards taxes. In the following section, tax behavior is discussed as a behavior in a social dilemma situation with decisions influenced by rational utility maximization and beliefs about other taxpayers’ behavior. In this section, we briefly present a survey conducted on emotions, the perceived power of authorities and compliance. In the penultimate section, we illustrate how the interaction between taxpayers and tax authorities determines cooperative interaction climates and corresponding forms of tax cooperation. We conclude with a discussion of current developments in the field and future research directions for tax psychology.

2

THE ECONOMIC APPROACH

The neoclassical economic approach to tax behavior is based on the assumption that taxpayers, aiming to maximize their utility, are confronted with a decision under uncertainty. It is suggested that taxpayers calculate which of the two options – paying taxes honestly or evading taxes – provides the greatest value. If effective audits with severe fines are in place and the probability of audit is high, a taxpayer should be honest because the risk of being caught and fined outweighs the small chance of gain through evasion. In contrast, if audits are rare and fines low, a rational taxpayer would choose to evade taxes because the risk of being caught is low and, even if that were to happen, the fine is low. Since taxpayers seek to accrue higher profits, they would always evade if audits and fines were absent (Allingham and Sandmo 1972; Srinivasan 1973). Audits and fines are the keys to ensuring tax compliance.

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Income tax compliance 333 Empirical research suggests that the effect of audits and fines is more complex than assumed in the economic theory of crime (Becker 1968). Indeed, the effect of audits and fines is smaller than theoretically expected and sometimes in the opposite direction (Andreoni et al. 1998; Kirchler et al. 2010). In many countries in the world, the probability of audit is low, which should result in low tax honesty; however, tax honesty is rather high compared to the low audit rates (Kirchler 2007). For instance, the Internal Revenue Service in the United States (IRS) audited only about 1 percent of the more than 137 million returns filed by taxpayers in 2008 (Bible 2010). Despite the low audit probability, about 80 percent of total reportable income is assumed to be reported correctly, while 18–23 percent of reports are incorrect (Cebula and Feige 2012). The contradictory effects of audits may be due to the fact that it is hard for people to deal with uncertainty. They often underestimate or overestimate the likelihood of events. Therefore, the perceived and objective probability of audit may differ significantly (Fischer et al. 1992). The effect of audits seems to vary more with their perceived severity than with their actual severity. The more severe audits and fines are perceived to be, the stronger their impact on tax compliance (Alm et al. 1992; Kirchler 2007; Mulder et al. 2009). For instance, frequent audits at the beginning of an individual’s professional life in contrast to later audits may increase a taxpayer’s perception of high audit probability and lead to sustainable tax compliance. Tax experiments in which participants file taxes on earned income over a ‘lifespan’ of 60 filing periods showed that audits conducted at the beginning of the 60 rounds (in contrast to later audits) lead to an increase in tax compliance and keep tax compliance high even if the frequency of audits decreases in the later rounds (Guala and Mittone 2002). Misperception of chance can also lead to the opposite of the intended effects. The same tax experiments also showed that tax compliance decreases immediately after an audit takes place (Guala and Mittone 2002; Kastlunger et al. 2009). It seems that after an audit, taxpayers feel safe from another audit in the next round and choose risk-seeking behavior. This phenomenon is referred to as the ‘bomb crater effect’ (Guala and Mittone 2002) following the observation in World War One that soldiers under heavy fire believed themselves to be safe in the craters of recent explosions, assuming it would be unlikely for two bombs to fall in the same spot. Likewise, taxpayers may underestimate the likelihood of upcoming audits immediately after they have been audited (Kastlunger et al. 2009) and therefore evade. Instead of the objective audit probability, it seems that the perceived audit probability determines tax compliance. Beside audits, fines are assumed to be a useful measure to diminish tax evasion. According to classical economic assumptions, the amount of a possible fine has a positive effect on taxpayers’ willingness to pay taxes honestly (Allingham and Sandmo 1972). This effect might be undermined by causing reactance instead of subordination (Kirchler 2007), that is, taxpayers become motivationally aroused by the tax law as a threat to their behavioral freedom (Brehm 1989). The implementation of fines per se may lead to reactance and negative attitudes towards the tax authority. As such, the imposition of a fine can also crowd out the intrinsic motivation to comply (Feld and Frey 2002). Gneezy and Rustichini (2000) studied parents’ behavior in Israeli daycare centers before and after the introduction of a fine for late picking up of their children. After the introduction of a monetary fine for latecomers, delayed picking-ups increased rather than went down. Instead of feeling guilty about their late picking-ups, parents had a clear conscience about

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leaving their children longer under custodial care and made less of an effort to be punctual. Rather than being perceived as a fine, the payment was gladly accepted as a ‘price’ for prolonged custody. Generally, audits and fines impact tax compliance positively, but the effect is smaller than theoretically expected, and sometimes audits and fines backfire (Alm et al. 1995; Gangl et al. 2013). Audits might be perceived as an unpleasant experience whose probability of occurrence decreases immediately after an audit has occurred; fines sometimes might be perceived as a ‘price’ people are willing to pay. Audits and fines can also be perceived as a signal of mistrust from the tax authority and elicit mistrust in the authorities and non-cooperative behavior (Alm et al. 2012; Feld and Frey 2007).

3

DIFFERENTIAL EFFECTS

Taxpayers are not a homogeneous group of people trying to evade taxes. Instead, based on different socio-demographic characteristics and experiences with the tax authorities, taxpayers can be assumed to differ in their motivation to pay taxes and in their compliance. Women and older taxpayers as well as employees taxed at source are found to be more compliant than men, younger taxpayers, and self-employed taxpayers (Kirchler 2007; Torgler 2006). Based on such differences, tax authorities could segment taxpayers into groups and take a different approach with each group. Audits and fines may be most appropriate to enforce compliance among taxpayers intentionally evading, whereas they should assist and educate those who want to comply but fail to do so owing to the complexity of tax law (Braithwaite 2003b). Women are often found to be more compliant than men. This effect seems to have social rather than biological reasons (Kastlunger et al. 2010). The classic social role of women is typically associated with pro-social and cooperative behavior, in contrast to the social role of men (Fallan 1999; Kastlunger et al. 2010). Empirical research shows that it is only those women who identify with the classical gender role that differ from men in their tax compliance (Kastlunger et al. 2010). However, self-employed women, who seem to identify less with the classical role and more with their occupational role, which is associated with values such as competitiveness and dominance, do not seem to be more compliant than self-employed men (Gangl et al. 2013). As a consequence, gender could be a criterion for tax authorities to differentiate among employed taxpayers but not among self-employed taxpayers. Tax compliance seems to increase with age. Older taxpayers are consistently found to be more tax compliant than younger taxpayers (Frey and Torgler 2007; Kirchler 2007; Lewis 1978; Sidani et al. 2014). The reason might be that age correlates with knowledge about taxes, experiences with taxes, and a better understanding of the tax law, which in turn enhances trust in tax law. Moreover, knowledge about taxes enhances the opportunities for a taxpayer to legally reduce their taxes instead of illegally evading them. For instance, younger taxpayers with less experience might not put money aside during the year in order to pay their taxes at the end of the year (Muehlbacher and Kirchler, 2013). In contrast, based on ‘mental accounting’, older taxpayers are more likely able to separate gross income into net income, tax duties and social security payments and consequently put tax money at least ‘mentally’ aside, which ‘prepares’ them to pay taxes. Rather than

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Income tax compliance 335 having the feeling that they are paying out of pocket, and thus experiencing a loss when paying taxes, mental accounting leads to the perception of a forgone gain in addition to the net income when paying taxes, and thus, to less risk-seeking behavior. While younger taxpayers may experience an unpleasant surprise when filing and paying taxes, older taxpayers are aware of their tax liability and know approximately how much money they have to put aside for taxes (Muehlbacher and Kirchler 2013). Based on these assumptions, it can be assumed that tax authorities who provide young self-employed people and less experienced businesspeople with services introducing them to the tax law and tax procedures would increase tax knowledge while improving money management related to taxes, which would lead to higher compliance. Income has been investigated as a potential moderator of tax compliance. From a theoretical perspective, high-income earners could be either more or less compliant than low-income groups, and empirically, income has been found to be both related and unrelated to compliance (Allingham and Sandmo 1972; Srinivasan 1973). High income might increase an individual’s willingness to take the risk of evading taxes because possible fines might be seen as easy affordable. Conversely, low income might also increase the motivation to evade taxes because the money spent on taxes might represents a larger fraction of available income. The results of empirical studies are inconclusive (Pickhardt and Prinz 2014). It seems that factors related to income, such as source of income, might play a relevant role (Durham et al. 2014). For instance, experiments show that taxes on windfall gains are more likely to be evaded than money earned through hard work (Muehlbacher et al. 2008). Occupation has been tested as an essential determinant of tax compliance and an important segmenting criterion. Employed and self-employed taxpayers differ in their opportunities to evade taxes. Also, in many countries, employed taxpayers’ income is taxed at source as compared to the self-employed, who take home their gross income and pay taxes out of pocket (Antonides and Robben 1995; Engström and Holmlund 2009; Webley et al. 1991, 2001). Self-employed people who pay taxes out of pocket not only face more opportunities to engage in ‘creative’ tax planning, in the concealment of income and the exaggerated deduction of expenditures, but also have a tendency to exhibit more risk-seeking behavior as they often perceive paying taxes as a loss (Antonides and Robben 1995). According to the prospect theory (Kahneman and Tversky 1979), taxpayers are expected to either be risk averse or to take the risk and evade, depending on whether taxpayers conceive of their tax decision as within a forgone gain frame or a loss frame. Prospect theory also explains withholding phenomena: it is to be expected that people who have, for example, already paid taxes in advance in the form of monthly contributions and then have an additional sum to pay at the end of the fiscal year experience this balance as a loss and are reluctant to pay additional taxes. In contrast, taxpayers with the same total amount of tax liability who have already paid all of their taxes in advance, resulting in a year-end refund, may experience their taxes when filing at the end of the year as a gain and tend to be loss averse, resulting in willingness to declare revenue and expenses correctly. A study by Cox und Plumley (1988, cited in Webley et al. 1991) shows that these assumptions might be accurate. The authors investigated 50 000 tax declarations in the US and found that willingness to pay taxes depended upon whether a taxpayer was expecting a refund from the tax authorities or faced a balance due. For wage earners, it has been demonstrated that willingness to pay taxes reaches 96 percent when a refund is

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Handbook of behavioural economics and smart decision-making 100 Wages and salaries Compliance rate in percentage

95 90 Business income

85 80 75 70 65 < –1000

< –500

< –100

0

> –100

> –500

> –1000

Size of refund or balance due ($) Source: Cox and Plumley (1988), cited in Webley et al. (1991, p. 84).

Figure 18.1

Willingness to pay taxes as dependent upon the type of tax and the size of refund or balance due at the end of the fiscal year

expected. On the other hand, when a balance due is expected, willingness to pay drops to 89 percent. People with business income act even more clearly according to the predictions of prospect theory: willingness to pay varies from 96 percent to 70 percent, depending on whether they expect to receive a refund or make an additional payment. Figure 18.1 illustrates the results of Cox and Plumley’s (1988, p. 84) study. Similar results were achieved by Schepanski und Kelsey (1990) and Schepanski und Shearer (1995) as well as by Elffers and Hessing (1997) and Kirchler et al. (2005). Taxpayers do not only differ in their socio-demographic characteristics, but also in their experiences and relationship with the tax authorities, factors which are also suggested to result in differences in individual motivations to be honest. Braithwaite (2003a) distinguishes among five motivational postures of tax compliance based on the social distance taxpayers perceive towards the tax authority. Two motivational postures (commitment, capitulation) represent an overall positive orientation to the tax authority. The motivational posture of commitment describes taxpayers who feel morally obliged to pay taxes; they are open to admitting mistakes and want to correct them. Taxpayers holding the motivational posture of capitulation are not as committed; rather, they accept the tax authority as a legitimate authority and give in to this authority. The other three motivational postures (resistance, disengagement, game playing) express a negative tendency to cooperate. Resistant taxpayers doubt that the tax authority intends to behave cooperatively and keep their guard up. Disengaged taxpayers see no sense in cooperating and try to keep their distance from the tax authorities. ‘Game playing’ reflects a motivational posture and attitude toward laws, whereby the law is seen as something that can be used for one’s own advantage (Braithwaite 2003a).

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Income tax compliance 337 Motivational postures

Disengagement (individuals, groups evade taxes) Resistance (individuals, groups do not want to be tax compliant)

Capitulation (individuals, groups try to be tax compliant) Commitment (individuals, groups feel a moral obligation to be tax compliant)

Strategies to promote tax compliance

Prosecution

Audit with/without penalty

Regulatory strategies

Command regulation (non-discretionary; exhaustion of all legal means)

Command regulation (discretionary; tax audits)

Real time business examinations; record keeping reviews (tax and business data)

Enforced self-regulation (support and assistance with tax declarations)

Education; record keeping (tax and business data); service delivery (convenience, access, choice, control)

Self-regulation (simplification of the tax declaration in order to facilitate and ease correct behavior)

Source: Adapted from Braithwaite (2003b, p. 3).

Figure 18.2

The responsive regulation approach based on different motivational postures of taxpayers

Based on these motivational postures, the Australian Tax Office has proposed a system of responsive regulation in order to address taxpayers effectively (Braithwaite 2003b). The responsive regulation approach requires tax authorities to apply different strategies to ensure tax compliance for taxpayers with differing motives to comply or not to comply. The regulatory pyramid (Figure 18.2) serves as a guide for a tax authority’s response to non-compliance and is based on the opinion that it is less costly to resolve a problem at the bottom of the pyramid than to allow it to escalate to the top of the pyramid (Braithwaite 2003b). Instead of treating all taxpayers alike, the tax authorities should approach taxpayers depending on their motivational posture. Taxpayers who intentionally evade taxes should be confronted with the full rigor of the law. In contrast, taxpayers who hold positive motivational postures of commitment and capitulation should get assistance and support from the tax authorities (Braithwaite 2003b). Figure 18.2 conceptualizes the responsive regulation approach. The bottom of the pyramid mirrors empirical findings indicating that most taxpayers hold motivational postures reflecting a compliance-minded attitude (Braithwaite 2003b). The appropriate strategy of the tax authorities for most taxpayers would be to facilitate compliance by educating taxpayers and providing services. Conversely, the tax authorities should apply the full force of the law only for the minority of intentional resistant evaders, represented by the top of the pyramid (Braithwaite 2003b, 2009).

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SOCIAL NORMS AND SOCIAL DILEMMAS

Taxpayers do not pay taxes in a social vacuum (Mittone and Patelli 2000) and do not only consider the balance between the tax burden and the provision of public goods, but also orient their behavior on other taxpayers’ behavior. Taxpayers’ conceptions of the attitudes and activities of other taxpayers impact their decision on whether to pay taxes honestly or not. As people feel part of social groups, their decisions are often motivated by group norms (Frey and Torgler 2007; Wenzel 2007). In this vein, social norms consistently prove to be a strong regulator of behavior since one’s own tax compliance is related to the perceived noncompliance of others (Wenzel 2005). The term ‘social norm’ is either defined as what is commonly done in a community (descriptive norm) or what is commonly approved and disapproved by the community (injunctive norm; Kallgren et al. 2000). Individuals will comply as long as they believe that other people pay taxes honestly and that compliance is the social norm for the group they identify with. Wenzel (2005) shows that people overestimate others’ acceptance of tax evasion. An intervention whereby participants are informed about the actual social norms on tax evasion could reduce tax cheating. In addition, a large-scale field experiment in the United Kingdom by Hallsworth et al. (2013) shows that social norm messages increase compliance. Over 100 000 taxpayers received letters from the tax authority which varied only in the inclusion of a short phrase after the first sentence. Some of these letters contained messages based on social norms (for example, ‘Nine out of ten people pay their tax on time’, Hallsworth et al. 2013, p. 34). The results show that including norm messages in a standard tax payment reminder letter enhances tax compliance, with the greatest effect obtained from the ‘minority norm’ treatment, where taxpayers were informed that ‘Nine out of ten people in the UK pay their tax on time. You are currently in the very small minority of people who have not paid us yet’ (Hallsworth et al. 2013, p. 34). Similar to perceived social norms, tax morale, civic duty, taxpayer identity, appeals to patriotism or conscience, and feelings of altruism and morality also impact tax compliance (Torgler 2007). As taxes are used to finance public goods for the good of the community, taxpaying represents a social dilemma. The term ‘social dilemma’ refers to a situation in which the interest of an individual is opposed to the interest of the community or the group (Dawes 1980). When people use a public resource, they are individually better off when they make use of it without contributing in return – for instance, without paying taxes. They can benefit by acting selfishly as ‘free riders’. However, if most people maximize their own utility and act selfishly, the outcome can be a disaster for everyone (Kollock 1998). Public goods will not be provided and as a consequence everyone will be harmed. Experimental evidence indicates that communication between individuals, opportunities to participate in setting up the rules of the game, and the public announcement of defection increase cooperation in social dilemma games (Kollock 1998). Emotions also seem to play an important role in social dilemmas and are assumed to influence people’s tendency to choose individual interests over collective interests (Polman and Kim 2013). An investigation concerning the effect of anger, disgust and sadness on people’s willingness to give shows that angry people give fewer shared resources to others, whereas the recall of disgusting and sad experiences lead to more resource-giving (Polman and Kim 2013). Coricelli et al. (2010) measured skin conductance and self-reports to show that emotional arousal is associated with cheating behavior: a high degree of emotional arousal

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Income tax compliance 339 correlates with the likelihood of tax evasion and the amount evaded. Further, tax compliance was higher when the pictures of evaders were made public. The impact of shaming on tax compliance remains beneficial over the long term if the contravener is reintegrated into the group after evading, but leads to higher levels of evasion when reintegration fails (Coricelli et al. 2014). Nevertheless, participants were initially less likely to evade in the treatment without reintegration, possibly because they anticipate negative emotions related with stigmatization. Tax decisions therefore may be driven by the willingness to avoid negative emotions, which may occur, for instance, during public denouncement. Emotions also play a role when taxpayers perceive the tax authority as unjust. People differ in their affect intensity, so they react more, or less, emotionally to their treatment by the tax authorities (Murphy 2009). When people feel unfairly treated by an authority, they feel anger and, in turn, reduce compliance (Murphy and Tyler 2008). Those who feel that the authority used procedural justice during its enforcement action are less likely to feel anger, and are therefore also less likely to evade taxes (Murphy 2009). Although research on emotions in the context of taxpaying is relatively scarce, the existing evidence indicates that the interplay of cognition and affect moderates the effectiveness of key economic variables like audit probabilities and fines (Maciejovsky et al. 2012).

BOX 18.1

SURVEY ON EMOTIONS RELATED TO TAXES, PERCEIVED PROTECTIVE POWER AND COMPLIANCE

We conducted a survey investigating the relationship between emotions, authorities’ perceived use of their power to detect and punish evasion, and tax compliance. A representative sample of 500 Austrian self-employed taxpayers (61 percent men, Mage = 44.46 years, SDage = 10.55 years) were presented with PANAS (20 items; Watson et al. 1988), an instrument to measure 20 qualities of emotions, and six additional items based on the PANAS-X (Watson and Clark 1994). Respondents were asked to indicate their emotions when thinking of the tax authority on a 7-point Likert scale (7 = high emotional intensity). They were also asked how likely it was that they had paid their taxes honestly in the past year (7-point Likert scale; 7 = very likely) and to indicate whether they feel that the tax authorities exert power in an undifferentiated way to combat tax evasion or in a well-targeted way towards wrong-doers in order to protect cooperative citizens (7-point Likert scale). The items read as follows: 1. 2. 3. 4.

As the tax authority takes targeted action against tax evaders, I feel protected by the authority. As the tax authority indiscriminately takes action against all taxpayers, I feel like I’m being harassed by the authority. People who pay their taxes honestly do not have to fear audits and fines. Even people who pay their taxes honestly have to fear indiscriminate audits and fines.

We computed the mean response to these four items, labeled as perceived protective power, and computed Pearson correlations between the 26 emotions, past tax compliance and protective power. Table 18.1 shows means, standard deviations and correlations. Respondents who perceive tax authorities as exerting protective power have a greater interest in taxes and indicate that they do not feel hostile, irritated, disgusted, upset, scornful, scared, afraid, nervous, sad, distressed, confused, ashamed, or guilty. Also, they indicate higher past compliance. In sum, although protective power seems unrelated to positive emotional qualities, the opposite, untargeted power, is related to strong negative emotions. If taxpayers perceive actions taken by the tax authority as illegitimate random threats, negative emotions towards the tax authority are evoked, like hostility or disgust. On the other hand, taxpayers react positively when they have the feeling they are protected by the tax authority’s power. The present survey shows that tax authorities therefore need to be careful in how they apply power in terms of audits and fines.

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Table 18.1 Protective power, tax compliance and emotions Quality of emotion Interested Proud Active Strong Excited Enthusiastic Inspired Attentive Unburdened Determined Alert Unemotional Surprised Guilty Ashamed Confused Distressed Sad Nervous Afraid Scared Scornful Upset Disgusted Irritable Hostile Protective power Past compliance

M (SD)

Protective power

Past compliance

2.99 (1.64) 2.05 (1.39) 2.93 (1.72) 2.40 (1.40) 1.74 (1.18) 1.87 (1.27) 2.24 (1.46) 3.54 (1.83) 2.21 (1.40) 2.89 (1.70) 3.20 (1.73) 3.41 (2.03) 2.29 (1.51) 1.92 (1.43) 1.98 (1.39) 2.36 (1.59) 3.08 (1.75) 2.34 (1.77) 2.48 (1.67) 2.28 (1.64) 2.46 (1.67) 2.40 (1.75) 3.61 (1.89) 2.94 (2.04) 3.39 (1.94) 2.69 (1.77) 4.55 (1.38) 1.65 (0.48)

.17* .06 .05 .03 .02 .01 .01 −.00 −.03 −.09 −.12 −.16 −.16 −.22* −.24* −.29* −.30* −.31* −.34* −.35* −.38* −.47* −.49* −.49* −.51* −.51*

−.03 −.21* −.06 −.18* −.23* −.21* −.21* −.05 −.24* −.06 −.06 −.06 −.19* −.31* −.31* −.25* −.15 −.13 −.23* −.23* −.21* −.19* −.06 −.12 −.08 −.17* .17*

Note: N 5 500; 7-point Likert-type scales; r > .16 significant at p < 0.01 (Bonferroni corrected).

5

INTERACTIONS BETWEEN THE TAX AUTHORITY AND TAXPAYERS

Tax behavior can be viewed as the result of a ‘psychological contract’ which regulates the interactions between taxpayers and the tax authority (Feld and Frey 2007). This relational contract involves strong emotional ties, building on a norm of reciprocity that goes beyond legal regulations. Commitments from taxpayers, on the one hand, require an equivalent commitment from the tax authority, on the other hand. The psychological contract approach assumes that as long as taxpayers are treated like equal partners instead of ‘robbers’ or subordinates, taxpayers will cooperate. The slippery slope framework (Kirchler 2007; Kirchler et al. 2008, 2014), presented in

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Income tax compliance 341 Voluntary cooperation

Enforced compliance

Maximum

Tax compliance

Minimum Maximum Maximum Trust in authorities

Power of authorities Minimum

Minimum

Source: Kirchler et al. (2008, p. 212).

Figure 18.3

The slippery slope framework of tax compliance

Figure 18.3, suggests that the relationship between tax authorities and taxpayers can be described in terms of the power of tax authorities and taxpayers’ trust in tax authorities. The power of tax authorities is the perceived ability of the tax authorities to prosecute tax evasion, whereas trust in the tax authorities is defined as the tax authorities’ perceived benevolence and competence in working for the common good. Whereas power is assumed to lead to an antagonistic interaction climate in which tax authorities and taxpayers work against each other and taxpayers only comply out of a fear of enforcement, trust in the tax authorities is believed to foster a synergistic and cooperative tax climate and taxpayers’ voluntary cooperation (Kirchler et al. 2008, 2014). Experimental evidence from different countries suggests that, independent of cultural differences, power and trust determine enforced and voluntary tax cooperation (Kogler et al. 2013). The transformation from an antagonistic climate with enforced compliance to a synergistic climate with voluntary and committed tax cooperation can be described on the basis of the distinction between different qualities of power, that is, coercive power and legitimate power, and different qualities of trust, that is, reason-based trust and implicit trust (Gangl et al. 2015). Coercive power based on audits and fines creates a hostile,

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antagonistic climate and fuels a vicious cycle of distrust between tax authorities and taxpayers. However, if coercive power is combined with legitimate power – that is, expertise, the provision of information, and following accepted procedures – coercive power might be perceived as a safeguard for the compliant majority and taxpayers develop reason-based trust in the competence, motivation, and benevolence of the tax authorities (Hofmann et al. 2014). Hence, a synergistic service climate develops in which the tax authorities and taxpayers interact in a professional relationship with each other, like a service provider and its clients. Further, over time and through positive experiences, trust initially based on rational consideration might become implicit and automatic, and tax authorities and taxpayers can come to mutually trust and respect each other. The synergistic climate deepens from a service climate to a confidence climate. In a confidence climate, tax authorities avoid coercive power and taxpayers, in turn, are committed and pay their taxes automatically. The dynamics between power and trust illustrate why it is difficult and time-consuming to build up trust and a synergistic tax climate and also, vice versa, why the destruction of a synergistic relationship between tax authorities and taxpayers can happen easily and quickly. A confidence climate can easily decay, transforming back to an antagonistic climate, if strong power measures alienate taxpayers, particularly if coercive power is applied without legitimate power. Although a service climate is assumed to be more stable than a confidence climate, it can also be destroyed if coercive power is applied without legitimate power (Gangl et al. 2015). Hence, coercive power in combination with low legitimate power easily destroys a synergistic tax climate and leads to an antagonistic climate. These assumptions on the dynamics between power and trust have gained empirical support from experiments with taxpayers (Hofmann et al. 2014). Taxpayers were asked to imagine a tax authority in a fictitious country wielding either low versus high coercive power or low versus high legitimate power as well as a combination of low versus high coercive and legitimate power. Results showed that coercive power as well as legitimate power and its combination ensure high tax compliance. In addition, the results support the assumption that the coercive power of authorities as well as perceived low or high legitimate power might determine whether an interaction climate is perceived as antagonistic or synergistic (Hofmann et al. 2014). These results highlight the practical value of high coercive power in combination with high legitimate power. In combination with legitimate power, coercive power seems to be perceived as a targeted safeguard of cooperation rather than a hostile threat, as protective power as described in the survey and in Table 18.1. Therefore, it is recommended that tax authorities enhance legitimate power by establishing professional and comprehensible tax procedures, web and telephone services and by having competent, motivated, and friendly tax officers to assist taxpayers (Alm and Torgler 2011). The attempt to describe tax behavior as a consequence of the relationship between tax authorities and taxpayers represents the latest development which has fueled research in tax psychology. The benefits of this approach are twofold. First, existing research on economic and psychological determinants of taxpaying behavior has been integrated. Second, theoretical and practical conclusions can be drawn and tested as to how a change in interaction climate between tax authorities and taxpayers can be accomplished in order to foster voluntary and committed tax compliance.

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6

CONCLUSION: FROM COMMAND AND CONTROL TO COOPERATIVE RELATIONSHIPS

Traditionally, tax authorities applied a command and control approach to enforce taxpayers’ cooperation, assuming that all taxpayers would otherwise evade taxes. However, there is hope that tax authorities can move on to establishing a cooperative relationship with taxpayers. In doing so, tax authorities avoid seeing taxpayers as potential criminals but instead as customers and partners. Tax authorities invest in their service provision and improve their assistance to reduce the time and effort required for taxpayers to comply with the law. Taxpayers, on the other hand, are believed to reciprocate this cooperative approach in the form of positive attitudes towards taxes and increased tax compliance. Currently, tax authorities in countries such as the Netherlands, the Scandinavian countries, Austria, Slovenia, and New Zealand are expanding their approaches to include trustbuilding measures to build a confidence climate with ‘enhanced relationships’ (OECD 2013). According to the Organisation for Economic Co-operation and Development (OECD), the concept of enhanced relationships requires tax authorities to dispense with audits of committed taxpayers going back several years. Instead, the tax authorities should resolve and settle uncertainties on the tax issues of a taxpayer immediately. On the other hand, taxpayers are required to fully disclose their tax files to the tax authorities and to sign a voluntary code in which they agree to not engage in aggressive tax planning (OECD 2013). For both tax authorities and taxpayers, such an arrangement involves trust which can be harmed; however, it pays off in the form of lower auditing costs for the tax authorities and in enhanced planning reliability for the taxpayer. Measures to increase voluntary and committed tax compliance are more important than ever. Tax avoidance among globally operating corporations has grown to a giant problem overshadowing tax evasion (Garside 2014). These global players do not evade taxes but legally avoid taxes through aggressive tax planning. Hence, classical command and control approaches fail to enforce cooperation. Research needs to further examine how the willingness of taxpayers to refrain from exploiting tax havens and to follow the spirit of the law rather than the letter of the law can be increased. Future research on tax behavior needs to recognize even more that tax behavior is embedded in the social world and includes many actors. Thus, not only the role played by other taxpayers or by the tax authorities needs to be examined, but also the impact of the government or of tax practitioners on individual tax decision-making. Likewise, more clarification is needed as to the cognitive and emotional processes involved in tax evasion, tax avoidance, or perceived fairness of the tax system. How do the self-employed mentally account for and represent the tax decision, and what emotions are involved when they decide to cheat or to avoid taxes? Tax research in the past has focused on income tax compliance. Future research needs to go beyond that and should theoretically and empirically analyze the determinants of compliance with other taxes, such as value added tax (VAT) or inheritance tax. Furthermore, the effects of tax amnesties on perceived fairness of the system among honest taxpayers versus the effects of integrating former tax evaders back into the formal system needs further examination. Democratic societies depend on the voluntary tax compliance of their citizens in order to be able to afford public goods. Psychological tax research shows that dealing with taxpayers on an equal footing might not only reduce negative attitudes towards paying taxes

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but might also increase the number of taxpayers who feel like responsible citizens willing to actively engage in their society.

NOTE *

This study was partly financed by the Austrian Science Fund (FWF), project number P24863-G16, and partly by the Jubiläumsfonds-project number 16042.

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PART V SMART MACROECONOMICS AND FINANCE

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19 Financial decisions in the household Bernadette Kamleitner, Till Mengay and Erich Kirchler

1

INTRODUCTION

What decisions are ‘about’ can influence the way decisions are made. When deciding about money, most people aim to make particularly ‘smart’ decisions. Mere reminders of money suffice to elicit decisions that are geared towards maximizing personal economic benefits (for example, Vohs et al. 2008; Kouchaki et al. 2013). Many decisions are in fact about money and of a financial nature. This also holds for decisions that are made at the household level (for example, Kirchler et al. 2008). Private households dispose of larger amounts of financial resources than any other ‘institution’ in the state; yet, financial literacy supposedly enabling smart decisions is surprisingly low (for example, Lusardi and Mitchell 2007). Financial decisions in a household focus on what money is used for, when, how, and by whom. These decisions range from small scale to large scale and from short term to long term. Notably, household decisions often involve a varying set of actors. Beyond leading to economic outcomes, they can also influence the relationship quality of household members. As a consequence, smart financial decision making in a household entails the need to balance social and economic aspects. Eventually these decisions play a key role for the financial and psychological well-being of individuals and households. In this chapter we provide an overview of the scope of financial household decisions and the complexity of the underlying dynamics. We do so by using a comprehensive framework of financial decision making as a starting point and by focusing on its key components in turn. We first provide a brief review of the different types of financial decisions made by individuals. We then extend the lens to multiple players in household decisions. One of the key questions in household decision making is which of these lenses, individual or joint, is more suitable. When are decisions made jointly by the household members, when are they made autonomously, and when does which member dominate (see Davis and Rigaux, 1974)? These questions are challenging because answers are influenced by the way people live together in a household. Given that concepts of family, gender and roles are changing over time, we conclude this chapter by an empirical look at what has been and what may be. We do so by contrasting the perceived decision dynamics observed from parents and the ideal decision dynamics striven for by students. Results provide insights into which lens tends to be best suited for which type of decision. Moreover, they allow for a glimpse into potential changes in the future.

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A COMPREHENSIVE FRAMEWORK OF FINANCIAL DECISION MAKING

The framework depicted in Figure 19.1 reflects the scope of financial decisions. A modified version of Kamleitner and Kirchler’s (2007) process model on credit choice highlights the interplay of what Ferber (1973) identified as the principal types of financial decisions: (1) spending decisions (that is, purchase decisions about acquiring goods), (2) decisions about saving and credit use (that is, decisions about whether to make an acquisition when funds are currently lacking and whether to hold money back for future spending decisions), (3) investment decisions (that is, decisions on whether and how to accumulate material wealth), and (4) money management (that is, decisions on how to budget available money). As shown in the framework, spending decisions are the starting point to understand and explain financial decisions in the household. Credit and saving decisions are secondary decisions: they are decisions made in order to ensure that enough money is available for more or less specified spending decisions. Once the decision is about the choice of saving or credit options, financial products take on the role of the ‘product’. For example, when buying a new car on a loan two potentially extensive decision processes – about the car and the loan – can be involved. Investment decisions, too, tend to follow the process of extensive spending decisions. Finally, money management can be seen as an underlying mechanism that tends to be involved in all other financial decisions. The framework also stresses the role of surrounding factors under which financial decisions take place (see Kamleitner et al. 2012). Among them there are general influences such as the individual situation persons are in (for example, social status and family background; Ashby et al. 2011) and their individual characteristics including factors such as financial literacy (for example, Dvorak and Hanley 2010; van Rooij et al. 2011), and personality characteristics (for example, Donnelly et al., 2012) like delay of gratification (for example, Norvilitis et al. 2006; Pyone and Isen 2011). Notably, the model is not necessarily specific to individuals as decision makers. It could just as well apply to the household as a decision making unit. The framework indicates the possibility that multiple members may be directly or indirectly involved in all steps by an arrow influencing the entire model.

3

INDIVIDUAL FINANCIAL DECISIONS

Following the logic of the framework, we first provide a review of previous findings on decisions about individual expenditures before moving on to individual credit use and saving decisions as well as individual investment decisions. Finally, we discuss money management as a financial decision in itself and as an important factor in other decisions. 3.1

Spending Decisions

The literature on the process of purchase decisions is vast. In particular, in consumer research several encompassing models of the individual decision-making process have been developed. Many of these models date back to the early days of consumer research

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Figure 19.1

Household members

Money management

Purchase/ investment

Use available funds

Immediate purchase

A framework of financial decision making in households

Credit use

Choice

Extensive decision process Alternatives, information search, evaluation, choice

Type of product/ investment

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Market information

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and they focus on depicting the decision process as faced by an individual decision maker (for example, Nicosia 1966; Howard and Sheth 1969; Borcherding 1983; Kroeber-Riel 1992; Engel et al. 1993, 2007). The framework offered in Figure 19.1 reflects some of the key premises that these models tend to share. Usually decision processes start with the need for a product or service that can be prompted by stimuli from the individual sphere or outside factors like social influences or market offers. The type of good desired (for example, Kotler 1982 distinguishes on the basis of the expected lifetime of a product between durable goods, convenience goods and services) plays a key role in how a decision process unfolds, whether multiple options are searched and how deeply different choice options are elaborated. Some acquisitions are made spontaneously in a shortened and impulsive decision process. Others, in particular expenditures for regularly purchased convenience goods, are made habitually. They do not involve a decision process as such. People tend to engage in extensive decision processes for products that are rarely purchased or involve risks (for example, expensive items such as washing machines). An extensive decision process is characterized by information search and comparison. It is only after an evaluation of multiple alternatives that a decision is made. Notably, the distinction of different types of decision processes is not restricted to the acquisition of goods. Financing decisions, too, vary in terms of level of involvement and depth of processing (for example, Kamleitner et al. 2012). 3.2

Decisions about Credit Use and Saving

If a desired good is not attainable with the currently available means, consumers are left with three options (see Figure 19.1). Either they abandon the acquisition, they borrow the money, or they postpone the acquisition and save until the desired good becomes attainable. These three paths are inherently linked. Research on when which path (saving or credit) would be taken has primarily been conducted by economists (for example, Duesenberry 1949; Modigliani 1966; Prelec and Loewenstein 1998; Shefrin and Thaler 1988). Simplified, the conclusion has been that credit use is preferred if (1) the (discounted) net benefits of borrowing outweigh the (discounted) net benefits of saving and if (2) income expectations turn credit use into a means of smoothing out lifetime income. The possibility that consumers would forgo acquisitions entirely has largely been neglected. Instead of viewing credit and saving as two explicit sides of the same coin, other – in particular, psychological – contributions focused on these decisions in isolation (for reviews see, for example, Lunt and Livingstone 1991; Berthoud and Kempson 1992; Groenland 1999; Kamleitner and Kirchler 2007; Kamleitner et al. 2012; Webley 2014). The propensity for credit use varies as a function of the product; with it being particularly acceptable for durable goods and investment products (for example, Engel et al. 1993; Prelec and Loewenstein 1998). This, however, only holds for those forms of credit that make the borrowing process salient. It does not hold for cases in which consumers are not fully aware that they are borrowing money and do so spontaneously or habitually, such as in the case of credit card usage (for instance, Lo and Harvey 2011; Thomas et al. 2011). For example, while people would mostly be averse to taking out a loan for a holiday, they may use their credit cards to do so without a second thought. This variability in decisions across credit vehicles is a fundamental factor in credit decisions. It is one of

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Financial decisions in the household 353 the main reasons why research on credit use, including reviews (for example, Kamleitner et al. 2012), tends to focus on specific credit vehicles. Saving decisions are less influenced by variability in terms of saving vehicles. In these decisions time horizons play a major role (Fisher and Montalto 2010; Rabinovich and Webley 2007). The more proximate a saving goal feels, the more likely people are to decide to save (for example, Hershfield et al. 2011 increased saving by using age-progressed renderings of participants). Whether a saving goal feels proximate and within reach is also a matter of the nature of this goal (for example, Canova et al. 2005; Ülkümen and Cheema 2011). People save for concrete (for example, a new car) and unspecific (for example, for a rainy day) purposes alike. The nature of saving goals is one of the key factors determining whether consumers can eventually implement saving decisions (for example, Rabinovich and Webley 2007). 3.3

Investment Decisions

Investment decisions are similar to saving decisions in that money is put aside for a future purpose. In abstract terms, the purpose, however, is not variable. The aim is wealth protection and accumulation rather than acquisition and usage. This goal can be achieved by investing in a wide range of investment vehicles which have to be purchased. Investment thus follows a similar process to extensive spending decisions. However, given that products are chosen because of their monetary value, potential fluctuations in value, that is, the perception of risks, move center stage. Consequently, risk preferences, that is, the extent of risk a person feels comfortable with, are a key determinant of the choice between investment options (for example, Dimmock and Kouwenberg 2010; Sachse et al. 2012). Notably, risk preferences may not always translate into adequate product choice. This is because risk perception is prone to biases. For example, when simultaneously focusing on potential gains and losses, the loss probability may sometimes be underestimated, yielding riskier decisions than intended (Diacon 2004). Another factor that sets investment decisions apart is that decisions often concern portfolios rather than individual options. Such ‘diversification’ makes it possible to balance the inherent risk of several products against each other. Similarly, time horizon takes on a special meaning in investment contexts. The general assumption is that longer investment horizons reduce the risk of losses (but see, for example, Strong and Taylor 2001 for results that do not entirely support this assumption). It is, however, not entirely clear whether decision makers truly understand the consequences of time horizons and diversification. Most evidence suggests that people struggle to fully understand the extent of these effects. For example, Goetzmann and Kumar (2008) find that a surprisingly high number of investors (75 percent) hold under-diversified portfolios that entail worse risk–return trade-offs than benchmark market portfolios (for example, the S&P 500 containing stock values of the 500 biggest US companies). 3.4

Money Management

People need to manage the funds available for all these decisions to be made. To a large extent this happens mentally. Thaler (1985) hence coined the term ‘mental accounting’. In several experiments he found that people establish so called mental accounts to keep

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track of their expenditures within a specific time period and/or for a specific purpose. Mental accounts (for example, €100 per month for eating out) are useful rules of thumb for budgeting and tracing available funds. Mental accounting, thus, can help to decide between competing usages of funds and acts as a self-control mechanism (Thaler 1980, 1999). However, these advantages do not hold universally. Mental accounts can equally be malleable and self-delusional (Cheema and Soman 2006; Shafir and Thaler 2006). For example, habitual expenditures such as the daily cup of coffee may be booked into a vague ‘other spending’ account and people can trick their own mental system by reframing decisions; for example, luxury goods can be ‘booked’ as ‘investments’ which justifies expenditures and turns eventual consumption ‘free’ of charge (Shafir and Thaler 2006). Mental accounting is perhaps the most prevalent money management practice and it permeates and blends with all other financial decisions (for instance, Kamleitner and Hölzl, 2009). For example, in the case of loans consumers can mentally link the pleasure of consuming the acquired good and the pain of paying back the loan (for example, Prelec and Loewenstein 1998; Kamleitner et al. 2009). In case they establish such a mental link and ‘book’ pain and pleasure on the same account, debt aversion for non-durables (that is, products for which repayment may extend beyond the time of product use) becomes more likely (Prelec and Loewenstein 1998). What is more, mental accounts may influence the decision to save or borrow. If an acquisition does not fit well with a mental saving account, credit use may be preferred despite available savings (Karlsson et al. 1997). Beyond mental, factual money management practices, such as the frequency with which accounts are checked or the amount of actual or symbolic accounts held, matter (Lea et al. 1995; Kidwell et al. 2003; Donnelly et al. 2012). Money management appears to be most effective when mental and factual practices align and reinforce each other (Kamleitner et al. 2011; Soman and Cheema 2011).

4

HOUSEHOLD FINANCIAL DECISIONS

The majority of insights on financial decisions regard individual decision makers. Yet, in reality, multiple household members may be involved in different ways (Kirchler et al. 2008). The question of whose needs are considered becomes as important as the question of how decisions about products are made. This opens the door for additional considerations such as relational power, relationship quality, and role stereotypes (for example, Kirchler 1988). It also puts a spotlight on formal practices such as money distribution and pooling by couples (for example, separate versus joint bank accounts in which partners’ incomes are pooled). In the following we briefly review key insights arising when the four financial decisions are viewed from a joint rather than individual decision makers’ point before moving on to an analyses of when which viewpoint may be most appropriate. 4.1

Spending Decisions in the Household

Living together mostly implies that many products are acquired for the household rather than for individual household members. Individual and potentially conflicting preferences of household members as well as their relationships add complexity to the decision

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Financial decisions in the household 355 process. Bizarrely, individual preferences tend to be stronger for everyday spending decisions than other financial decisions. Whereas most people are indecisive as to which kind of blue chip stock or bank bond they prefer, the color of a car or even the topping of a pizza can be a crucial test for a relationship. Kirchler (1989) provided one of few household decision models that incorporates the dynamics found in most individual decision models as well as insights on decisions by couples (for example, Pollay 1968; Sheth 1974; Corfman 1987; Scanzoni and Polonko 1980). Similar to individual decisions, the decision process begins with the desire for a good by at least one of the partners. Product type, relational aspects (in particular, quality of and power in a relationship), and the impact of the decision on the relationship determine whether the ensuing decision is spontaneous, habitual or extensive; joint or individual. This last aspect, that is, the degree to which partners are involved, was the topic of interest in a seminal paper by Davis and Rigaux (1974). They provide a classification for household decisions that holds across all financial decisions. Basing their analyses on married couples, they distinguished between: (1) autonomous decisions by one of the spouses, (2) husband or wife dominated decisions, and (3) jointly made or syncratic decisions. In particular, extensive decisions open up the potential for syncratic decisions because they enable the partners to become differentially involved in information search, the evaluation of alternatives, and the eventual decision. Notably, a partner’s involvement in a decision does not have to be active. Even if only one partner is in charge of a decision, he or she is likely to account for the assumed preferences1 of the partner – even in an exploitative relationship (Maccoby 1986). A key aspect in decisions for more than one person is the potential of disagreement and conflict. Multiple types of household conflict have been identified by Kirchler et al. (2001). Depending on the type of conflict the partners are more or less motivated to solve the problem in a way that either reduces the negative impact on their relationship or maximizes their benefit (Ben-Yoav and Pruitt, 1984). Probability conflicts relate to judgments about objective truths and outcomes and the likelihood with which they will happen. For example, partners may agree about the social significance of an item and have similar design preferences. Yet, they may be finding the joint decision difficult because they hold different views on the quality of alternatives. In such probability conflicts, partners are not seeking to influence each other. Rather they are having an objective disagreement in which the crucial elements are items of information. Normative pressure is kept to the background. The situation is very different for value conflicts for which there is no verifiably correct solution. Value conflicts exist if there are fundamental differences in goals and values between the partners. Purchasing decisions present a value conflict if partners have fundamental differences with regard to the symbolic power of a product rather than the specific features. Value conflicts are genuine conflict situations, in which partners try to persuade each other (March and Simon 1958; Madden 1982) or even impose their views on each other, using several influencing tactics (for an overview of commonly used tactics, see also Kirchler 1990). The third type of conflict is distributional. A distributional conflict exists if the discussion revolves around the division of costs and benefits. Even if both partners are convinced that a particular product represents the optimal alternative and is desirable, so that

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there is no value conflict, one partner may still argue against the purchase on the grounds that the product largely benefits the other partner or would mainly be used by them. In distributional conflicts, partners will try to reach a compromise using their negotiating skills. (Kirchler et al. 2001, p. 75). These types of conflicts can occur for all types of financial decisions, including credit and saving decisions. 4.2

Decisions about Credit Use and Saving in a Household

Research that looks at decisions about credit use and saving from a household perspective is scarce. Actual money management practices (for example, do partners hold individual credit cards) but also role specialization and quality of a relationship will influence the way a household saves and uses credit. For example, the breadwinner role affects credit card usage. Pahl (2008) has shown that an observed higher rate of credit card usage by the male partner disappears when employment status is taken into account. 4.3

Investment Decisions in the Household

Investment poses a particular challenge for joint decision making because partners’ risk preferences are likely to diverge. In a study by Mazzocco (2004), only half of the examined couples held similar risk preferences. The question then arises of how couples come to a joint risk preference. As, for example, Abdellaoui et al. (2013) show, couples do not simply average their individual risk preferences. In one study the man had more influence initially, whereas the woman’s influence rose over the course of investments (de Palma et al. 2011). This may match with insights by Meier et al. (1999) who found that the spouse believed to be more experienced had more influence on the decision. The type of relationship also plays an important role. Decisions in egalitarian relationships are more likely to be autonomous and dominated by the female spouse than decisions in relationships with traditional attitudes toward marital roles (Meier et al. 1999). To some extent this may also be caused by differences in bargaining power (see Yilmazer and Lich 2015) which determines who of the partners has more say in terms of the risk taken. 4.4

Money Management in the Household

A purely mental money management system is unlikely to work for an entire household. Formal ways of managing the household finances have to be established and responsibilities have to be assigned. The answer to the question ‘Who manages the household’s finances?’ is informed by marital and breadwinner roles, relationship satisfaction, power in the relationship, equity perceptions, and the meaning of money for each partner (for example, Jasso 1988; Burgoyne and Kirchler 2008). The most common way in which households manage their money is through pooling (Pahl 2008) which refers to the uniting of both partners’ income on a joint banking account. However, in blended and patchwork families (that is, couples that are in a new relationship after a divorce or separation, with at least one child from the previous relationship) an increase of the practice of separate banking accounts (Raijas 2011) and separate money management has been observed.

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Financial decisions in the household 357

5

WHICH DECISIONS ARE MADE JOINTLY? AN EMPIRICAL INVESTIGATION

As this review has shown, financial decisions are complex phenomena – in particular when they are made jointly by household members. A crucial question therefore asks which decisions are being made jointly. Already in the 1970s Davis and Rigaux (1974) addressed this question. They investigated which product categories are decided upon primarily by one partner of a particular gender, autonomously by both partners, and jointly. In addition, they distinguished between the respective influence of partners across the different stages of a decision process (need recognition, information search and final decision). Results were reported in the so-called decision triangle (see Figure 19.2 for an exemplar with data from this study). The y-axis depicts whether, if any, of the partners dominates the decision (1 5 male dominated to 3 5 female dominated with 25 joint decisions in the middle). The extent of role specialization is displayed on the x-axis. It reflects the percentage of participants stating that a specific phase has been decided on jointly. The phases of the decision process are displayed in form of a line flowing from problem recognition (rhomb) to information search (dot) to the final decision (triangle). As discussed, the role of partners in the decision process is influenced by gender dynamics, breadwinner and marital roles and partners’ bargaining power (for example, Burgoyne and Kirchler 2008). All of these aspects have seen at least some changes in the past decades (for example, Gere and Helwig 2012; Lewis and Sussman 2014). For instance, Pahl (2008) observed a decrease in the number of couples pooling their money. Here we empirically examine the way decisions are made in all four domains of financial decisions. Moreover, we aim to capture what is and what may yet come. We assess the decision processes observed from parents as well as the ideal decision processes striven for by their children. 5.1

Sample and Procedure

Overall, 300 Austrian business students (mean age 5 23.51 years, 54.7 percent female) participated in a laboratory-based survey on decision making in partnerships. Participants were first asked to report how 13 financial decisions were made by their parents. Subsequently, they reported on how they anticipated to make those decisions once they share a household with a partner. The target decisions were chosen so as to reflect all four areas of financial decision making. In addition, goods that had emerged as particularly prone to be decided on by the husband or wife in the original Davis and Rigaux study were used. For all 13 decisions participants were asked to indicate who (man, woman, jointly) would usually recognize the need for a product, who would search for information, and who would make the final decision. To keep insights comparable with the original studies (for example, Negrusa and Oreffice 2011 find some differences across couples’ sexual orientations), only heterosexual relationships were taken into account. Controlling for the actual relationship status of students did not change results of students’ anticipated decision roles.

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Observed (parents) and intended patterns of spending decisions (a) and saving and credit use (b) across genders

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Figure 19.2

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Financial decisions in the household 359 5.2

Results and Discussion

To facilitate interpretation of results and following Davis and Rigaux (1974), each roletriangle is separated into four sections: female-dominated decision steps are in the upperright corner, male-dominated decisions in the lower-right corner, autonomous decisions that are equally likely to be independently taken by either of the partners are in the middle of the triangle and truly syncratic (or joint) decisions are in the outer-right corner of the triangle. The discussion of results has been split according to the four financial decisions. 5.2.1 Decision roles involved in spending decisions Figure 19.2a shows parents’ observed actual (dotted line) and students’ ideal (grey for females and black for males) decision processes for seven different spending decisions. Focusing on parents’ decision processes, it becomes evident that, as already observed by Davis and Rigaux (1974), all four sections of the triangle are populated. As in the 1970s, cleaning products and groceries are still female dominated. Moreover, cars still tend to be male dominated, primarily during the phase of information search. Unlike in the 1970s, the decision for a car is likely to be made syncratic. Getting an Internet connection seems to be the task for either one of the partners. Despite being a technological topic it is not necessarily male dominated. The decision about living room furniture is autonomic during problem recognition and information search, but the final decision is made jointly. The decision process about which holidays to go on is similar to that for furniture. However, for holidays, problem recognition appears more likely to be a joint process. In sum, reports on parents’ spending decisions are similar to those observed in earlier research (Davis and Rigaux 1974). The final decision, however, seems to have become more syncratic. Interestingly, the stereotypical female domains have remained untouched but the male domains have made some way for joint decisions. Moving on to how students described the way they anticipate making decisions in their own future households, a glance at Figure 19.2a proves revealing. First, the fact that the black lines tend to be lower in the graph than the grey lines suggests that each gender assigns itself slightly more say in decisions. The perhaps surprising exception is cars. At least when it comes to information search, many women appear happy to leave that task to their future partner. Second, ideals cluster more strongly in the syncratic section of the triangle. This suggests that students intend to make less autonomous and gender-dominated decisions than observed by their parents. In fact, students’ ideals see barely any autonomous decisions. Cleaning products are the only product category for which both genders expect that one person will decide it all. 5.2.2 Decision roles involved in credit use and saving Figure 19.2b depicts decision roles for credit (credit cards and loans) and saving (saving book and life insurance) decisions. It shows that students’ ideals and parents’ reality tend to crowd together in the syncratic section of the decision triangle. Even the decision about a product focusing on one individual’s life, that is, life insurance, has become more syncratic than in the 1970s. It is only during the information search that one of the partners is in charge.

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Overall, female ideals are clearly syncratic across all decision phases. Male ideals and parents’ actual behaviors are also mostly syncratic except for the information search phase in which they are situated at the edge to autonomous decisions. 5.2.3 Decision roles involved in investment decisions Figure 19.3a shows decision patterns for investment decisions in general and stock in particular. A first glimpse reveals that investment decisions are more prone to be made autonomously (specifically during problem recognition and information search) than saving and credit decisions. While both partners appear to recognize the need to save or borrow, recognizing the need to invest seems often to be down to one of the partners. Moreover, Figure 19.3a reveals that the general decision to invest is more likely to be syncratic than the decision about stocks as an actual investment vehicle. In particular for men, the decision to acquire stocks seems to lie in their domain. Women generally expect to have more influence on investment decisions than either their mothers have or their male colleagues and potential future partners anticipate. Results suggest that couples are likely to decide on an investment strategy jointly but that the choice for a high-risk investment product may be male dominated; in particular in the perception of men themselves. 5.2.4 Decision roles involved in money management Figure 19.3b depicts decision patterns with regard to money management. Participants were asked to indicate who would express the need to decide on whether to pool the respective funds available, who would think about possible distributions of money, and who finally decides which kind of distribution is implemented. Interestingly, in this decision parents are observed to decide more syncratically than their adult sons’ intent to do. Parents were observed to, and female participants would like to, jointly go through all decision stages. Male participants differ in that they consider problem recognition and information search as the domain of only one of the partners.

6

CONCLUSION

These results provide a glimpse at contemporary financial decision making in the household and reveal some general patterns across the four main areas of financial decision making: spending decisions, saving and credit use, investment, and money management. Despite increasing degrees of financial autonomy of the spouses, most financial decisions tend to be made jointly and the future generation intends to further increase this trend. Notably, this intention differs across the genders. In particular with respect to decisions that involve money only (that is, money management, investment, saving and credit use), male students anticipate that the decision process would be more autonomous than female participants. It is only with regard to spending decisions, that is, decisions that involve non-financial products, that female participants considered autonomous decisions at least as likely as their male counterparts. Given substantial variations in the ways that decisions are made across contexts, knowledge about individual decisions is as necessary as it is limited in order to understand financial decision making in the household. Admittedly, there is no way to know whether our samples’ intended practices will reflect

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Figure 19.3

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their decision making once they have been sharing a household with a partner for some time. The lack of longitudinal insights is, however, not a limitation that is specific to the study at hand. There is very little evidence on how decision dynamics change within a relationship (for cross-sectional evidence on couples’ dynamics, see Scheibehenne et al. 2011) and on the extent to which a potentially observed change is due to the maturation of the relationship and societal trends, respectively. Although we have no means of ensuring that results generalize to different samples, they do hold an important message. The increase in financial independence of women, which has been observed in many industrialized countries, does not necessarily entail more autonomous financial decisions. In fact, nearly all stereotypical financial decisions were deemed syncratic and this appears to be an aspired practice by the future generation of (well-educated) households. The only exception appears to be spending decisions; in particular about everyday goods and services. Yet, even for groceries, students seem to aspire to joint decisions. However, whether this is a valid prediction of what will be practiced in the future remains to be seen. This finding may be the result of romantic expectations of limitless ‘togetherness’ or of a generally perceived choice overload. Given that many goods and services (including financial services) are marketed to individuals rather than couples there seems to be a mismatch between what is offered and what is actually needed by households. Especially the differential influence across the three stages of the decision process, in particular the tendency to search autonomously, may hold implications for marketers. Our results also imply a noteworthy asymmetry between the factors that likely matter to decision makers and the factors that occupy decision researchers. A short glimpse into the latest issues of journals such as the Journal of Consumer Research, the Journal of Economic Psychology, and Judgment and Decision Making suffices to reveal that most disciplines involved in the study of financial decision making tend to overlook the fact that decisions happen on a household as well as on an individual level. Topics inherent to household-level decisions such as relationship quality, power, resource distribution, gender dynamics, and conflicts may be more relevant than ever before. It seems high time that academic research on financial decisions systematically considers (and asks) who is making them.

NOTE 1. Note that couples do not tend to be good at making these predictions and that the ability to predict a partner’s preferences does not improve with relationship duration (Scheibehenne et al. 2011).

REFERENCES Abdellaoui, M., O. l’Haridon and C. Paraschiv (2013), ‘Individual vs. couple behavior: an experimental investigation of risk preferences’, Theory and Decision, 75 (2), 175–91. Ashby, J.S., I. Schoon and P. Webley (2011), ‘Save Now, save later?’, European Psychologist, 16 (3), 227–37. Ben-Yoav, O. and D.G. Pruitt (1984), ‘Accountability to constituents: a two-edged sword’, Organizational Behavior and Human Performance, 34 (3), 283–94. Berthoud, R. and E. Kempson (1992), Credit and Debt: The PSI Report, London: Policy Studies Institute.

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Financial decisions in the household 363 Borcherding, K. (1983), ‘Entscheidungstheorie und Entscheidungshilfeverfahren für komplexe Entscheidungssituationen’ (‘Decision theory and decision aids for complex decision situations’), in M. Irle and W. Bussmann (eds), Methoden und Anwendungen in der Marktpsychologie (Methods and Applications in Market Psychology), vol. 2, Göttingen: Hogrefe, pp. 64–173. Burgoyne, C.B. and E. Kirchler (2008), ‘Financial decision in the household’, in A. Lewis (ed.), The Cambridge Handbook of Psychology and Economic Behaviour, Cambridge: Cambridge University Press, pp. 132–54. Canova, L., A.M.M. Rattazzi and P. Webley (2005), ‘The hierarchical structure of saving motives’, Journal of Economic Psychology, 26 (1), 21–34. Cheema, A. and D. Soman (2006), ‘Malleable mental accounting: the effect of flexibility on the justification of attractive spending and consumption decisions’, Journal of Consumer Psychology, 16 (1), 33–44. Corfman, K.P. 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Hölzl (2009), ‘Cost–benefit associations and financial behavior’, Applied Psychology: An International Review, 58 (3), 435–52. Kamleitner, B. and E. Kirchler (2007), ‘Consumer credit use: a process model and literature review’, Revue Européenne de Psychologie Appliquée/European Review of Applied Psychology, 57 (4), 267–83. Kamleitner, B., E. Hoelzl and E. Kirchler (2009), ‘Cost–benefit associations and their influence on loan experience’, in A.L. McGill and S. Shavitt (eds), Advances in Consumer Research, vol. 36, Duluth, MN: Association for Consumer Research, pp. 607–98. Kamleitner, B., E. Hoelzl and E. Kirchler (2012), ‘Credit use: psychological perspectives on a multifaceted phenomenon’, International Journal of Psychology, 47 (1), 1–27. Kamleitner, B., B. Hornung and E. Kirchler (2011), ‘Over-indebtedness and the interplay of factual and mental money management: an interview study’, New Zealand Economic Papers, 45 (1), 139–60. Karlsson, N., T. Gärling and M. 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Kirchler, E. (1988), ‘Diary reports on daily economic decisions of happy versus unhappy couples’, Journal of Economic Psychology, 9 (3), 327–57. Kirchler, E. (1989), Kaufentscheidungen im privaten Haushalt (Purchase Decisions in the Private Household), Göttingen: Hogrefe. Kirchler, E. (1990), ‘Spouses’ influence strategies in purchase decisions as dependent on conflict type and relationship characteristics’, Journal of Economic Psychology, 11 (1), 101–18. Kirchler, E., E. Hoelzl and B. Kamleitne (2008), ‘Spending and credit use in the private household’, Journal of Socio-Economics, 37 (2), 519–32. Kirchler, E., E. Hölzl, K. Meier and C. Rodler (2001), ‘Editorial’, Zeitschrift für Sozialpsychologie, 32, 129–32. Kotler, P. (1982), Marketing-Management: Analyse, Planung und Kontrolle, Stuttgart: Poeschel. Kouchaki, M., K. Smith-Crowe, A.P. Brief and C. 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Todd (2011), ‘Older but not wiser – predicting a partner‘s preferences gets worse with age’, Journal of Consumer Psychology, 21 (2), 184–91. Shafir, E. and R.H. Thaler (2006), ‘Invest now, drink later, spend never: on the mental accounting of delayed consumption’, Journal of Economic Psychology, 27 (5), 694–712.

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Financial decisions in the household 365 Shefrin, H.M. and R.H. Thaler (1988), ‘The behavioral life-cycle hypothesis’, Economic Inquiry, 26 (4), 609–43. Sheth, J.N. (1974), ‘A theory of family buying decisions’, in J.N. Sheth (ed.), Models of Buyer Behavior: Conceptual, Quantitative, and Empirical, New York: Harper & Row. Soman, D. and A. Cheema (2011), ‘Earmarking and partitioning: increasing saving by low-income households’, Journal of Marketing Research, 48 (special issue), S14–S22. Strong, N. and N. Taylor (2001), ‘Time diversification: empirical tests’, Journal of Business Finance & Accounting, 28 (3–4), 263–302. Thaler, R.H. (1980), ‘Toward a positive theory of consumer choice’, Journal of Economic Behavior & Organization, 1 (1), 39–60. Thaler, R.H. (1985), ‘Mental accounting and consumer choice’, Marketing Science, 4 (3), 199–214. Thaler, R.H. (1999), ‘Mental accounting matters’, Journal of Behavioral Decision Making, 12 (3), 183–206. Thomas, M., K.K. Desai and S. Seenivasan (2011), ‘How credit card payments increase unhealthy food purchases: visceral regulation of vices’, Journal of Consumer Research, 38 (1), 126–39. Ülkümen, G. and A. Cheema (2011), ‘Framing goals to influence personal savings: the role of specificity and construal level’, Journal of Marketing Research, 48 (6), 958–69. Van Rooij, M., A. Lusardi and R. Alessie (2011), ‘Financial literacy and stock market participation’, Journal of Financial Economics, 101 (2), 449–72. Vohs, K.D., N.L. Mead and M.R. Goode (2008), ‘Merely activating the concept of money changes personal and interpersonal behavior’, Current Directions in Psychological Science, 17 (3), 208–12. Webley, P. (2014), ‘The development of saving’, in S. Preston, M.L. Kringelbach and B. Knutson (eds), The Interdisciplinary Science of Consumption, Cambridge, MA: MIT Press, pp. 243–62. Yilmazer, T. and S. Lich (2015), ‘Portfolio choice and risk attitudes: a household bargaining approach’, Review of Economics of the Household, 13 (2), 219–41.

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20 Employing priming to shed light on financial decision-making processes Doron Kliger

Economic decision-making under risk and uncertainty is conventionally modeled within the cognitive framework, using expectation-based calculus which is performed on an underlying utility function. Presumably, this function describes all the relevant information regarding individual preferences, the individual’s ‘type’ (expected utility, EU; von Neumann and Morgenstern 1944). Recently, descriptive models deviating from economic rationality have been devised and employed. Often, these models also rely on sets of cognitive rules. A prominent modeling taking this approach is manifested by the celebrated prospect theory (PT; Kahneman and Tversky 1979) and cumulative prospect theory (CPT; Tversky and Kahneman 1992). The cognitive, rational, framework builds on the notion that deciding advantageously in complex situations requires decision makers to utilize declarative knowledge and employ overt reasoning. That is, rational individuals arrive at decisions by a process of contemplating facts regarding assumptions, and considering the outcomes of each possible action. However, an alternative process may take place, namely, that the overt reasoning phase is primed by a phase of nonconscious biasing that employs neural systems other than those that support declarative knowledge. In a gambling task given by Bechara et al. (1997) to two groups, one consisting of normal participants and the other consisting of patients with prefrontal damage, the normals began to choose advantageously before realizing which strategy worked best, while the prefrontal patients continued to choose disadvantageously even after they knew the correct strategy. Moreover, the normals’ actions were preceded by anticipatory skin conductance responses (SCRs) whenever they pondered a risky choice, before knowing explicitly that it was, indeed, risky. The lesson from the experiment conducted by Bechara and colleagues is that, normally, nonconscious biases guide behavior before conscious knowledge does. Importantly, without the help of the nonconscious biases, overt knowledge might be insufficient to bring about advantageous behavior. This chapter employs priming to shed light on financial decision-making processes. Priming is an implicit memory process, where exposure to stimuli or events affects the availability of specific information categories and, thus, the response to subsequent events (Baron and Byrne 1997, ch. 3). Priming may also be viewed as changes in preliminary conditions that impel the probability that the stimulus will be followed by a particular response (see Cramer, 1968). According to Tulving (1983), priming consists of facilitation of performance in one task on following identical or similar tasks. Priming processes are routinely activated by individuals. An illustrative example might be provided by considering the activity of watching a horror movie, a stimulus which may intensify attention and modify interpretation and reaction to subsequent stimuli, such as a squeaking gate, causing the exposed individual to act as in alarming situations. The 366

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Employing priming to shed light on financial decision-making processes 367 same situation, while not taking place after the exposure to the horror movie, may be left consciously unnoticed, or interpreted in a different, unexciting manner. Studying priming effects in the laboratory is usually devised in two successive stages, exposure and testing. In the exposure stage, the subjects are exposed to the priming stimulus, and in the testing stage, they are requested to perform particular actions, make decisions, or interpret the meaning of some given substance. The exposure stage may be subconscious, also known as automatic priming (for example, briefly flashing words or pictures such that the subjects are not aware of seeing them), or conscious, gaining the awareness of the participating subjects. Next, I describe several research projects involving priming and financial decisions, and then I suggest some insights about the underlying decision-making process. Gilad and Kliger (2008) explored priming effects on financial decisions by reinforcing participants’ risk-seeking behavior under uncertainty. In addition to investigating the conduct of economics undergraduates, the actions of professionals (commercial banks’ investment advisors and accountants in certified public accountant, CPA, firms) were also investigated. The results detect priming effects on risk attitudes and investment decisions in both participant groups. Further, the decisions made by the professionals were affected more than the decisions made by the undergraduates, suggesting that the former employ a more intuitive and less analytic approach in making their decisions. Kliger and Gilad (2012) explored the role of colors as a priming substance. Colors are prevalent in the financial industry, with red and green prominently employed. A betweensubject experimental analysis was employed to expose the participants to financial information on colored backgrounds and investigate the effect on their investment decisions. The results indicate that red color, compared to green, emphasizes value losses of the underlying asset: the participants who were exposed to red assigned higher valuations and probabilities to events involving the loss domain than to events involving the gain domain. Cohn et al. (2015) employed priming to delve into the issue of investors’ countercyclical risk aversion, a feature used in many asset pricing models to explain economic puzzles such as time variation in risk premia and high volatility of asset prices. Empirical evidence on this issue is scarce, due to the difficulty to control for a host of factors that change simultaneously during financial booms and busts. Specifically, they devised a couple of experimental investment tasks to prime financial professionals with either a boom or a bust scenario and measured their subsequent risk aversion. They found that professionals who were primed with a financial bust manifested increased risk aversion, compared to those who were primed with a financial boom. Cohn et al. (2015) have also found that their treatments evoked different emotional reactions, which were related to the investment decisions: the financial professionals who were primed with a bust were more fearful than those that were primed with a boom condition, and their fear was negatively related to investments in the risky asset. They conclude with a suggestion that fear reduces investors’ willingness to take risk, even when that fear is unrelated to economic events. As a basis for suggesting some insights about the underlying decision-making process, we now delve into some details of the first experiment reported in Gilad and Kliger (2008). Design. The experiment was constructed in a 2 × 2, between-subject, design, of two populations (investment advisors in large commercial banks and accountants in CPA firms – hereafter, professionals – versus economics undergraduates) and two priming

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treatments (risk seeking, RS, versus risk aversion, RA). The participants from each population were randomly split between the two priming treatments, enabling the investigation of within-population priming of risk attitudes, and comparative analysis of the extent of effect between the populations. At the introduction of the experiment, all the participants received the following general description: In the experiment, you will be requested to answer two questionnaires. The first contains economics questions, and the second memorization questions. For the first questionnaire, we will show you financial information of a specific stock, whose name is kept with us, which was published in economics sites. In this questionnaire, you will be requested to answer several questions regarding investment in the stock. In the second questionnaire you will be asked to identify adjectives from a short story which we will show you. The story will be presented before the first question set. Please note, in the memorization questionnaire (the second questionnaire in the experiment) you will be asked to recollect as many adjectives as you can, while coping with the time gap between reading the story and marking your answers.

The stories given to the RS and RA groups primed risk-seeking and risk-averse behavior, respectively. The RS story described a daring person who has chosen to gamble in a casino, and consequently gained a lot of money, whereas the RA story was about a responsible person who has chosen to avoid taking risk at the casino, an action which prevented a large monetary loss. The financial questions, presented to all of the participants in an identical manner, regarded investments which depended on the stock of MagStar Technologies Incorporated (MGST.OB), without revealing the stock’s identity. As a basis for the financial decisions, the participants received: financial information, which was extracted from Yahoo! Finance (http://finance.yahoo.com), consisting of the company profile (MagStar is a prototype developer and manufacturer of centrifuges, conveyors, medical devices, spindles and subassemblies); short financial reports (for example, balance sheet and income statement); and a graph exhibiting the stock’s price fluctuations in the preceding 330 days, compared to the Standard and Poors (S&P) and the National Association of Securities Dealers Automated Quotations (NASDAQ) indices. The economics questionnaire, which followed the priming substance, consisted of five stock related investment questions (an additional question, not discussed here, requested the participants to rank the degree of risk they attributed to investing in the stock). In each of the stock related investment questions, the participants of all groups were requested to choose between (1) investing in a binomial lottery yielding NIS 75 (approximately US$17, at the time the experiment was conducted) or 0, depending on the stock’s return by the subsequent month, and (2) getting certain sums of money ranging from NIS 0 to NIS 75. Then, the participants were requested to assign the certain amount for which they were indifferent between the two alternatives, as well as to assess the probability the investment would end up yielding NIS 75 (for more details, see Gilad and Kliger (2008)). Formally, the indifference values assigned by the participants are their certainty equivalents (CEs) of the respective investments, that is, represent the investments’ subjective valuations by these participants. The stock related investment questions were constructed according to two cutoff returns, of –15 percent and 18 percent (see Figure 20.1). The memorization questionnaire which concluded the experiment contained a list of adjectives the participants were asked to check according to their recollection of the

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Question 5

Question 4

Question 3

Question 2

Question 1

–15

0

18

Note: The figure portrays the conditions for the binomial lotteries stated in the stock related investment questions to yield NIS 75. The lottery described in each of the five questions would yield NIS 75 (otherwise, 0) in case the stock return by the subsequent month belongs to the interval marked by the respective segment.

Figure 20.1

A schematic description of the stock related investment questions

adjectives in the version of the story they have read. Some of the adjectives in the stories were extracted from Erb et al. (2002). Hypothesis. Overall, the two stories, RS and RA, were expected to prime risk seeking and risk aversion, respectively. That is, the activation of risk attitudes via priming was hypothesized to make the participants in the RS group willing to state higher CEs for the gambles on the stock’s performance, as well as to assign higher probabilities to the events making the investments profitable. As regards the comparison of the two populations (professionals and students), the professionals’ decisions were expected to be affected by the priming manipulation more than the students’ (see Haigh and List 2005; Abbink and Rockenbach 2006), possibly because professionals tend to use a less analytic and more intuitive approach in decisionmaking. We delve into this aspect later on, in a discussion of the underlying decisionmaking process. Results. The main results of the experiment are summarized in Table 20.1. The left- and right-hand sides of the table describe the replies to the stock related investment questions by the professionals and the undergraduates, respectively. Evidently, both populations’ risk attitudes were affected by the priming stimuli. This is seen in the participants’ overall willingness to take risky positions, as reflected by the aggregated CE they assign to the investments across all the investment questions. The aggregated amount of the RS participants was significantly higher than that of the RA participants, for the professionals (NIS 42, prob. 0.01), as well as for the students (NIS 24, prob.0.04). Further, checking each

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Table 20.1

Some experimental results from Gilad and Kliger (2008)

Stock related investment questions, average monetary certainty equivalent Professionals Question 1 2 3 4 5 Aggregated

Students

RS

RA

Difference

Prob.

RS

RA

Difference

Prob.

49.52 54.90 32.57 53.72 50.07

40.55 41.67 23.92 43.86 48.48

8.97 13.23 8.65 9.86 1.59

0.02 0.02 0.08 0.03 0.41

39.68 37.84 28.14 50.79 58.68

35.02 36.50 25.27 44.71 49.69

4.66 1.34 2.87 6.08 8.99

0.16 0.40 0.31 0.10 0.04

240.77

198.46

42.31

0.01

215.13

191.19

23.94

0.04

question separately, the average monetary CEs of the RS participants were always larger than those of the RA participants, for both populations, the effect manifested by the replies of the professionals being more pronounced, as all the differences but one (question 5) were larger than those of the undergraduates, and more significant. Considering the size of the priming effect, we note that the professional and student participants who were given the RA treatment were valuing the risky investments, on average, roughly the same (that is, total value of NIS 198 and NIS 191, respectively). The RS treatment, in comparison to the RA treatment, increased the students’ average valuation considerably, by 13 percent. The picture emerging from inspecting the effect on the professionals’ valuation is even more overwhelming, amounting to a corresponding increase of 21 percent. We turn now to discussing the underlying decision-making process. To convey the idea in a simple manner, we refer to a static, one-period setup. Figure 20.2 provides a sketch of the rational decision-making process. Trivially, under the rational framework, the process starts when the decision maker is confronted by a decision-making problem. The decision maker then enters a phase of information recollection, yielding cognitive evaluation in which probabilities of the possible realizations are assessed. For instance, in the case of contemplating the purchase of a specific stock, the phase yields the assessed distribution of possible future states of nature, and related stock returns. Then, depending on the decision maker’s shape of utility function (‘type’), a decision (that is, whether to purchase the stock given its current price) is arrived at. Finally, the state of nature is realized, that is, the stock gains a specific return, determining the payoff to the action the decision maker has taken. A prominent contribution of psychology to the understanding of decision-making and behavior takes the shape of two-system models. Noteworthy, Kahneman (2011) has adopted terms, originally coined by Stanovich and West (see Stanovich and West 2000; they now prefer speaking of type 1 and type 2 processes), and refers to two systems in the mind, namely, system 1 and system 2. System 1 functions in a quick, automated manner, exerting little or no effort from the decision maker, hence bearing no sense of voluntary control. System 2 involves effortful mental activities, such as complex computations, thus the operations involving it are often associated with choice and concentration. Figure  20.3 depicts the behavioral decision-making process. Specifically, it appends a channel of behavioral evaluation alongside the cognitive evaluation channel, in accordance with two-system models. Roughly speaking, we may link the behavioral evaluation

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Decision-making process =>Rational model Decision-making problem Information recollection

Cognitive evaluation

Probability assessment

Util. function (‘TYPE’)

Decision

Payoff realization

1¢ Figure 20.2

Rational decision-making

Decision-making process => Behavioral model Decision-making problem Information recollection

Cognitive evaluation

Behavioral evaluation

Decision Weights (PWFs)

Value function

(‘TYPE’)

Decision

Payoff realization

1¢ Figure 20.3

Behavioral decision-making

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Decision-making process => Behavioral model , under priming Decision-making problem

Information recollection

Cognitive evaluation

Exposure (priming)

Perception/ info processing

Behavioral evaluation

Decision weights (PWFs) Case dependent!

Which evaluation channel is more active?

Value function

(‘TYPE’)

Decision

For students? For professionals?

Payoff realization

1¢ Figure 20.4

Behavioral decision-making, under priming

channel and cognitive evaluation channel with system 1 and system 2, respectively. Thus, the assessment of probabilities of the possible realizations is generalized to the assignment of decision weights, possibly in the shape of probability weighting functions (PWFs). The carrier of values is also modified, for example, in the case of prospect theory, the traditional utility function is replaced by a value function (VF). A prominent difference between the utility function and the value function lies in the definition of the latter on gains and losses, compared with the definition of the former on wealth levels. From this point on in the decision-making process, the flow meets again the rational model, presented in Figure 20.2. That is, a decision is arrived at depending on the decision maker’s ‘type’ and decision weights and, subsequently, the state of nature is realized, determining thereby the payoff to the decision maker. For the sake of accuracy, it is worth mentioning here that decision makers may also assign different subjective decision weights, so we may choose to append the properties of PWFs (in addition to the properties of VFs) to the decision maker’s ‘type’. Figure 20.4 presents the dually channeled behavioral decision-making process, under priming. Incorporating priming exposure effects into the analysis explains how seemingly irrelevant information may enter the decision process. Specifically, the priming substance affects the decision maker’s perception and information processing and, thereby, the two components that play a role in arriving at decisions, namely, the shapes of the PWF and

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Employing priming to shed light on financial decision-making processes 373 VF. The resulting decisions are, therefore, case dependent. That is, we may argue that the traditional notion of the decision maker’s ‘type’ is insufficient, and has to be augmented to incorporate the effects of surrounding information on the decision maker, as well as the decision maker’s subjective sensitivity to priming stimuli. Having established a sketch of the dually channeled behavioral decision-making process under priming, we turn now to the discussion on the differential priming effect between students and professionals. The explanation suggested here involves the relative prominence of each channel (cognitive or behavioral) in the decision-making process of the two populations. More specifically, the increased sensitivity to priming by the professionals may be owing to their relative reliance on the behavioral evaluation channel being more prominent than that of the students. To elucidate the difference between professionals and students, ponder the following question: what does the situation of answering a questionnaire resemble in students’ lives? Or, in which event in a typical academic learning environment are the students welcomed into a class and given a questionnaire to be answered within a short time frame? An associative reply you may have arrived at is that the event in question is a course’s examination. And what are students implicitly requested to do in exams? Commonly, to search for the relevant model that was introduced in the course material, detect the relevant information, and employ the model on the information to arrive at answers to the examination’s questions. Putting this reply in the boxes of the decision-making process presented in Figure 20.4, the students are requested to activate the cognitive evaluation channel. Now consider the case of the professionals. Would it be conceivable to assume that, when operating in their vastly changing, information-swamped, working environment, the professionals would activate the cognitive evaluation channel as intensely as do students in examination-like situations? To answer this question, consider the students again, and their (justified) reaction to a situation where their lecturer enters in the middle of the examination in order to apologize for a typographical error in one of the examination’s questions, which should be corrected before answering. Having relied on the (slow) cognitive evaluation channel, the students would have to erase the answer they have arrived at, and start the answering procedure all over again. Would professionals, in their crowded working environment, be able to act as the students did in the examination situation? Apparently not. They would probably have to resort to a faster, less effortful, decisionmaking process, hence to activate (relatively more than the students) the behavioral evaluation channel. To summarize, comparing professionals and students, the decisions of the former participants were affected by the priming manipulation more than the students. Seemingly, the differential effect may be attributed to the professionals’ tendency to practice less analytic and more intuitive decision-making. Presumably, professionals’ decisions are based more heavily on experience. Nevertheless, keep in mind that the pricing relevant information given to both groups was identical, and the effect was detected with each group. We should not deduce from the analysis presented here any conclusions regarding the quality of the decisions made by the two populations. Merely, the relative prominence of the cognitive and behavioral channels each population has adapted is commensurate with the characteristics of their ‘natural’ decision-making environments. To conclude, we find that individuals’ financial decisions, whether these individuals be students or professionals, involve aspects beyond utilization of declarative knowledge and employment of

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overt reasoning. Specifically, overt reasoning may be continuously affected by exposure to priming substance. Arguably, this exposure is guiding behavior before conscious knowledge does, playing thereby an important role in the decision-making process.

REFERENCES Abbink, K. and B. Rokenbach (2006), ‘Option pricing by students and professional traders: a behavioural investigation’, Managerial and Decision Economics, 27 (6), 497–510. Baron, R.A. and D. Byrne (1997), Social Psychology: Understanding Human Interaction, 7th edn, Boston, MA: Allyn & Bacon. Bechara, A., H. Damasio, D. Tranel and A.R. Damasio (1997), ‘Deciding advantageously before knowing the advantageous strategy’, Science, 275 (5304), 1293–5. Cohn, A., J. Engelmann, E. Fehr and M.A. Maréchal (2015), ‘Evidence for countercyclical risk aversion: an experiment with financial professionals’, American Economic Review, 105 (2), 860–85. Cramer, P. (1968), Word Association, New York: Academic Press. Erb, H.P., A. Bioy and D.J. Hilton (2002), ‘Choice preference without inferences: subconscious priming of risk attitudes’, Journal of Behavioral Decision Making, 15 (3), 251–62. Gilad, D. and D. Kliger (2008), ‘Priming the risk attitudes of professionals in financial decision making’, Review of Finance, 12 (3), 567–86. Haigh, M.S. and J.A. List (2005), ‘Do professional traders exhibit myopic loss aversion? An experimental analysis’, Journal of Finance, 60 (1), 523–34. Kahneman, D. (2011), Thinking Fast and Slow, London: Penguin Books. Kahneman, D. and A. Tversky (1979), ‘Prospect theory: an analysis of decision making under risk’, Econometrica, 47 (2), 263–91. Kliger, D. and D. Gilad (2012), ‘Red light, green light: color priming in financial decisions’, Journal of SocioEconomics, 41 (5), 738–45. Stanovich, K.E. and R.F. West (2000), ‘Individual differences in reasoning: implications for the rationality debate’, Behavioral and Brain Sciences, 23 (5), 645–65. Tulving, E. (1983), Elements of Episodic Memory, New York: Oxford University Press. Tversky, A. and D.E. Kahneman (1992), ‘Advances in prospect theory: cumulative representation of uncertainty’, Journal of Risk and Uncertainty, 5 (4), 297–323. Von Neumann, J. and O. Morgenstern (1944), Theory of Games and Economic Behavior, Princeton, NJ: Princeton University Press.

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21 Experimental asset markets: behavior and bubbles Owen Powell and Natalia Shestakova

1

INTRODUCTION

The efficiency of markets is a topic of both popular and academic discussion. The long-standing debate is to what extent market prices reflect underlying preferences of society. This is important because prices serve as economic indicators of the relative, or fundamental, values of assets. When markets operate efficiently, they help channel investment and resources into assets which society values the most. On the other hand, when they fail, misallocation of resources that leads to unnecessary waste and loss can result. While the actual degree of mispricing remains a matter of debate (see Shiller 2003 for a review), even the potential presence of mispricing has strong implications for optimal individual behavior. In particular, in markets with fully rational and informed agents (and common knowledge thereof), there is no reason for anyone to speculate: assets will be bought and sold at their fundamental values. However, once the possibility of deviations of prices from fundamentals is introduced (either through imperfect rationality, information, or common knowledge thereof), it may become optimal for even fully rational and informed participants to engage in speculative behavior. Thus the behavior of ‘smart’ agents, who may have perfect information about the relative value of assets, may induce mispricing that they know to be at odds with the actual value of the assets. One particular pattern of mispricing is a price bubble. Under such a scenario, prices consistently increase over a period of time relative to the underlying fundamental value, until eventually falling precipitously back to efficient levels. In addition to the concern that these episodes of mispricing can often last for long periods of time, the large swings in asset prices often mean extreme trading losses are concentrated in the hands of a few market participants. This can create additional problems when, for example, moral hazard causes market participants to assume too much risk (Farhi and Tirole 2012). Such bubble scenarios are at odds with traditional economic theories of market equilibrium. With fully rational agents and common knowledge thereof, equilibrium prices should adjust quickly and accurately to represent underlying preferences. Trade should occur only insofar as agents seek to rebalance their initial portfolios. However, in various situations bubble episodes may arise due to speculation, lack of common knowledge of rationality and/or imperfect information (Blanchard 1979; Abreu and Brunnermeier, 2003). Regardless of the exact pattern and cause of mispricing, judging the efficiency of markets remains a difficult problem. As in other areas of economics, the experimental method offers the advantage of being able to isolate and examine specific issues of interest. Experiments are particularly useful in the case of market behavior because underlying fundamentals are typically unobservable in the field, meaning that empirical tests of market efficiency are in fact joint tests of efficiency and a set of assumptions regarding 375

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fundamentals.1 In the laboratory setting, asset values are defined (and hence observed) by the experimenter, making it possible to unequivocally compare them to prices.2 Although market experiments have a rich history in economics, this chapter focusses exclusively on the behavior of experimental asset markets. For the purposes of this chapter the defining feature of an asset market is that the monetary value of the asset is homogeneous across all agents.3 The implicit value of the asset comes from the cash revenue generated by holding the asset. When, as is often the case, this takes the form of periodic dividends and is perfectly known to all traders, this creates a fundamental value that decreases steadily over time. The baseline for market experiments of this type is given in Smith et al. (1988). In spite of the fact that fundamental values decrease over time, prices in these markets tend to persistently increase for a large portion of the market, before crashing to near fundamental value at the end of the market. This clear evidence shows that market bubbles can occur, even in relatively simple environments. Subsequent work showed that this was robust to a wide variety of factors, and sparked an interest in determining both the institutional factors and the associated trading behaviors underlying bubbles, and how well the results extend to more realistic market settings. The literature on experimental asset markets has been reviewed before, most recently by Palan (2013a). This chapter concentrates on the results of recent publications and divides the analysis into the following threads of research: the characteristics of traders (section 2), the properties of the traded asset (section 3) and the structure of the market (section 4). Measurement issues are discussed in section 5, and the chapter ends with a discussion of useful tools and concluding remarks.

2

TRADER CHARACTERISTICS

One line of research investigates how mispricing is affected by characteristics of market participants and their understanding of market structure. The effects that have been studied are summarized in Table 21.1. Table 21.1

Effects of trader characteristics on bubbles

Characteristic

Effect

Indirect learning Common knowledge Mixed-experience markets

Reduces mispricing Reduces mispricing Under complete knowledge of fundamental value (FV), sensitive to the past success of experienced traders; increases mispricing relative to experienced markets under incomplete knowledge of FV Reduces mispricing Markets populated by males have higher mispricing Lower mispricing with higher risk aversion No effect Lower mispricing in homogeneous markets; higher mispricing in heterogeneous markets Mispricing increases with proportion of speculators

Ethnic diversity Gender Risk aversion Loss aversion CRT/intelligence Individual trading strategy

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Experimental asset markets 377 2.1

Market Experience

One of the most consistently replicated results in experimental asset market research is that repeated participation in identical markets eventually eliminates mispricing (Smith et al. 1988; King 1991; Haruvy et al. 2007). There are a few caveats to this result: changing the parameters of the market may reintroduce mispricing (Hussam et al. 2008) and the speed of adjustment to fundamentals is faster under some fundamental value regimes than others (Noussair and Powell 2010). Nevertheless there is substantial evidence that experience, as well as training and understanding in general, play a key role in achieving efficient prices. 2.2

Indirect Learning

Direct experience is only one way in which subjects may improve their understanding of the market setting. Allowing experienced traders to leave advice to subsequent cohorts of traders reduces bubble size proportional to the number of traders who receive advice (Alevy and Price 2014). In a similar vein, Cason and Samek (2015) devise a treatment where subjects observe and are rewarded according to the trading behavior of another subject in a previous market. Again, the results suggest that subjects learn to the same degree from observing another trader as they do from participating in a market themselves. 2.3

Common Knowledge/Expectations

A related line of research deals with the role of expectations and common knowledge. Common knowledge about the use of training in a market significantly reduces mispricing above and beyond the effect of training itself (Cheung et al. 2014). Two other studies look at the effect of knowledge about the presence of computer traders on market outcomes. Traders react to the mere possibility that robot traders might be active in the market (Farjam and Kirchkamp 2015). Elicited price forecasts show that a significant proportion of mispricing in mixed human-robot markets is due to strategic uncertainty about the behavior of others, rather than individual irrationality alone (Akiyama et al., 2013). 2.4

Mixed-experience Markets

The evidence so far clearly points to experience having a dampening impact on bubble formation. However, in the traditional setup all traders in a market gain experience at the same rate, whereas the distribution of experience in real-world markets is certainly more heterogeneous. One series of studies considers the ‘inflow effect’, which looks at heterogeneously experienced markets. In these markets a proportion of traders are periodically replaced with new inexperienced traders. Dufwenberg et al. (2005) show that markets populated by even a small proportion of experienced traders can operate efficiently. Subsequent work reveals that experienced traders act as price stabilizers when inexperienced traders enter the market (Akiyama et al. 2014). However, this result is sensitive to the previous success of experienced traders. Bubbles are larger when experienced traders are those with the most extreme (both highest and lowest) earnings (Gladyrev et al. 2015). In an overlapping generations setting, bubbles are formed every time a new generation

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enters the market and brings additional liquidity, while crashes occur every time an old generation exit the market and withdraw some cash (Deck et al. 2014). These two ingredients of a shock, inflow of traders and inflow of cash, seem to both be necessary to trigger market inefficiency, at least in the constant fundamental value setting (Kirchler et al. 2015). 2.5

Socio-demographic Characteristics

Populating experimental markets with traders who are familiar with real world financial markets still results in a typical bubble-and-crash pattern (Smith et al. 1988; King et al. 1993). However, market outcomes have been shown to be sensitive to other socio-demographic characteristics of traders. Ethnically homogeneous markets produce substantially larger bubbles and more severe crashes than markets where at least one trader is of a different ethnicity, regardless of whether this ethnicity is population majority or minority (Levine et al. 2014). This might be the case because in ethnically homogeneous markets traders are overconfident in the others’ decisions and, conversely, they scrutinize the others’ behavior in heterogeneous markets. The fraction of female traders is negatively correlated with the resulting deviation of markets prices from fundamentals (Eckel and Fullbrunn 2015). 2.6

Risk and Loss Preferences and Other Personality Traits

It is often speculated that non-zero trade volume is observed in the standard experimental asset market due to the diversity in traders’ risk and loss preferences. Breaban and Noussair (2015) reveal a significant negative correlation between the average risk aversion of traders weighted by their market power and price bias for markets in which the fundamental value increases over time, but no strong relation in other fundamental value regimes. The same study also finds essentially no evidence for the correlation between the average loss aversion of traders and market dynamics. At the individual level, more riskaverse traders tend to sell to those who are less risk averse and more loss-averse traders tend to engage in fewer trades. Eckel and Fullbrunn (2015) find smaller and shorter bubbles in sessions with more riskaverse subjects, although this cannot be disentangled from a gender effect, which is their main focus. Their markets with more competitive participants produce higher bubbles even though their male and female participants do not differ in competitiveness score.

2.7

COGNITIVE ABILITIES

Much of the previous research on experimental asset markets indicates that market efficiency improves with traders’ understanding of the market rules and structure (Lei and Vesely 2009; Kirchler et al. 2012). It is natural to assume that such understanding is easier for traders with better cognitive abilities. One way of measuring cognitive abilities of traders is the cognitive reflection test (CRT), and the available evidence suggests that prices stay closer to fundamentals when the average CRT-score of traders is higher

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Experimental asset markets 379 (Breaban and Noussair 2015; Noussair et al. 2014; Bosch-Rosa et al. 2015), and that traders with higher CRT-scores earn more (Gladyrev et al. 2015).

2.8

EMOTIONAL STATE

A number of studies induce moods on their subjects and consistently find that increasingly positive moods lead to higher prices (Heap and Zizzo 2011; Lahav and Meer 2012). Analysis based on software-based emotion recognition shows that price level is positively correlated with a more positive emotional state before the market opens and negatively correlated with the average trader’s fear before the market opens (Breaban and Noussair 2013). On the individual level, traders who exhibit greater neutrality during a crash achieve higher earnings.

2.9

TRADING STRATEGIES

Another explanation for the bubble-and-crash phenomenon is a speculation motive pursued by some traders. To this end, patterns in mispricing have been explained by classifying trader behavior according to different models of speculation. The model of De Long et al. (1990) classifies traders as fundamental value traders, momentum traders, and rational speculators. Estimates of the distribution of types based on behavioral rules vary across studies, but are generally consistent with the model predictions and change as traders gain market experience (Haruvey and Noussair 2006; Haruvy et al. 2014). Trading strategies also tend to be correlated with the traders’ risk and loss preferences and cognitive abilities (Breaban and Noussair 2015; Baghestanian et al. 2014a).

3

ASSET PROPERTIES

Mispricing has also been shown to be sensitive to various properties of the assets traded in the market. These effects are summarized in Table 21.2. Table 21.2

Effects of asset properties on bubbles

Characteristic

Effect

House-money Fundamental value

No effect on mispricing Higher variance in fundamental value (FV) increases mispricing; mispricing sensitive to structure of FV Relative price of an asset decreases with its relative supply Mispricing decreases with private information. Individual earnings lowest with intermediate level of private information

Asset supply Information provision

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3.1

House Money

A well-documented finding in various experimental settings is a house money effect. This refers to the fact that subject behavior is in many cases sensitive to whether or not subjects perceive that they are making decisions with their own or the experimenter’s (the house) money. Of course, in many market settings it is normal for principals to make decisions with other people’s money, but there is nevertheless concern that experimental subjects may be especially careless with experimental endowments. There is evidence that market bubbles are similar in magnitude regardless of whether subjects play with earned or endowed money (Paul et al. 2013; Corgnet et al. 2015), even though some differences in other market attributes (such as transaction volume) are observed. It would appear then that aggregate market prices are relatively robust to different endowment regimes. 3.2

Fundamental Value

In the original design by Smith et al. (1988), the asset pays a periodic dividend to its owner, which creates a monotonically declining fundamental value over the course of the market. This is at odds with many popular classes of assets, which tend to maintain or even increase in value over time; therefore, various authors have studied alternative time paths for the fundamental value. Several studies include markets with a constant fundamental value but do not allow for the identification of whether mispricing is sensitive to the actual time path of fundamentals. For example, Smith et al. (2000) change other aspects of the market (dividend timing) across treatments, and Noussair et al. (2001) do not include a baseline treatment. Bostian and Holt (2009) present the results from a non-incentivized classroom experiment, and Kose (2016) studies a market with an indefinite horizon, which means that market length and subject experience are not constant across observations. Nevertheless, the results suggest that markets may behave differently when fundamentals follow a non-decreasing time path, and further work comparing treatments within one study firmly establishes that markets with constant fundamentals have very little mispricing, especially compared with the case of declining fundamentals (Kirchler et al. 2012; Cheung and Coleman 2014). More generally, research has shown that market efficiency tends to increase with the amount of variation in fundamentals (Noussair and Powell 2010; Stöckl et al. 2015). In particular, markets are efficient under constant fundamentals, and prices tend to be flatter than fundamentals in markets with non-constant fundamentals, regardless of whether fundamentals increase or decrease and do so deterministically or stochastically over time. This echoes the results of Kirchler (2009) which considers only the case of random fluctuations. Most of the previous studies use a combination of taxes, dividends and final buyouts to vary the path for fundamental values. There is some evidence that bubble episodes are robust to an increase in the frequency of dividend payments (Smith et al. 2000; Jaworski and Kimbrough 2016). Alternatively, interest on cash has also been used to induce declining or increasing fundamentals in the risky asset. In contrast to Stöckl et al. (2015), who study an interest-free environment, Giusti et al. (2013) find less price inflation when interest on cash is used to induce an increasing fundamental value. Giusti et al. (2014) also consider several treatment variations that include a debt swap that increases fundamental values, and show evidence that this action is conducive to bubble formation.

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Experimental asset markets 381 Business cycles are often discussed phenomena in economics and finance, and they are characterized by regular reversals in the time trend of economic activity. The study of asset values whose values mirror this reversal in trend has identified significant asymmetries in the performance of markets during these episodes of recovery and downturn (Noussair and Powell 2010). By considering treatments in which the assets value reaches either a peak or a minimum at a certain point in the market, the authors are able to show that markets adjust more quickly to efficient pricing during a downturn than during a recovery. Breaban and Noussair (2015) extend this idea to the study of markets with a period of constant fundamental values, followed by either an increase (bull market) or decrease (bear market) in value. In contrast to the case of a constantly increasing or decreasing fundamental value, they conclude that increasing markets have a harder time tracking fundamentals than in the standard declining fundamental design. Laboratory assets typically have a predetermined fundamental value, as the time path of fundamentals is fixed by design. Endogenizing the value of the asset by placing it under the control of a subject has no effect on initial bubble sizes, although the ability of experience to mitigate mispricing is diminished (Jaworski and Kimbrough 2016). 3.3

Asset Supply

In a completely rational model, demand for assets is determined only by underlying fundamentals. However, various studies have shown that demand in experimental asset markets depends critically on the relative supply of cash and other assets (the so-called cash-to-asset ratio). In the standard experimental design, dividends are paid out throughout the lifetime of the asset, which implies that over time the supply of cash available for trading increases relative to the supply of the asset. This has been shown to be a significant driver of bubbles, under both declining (Kirchler et al. 2012) and constant (Noussair and Tucker 2014) fundamental value regimes. This is consistent with the effect of nominally changing the value of currency in the middle of a market (Noussair et al. 2012). 3.4

Information Provision

In the typical asset market setting, all information regarding the fundamental value is known to all participants; however, this is clearly a special case that is not satisfied in many real world-scenarios. One strand of literature focusses on the ability of markets to disseminate and aggregate private information efficiently. To address this issue, it is natural to distribute information asymmetrically among market participants. As long as the asset value is the same for all traders, price convergence to the fundamental value is observed in such markets (Plott and Sunder 1988; Oechssler et al. 2011). Allowing the purchase of information on dividend realizations does not eliminate bubble-and-crash patterns (King 1991; Hey and Morone 2004; Alfarano et al. 2011). Providing traders with information on dividend realizations helps in preventing bubbles and subsequent crashes, regardless of whether this information is provided to all subjects, or only to a subset of them, potentially because traders’ decisions are more thoughtful when they are aware that others have an advantage over them (Sutter et al. 2012). However, it has been shown that noisy information signals are heavily over-weighted by

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traders (Smith 2012). Overall, it appears that price efficiency of the market is improved by providing traders with better information, either in terms of quality or quantity. In terms of individual performance, private information has a non-monotonic effect on earnings, even when the information level is endogenous (Stöckl and Kirchler 2014; Huber et al. 2011). In particular, those with intermediate levels of information tend to fare worse than the completely uninformed. With the option to purchase price information, the existence of informed traders has no effect on aggregate market prices; however, it produces lower price volatility (Alfarano et al., 2006). Market-based messages that compare current prices with the fundamental value have been shown to reduce market mispricing (Corgnet et al. 2010). This is not simply a coordination mechanism, since uninformative messages generally have no or only a weak effect (Stoian 2014). The evidence suggests that both prices and forecasts underreact to public messages that sequentially reveal the dividend value, and the under-reaction is generally stronger for positive information than negative information (Gillette et al. 1999; Stevens and Williams 2004; Palfrey and Wang 2012). Under-reaction is stronger the more asymmetrically information is distributed in the market (Kirchler 2009).

4

MARKET STRUCTURE

More generally, various properties of the market can influence price efficiency. This is summarized in Table 21.3. 4.1

Information Presentation

Perhaps not surprisingly, presenting information in graphical format, rather than as tables of text, increases the efficiency of the market (Cason and Samek 2015). This finding is consistent with the idea that increased knowledge and subject understanding of the market increase efficiency (Lei and Vesely 2009; Kirchler et al. 2012). Recent work also shows that minimal visual anchoring of prices, irrespective of how far they are from Table 21.3

Effect of market structure on bubbles

Characteristic

Effect

Information presentation

Prices respond to the use of informative graphical (versus textual) information Trading institution Mispricing is highest in double auctions, smaller in call markets, and lowest under tatonnement Trading restrictions Prices of an asset are decreasing in the ability to borrow that asset. Performance incentives Bonus schemes increase prices Multiple markets Additional assets and futures markets do not eliminate mispricing Market interventions Endogenous interest rate policy does not affect mispricing. Prices respond to share repurchases and new issues. Pre-trading auction phases consistently underpriced Coordination mechanisms Aberrant orders decrease mispricing. Public messages have no effect

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Experimental asset markets 383 fundamental values, can have long-lasting effects on market behavior (Caginalp et al. 2000; Baghestanian and Walker 2015). 4.2

Trading Institution

One defining feature of a market is the means by which trade is conducted. Two of the most popular options are a double auction and a call market. Continuous double auctions allow traders to make offers in real-time that remain open until they are either accepted by another party or explicitly cancelled by the originating trader. Conversely, call markets typically require that offers be submitted simultaneously. After offers have been submitted, an equilibrium price is calculated and trades are determined. Various studies (van Boening et al. 1993; Gillette et al. 1999; Baghestanian et al. 2014b) find that mispricing bubbles are equally likely under each of the two institutions. The tatonnement trading institution produces significantly lower bubbles than a double auction by reducing speculative behavior (Baghestanian et al. 2014b). 4.3

Trading Restrictions

An indirect way of increasing relative supply of assets in a market is through trading restrictions. Borrowing and lending have been shown to play important roles during recessionary periods (Ivashina and Scharfstein 2010). As such, regulatory agencies have returned periodically to the question of whether to allow activities such as short-selling and trading on the margin. Several studies have looked at the role of trading restrictions in experimental asset markets. Short-selling is found to significantly decrease prices towards, but not beyond, fundamentals (King et al. 1993; Haruvy and Noussair 2006). Ackert et al. (2006) find similar results in markets where multiple assets are traded simultaneously. Recent work by Fellner and Theissen (2014) extends this result to a one-period setting with an uncertain fundamental value. Margin trading (borrowing cash) is shown to have an equal and opposite effect as short selling in Ackert et al. (2006), however, the precise type of restriction used in the above studies is sometimes arbitrary. Füllbrunn and Neugebauer (2012) use a design in which borrowing is only restricted by margin requirements. This allows them to compare the relative size of the two effects, suggesting that margin trading may outweigh the effect of short selling when both types of borrowing are simultaneously permitted. Asset holding caps have also been shown to restrict bubble formation (Lugovskyy et al. 2014) Specifically, early prices decrease with permanent caps, however, overall mispricing stays the same since prices subsequently fall below fundamentals later in the market. Surprisingly, temporary caps eliminate both types of mispricing and result in an efficient market. 4.4

Performance Incentives

In many economics experiments, including the standard design by Smith et al. (1988), subjects are compensated proportional to their own personal performance market earnings. However, in many real-world settings, compensation packages include non-linear and relative payment mechanisms. Holmen et al. (2014) design a treatment in which traders receive

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a guaranteed base salary combined with a bonus proportional to earnings above a certain amount. Such convex incentive schedules significantly increase both risk-taking and prices of the traded assets. Kleinlercher et al. (2014) build on this design to allow for bonus caps and penalties, and find that both types of mechanisms tend to reduce overpricing. These results show that aggregate market behavior may deviate from rational equilibrium, even though individual agents tend to respond rationally to different incentive schemes. Certain types of markets are best described as contests among agents, where performance is rewarded according to relative, rather than just absolute, performance. Cheung and Coleman (2014) study an environment in which traders are regularly rewarded based on the market value of their current portfolio. In this setting, while bubbles initially do not differ between compensation schemes, a large asymmetry exists as compared to markets with once-experienced traders. With tournament incentives and declining fundamental values, bubbles fail to dissipate with repetition of the market setting, although this effect appears to be sensitive to the time path of fundamental value. Finally, there is some evidence that knowledge of relative performance alone is enough to affect individual behavior (Baghestanian et al. 2015). 4.5

Multiple Markets

In the real world, markets do not operate in isolation. Multiple markets, for the same or distinct assets, may operate and simultaneously influence each other. Futures markets allow market participants to hedge against future price changes by buying and selling ownership of the asset in the future, as opposed to the spot market in which trades are executed immediately. Consistent with theory, the presence of these markets tends to decrease overall mispricing in the current spot market when a single futures market is available (Porter and Smith 1995; Noussair et al. 2014), however the efficiency gains in the spot market are offset by mispricing in the futures markets themselves when a full set of futures markets are open (Noussair and Tucker 2006). Mispricing has also been shown to increase proportionally with the level of the fundamental value in markets with multiple assets (Chan et al. 2013). Such markets also show that relative asset prices for similar assets remain close to parity, but deviations increase as the mean and variance of one of the asset values increases (Fisher and Kelly 2000; Childs and Mestelman 2006; Childs 2009). 4.6

Market Interventions

The actions of economic agents in asset markets are not restricted to trading. Both private and public entities engage in various forms of non-trading behavior that can influence market behavior. A classic form of intervention is the actions undertaken by central banks. Fischbacher et al. (2013) examine how mispricing is affected by central bank interest rate policy by including an interest-paying bond as an additional asset. In general, the interest-bearing assets do not significantly affect mispricing, although they do reduce the liquidity of the market. This finding is robust to several variations: the rule for fixing interest rates (exogenous or adaptive to prices), the precise timing of interest payment and type of information provided. The one treatment that is found to have an effect on prices is the potential presence of reserve requirements.

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Experimental asset markets 385 Publicly traded shares represent a significant source of funding for many private companies, and firms regularly seek to repurchase from or float new shares in the marketplace. Haruvy et al. (2014) study a standard asset market in which shares are either sold to or bought and removed from the market at a predetermined moment in time in the market. These market interventions are shown to have significant impacts on prices: bubbles increase following a repurchase, and decrease following a share issue. The results are consistent with a downward sloping demand for the asset, although they also suggest that the effects are asymmetric because of resistance of prices to fall below the fundamental value. Firm takeovers are an additional source of market activity which may affect mispricing. Füllbrunn and Haruvy (2014) design an asset market setting where firm profits are uncertain and dividend payments are endogenously chosen by a manager. Shareholders periodically have the opportunity to sell the firm for an exogenously given price if they believe that the manager is not sharing enough of the profits. Prices in these markets indicate initial overvaluation of the firm, yet with repetition prices eventually reach the fundamental value. The underpricing of initial public offerings (IPOs) is an established empirical regularity. This has been studied in experimental markets by adding an initial auction phase to determine the initial allocation of shares in the market. Füllbrunn et al. (2014) consider three different choices for the auction mechanism: a closed book auction, in which all bids are made privately; an open book auction, where traders observe and are able to react to bids and the implied equilibrium price; and a book-building approach, which consists first of a closed-book auction, followed by a second stage in which actual quantities purchased are determined. Independent of auction mechanism, prices in the auction stage are shown to be persistently lower than both fundamental values and prices in the aftermarket. These differences are reduced but not completely eliminated with experience. 4.7

Coordination Mechanisms

While the causes of price increases are still being determined, an equally important part of the bubble-and-crash puzzle is the cause of the crash. For whatever reason, prices begin to increase, and this may be followed by further destabilizing speculation, resulting in a feedback loop that drives prices even further away from fundamentals. At some point, the feedback loop is broken, and the trend in prices is reversed. It is unclear whether this break occurs because of nominal events or as a function of underlying market properties. Ackert et al. (2014) consider the possibility of aberrant orders. At a predetermined point in the market (chosen to be a point where significant mispricing typically exists), an order to buy or sell a large number of shares at an obviously irregular price is entered into the market. In line with expectations, orders to buy tend to increase prices compared to orders to sell. However, their results suggest that the coordinating ability of the aberrant order, independently of its actual details, can serve to dampen bubbles. This means that out-of-the-ordinary signals may act as circuit breakers that have larger than expected effects on market performance. Another type of potential coordination device is public messages. Various public entities, such as central banks and regulators, regularly state their opinions regarding the current state of markets. These messages may contain new information regarding intentions and beliefs, but at the same time they may also increase the likelihood of traders

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re-evaluating the market and thus breaking a feedback of mispricing. Stoian (2014) compares a regular asset market with a market in which a message re-affirming publiclyknown information about the fundamental value is released at a predetermined time in the market. No clear pattern emerges, suggesting that public messages may not be so important in promoting the end of a bubble.

5

MEASURING MISPRICING

For the most part, measures can be classified into one of four types described by two factors (see Table 21.4). It is clear in a general sense what is meant by the phrase ‘bubble-and-crash’. In practice, however, a precise definition is hard to come by. Even focusing on the simpler problem of defining ‘mispricing’ given a set of price indices and fundamental values, we can imagine various definitions. For the most part, measures can be classified into one of four types described by two factors: (1) whether they attempt to measure a temporal or price amount, and (2) whether they consider the absolute (mispricing) or signed (overpricing or underpricing) deviation of prices from fundamentals. There are arguments to be made in favor and against each type of measure – each represents a different way of representing market performance. However, even within a given type there are multiple ways to measure efficiency. We have reviewed the literature and identified no fewer than 18 such measures that have been used. Additional dimensions to the problem (such as how prices are averaged to arrive at an index and the length of indexing period) multiply the set of measures. This is not an ideal state of affairs, since it is not clear which results are most relevant and to what extent they are sensitive to the choice of measure. To this end, a strand of literature has developed that seeks to identify reasonable conditions for restricting the set of admissible measures. The conditions identified in Stöckl et al. (2010) require that a measure relates prices and fundamentals and that it is independent with respect to various quantities (the number of periods and average fundamental value). As a result, the new measures they propose are essentially scaled versions of previous measures. Powell (2016) continues in this vein but shows that the measures analyzed and proposed by Stöckl et al. (2010) are not independent with respect to one important variable: the choice of units used to represent prices. Prices and fundamental values have multiple equivalent representations since they are in fact rates of exchange, where each Table 21.4

Bubble measures by type

Time- or price-based

Price level or mispricing

Examples

Price-based

Price level Mispricing Price level Mispricing

Bias; amplitude Dispersion Boom duration; bust duration (none)

Time-based

Note: Measure definitions are given in Haruvy and Noussair (2006).

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Experimental asset markets 387 representation is defined by the choice of an appropriate numeraire. This motivates the requirement that a measure of mispricing is independent from the nominal choice of price numeraire, and it is shown that all arithmetic-mean based measures fail this test. As a result, the geometric average deviation (GAD) is proposed as an alternative (although still not unique) measure of mispricing that satisfies both the condition of numeraire independence and the original conditions from Stöckl et al. (2010).

5

SUMMARY AND FUTURE RESEARCH

This study has reviewed the latest research on asset market experiments. The main result, that market bubbles can readily occur in simple settings, has been widely replicated. The propensity of markets to bubble has been shown to be sensitive to a variety of factors. Markets populated with less risk-averse traders tend to have higher prices, and overall uncertainty about the knowledge and intelligence of other traders increases mispricing. In terms of the assets themselves, market prices have a tendency to exhibit less variation than underlying fundamentals, and there appears to be a non-monotonic relationship between private information and individual earnings. Finally, the structure of the market plays an important role: trading restrictions tend to impact prices, the double auction environment seems to be especially conducive to speculative behavior, and compensation schemes can create an especially bubbly environment that does not even disappear with repetition. The previous sections show that much has been learned about experimental asset markets, but also that many open questions remain in the field. There are several resources available to those looking to further investigate the topic. In addition to the various surveys of the literature, Palan (2013b) includes a structured database of bubble measures from many of the studies mentioned above. The same author has also developed specialized software for running asset market experiments (Palan 2015). Finally, the website maintained by Powell (2015) contains a comprehensive list of studies related to the topic. This chapter has reviewed work that shows that market bubbles can occur for a combination of reasons. A combination of irrationality and the lack of common knowledge thereof create an atmosphere that lends itself to the bubble-and-crash phenomena. The research reviewed above shows that this phenomenon is sensitive to various factors, both at the individual level, the assets being traded and the market. The continued importance of markets as mechanisms for potentially aggregating information about preferences suggests that this topic will continue to be an active research area for the near future.

NOTES 1. Although rare, exceptions do exist. See, for example, Xiong and Yu (2011), who study bubbles in essentially worthless Chinese put warrants. 2. Naturally, asset values defined in terms of expected monetary value may not exactly describe their value to individuals (owing to idiosyncratic risk and wealth preferences, for example). However, as a first approximation, they may still be useful. 3. In contrast, markets populated with heterogeneous agent values and specialized roles tend to behave relatively efficiently (Chamberlin 1948; Smith 1962). Recent work has shown that specialization plays a key role in generating efficient prices (Dickhaut et al. 2012).

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REFERENCES Abreu, D. and M.K. Brunnermeier (2003), ‘Bubbles and crashes’, Econometrica, 71 (1), 173–204. Ackert, L.F., N. Charupat, B.K. Church and R. Deaves (2006), ‘Margin, short selling, and lotteries in experimental asset markets’, Southern Economic Journal, 73 (2), 419–36. Ackert, L.F., J. Lei and Q. Li (2014), ‘Liquidity shocks in experimental asset markets’, Working Paper FALL1402, Coles College of Business, Kennesaw State University, Kennesaw, GA, accessed 11 January 2016 at http:// coles.kennesaw.edu/coles-overview/faculty-and-research/working-paper-series/documents/FALL14-02.pdf. Akiyama, E., N. Hanaki and R. Ishikawa (2013), ‘It is not just confusion! Strategic uncertainty in an experimental asset market’, Strategic Uncertainty in an Experimental Asset Market, 8 August, accessed 11 January 2016 at http://ssrn.com/abstract52307791. Akiyama, E., N. Hanaki and R. Ishikawa (2014), ‘How do experienced traders respond to inflows of inexperienced traders? 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Walker (2015), ‘Anchoring in experimental asset markets’, Journal of Economic Behavior and Organization, 116 (August), 15–25. Baghestanian, S., P.J. Gortner and J.J. Van der Weele (2015), ‘Peer effects and risk sharing in experimental asset market’, Sustainable Architecture for Finance in Europe (SAFE) Working Paper No 67, Goethe University, Frankfurt, accessed 11 January 2016 at http://ssrn.com/abstract52504541. Baghestanian, S., V. Lugovskyy and D. Puzzello (2014a), ‘Traders’ heterogeneity and bubble-crash patterns in experimental asset markets’, Journal of Economic Behavior & Organization, 117 (C), 82–101. Baghestanian, S., V. Lugovskyy, D. Puzzello and S. Tucker (2014b), ‘Trading institutions in experimental asset markets: theory and evidence’, accessed 14 January 2017 at https://www.semanticscholar.org/paper/TradingInstitutions-in-Experimental-Asset-Markets-Puzzello-Baghestanian/698201a1934291921feb206dfc0a8d2aec c77b77. Blanchard, O.J. (1979), ‘Speculative bubbles, crashes and rational expectations’, Economics letters, 3 (4), 387–9. Bosch-Rosa, C., T. Meissner and A. Bosch i Domènech (2015), ‘Cognitive bubbles’, paper, 10 November, accessed 11 January 2016 at http://ssrn.com/abstract52553230. Bostian, A.J. and C.A. Holt (2009), ‘Price bubbles with discounting: a web-based classroom experiment’, Journal of Economic Education, 40 (1), 27–37. Breaban, A. and C.N. Noussair (2013), ‘Emotional state and market behavior’, CentER Discussion Paper Series No. 2013-031, 10 June, accessed 11 January 2016 at http://ssrn.com/abstract52276905. Breaban, A. and C.N. Noussair (2015), ‘Trader characteristics and fundamental value trajectories in an asset market experiment’, Journal of Behavioral and Experimental Finance, 8 (December), 1–17. Caginalp, G., D. Porter and V. Smith (2000), ‘Momentum and overreaction in experimental asset markets’, International Journal of Industrial Organization, 18 (1), 187–204. Cason, T.N. and A. Samek (2015), ‘Learning through passive participation in asset market bubbles’, Journal of the Economic Science Association, 1 (2), 170–81. Chamberlin, E.H. (1948), ‘An experimental imperfect market’, Journal of Political Economy, 56 (2), 95–108. Chan, K.S., V. Lei and F. Vesely (2013), ‘Differentiated assets: an experimental study on bubbles’, Economic Inquiry, 51 (3), 1731–49. Cheung, S.L. and A. Coleman (2014), ‘Relative performance incentives and price bubbles in experimental asset markets’, Southern Economic Journal, 81 (2), 345–63. Cheung, S.L., M. Hedegaard and S. Palan (2014), ‘To see is to believe: common expectations in experimental asset markets’, European Economic Review, 66 (C), 84–96. Childs, J. (2009), ‘Rate of return parity and currency crises in experimental asset markets’, Journal of International Financial Markets, Institutions and Money, 19 (1), 157–70. Childs, J. and S. Mestelman (2006), ‘Rate-of-return parity in experimental asset markets’, Review of International Economics, 14 (3), 331–47. Corgnet, B., R. Hernán-González, P. Kujal and D. Porter (2015), ‘The effect of earned versus house money on price bubble formation in experimental asset markets’, Review of Finance, 19 (4), 1455–88.

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Experimental asset markets 389 Corgnet, B., P. Kujal and D. Porter (2010), ‘The effect of reliability, content and timing of public announcements on asset trading behavior’, Journal of Economic Behavior and Organization, 76 (2), 254–66. De Long, J.B., A. Shleifer, L.H. Summers and R.J. Waldmann (1990), ‘Noise trader risk in financial markets’, Journal of Political Economy, 98 (4), 703–38. Deck, C., D. Porter and V. Smith (2014), ‘Double bubbles in assets markets with multiple generations’, Journal of Behavioral Finance, 15 (2), 79–88. Dickhaut, J., S. Lin, D. Porter and V. Smith (2012), ‘Commodity durability, trader specialization, and market performance’, Proceedings of the National Academy of Sciences, 109 (5), 1425–30. Dufwenberg, M., T. Lindqvist and E. Moore (2005), ‘Bubbles and experience: an experiment’, American Economic Review, 95 (5), 1731–7. Eckel, C.C. and S.C. Füllbrunn (2015), ‘Thar she blows? Gender, competition, and bubbles in experimental asset markets’, American Economic Review, 105 (2), 906–20. Farhi, E. and J. Tirole (2012), ‘Collective moral hazard, maturity mismatch, and systemic bailouts’, American Economic Review, 102 (1), 60–93. Farjam, M. and O. Kirchkamp (2015), ‘Bubbles in hybrid markets – how expectations about algorithmic trading affect human trading’, CESifo Working Paper No. 5631, November, accessed 11 January 2016 at http://ssrn. com/abstract52706506. Fellner, G. and E. Theissen (2014), ‘Short sale constraints, divergence of opinion and asset prices: evidence from the laboratory’, Journal of Economic Behavior and Organization, 101 (May), 113–27. Fischbacher, U., T. Hens and S. Zeisberger (2013), ‘The impact of monetary policy on stock market bubbles and trading behavior: evidence from the lab’, Journal of Economic Dynamics and Control, 37 (10), 2104–22. Fisher, E.O. and F.S. Kelly (2000), ‘Experimental foreign exchange markets’, Pacific Economic Review, 5 (3), 365–87. Füllbrunn, S. and E. Haruvy (2014), ‘The takeover game’, Journal of Behavioral and Experimental Finance, 1 (January), 85–98. Füllbrunn, S. and T. Neugebauer (2012), ‘Margin trading bans in experimental asset markets’, Jena Economic Research Paper 2012,058, accessed 11 January 2016 at http://www.econstor.eu/handle/10419/70137. Füllbrunn, S., T. Neugebauer and A. Nicklisch (2014), ‘Underpricing of initial public offerings in experimental asset markets’, NiCE Working Paper 14-107, Nijmegen Center for Economics (NiCE), Institute for Management Research, Radboud University, Nijmegen, November, accessed 11 January 2016 at http://ssrn. com/abstract52545015. Gillette, A.B., D.E. Stevens, S.G. Watts and A.W. Williams (1999), ‘Price and volume reactions to public information releases: an experimental approach incorporating traders’ subjective beliefs’, Contemporary Accounting Research, 16 (3), 437–79. Giusti, G., C.N. Noussair and H.-J. Voth (2013), ‘Recreating the South Sea bubble: lessons from an experiment in financial history’, CentER Discussion Paper Series No. 2013-042, August, accessed 11 January 2016 at http://ssrn.com/abstract52313037. Giusti, G., J.H. Jiang and Y. Xu (2014), ‘Interest on cash, fundamental value process and bubble formation on experimental asset markets’, Staff Working Paper 2014-18, Bank of Canada, Ottawa, accessed 11 January 2016 at http://www.bankofcanada.ca/2014/05/working-paper-2014-18/. Gladyrev, D., O. Powell and N. Shestakova (2015), ‘Bubbles, experience, and success’, accessed 14 January 2017 https://ideas.repec.org/p/vie/viennp/1404.html. Haruvy, E. and C.N. Noussair (2006), ‘The effect of short selling on bubbles and crashes in experimental spot asset markets’, Journal of Finance, 61 (3), 1119–57. Haruvy, E., Y. Lahav and C.N. Noussair (2007), ‘Traders’ expectations in asset markets: experimental evidence’, American Economic Review, 97 (5), 1901–20. Haruvy, E., C.N. Noussair and O. Powell (2014), ‘The impact of asset repurchases and issues in an experimental market’, Review of Finance, 18 (2), 681–713. Heap, S.P. and D.J. Zizzo (2011), ‘Emotions and chat in a financial markets experiment’, paper, 11 March, accessed 11 January 2016 at http://ssrn.com/abstract51783462. Hey, J. D. and A. Morone (2004), ‘Do markets drive out lemmings – or vice versa?’, Economica, 71 (284), 637–59. Holmen, M., M. Kirchler and D. Kleinlercher (2014), ‘Do option-like incentives induce overvaluation? Evidence from experimental asset markets’, Journal of Economic Dynamics and Control, 40 (March), 179–94. Huber, J., M. Angerer and M. Kirchler (2011), ‘Experimental asset markets with endogenous choice of costly asymmetric information’, Experimental Economics, 14 (2), 223–40. Hussam, R.N, D. Porter and V.L. Smith (2008), ‘Thar she blows: can bubbles be rekindled with experienced subjects?’, American Economic Review, 98 (3), 924–37. Ivashina, V. and D. Scharfstein (2010), ‘Bank lending during the financial crisis of 2008’, Journal of Financial Economics, 97 (3), 319–38. Jaworski, T. and E.O. Kimbrough (2016), ‘Bubbles, crashes and endogenous uncertainty in linked asset and product markets’, International Economic Review, 57 (1), 155–76.

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King, R.R. (1991), ‘Private information acquisition in experimental markets prone to bubble and crash’, Journal of Financial Research, 14 (3), 197–206. King, R.R., V.L. Smith, A.W. Williams and M. Van Boening (1993), ‘The robustness of bubbles and crashes in experimental stock markets’, in I. Prigogine, R. Day and P. Chen (eds), Nonlinear Dynamics and Evolutionary Economics, Oxford: Oxford University Press, pp. 183–200. Kirchler, M. (2009), ‘Underreaction to fundamental information and asymmetry in mispricing between bullish and bearish markets. An experimental study’, Journal of Economic Dynamics and Control, 33 (2), 491–506. Kirchler, M., C. Bonn, J. Huber and M. Razen (2015), ‘The “inflow-effect” – trader inflow and bubble formation in asset markets’, European Economic Review, 77 (July), 1–19. Kirchler, M., J. Huber and T. Stöckl (2012), ‘Thar she bursts: reducing confusion reduces bubbles’, American Economic Review, 102 (2), 865–83. Kleinlercher, D., J. Huber and M. Kirchler (2014), ‘The impact of different incentive schemes on asset prices’, European Economic Review, 68 (C), 137–50. Kose, T. (2016), ‘Price convergence and fundamentals in asset markets with bankruptcy risk: an experiment’, International Journal of Behavioural Accounting and Finance, forthcoming. Lahav, Y. and S. Meer (2012), ‘The effect of induced mood on prices in asset markets-experimental evidence’, paper, 3 May, accessed 11 January 2016 at http://ssrn.com/abstract52050299. Lei, V. and F. Vesely (2009), ‘Market efficiency: evidence from a no-bubble asset market experiment’, Pacific Economic Review, 14 (2), 246–58. Levine, S.S., E.P. Apfelbaum, M. Bernard, V.L. Bartelt, E.J. Zajac and D. Stark (2014), ‘Ethnic diversity deflates price bubbles’, Proceedings of the National Academy of Sciences, 111 (52), 18524–9. Lugovskyy, V., D. Puzzello, S. Tucker and A. Williams (2014), ‘Asset-holdings caps and bubbles in experimental asset markets’, Journal of Economic Behavior and Organization, 107 (B), 781–97. Noussair, C.N. and O. Powell (2010), ‘Peaks and valleys: price discovery in experimental asset markets with non-monotonic fundamentals’, Journal of Economic Studies, 37 (2), 152–80. Noussair, C.N. and S.J. Tucker (2006), ‘Futures markets and bubble formation in experimental asset markets’, Pacific Economic Review, 11 (2), 167–84. Noussair, C.N. and S.J. Tucker (2014), ‘Cash inflows and bubbles in asset markets with constant fundamental values’, Working Papers in Economics No. 14/03, Department of Economics, University of Waikato, accessed 11 January 2016 at ftp://wms-webprod1.mngt.waikato.ac.nz/RePEc/wai/econwp/1403.pdf. Noussair, C.N., G. Richter and J.-R. Tyran (2012), ‘Money illusion and nominal inertia in experimental asset markets’, Journal of Behavioral Finance, 13 (1), 27–37. Noussair, C., S. Robin and B. Ruffieux (2001), ‘Price bubbles in laboratory asset markets with constant fundamental values’, Experimental Economics, 4 (1), 87–105. Noussair, C.N., S.J. Tucker and Y. Xu (2014), ‘A futures market reduces bubbles but allows greater profit for more sophisticated traders’, CentER Discussion Paper No. 2014-051, 2 September, accessed 11 January 2016 at http://ssrn.com/abstract52490326. Oechssler, J., C. Schmidt and W. Schnedler (2011), ‘On the ingredients for bubble formation: informed traders and communication’, Journal of Economic Dynamics and Control, 35 (11), 1831–51. Palan, S. (2013a), ‘A review of bubbles and crashes in experimental asset markets’, Journal of Economic Surveys, 27 (3), 570–88. Palan, S. (2013b), ‘A review of research into Smith, Suchanek and Williams markets’, Working Paper Social and Economic Sciences No. 2013-04, Faculty of Social and Economic Sciences, Karl-Franzens-University, Graz, accessed 11 January 2016 at http://static.uni-graz.at/fileadmin/sowi/Pics/2013-04_Palan_01.pdf. Palan, S. (2015), ‘GIMS — software for asset market experiments’, Journal of Behavioral and Experimental Finance, 5 (March), 1–14. Palfrey, T.R. and S.W. Wang (2012), ‘Speculative overpricing in asset markets with information flows’, Econometrica, 80 (5), 1937–76. Paul, D.J., J. Henker and S. Owen (2013), ‘House money effects in experimental asset markets’, accessed 11 January 2016 at http://www.busman.qmul.ac.uk/newsandevents/events/eventdownloads/bfwgconfer ence2013acceptedpapers/114920.pdf. Plott, C.R. and S. Sunder (1988), ‘Rational expectations and the aggregation of diverse information in laboratory security markets’, Econometrica, 56 (5), 1085–118. Porter, D.P. and V.L. Smith (1995), ‘Futures contracting and dividend uncertainty in experimental asset markets’, Journal of Business, 68 (4), 509–41. Powell, O. (2015), ‘Literature on experimental asset markets’, paper, 9 March, accessed 11 January 2016 at http:// sites.google.com/site/opowell/market-experiments. Powell, O. (2016), ‘Measuring mispricing in experimental markets’, Journal of Behavioral and Experimental Finance, forthcoming. Shiller, R.J. (2003), ‘From efficient markets theory to behavioral finance’, Journal of Economic Perspectives, 17 (1), 83–104.

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Experimental asset markets 391 Smith, N. (2012), ‘Private information and overconfidence in experimental asset markets’, paper, 24 June, accessed 11 January 2016 at http://www-personal.umich.edu/~nquixote/privateinfo.pdf. Smith, V.L. (1962), ‘An experimental study of competitive market behavior’, Journal of Political Economy, 70 (2), 111–37. Smith, V.L., M. van Boening and C.P. Wellford (2000), ‘Dividend timing and behavior in laboratory asset markets’, Economic Theory, 16 (3), 567–83. Smith, V.L., G.L. Suchanek and A.W. Williams (1988), ‘Bubbles, crashes, and endogenous expectations in experimental spot asset markets’, Econometrica, 56 (5), 1119–51. Stevens, D.E. and A.W. Williams (2004), ‘Inefficiency in earnings forecasts: experimental evidence of reactions to positive vs. negative information’, Experimental Economics, 7 (1), 75–92. Stöckl, T. and M. Kirchler (2014), ‘Trading strategies and trading profits in experimental asset markets with cumulative information’, Journal of Behavioral and Experimental Finance, 2 (June), 18–30. Stöckl, T., J. Huber and M. Kirchler (2010), ‘Bubble measures in experimental asset markets’, Experimental Economics, 13 (3), 284–98. Stöckl, T., J. Huber and M. Kirchler (2015), ‘Multi-period experimental asset markets with distinct fundamental value regimes’, Experimental Economics, 18 (2), 314–34. Stoian, A. (2014), ‘Public messages and asset prices’, Atlantic Economic Journal, 42 (4), 441–54. Sutter, M., J. Huber and M. Kirchler (2012), ‘Bubbles and information: an experiment’, Management Science, 58 (2), 384–93. Van Boening, M.V., A.W. Williams and S. LaMaster (1993), ‘Price bubbles and crashes in experimental call markets’, Economics Letters, 41 (2), 179–85. Xiong, W. and J. Yu. (2011), ‘The Chinese warrants bubble’, American Economic Review, 101 (6), 2723–53.

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22 To consume or to save: are we maximizing or what? Tobias F. Rötheli*

1

INTRODUCTION

One of the most important relationships in macroeconomics is the so-called consumption function. It relates aggregate consumption to other macroeconomic variables such as income and wealth. Understanding the forces driving consumption is very important for two reasons. First, changes in consumption are key determinants of changes in aggregate income in the short term and, second, savings – the difference between income and consumption – by affecting the accumulation of capital (physical and human) is a crucial determinant of the longer-term course of an economy. As documented in this chapter, economists have considered various explanations of the consumption–savings behavior. It is remarkable that practically all of these approaches attribute some basic rationality to economic decision makers. However, scientific explanations of consumption differ significantly in the complexity of the decision making that various theories consider to be the basis of behavior. Currently popular theories of consumption portray the individual as solving a difficult intertemporal maximization problem that requires trading off utility from current consumption against expected utility derived from future consumption. This is difficult to believe and – in its specific implications – hard to square with empirical facts. As a response, some behavioral economists have explored the notion that behavior is guided by simple heuristics that perform almost as well as maximization routines. In this account, simple behavioral rules are structured to lead agents to approximate fully rational behavior. We develop a different explanatory strategy and see humans as endowed with preferences that are both straightforward to balance and conducive to survival. In the words of theory, nature has provided us with a utility function that is simple to maximize and that makes us behave in a smart way. The resulting human behavior may look to be heuristic driven (that is, rule of thumb behavior) when in fact it is optimal given a smart and solvable decision problem. Figure 22.1 illustrates in general terms the difference between the standard heuristics approach and the approach of simple behavioral rules derived from maximization developed here. Decision rules vary with respect to the performance achieved (utility attained) and computational complexity (that is, required information processing capacities). In the standard interpretation of rule-of-thumb behavior (depicted on the left) maximization leads to a high level of utility but comes at a high (and often unmanageable) level of complexity. Heuristics are seen to yield a performance somewhat short of what the maximization routine can achieve but they are significantly less demanding in terms of complexity.1 The competing approach which we develop here for the purpose of explaining consumption behavior is depicted on the right hand side of Figure 22.1. Here we see 392

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To consume or to save 393 (a) The heuristics approach Performance

(b) The alternative approach Performance

Maximization Maximization Simple rule-following Heuristics

Complexity

Figure 22.1

Complexity

Maximization and simple rule-following behavior

the decision maker as endowed with a utility function that is computationally straightforward and thus implies that behavior follows a simple rule. Here, maximization and simple rule-following behavior are the same. The text starts with a description of theoretical and empirical studies that have shaped the field. In particular we focus on the life-cycle permanent income hypothesis of consumption. This backbone of analysis will be used to discuss various issues of bounded rationality, such as errors regarding expectations or problems of self-control. Thereafter, the text explores an alternative explanation of the consumption–savings relationship that strictly relies on variables currently observed and felt by the individual, that is, consumption and wealth. It is argued that such a form of atemporal maximization is biologically plausible and the theories’ predictions concerning aggregate behavior are in line with empirical observations.

2

A REVIEW OF THEORETICAL DEVELOPMENTS

The typical starting point for a discussion of consumption is Keynes’s fundamental psychological law that states that humans tend to increase consumption (C) with a rising income (Y) but not by as much as the increase of income (Keynes 1936, p. 96). Although Keynes acknowledges that a host of other factors influence the consumption–savings decision, his consumption function as presented in countless textbooks can be displayed in a simple two-dimensional C–Y chart with a positive intercept and a slope of less than one. This functional relationship is the basis of presentations of the well-known multiplier effect of consumption. Within a few years of Keynes’s contribution, critics such as Kuznets pointed out that one of the implications of this simple model of consumption appeared to be at variance with empirical observations. According to the simplest Keynesian consumption function an increase in income should lead to an increase in the savings rate (a decrease in C/Y) over time as income rises because consumption would

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increase less than proportional to income. Evaluation of long time series of income and consumption tended to contradict this implication of the Keynesian consumption function. At least over the period 1870 to 1940 savings as a proportion of income in the US appeared to remain remarkably stable (Kuznets 1946). Several theoretical developments have helped to explain this empirical regularity and have come to form the core of the modern understanding of consumption and savings. The crucial first element is Fisher’s notion of the individual (or the household) as being engaged in an intertemporal assessment of consumption possibilities. According to Fisher (1930), people constantly compare utility derived from current consumption with the utility derived from extra consumption made possible in the future when today’s consumption is curbed. This intertemporal tradeoff is seen to be just the generalization of the timeless utility maximization determining, for example, the demand for apples and bananas. Fisher’s understanding of intertemporal utility tradeoffs led to two major developments or variants of consumption theories. In the version of Modigliani and Brumberg (1954, 1979) agents are seen to maximize utility over a finite lifetime.2 In Friedman’s (1957) contribution the focus is on a consumer living indefinitely who faces changes in income that can be either permanent or transitory. The former concept has come to be known as the life-cycle model of consumption and savings and the latter as the permanent income hypothesis. Both concepts propose that to understand consumption we need to look at more than just the current level of income. In the following I will first concentrate on the life-cycle hypothesis. The life-cycle hypothesis appears to be the more plausible account of behavior since it does not assume – as the permanent income hypothesis does – that people in general show intergenerational altruism.3 Hence, the new formulation of the consumption function introduced in section 4 will be compared with implications of the life-cycle theory. The basic insight of Modigliani and Brumberg into the determinants of consumption and savings are most easily understood by imagining an individual who faces a (certain) remaining lifetime of L years and a retirement age N years into the future. In the absence of discounting, and with a constant income Y until retirement, no income afterwards and a current level of wealth, W, a utility maximizer as proposed by Fisher would distribute consumption evenly over his remaining life. Hence, in each period the individual would consume (YN 1 W) /L. This in turn would imply positive savings before retirement (with a steady increase in wealth) followed by dissaving during retirement with a steady decline in wealth.4 An economy populated with individuals of this kind but of different ages would not necessarily generate aggregate savings at all. Instead, the savings of the young would just be offset by the dissaving of the old. Only in a growing economy would there be positive savings which would be the greater the higher the growth rate. With positive population growth there are more young people who save than older people who dissave. Abstracting from complications owing to uncertainty, the described life-cycle approach can deal with the key empirical challenge to the simplest version of the Keynesian theory of consumption. Simply put, consumption of the individual is a function of income (current and future) and of financial wealth. In the basic version of the theory described above the propensity to consume out of income would be N/L e), μ represents calorie expenditure per instant of physical activity, and BMR is the

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Behavioral aspects of obesity 435 Basal metabolic rate.11 A few assumptions allow obesity to be expressed as a function of F and x alone, S(F, x), into which healthy meals do not enter explicitly albeit moderate the calorie contribution of junk-food by substituting junk food in satiating hunger. A weight-unconscious individual determines his consumption composition and allocation of time so as to maximize his utility: U 5 U(C, ),

(24.6)

where C is consumption of meals (composed of both junk-food meals and healthy meals) and  is genuine leisure. A weight-conscious individual chooses differently, so as to maximize his net utility: NetU 5 U(C, ) − S(F, x).

(24.7)

The authors examine the outcomes of two alternative policy measures for combating obesity, a fat tax on the purchase of junk-food meals and a thin subsidy to the purchase of ingredients for the cooking of healthy meals. The motive behind fat tax is to increase the cost of high-calorie low-nutrition foods in order to reduce their high accessibility to the public, and also to find a way to finance public health initiatives with respect to diet and exercise. It is believed that a fat tax would decrease the demand for high-fat foods and would subsequently decrease the weight of the population. However, there is no evidence that taxes on food affect obesity. Yaniv et al. (2009) argue that even if a fat tax decreases fat consumption, and even if it were technically feasible to apply a fat tax, it is not clear how much success such a tax would have in reducing obesity levels. Their model shows that a fat tax will unambiguously reduce junk-food consumption and the obesity level of a weight-unconscious individual, however, it will not necessarily do so for a weight-conscious individual. Furthermore, for a weight-conscious individual who is physically active, a fat tax may even increase obesity. This is because a fat tax generates substitution away from junk-food meals towards healthy meals. The preparation of healthy meals necessitates time for cooking and healthingredient shopping, thereby leaving less time for physical activity. Hence not just the consumption of junk food will be reduced, but also the time devoted to physical activity. Although calorie intake will fall, calorie burning may fall by more. Consequently, obesity might rise in spite of the fall in junk-food consumption, exacerbating the problem the fat tax proposal intended to eliminate. A thin subsidy is even more problematic, as it will unambiguously increase the junk-food consumption and the obesity level of a weight-conscious physically inactive individual. Otherwise, the effect of a thin subsidy is ambiguous. This is so because the substitution effect acts to increase the purchase of cooking ingredients (at the expense of junk-food consumption), whereas the income effect acts to increase leisure, reducing the time left for cooking. It is only by adopting a specific utility function that clear-cut results can be obtained.

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3.4

Rational Addiction

Addiction to food could present a logical explanation for why consumers persist in purchasing and consuming more food than is necessary for survival. Becker and Murphy (1988) develop a theory of rational addiction, which models rationality as a dynamic maximization of utility from stable preferences, and can be used to explain a wide variety of addictive behaviors, including overeating. A consumer is considered rationally addicted if an increase in his current consumption increases both future consumption and marginal utility from future consumption. That is, he takes costs and benefits into account and does not overeat out of some pathological obsession. According to the model, the addicted person reaches an unstable steady state of growing consumption over time. Several researchers apply the rational addiction model to food consumption and provide evidence of rational addiction to caloric intake (Cawley 1999), specific food nutrients (fat, protein, carbohydrates and sodium) with a particularly strong addiction to carbohydrates (Richards et al. 2007), and carbonated soft drinks (Liu and Lopez 2012). Despite the empirical evidence, Auld and Grootendorst (2004) criticize this, raising the difficulty in implying that nearly half of the population of developed countries became addicted to food. They claim that time series data are often insufficient to differentiate rational addiction from serial correlation, and show that rational addiction can be demonstrated even for consumption of non-addictive goods (such as milk and eggs). Therefore, it seems that addiction is not the root cause of individuals losing control of their eating behavior. 3.5

Long-term and Short-term Inconsistencies

While the incidence of obesity has been steadily increasing in Western societies, there has been a parallel rise in sales of healthy low-fat foods and in recreational exercising. It therefore seems that, on the one hand, people become heavier and increase their risk of suffering from diet-related illnesses but, on the other hand, they eat better and have a healthier lifestyle. Mancino and Kinsey (2004) point to this inconsistency and suggest that individuals attempt to incorporate beliefs about healthy eating into their food choices but then forgo good intentions for more immediate gratification owing to situational factors such as time pressure, hunger, and demand for convenience. Their calorie-choice model reveals elements that induce actual behaviors contrary to personal long-term health objectives and self-interest: consuming food prepared away from home, less knowledge about health and nutrition, high opportunity cost of time, and low price savings from food preparation. 3.6

Time Preference

Time preference is equal to the rate at which people are willing to trade current utility for future benefit, and is influenced by various social, cultural, and psychological factors. Time preference can impact current food consumption decisions, as immediate gratification from eating is forgone in order to gain future potential health benefits. A higher rate of time preference can reduce investment in exercise and increase caloric intake, therefore

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Behavioral aspects of obesity 437 an increase in the marginal rate of time preference may be a contributing factor to the rise of obesity. Komlos et al. (2004) build a model in which individuals maximize lifetime utility: T

U (Ct,Ht ( Ht21,It21)) dt,

2st

3e

(24.8)

0

where C is consumption of goods and services, T is expected life time and s is marginal rate of time preference. Health status, H, depends on past investments in health, I, and on past levels of health. The lifetime budget constraint equals the present value of lifetime income: T 2rt

3e

(PctCt 1 PIt,It) ,

(24.9)

0

where r is the market interest rate, Pc is the price of consumption and PI is the price of health investment. The model shows that individuals with high rates of time preference prefer current utility to future potential health benefits. They therefore consume more high-calorie foods and invest less in physical exercise, at the expense of lower levels of health and utility in the future. With saving rate and consumer debt as indicators of the rate of time preference, Komlos et al. (2004) do not rule out a positive link between obesity and the marginal rate of time preference. Smith et al. (2005) provide evidence of a positive link between time preference and BMI among American youth, using savings data as proxies for time preference. They examine the data by gender and find that a higher time preference is associated with a greater mean body weight among men and to a lesser extent among women. Breaking the data down by both gender and ethnicity, they find that time preference is positively associated with BMI among black and Hispanic men and black women. Zhang and Rashad (2008) also find evidence of a positive association between time preference for the present and BMI, particularly for males. Borghans and Golsteyn (2006) conclude that being overweight might be related to the way people discount future health benefits; however, the increase in BMI is more likely explained by shifts in other parameters that determine the intertemporal decisions regarding the trade-off between current health, future health and satisfaction. 3.7

Hyperbolic Discounting

Weight gain could be explained by time-inconsistent present-biased preferences, in the sense of O’Donoghue and Rabin (1999).12 Eating is an immediate-reward activity as it involves immediate gratification, whereas the costs of gaining weight are delayed. Furthermore, weight-loss dieting is an immediate-cost activity as it involves immediate costs, whereas the reward of being slimmer is delayed. However, it is not only that people are impatient, which means they want to receive rewards sooner and delay costs until later; when considering trade-offs between two future points in time, present-biased preferences give stronger relative weight to the earlier point as that point is approached. The presentbias effect affects people in two opposite ways: when actions involve immediate costs, the

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present-bias effect causes people to procrastinate (that is, wait instead of acting); when actions involve immediate rewards, the present-bias effect causes people to act before the proper time. Another behavioral distinction is between naive and sophisticated people. Sophisticated people correctly predict their future behavior. For example, they foresee that they will have self-control problems in the future. Naive people do not foresee their self-control problems. They always believe they will be time-consistent in the future. They plan to behave one way but in fact behave differently. Rosin (2012) presents a model that explains weight cycling, which means a repeated loss and regain of body weight over time, using the above distinctions and assuming that most people are naïve regarding weight-loss dieting as they are incorrectly optimistic about their future behavior.13 The model considers an individual who obtains satisfaction from the consumption of food and other goods, has disutility from being overweight and makes a decision to start a weight-loss diet. The individual is rational in the sense that he compares his costs and benefits and selects the best alternative to maximize his utility. However, his naivety prevents him from foreseeing that the next diets will demand exerting more and more effort. The model shows that dieting efforts do not fully offset a higher initial body weight or metabolic slowdowns attributed to aging and menopause. 3.8

Technological Change

People in developed countries might have gained weight as a result of technological changes that caused overall physical activity to decline. Employment has shifted from manufacturing and mining to services and sedentary jobs that involve less on-the-job exercise, and caloric expenditure in household work has been reduced owing to laborsaving devices. As a result, people must pay for undertaking physical activity, in terms of forgone leisure. Philipson and Posner (1999) claim that technology has lowered the cost of food through agriculture innovation and raised the cost of physical activity, causing more calories to be consumed but fewer calories to be expended. They define utility as: U 5 U(W(F, S), F, C)

(24.10)

where W is weight, F is food intake, S denotes calorie expenditure in physical activity, and C is alternative consumption. Utility is maximized under the budget constraint: C + rF ≤ I,

(24.11)

where r is the relative price of food and I is income. The optimal choice of calories balances the joy of eating, plus the effect of the weight change induced by eating, against the forgone consumption of alternative goods. Beyond a certain caloric intake level, the utility loss from gaining weight dominates the joy of eating. The model demonstrates that obesity is technologically induced, but also predicts that the growth in obesity is selflimiting. As technological change lowers the price of food and thereby frees up time to raise income by other forms of production, weight will not continue to grow indefinitely.

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Behavioral aspects of obesity 439 Leisure devoted to off-the-job exercise may offset the rise in obesity owing to work-related technological change. Cutler et al. (2003) argue that the rise in obesity in the US is primarily a result of increased food consumption rather than reduced exercise. The increase in food consumption itself is related to technological innovations in food production (for example, food processing, food packing, deep freezing, artificial flavors, preservatives, and kitchen appliances such as microwaves) and transportation. Technology has made it increasingly possible for firms to mass prepare food and ship it to consumers for ready consumption, thereby taking advantages of scale economies in food preparation. The result has been a significant reduction in time spent cooking and cleaning at home, thus a reduction in the time costs of food, that has led to increased quantity and variety of foods consumed. Their theory is nicely illustrated by consumption data of potatoes. Before the technological innovations in food production, people usually prepared baked, boiled or mashed potatoes at home. From 1977 to 1995, total potato consumption in the US increased by about 30 percent, accounted for almost exclusively by increased consumption of potato chips and French fries. Cutler et al. (2003) present evidence that the increase in caloric intake mainly came from snacks consumed at home, and also that especially married woman who experienced a large reduction in the time spent preparing food had large increases in BMI. Increases in energy expenditure activities, such as sports or walking, were offset by decreases in energy spent on-the-job and by increases in sedentary activities, such as watching television. The authors reject several theories that explain the trend of rising obesity, and propose a theory based on the division of labor in food preparation. Utility is derived from consumption and lost from being overweight: Ut 5 Ct + U(Kt) − h·Weightt,

(24.12)

where C is consumption of durable composite commodity, K is caloric intake, and the costs that underlie utility loss are linear with slope h. The income constraint is: Ct 5 Y − P·Kt,

(24.13)

where Y is income and P is the cost of food. A rational consumer will consume food until the marginal consumption benefit is equal to the marginal cost. Technological innovation that allows mass preparation of food affects consumption through reducing two variables: the cost of food (including both time and money costs), and the time delay before consumption, which is the time taken to prepare the food. Thus, the benefits of consumption are discounted for that interval of time. Hyperbolic consumers, who are very sensitive to changes in time delay, will gain more weight with further improvements in food technology. Therefore, the dramatic time savings in food preparation, that are supposed to lead to a pure economic benefit, may actually be welfare reducing in the presence of self-control problems. Lakdawalla and Philipson (2009)14 examine the increase in weight that has resulted from technological changes over the last few decades, and decompose it into supply and demand components. They find that about 40 percent of the increase in weight was owing to expansion in the supply of food through agriculture innovation that has lowered food

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prices, while 60 percent was owing to demand factors through more sedentary market and household work. 3.9

Relative Prices

Variations in relative prices may underlie the variations in weight. French et al. (1997, 2001)15 bring some experimental evidence from vending machines, showing that price reduction strategies which change the price differentials between high-fat and low-fat snacks may cause people to alter their consumption behavior. Chou et al. (2004) examine changes in prices of food consumed at home, meals in fastfood and full-service restaurants, cigarettes and alcohol, and show that, controlling for age and race, relative prices affect obesity and explain a substantial amount of its trend: (1) weight rises when relative prices of food at home decline; (2) the demand for convenience food and for unhealthy fast food is to a large extent a response to expanded labor market opportunities for women, which increase the value of household time; and (3) increases in the relative price of cigarettes reduce smoking and contribute to increased body weight. The effects of relative prices on obesity are also examined by Rashad et al. (2006). They find that the rapid increase in obesity over time, especially during the 1980s, is due in part to a great increase in the per capita number of restaurants, and is partly an unintended consequence of the campaign to reduce smoking, as female BMI is responsive to changes in cigarette taxes.16 Courtemanche (2011) finds that increases in gasoline prices are associated with additional walking and a reduction in the frequency with which people eat at restaurants, therefore affecting weight. His estimates imply that 8 percent of the rise in obesity between 1979 and 2004 can be attributed to the concurrent drop in real gas prices. 3.10

Economic Predispositions to Obesity

It is well known that poverty undermines health and that health inequality is linked to income inequalities. Obesity as well is not evenly distributed across socio-demographic groups. In Western countries, the highest rates of obesity are observed among population groups with the lowest levels of education and the highest poverty rates, and in the most deprived areas, especially among women.17 Consequently, obesity may be related to changes in the economic environment over the past few decades, which affect diet structure and diet costs (Drewnowski 2009). Since food insufficiency and overeating seem to contradict each other, researchers tried to explain how it is that obesity often coexists with food insecurity and even with undernutrition. Drewnowski and Specter (2004) find that poverty and food insecurity are associated with lower food expenditures, low fruit and vegetable consumption, and lower-quality diets. They also find an inverse relation between energy density and energy costs, such that energy-dense foods represent the lowest cost option. Moreover Baum and Ruhm (2009) find that excess body weight is inversely related to socioeconomic status at all observed points of the lifecycle and these disparities increase with age. As incomes drop, cheap foods become the best way to provide daily calories. Diets based on refined grains, added sugars and fats are more affordable than diets based on

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Behavioral aspects of obesity 441 lean meats, fish, fresh vegetables and fruits. As the lowering of energy costs through technological innovation has been most marked for highly processed foods containing added sugars and fat, food-insufficient people consume energy-dense nutrient-poor foods, and hence gain weight. Moving to a nutrient-rich and healthier diet implies increasing food expenditures.18 Inequitable access to healthy foods is one mechanism by which socioeconomic factors influence diet and health. Another aspect of poverty is limited accessibility to physical activity. Sallis and Glanz (2006) show that not only do low-income neighborhoods have fewer supermarkets with fresh fruits and vegetables, but they also have fewer recreational facilities and more environmental barriers to physical activity. Smith et al. (2009) propose that weight gain is an optimal physiological and behavioral response to the presence of economic insecurity, just as in ancient times storing food was the response to the presence of food scarcity and starvation risk. Using data on American working-age men, they find that weight gain is positively related to increases in the probability of becoming unemployed and drops in annual income. The mechanism also appears to work in reverse, with health insurance and intra-family transfers protecting against weight gain. Offer et al. (2010) state that affluent market-liberal countries, with high levels of competition, uncertainty, and inequality, tend to have the highest prevalence of obesity. They analyzed 96 body-weight surveys from 11 countries and found that economic insecurity, measured in several different ways, is a much more powerful indicator of economic stress compared to inequality. Smith (2012) further argues that economic stress and economic household-level insecurity promote seeking comfort foods, which are carbohydrate-rich and fattening. In his view, it is no coincidence that obesity has increased in parallel with the rise in economic insecurity in the US and around the world, and that consumption of fast foods and sweetened beverages, in which caloric density is paired with a strong glycemic effect, has increased at the same time. These studies suggest a promising path to better understanding of the causes of obesity and effective measures of dealing with it, and request more research in this direction. 3.11

Information

Unhealthy food choices and weight gain could be explained by information problems, either a lack of information on the nutritional value of food or misleading information on health consequences of poor eating habits. Nayga (2000) finds a significant effect of individual health knowledge on decreasing the probability of being obese. Food labeling is an important source of nutritional information. It may influence health beliefs and purchase intentions, and is expected to help consumers choose more healthful and nutritious diets that can help reduce obesity.19 So far most demographic groups do not seem to benefit from food labeling, at least in terms of body weight. Many countries have implemented mandatory nutrition labeling regulations, yet the trend of rising obesity has continued. However, Variyam and Cawley (2006) show that the release of food-labeling regulations in the US in 1990 were only effective in decreasing body weight among nonHispanic white women. Loureiro et al. (2012) also find that nutritional labels play a role in reducing BMI among

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users of nutritional labels, notably among women, who are considered more frequent users of nutritional labels. They estimate that men who read labels have 0.36 points lower BMI than men who never/rarely read them, whereas women who read labels have 1.93 points lower BMI than women who do not read them. Considering the average American height and weight, these reductions in BMI correspond with a loss of 1.2 kilograms for men and 5.05 kilograms for women. Loureiro et al. (2012) further find a considerable gap in the effect of labels on BMI reduction across races. For women, the highest BMI reduction is seen in white women – those who read labels have a BMI of 2.18 points lower than those who do not read them. The second largest effect of nutritional labels on BMI is seen in black women – those who read labels have 1.10 lower BMI than those who do not read labels. The smallest reduction in BMI is found for women of other races who read labels, with a 0.74 lower BMI than those who do not read labels. These findings imply that nutrition and health education campaigns can employ nutritional-label use as one of the instruments for reducing obesity. Hedley et al. (2004) are concerned with the risk that low-fat labels may lead to overconsumption of nutrient-poor and calorie-rich snack foods by consumers who are already overweight. Wansink and Chandon (2006) show that all consumers in a laboratory overeat more calories of snack food during a single consumption occasion when it is labeled as ‘low fat’. Low fat nutrition claims decrease feelings of guilt and also cause consumers to underestimate calorie content of snacks and increase their perception of the appropriate serving size. These findings are robust across both hedonic (chocolate candies) and utilitarian (granola) snacks, young and old consumers, self-reported nutrition experts and novices, in public and private consumption settings and regardless of whether people serve themselves or not. Interestingly, people who are overweight seem to be more sensitive to low-fat labeling. For normal weight people, low-fat labels increase consumption most with foods that are believed to be relatively healthy, whereas for overweight people, low-fat labels increase their consumption of all foods. The authors further demonstrate that salient objective serving-size information (for example, ‘contains 2 Servings’) reduces overeating among guilt-prone, normal weight consumers but not among overweight consumers. Objective serving-size information prevents normal weight people from overestimating serving sizes and from overeating foods labeled as low fat, nevertheless does not influence overweight people. Cawley (2006) notes that consumers typically have less information about the calorie content of foods they eat away from home, which are exempted from mandatory labeling. Another important source of information is advertising. Advertisements of high calorie food and fast-food restaurants may influence food purchasing patterns and eating patterns of adults and children.20 Smith (2004) hypothesizes that food advertising provides information that once, in the pre-industrial world of food scarcity, served as a signal of nutritional value and product quality: foods that are eaten by others, taste sweet or salty, and are associated with post-ingestive satiety; all these were social and chemical signals that meant that the foods were safe and nutritionally valuable. Our food preferences have been designed by these signals and cannot be altered within the span of a single lifetime. Current food producers, fast-food restaurants have learned to isolate these signals and have featured a variety of sweetened, salty, calorie dense foods.

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Behavioral aspects of obesity 443 Chou et al. (2008) state that in the period during which childhood obesity increased so drastically, the share of fast-food restaurant advertising in total food product advertising has increased (from 5 percent in 1980 to 28 percent in 1997), hence the number of restaurant and fast-food advertisements viewed by children has increased. Chou et al. explore this causal link and find that increasing exposure to fast-food advertising by half an hour per week will increase the probability of children ages 3–11 being overweight by 2.2 percent for boys and by 1.6 percent for girls. This translates to a 15 percent increase in the number of overweight boys in a fixed population, and 12 percent for girls. For adolescent boys and girls ages 12–18, they obtain an increase of 2.5 percent in the probability of being overweight for boys and 0.6 percent for girls. This translates to a 17 percent increase in the number of overweight adolescent boys in a fixed population, and 4 percent for adolescent girls. Grossman et al. (2012) find further that the exposure to fast-food restaurant advertising causes changes in body composition, namely, increases body fat percentage. These results can be used to justify policy measures such as a fast-food restaurant advertising ban on television, or an elimination of the tax deductibility of food advertising costs, which will increase the price of advertising. 3.12

Misperceptions and Consumption Norms

Cues or norms that are present in the environment, such as plate size, package size, variety of foods, eating atmospherics, and the presence of other people, may contribute indirectly to consumption volume by influencing self-monitoring or by suggesting consumption norms. Such effects seem to be relatively automatic and may often occur outside of conscious awareness. Young and Nestle (2002) provide evidence for a significant increase in portion sizes in the US. Food portions began to grow in the 1970s, rose sharply in the 1980s, and have continued in parallel with increasing body weights. Wansink (1996) discusses the ways in which large packages and portion sizes raise consumption. A common explanation is that they cause people to underestimate their consumption and suggest a lower cost per unit. Unfortunately, this explanation does not explain more than 21 percent of the variance in consumption, nor does it explain increased consumption in environments where food is abundant and provided at no charge (such as receptions, parties, and allyou-can-eat buffets). A more robust explanation as to why large packages and portions increase consumption may be because they suggest larger consumption norms. That is, the amount of food on a plate or in a bowl may implicitly, or at least perceptually, suggest what might be construed as an appropriate or an acceptable amount to consume, therefore might determine how much people expect to consume and how much they eventually consume. Using self-refilling soup bowls, Wansink et al. (2005) examined whether visual cues related to portion size influence intake volume, estimated intake, and satiation. This was done by serving soup in bottomless bowls and refilling them through concealed tubing that ran through the table and into the bottom of the bowls. Participants who were unknowingly eating from these self-refilling bowls ate 73 percent more soup than those eating from normal bowls. Moreover, despite consuming more they did not believe they had consumed more nor did they perceive themselves as more sated. It seems as though

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they use their eyes to estimate food intake and not their stomachs. Paradoxically, people who tend to be most focused on food consumption and weight control may be particularly susceptible to such environmental factors. Also in the context of misperceptions, Wansink (2004) reviews psychological mechanisms behind consumption and reveals how various environmental cues influence eating duration and food intake. Aside from hunger, people start eating because they see or smell food, or because they want to be with other people, or because eating provides them with something to do (while watching television or reading). Aside from satiety, people stop eating because of the completion of the meal by others that serves as  external signals that the meal should be over, or when they ran out of food, or because their television program finished. People might continue to eat if they are given more food or more time to eat. Wansink also discusses eating atmospherics, which have an indirect or mediated impact on eating duration and consumption volume. It is known that cold temperatures lead to higher consumption compared with warm temperatures. In addition, dimmed or soft lighting, and soft or preferred music, make it comfortable or enjoyable for a person to spend more time eating, and encourages a slower rate of eating and a longer meal duration. People are less inhibited and less self-conscious, and therefore are likely to consume more food and drinks than they otherwise would. Variyam et al. (2001) estimate the degree of consumer misperception regarding our own diet quality, and find that 40 percent of household meal-planners perceived the quality of their diets to be better than the actual diet quality.

4

CONCLUSION

This chapter presents a number of ways in which overweight and obesity can be caused by decision-making of intelligent boundedly rational individuals. The models and empirical research reviewed in this chapter indicate that obesity is not solely, or perhaps primarily, a medical problem. Diverse factors have been suggested in the literature as possible contributors to rising obesity over time, some of them remain under debate and are still open to further research. An understanding of the causes and consequences of obesity provides us with the theoretical and empirical basis for considering effective policy responses. Decisions are required about whether government intervention is at all justified. This requires, among others, forecasts as to whether obesity rates are likely to continue to rise or to reverse in the future without government regulation. The possible answers determine the role of government in reducing obesity levels and provide input for evaluating the feasibility and cost-effectiveness of different policy measures that might be used, either through prices (taxes and subsidies that change the expected future costs and benefits), or through information (advertising and education campaigns).

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Behavioral aspects of obesity 445

NOTES 1.

2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

The common classification of weight is based on Body Mass Index (BMI), calculated as a person’s weight in kilograms divided by the square of their height in meters (kg/m2). Being overweight is defined as having a BMI from 25 to 30. Being obese is defined as having a BMI of 30 and above. Being extremely obese is defined as having a BMI of 40 and above. For more details, see Rosin (2008). For more on health consequences of obesity, see, for example, Sturm (2002), Sturm et al. (2004), and Flegal et al. (2005). See global health indicators in World Health Organization (2015). For more on economic consequences of obesity see, for example, Ludwig and Pollack (2009), Cawley and Danziger (2005), and Bhattacharya and Bundorf (2005). For more on the impact of obesity on the labor market see, for example, Barkin (2010), Wada and Tekin (2010), Gates et al. (2008), Baum and Ford (2004), Cawley (2004), Zagorsky (2004, 2005), Cawley and Danziger (2005), Averett and Korenman (1996). For further explanations, see Yaniv et al. (2009). For example, an ability to produce more fat tissue from given calorie intakes, a susceptibility to chronic overfeeding or a sensitivity to negative energy balance. Anderson and Butcher (2006) explain that it is difficult to differentiate the parents’ influence between genetics and behavior. Children’s food selection is affected by their parents. In addition, children’s physical activity can be affected by how active their parents are. For a broad survey on the explanations of obesity in the economic literature, see the literature survey of Rosin (2008). ‘Weight conscious’ is largely a synonym for ‘health conscious’. Basal metabolic rate (BMR) reflects the amount of calories needed to sustain life in a resting individual. BMR is determined by physical characteristics (such as gender, age, weight and height) and is actually the largest source of energy expenditure. Identical preferences were used by Laibson (1997), who used the term hyperbolic discounting. Diets always start tomorrow, meaning that people continually delay their diet yet another day. However, while naive people believe that when tomorrow comes, they will really start dieting, sophisticated people know that they are lying to themselves when they say they will start dieting tomorrow. See also Lakdawalla et al. (2005). See also French (2003). For more on the link between smoking and obesity, see, for example, Courtemanche (2009), Fang et al. (2009), and Gruber and Frakes (2006). By contrast, in poor or early societies the obese are relatively wealthier. See also, for example, Drewnowski (2003, 2009), Hayden and Blisard (2008), Shahar et al. (2005), Loureiro and Nayga (2005), and Basiotis and Lino (2002). For more on labeling regulations, see Variyam (2008). Note that in addition to the exposure to advertisements, television watching is a sedentary behavior and provides cues for snacking, hence contributes to weight gain in several ways (see, for example, Taras et al. 1989; Gore et al. 2003).

REFERENCES Anderson, P.M. and K.F. Butcher (2006), ‘Childhood obesity: trends and potential causes’, The Future of Children, 16 (1), 19–45. Auld, M.C. and P. Grootendorst (2004), ‘An empirical analysis of milk addiction’, Journal of Health Economics, 23 (6), 1117–33. Averett, S. and S. Korenman (1996), ‘The economic reality of the beauty myth’, Journal of Human Resources, 31 (2), 304–30. Barkin, S. (2010), ‘The impact of obesity on health and productivity’, Journal of Business and Psychology, 25 (2), 239–45. Basiotis, P.P. and M. Lino (2002), ‘Food insufficiency and prevalence of overweight among adult women’, Nutrition Insights No. 26, July, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA. Baum, C.L. and W.F. Ford (2004), ‘The wage effects of obesity: a longitudinal study’, Health Economics, 13 (9), 885–99.

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Baum, C.L. and C.J. Ruhm (2009), ‘Age, socioeconomic status and obesity growth’, Journal of Health Economics, 28 (3), 635–48. Becker, G.S. and K.M. Murphy (1988), ‘A theory of rational addiction’, Journal of Political Economy, 96 (4), 675–700. Bernheim, B.D. and A. Rangel (2008), ‘Behavioral public economics’, in S.N. Durlauf and L.E. Blume (ed.), The New Palgrave Dictionary of Economics, 2nd edn, Basingstoke: Palgrave Macmillan. Bhattacharya, J. and M.K. Bundorf (2005), ‘The incidence of the healthcare costs of obesity’, NBER Working Paper No. 11303, National Bureau of Economic Research, Cambridge, MA. Borghans, L. and B.H.H. Golsteyn (2006), ‘Time discounting and the body mass index: evidence from the Netherlands’, Economics and Human Biology, 4 (1), 39–61. Cawley, J. (1999), ‘Rational addiction, the consumption of calories, and body weight’, PhD dissertation, Department of Economics, University of Chicago, Chicago, IL. Cawley, J. (2004), ‘The impact of obesity on wages’, Journal of Human Resources, 39 (2), 451–74. Cawley, J. (2006), ‘Markets and childhood obesity policy’, The Future of Children, 16 (1), 69–88. Cawley, J. and S. Danziger (2005), ‘Morbid obesity and the transition from welfare to work’, Journal of Policy Analysis and Management, 24 (4), 727–43. Chou, S.Y., M. Grossman and H. Saffer (2004), ‘An economic analysis of adult obesity: results from the behavioral risk factor surveillance system’, Journal of Health Economics, 23 (3), 565–87. Chou, S.Y., I. Rashad and M. Grossman (2008), ‘Fast-food restaurant advertising on television and its influence on childhood obesity’, Journal of Law and Economics, 51 (4), 599–618. Courtemanche, C. (2009), ‘Rising cigarette prices and rising obesity: coincidence or unintended consequence?’, Journal of Health Economics, 28 (4), 781–98. Courtemanche, C. (2011), ‘A silver lining? The connection between gasoline prices and obesity’, Economic Inquiry, 49 (3), 935–57. Cutler, D.M., E.L. Glaeser and J.M. Shapiro (2003), ‘Why have Americans become more obese?’, Journal of Economic Perspectives, 17 (3), 93–118. Drewnowski, A. (2003), ‘Fat and sugar: an economic analysis’, Journal of Nutrition, 133 (3), 838S–840S. Drewnowski, A. (2009), ‘Obesity, diets, and social inequalities’, Nutrition Reviews, 67 (supplement 1), S36–S39. Drewnowski, A. and S.E. Specter (2004), ‘Poverty and obesity: the role of energy density and energy costs’, American Journal of Clinical Nutrition, 79 (1), 6–16. Fang, H., M.M. Ali and J.A. Rizzo (2009), ‘Does smoking affect body weight and obesity in China?’, Economics and Human Biology, 7 (3), 334–50. Finkelstein, E.A., C.J. Ruhm and K.M. Kasa (2005), ‘Economic causes and consequences of obesity’, Annual Review of Public Health, 26, 239–57. Flegal, K.M., B.I. Graubard, D.F. Williamson and M.H. Gail (2005), ‘Excess deaths associated with underweight, overweight and obesity’, Journal of the American Medical Association, 293 (15), 1861–7. French, S.A. (2003), ‘Pricing effects on food choices’, Journal of Nutrition, 133 (3), 841S–843S. French, S.A., R.W. Jeffery, M. Story, K.K. Breitlow, J.S. Baxter, P. Hannan and M.P. Snyder (2001), ‘Pricing and promotion effects on low-fat vending snack purchases: the CHIPS study’, American Journal of Public Health, 91 (1), 112–17. French, S.A., R.W. Jeffery, M. Story, P. Hannan and M.P. Snyder (1997), ‘A pricing strategy to promote low-fat snack choices through vending machines’, American Journal of Public Health, 87 (5), 849–51. Gates, D.M., P. Succop, B.J. Brehm, G.L. Gillespie and B.D. Sommers (2008), ‘Obesity and presenteeism: the impact of body mass index on workplace productivity’, Journal of Occupational and Environmental Medicine, 50 (1), 39–45. Geiss, L.S., J. Wang, Y.J. Cheng, T.J. Thompson, L. Barker, Y. Li, A.L. Albright and E.W. Gregg (2014), ‘Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980–2012’, Journal of the American Medical Association, 312 (12), 1218–26. Gore, S.A., J.A. Foster, V.G. Dilillo, K. Kirk and D.S. West (2003), ‘Television viewing and snacking’, Eating Behavior, 4 (4), 399–405. Grossman, M., E. Tekin and R. Wada (2012), ‘Fast-food restaurant advertising on television and its influence on youth body composition’, NBER Working Paper No. 18640, National Bureau of Economic Research, Cambridge, MA. Gruber, J. and M. Frakes (2006), ‘Does falling smoking lead to rising obesity?’, Journal of Health Economics, 25 (2), 183–97. Hayden, S. and N. Blisard (2008), ‘Are lower income households willing to budget for fruits and vegetables?’, Economic Research Report No. 54, US Department of Agriculture, Alexandria, VA. Hedley, A.A., C.L. Ogden, C.L. Johnson, M.D. Carroll, L.R. Curtin and K.M. Flegal (2004), Journal of the American Medical Association, 291 (23), 2847–50. Komlos, J., P.K. Smith and B. Bogin (2004), ‘Obesity and the rate of time preference: is there a connection?’, Journal of Biosocial Science, 36 (2), 209–19.

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Behavioral aspects of obesity 447 Laibson, D. (1997), ‘Golden eggs and hyperbolic discounting’, Quarterly Journal of Economics, 112 (2), 443–77. Lakdawalla, D. and T. Philipson (2009), ‘The growth of obesity and technological change’, Economics and Human Biology, 7 (3), 283–93. Lakdawalla, D., T. Philipson and J. Bhattacharya (2005), ‘Welfare-enhancing technological change and the growth of obesity’, American Economic Review, 95 (2), 253–7. Levy, A. (2002a), ‘Rational eating: can it lead to overweightness or underweightness?’, Journal of Health Economics, 21 (5), 887–99. Levy, A. (2002b), ‘A theory of rational junk-food consumption’, Working Paper No. 02-11, Department of Economics, University of Wollongong, Wollongong, NSW. Levy, A. (2003), ‘A theory of LTR junk-food consumption’, Economics Working Paper No. 03-06, University of Wollongong, Wollongong, NSW. Liu, X. and R. Lopez (2012), ‘Evidence of rational addiction to carbonated soft drinks?’, China Agricultural Economic Review, 4 (3), 300–317. Loureiro, M.L. and R.M. Nayga (2005), ‘International dimensions of obesity and overweight related problems: an economics perspective’, American Journal of Agricultural Economics, 87 (5), 1147–53. Loureiro, M.L., S.T. Yen and R.M. Nayga (2012), ‘The effects of nutritional labels on obesity’, Agricultural Economics, 43 (3), 333–42. Ludwig, D.S. and H.A. Pollack (2009), ‘Obesity and the economy: from crisis to opportunity’, Journal of American Medical Association, 301 (5), 533–5. Mancino, L. and J. Kinsey (2004), ‘Diet quality and calories consumed: the impact of being hungrier, busier, and eating out’, Working Paper No. 04-02, The Food Industry Center, University of Minnesota, Minneapolis, MN. Nayga, R.M. (2000), ‘Schooling, health knowledge and obesity’, Applied Economics, 32 (7), 815–22. O’Donoghue, T. and M. Rabin (1999), ‘Doing it now or later’, American Economic Review, 89 (1), 103–24. Offer, A., R. Pechey and S. Ulijaszek (2010), ‘Obesity under affluence varies by welfare regimes: the effect of fast food, insecurity, and inequality’, Economics and Human Biology, 8 (3), 297–308. Ogden, C.L., M.D. Carrol, B.K. Kit and K.M. Flegal (2014), ‘Prevalence of childhood and adult obesity in the United States, 2011–2012’, Journal of the American Medical Association, 311 (8), 806–14. Organisation for Economic Co-operation and Development (OECD) (2014), ‘Obesity update’, accessed 27 March 2016 at http://www.oecd.org/els/health-systems/Obesity-Update-2014.pdf. Philipson, T.J. and R.A. Posner (1999), ‘The long-run growth in obesity as a function of technological change’, NBER, Working Paper No. 7423, National Bureau of Economic Research, Cambridge, MA. Raebel, M.A., D.C. Malone, D.A. Conner, S. Xu, J.A. Porter and F.A. Lanty (2004), ‘Health services use  and  health care costs of obese and nonobese individuals’, Archives of Internal Medicine, 164 (19), 2135–40. Rashad, I., M. Grossman and S.Y. Chou (2006), ‘The super size of America: an economic estimation of body mass index and obesity in adults’, Eastern Economic Journal, 32 (1), 133–48. Richards, T.J., P.M. Patterson and A. Tegene (2007), ‘Obesity and nutrient consumption: a rational addiction?’, Contemporary Economic Policy, 25 (3), 309–24. Rosin, O. (2008), ‘The economic causes of obesity: a survey’, Journal of Economic Surveys, 22 (4), 617–47. Rosin, O. (2012), ‘Weight-loss dieting behavior: an economic analysis’, Health Economics, 21 (7), 825–38. Sallis, J.F. and K. Glanz (2006), ‘The role of built environments in physical activity, eating, and obesity in childhood’, The Future of Children, 16 (1), 89–108. Shahar, D., I. Shai, H. Vardi, A. Shahar and D. Fraser (2005), ‘Diet and eating habits in high and low socioeconomic groups’, Nutrition, 21 (5), 559–66. Smith, P.K., B. Bogin and D. Bishai (2005), ‘Are time preference and body mass index associated? Evidence from the National Longitudinal Survey of youth’, Economics and Human Biology, 3 (2), 259–70. Smith, T.G. (2004), ‘The McDonald’s equilibrium’, Social Choice and Welfare, 23 (3), 383–413. Smith, T.G. (2012), ‘Economic stressors and the demand for “fattening’ foods”’, American Journal of Agricultural Economics, 94 (2), 324–30. Smith, T.G., C. Stoddard and M.G. Barnes (2009), ‘Why the poor get fat: weight gain and economic insecurity’, Forum for Health Economics and Policy, 12 (2), art. 5. Sturm, R. (2002), ‘The effects of obesity, smoking, and drinking on medical problems and costs’, Health Affairs, 21 (2), 245–53. Sturm, R., J.S. Ringel and T. Andreyeva (2004), ‘Increasing obesity rates and disability trends’, Health Affairs, 23 (2), 199–205. Taras, H.L., J.F. Sallis, T.L. Patterson, P.R. Nader and J.A. Nelson (1989), ‘Television’s influence on children’s diet and physical activity’, Journal of Developmental and Behavioral Pediatrics, 10 (4), 176–80. Variyam, J.N. (2008), ‘Do nutrition labels improve dietary outcomes?’, Health Economics, 17 (6), 695–708. Variyam, J.N. and J. Cawley (2006), ‘Nutrition labels and obesity’, NBER Working Paper No. 11956, National Bureau of Economic Research, Cambridge, MA.

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Variyam, J.N., Y. Shim and J. Blaylock (2001), ‘Consumer misperceptions of diet quality’, Journal of Nutrition Education, 33 (6), 314–21. Wada, R. and E. Tekin (2010), ‘Body composition and wages’, Economics and Human Biology, 8 (2), 242–54. Wansink, B. (1996), ‘Can package size accelerate usage volume?’, Journal of Marketing, 60 (3), 1–14. Wansink, B. (2004), ‘Environmental factors that increase the food intake and consumption volume of unknowing consumers’, Annual Review of Nutrition, 24, 455–79. Wansink, B. and P. Chandon (2006), ‘Can ‘low-fat’ nutrition labels lead to obesity?’, Journal of Marketing Research, 43 (4), 605–17. Wansink, B., J.E. Painter and J. North (2005), ‘Bottomless bowls: why visual cues of portion size may influence intake’, Obesity Research, 13 (1), 93–100. World Health Organization (2015), World Health Statistics 2015: Part II, Global Health Indicators, accessed 30 December 2016 at http://who.int/gho/publications/world_health_statistics/EN_WHS2015_Part2.pdf. Yaniv, G., O. Rosin and Y. Tobol (2009), ‘Junk-food, home cooking, physical activity and obesity: the effect of the fat tax and the thin subsidy’, Journal of Public Economics, 93 (5–6), 823–30. Young, L.R. and M. Nestle (2002), ‘The contribution of expanding portion sizes to the U.S. obesity epidemic’, American Journal of Public Health, 92 (2), 246–9. Zagorsky, J.L. (2004), ‘Is obesity as dangerous to your wealth as to your health?’, Research on Aging, 26 (1), 130–52. Zagorsky, J.L. (2005), ‘Health and wealth: the late-20th century obesity epidemic in the U.S.’, Economics and Human Biology, 3 (2), 296–313. Zhang, L. and I. Rashad (2008), ‘Obesity and time preference: the health consequences of discounting the future’, Journal of Biosocial Science, 40 (1), 97–113.

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25 Time inconsistent preferences in intertemporal choices for physical activity and weight loss: evidence from Canadian health surveys Nazmi Sari

1

INTRODUCTION

It has been consistently shown that healthy lifestyles such as participation in sports and exercise, healthy diet, and non-smoking practices give substantial short-term and longterm benefits to individuals. For instance, studies show that physical activity decreases the likelihood of any use of healthcare services (Sari 2009, 2010, 2011, 2014), and improves individuals’ labor market (Lechner 2009; Lechner and Sari 2015) and health outcomes in the short run as well as in the long run (Sari and Lechner 2015). There is similar literature for smoking and healthy diet (US Department of Health and Human Services 2004). Although the benefits of healthy lifestyles including participation in sports and exercise have been widely known, we have an increasing number of people1 who are not following the healthy lifestyles recommended by the national and international health agencies (World Health Organization 2010). Given their direct impacts on individuals’ health (US Department of Health and Human Services 2010; Warburton et al. 2006), why people are not following these recommendations has been an ongoing interest in the research and policy world. Is this simply owing to a lack of interest in improving their health, or a selfcontrol and commitment problem? If individuals are aware of the health consequences of not following the recommended guidelines, does this mean that they are not rational decision makers? The purpose of this chapter is to shed some light on the questions raised above. Using cross-sectional representative health surveys in Canada, I examine individuals’ intentions to improve their health among Canadians adults. Given that a Canadian survey provides specific information regarding the individuals’ intentions to improve their health, we use this dataset. The issue studied here is not specific to the Canadian context, and therefore our findings can easily be generalized to population groups across the developed world. This dataset shows that more than half of the Canadian population aged 20–59 has reported that they will do something to improve their health during the next year. When asked for the specific type of health improvement, 34 percent reported that they will start to or increase exercise during the next year. This is followed by improving diet (11 percent), losing weight (8 percent), and quitting smoking (8 percent). In spite of a substantial proportion of people reporting that they will improve their health during the next year, we have not seen any substantial change in health behavior in the form of higher participation in sports and exercise, decrease in body weight or smoking rate. It seems that individuals, for the most part, fail to follow the plans they make today for a specific period in the future. That is, their future behavior is inconsistent with their optimal plan determined as of today.2 449

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There has been growing literature emphasizing the time inconsistent preferences in the context of intertemporal decision making. As consistently noted in behavioral economics in the context of saving behavior, intertemporal decisions concerning physical activity and weight loss are other examples of time-inconsistent preferences people exhibit that require economic models departing from standard assumptions. For instance, a model of intertemporal choices that is based on an assumption of exponential discounting or not incorporating self-control and commitment problems does not predict time-inconsistent preferences. One approach would be to insist that we should still use economic models with standard assumptions to examine the behavior of individuals regarding their health choices. This approach would simply assume that individuals who exhibit behavior that deviates from the predictions of these models are not rational. Instead of relying on this line of argument, we offer alternative arguments in which individuals still tend to be ‘smart’ in the decision-making process given the constraints and opportunities they face. For instance, individuals who predict that their future selves would deviate from the decisions made for future behavior could choose to use some self-limiting devices for their future selves in order to achieve long-term benefits of their healthy lifestyles. These alternative approaches (that is, incorporating more general discounting, self-control and commitment problems into economic models) that are consistent with time-inconsistent preferences are discussed in section 4 of this chapter. We offer these alternatives as additional directions that can improve our understanding of intertemporal choices in the context of health behavior.

2

BEHAVIORAL INTENTIONS TO IMPROVE PHYSICAL HEALTH AMONG CANADIAN ADULTS

There are specific times of the year that we decide to make commitments to change our behavior in the future (that is, New Year resolutions). Some of these promises could be related to behavioral changes related to healthy eating and improving diet, quitting smoking or participating more in sports and exercise. Although we have anecdotal evidence for this type of behavioral promises, there are no population-based estimates showing the extent of these intentions. In this section, I present evidence from a large sample of Canadians to show the level of promises reported to improve health in general and specifically for diet, smoking and exercise. These are illustrated in Table 25.1 for selected types of health improvement that are intended to be undertaken during the next year following the year of the corresponding health survey. The descriptive statistics reported in Table 25.1 are computed using the Canadian Community Health Survey 3.1 (CCHS 3.1) conducted in 2005. The CCHS is a survey conducted every other year to collect rich information related to various aspects of individuals’ health including general health status, health behavior (that is, diet, exercise, and smoking), and the utilization of healthcare services. The survey also includes detailed variables on socioeconomic and demographic backgrounds of Canadians. The sample includes around 130 000 individuals covering all Canadians aged 12 and older. In addition to standard survey questions administered in each cycle, Statistics Canada includes an additional module to each cycle focused on a specific topic. For Cycle 3.1, there is an addi-

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Time inconsistent preferences in choices for physical activity and weight loss 451 Table 25.1

Reported intention to improve own health by planned actions for the following year (percentage) Intention to improve health

Types of health improvement More exercise

Diet

Lose weight

Quit smoking

All (ON, MB, NFL)

53

34

11

8

8

Sex Female Male

56 50

36 31

12 10

10 6

7 9

Marital status Married/common law partner Not married

54 53

35 32

11 11

9 7

7 9

Household income Less than $15 000 $15 000–$29 999 $30 000–$49 999 $50 000–$79 999 $80 000 and above

51 50 53 56 57

29 29 32 36 38

10 9 10 11 11

8 8 9 9 8

11 10 9 8 7

Education Less than secondary graduate Secondary graduate Some post-secondary Post-secondary graduate

45 50 59 56

23 30 36 37

7 10 12 11

8 8 7 8

12 9 11 6

Notes: The results reported in the table are my own computation based on a sample that includes all survey respondents aged 20–59, who live in ON, MB, or NFL. These are the participants’ completed optional module of the CCHS 3.1.

tional module comprising a section related to individuals’ perceptions, and intentions/ plans to improve their health during the following year. This section of the survey was asked of the survey participants in three provinces (Ontario, Manitoba, Newfoundland and Labrador – NFL), that is a large sub-sample, and representative for the population in these three provinces (Statistics Canada 2005). There are two sets of questions in this module that are particularly relevant to the topic of this chapter. The first question was asked of all participants of this module: ‘Is there anything you intend to do to improve your physical health in the next year?’ For those respondents who reported an affirmative answer to this question, there are a set of questions regarding the types of actions that the respondents have planned to do to improve their health. Eight explicit types of actions that are listed in the follow-up question in order are: (1) start/increase exercise, sports and physical activity, (2) lose weight, (3) improve eating habits, (4) quit smoking, (5) drink less alcohol, (6) reduce stress level, (7) receive medical treatment, (8) take vitamins, and other (needs to be specified by the respondent). The survey respondents were asked to answer each follow-up question independently (that is, ‘Do you intend to start/increase exercising to improve your health in

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the next year?’). Therefore, the respondents could have multiple action plans to improve their health (that is, exercising, losing weight and improving their diet). Based on these survey questions, I computed the percentage of individuals who reported that they intended to take action to improve their health. This is shown in the first column of Table 25.1 for individuals aged 20–59. For specific types of actions, I report the results for the top four action types (exercising more, improving diet, losing weight, and quitting smoking). Table 25.1 presents health improvement intentions by types of actions for all participants as well as by selected socioeconomic and demographic characteristics. The first particularly striking observation is that more than half of the population aged 20–59 has reported that they will do something to improve their health during the following year. When asked by types of specific actions that will be undertaken, 34 percent reported that they will start to or increase exercise more during the next year. This is followed by improving diet (11 percent), losing weight (8 percent), and quitting smoking (8 percent). When we examine it by selected set of socioeconomic characteristics, we can observe that females and individuals with higher income and education are more likely to report that they intend to improve their physical health. In order to examine underlying factors, and to understand major determinants of health improvement intentions, I estimate probit models using two dependent variables, and present the results in Table 25.2. The table shows the regression results from probit models that examine the determinants of intention to improve physical health for age group 20–59. In the first model, the dependent variable is whether the individual intends to do something to improve physical health during the course of the next year. The second model, however, is specific to physical activity. In this case, the dependent variable measures the individual’s intention to start or increase participation in sports and exercise to improve his or her physical health. In both models, we use location, and the most important socioeconomic and demographic characteristics as independent variables. Table 25.2 shows the estimated coefficients and corresponding p-values for statistical significance of the coefficients. The results from the probit models show that some of the factors such as location, being an immigrant and income have no association with individuals’ intentions to improve their physical health. However, other factors such as age, being male or level of education have statistically significant associations. While males (about 8 percentage points), whites (2–3 percentage points), or married people (1.5 percentage points) are less likely to report that they have any intention to do something (or specifically exercise more) to improve their health, more educated people are 5–10 percentage points more likely to have any intention to improve their health during the next year. The results in these regressions are consistent with the descriptive statistics. Among all factors mentioned above, level of education and being male is one of the factors consistently associated with individuals’ intentions to do something to improve their health. As shown in this section, the majority of Canadians plan to do something to improve their health during the next year, and the intention to increase physical activity is at the top of their action plan. Given that 34 percent of Canadians living in one of the three provinces report that they will increase or start to exercise during the next year, this should lead to a considerable reduction in insufficient participation in physical activity, and body weight. Is this what we observe based on population surveys on physical activity and body weight? This issue is examined in the following section.

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Time inconsistent preferences in choices for physical activity and weight loss 453 Table 25.2

Determinants of intention to improve physical health: results from probit regressions Dependent variable: Improve health ME

p-value

Dependent variable: More exercise ME

p-value

Province (reference group: Newfoundland and Labrador) Ontario −0.013 Manitoba 0.004

0.28 0.78

−0.011 0.005

0.46 0.77

Age (reference group: 20–24 age group) 25–29 30–34 35–39 40–44 45–49 50–54 55–59

0.78 0.55 0.47 0.14 0.42 0.05 0.07

−0.009 −0.033 −0.031 −0.055 −0.046 −0.066 −0.044

0.60 0.06 0.07 0.00 0.01 0.00 0.02

Education (reference group: less than secondary school graduate) Secondary graduate 0.009 0.49 Some post-secondary 0.059 0.00 Post-secondary graduate 0.048 0.00

0.041 0.097 0.108

0.02 0.00 0.00

0.004 −0.008 −0.010 −0.020 −0.011 −0.028 −0.026

Household income (reference group: less than $15 000) $15 000–$29 999 −0.023 $30 000–$49 999 −0.009 $50 000–$79 999 0.009 $80 000 or more 0.008

0.16 0.57 0.55 0.59

−0.028 −0.003 0.032 0.036

0.19 0.88 0.10 0.07

Other socio-demographic variables Male Married/common law partner Immigrant White Household size

0.00 0.05 0.60 0.02 0.42

−0.089 −0.010 0.010 −0.030 0.003

0.00 0.32 0.44 0.02 0.37

Sample size

−0.067 −0.015 −0.005 −0.023 0.002 19869

14471

Note: ME stands for marginal effects.

3

BODY MASS INDEX (BMI) AND PARTICIPATION IN SPORTS AND EXERCISE IN CANADA

The previous section presents evidence from the population-based survey in regard to Canadians’ intentions to change or modify their unhealthy behavior. Given that these promises have implications for body weight and participation in exercise and sports activities, I present the pattern of changes in body weight and participation in sports and exercise in Canada during the last decade.

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The primary dataset for this analysis is various cycles of the Canadian Community Health Survey, a cross-sectional survey conducted in 2001, 2003, 2005, 2007 and 2008 with a sample size of more than 100 000 individuals. The survey has detailed questions related to the participation in sports and exercise and other health behaviors (that is, dietary details, smoking and alcohol consumption), as well as health-related information (BMI, chronic conditions, and self-reported health). There is a series of questions in the CCHS related to 23 types of leisure time physical activities (LTPAs). The participants have been asked whether they have participated in each activity during the past three months. For those individuals who have participated in the corresponding LTPA, there are follow-up questions related to the frequency (episode) and the typical duration of each episode. Using this detailed information, a summary variable for total daily energy expenditure from all LTPAs is computed and included with the survey. This variable is calculated by multiplying hours of daily activity by the equivalent energy expenditure from this activity, expressed as daily kilocalories (kcal) per kilogram (kg) of individual’s body weight.3 Then the daily energy expenditure from each LTPA is summed over all LTPAs to obtain the summary measure. This summary variable is used to compute the pattern of participation in sports and exercise in Canada from 2001 to 2008. The participation pattern in sports and exercise is displayed for the Canadian adult population (Figure 25.1) by men and women. The vertical axis indicates the total daily energy expenditure (kilocalories) from all types of LTPAs per kilogram of body weight while the horizontal axis shows the corresponding year that

2.5

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0 2001

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Figure 25.1

Participation in LTPA for adult men and women in Canada

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Time inconsistent preferences in choices for physical activity and weight loss 455 the information is estimated. For those years that the information is not available, the values are linearly interpolated. Figure 25.1 presents that the level (intensity) of physical activity is higher for men than women in all years suggesting that there are substantial variations between men and women in terms of their participation in sports and exercise. As displayed here, and also consistently shown in the literature, men are more active than women. The figure indicates that for both groups physical activity has an increasing trend in the period 2001–03. Then it stays the same until 2005 and then shows a declining trend after 2005. This declining trend is very clear from 2005 to 2007 for men and women, suggesting that there seems to be some inconsistency between Canadians’ intentions related to their health behavior (discussed in the previous section), and their actual behavior. Overall intensity of physical activity is slightly higher than the benchmark daily energy expenditure of 1.5 kcal.kg−1 recommended by the agencies such as the Centers for Disease Control and Prevention (CDC), and the American College of Sports Medicine (ACSM). This recommended benchmark level of physical activity that is also consistent with the World Health Organization (WHO) guidelines corresponds to about a ‘30 minutes or more of moderate intensity physical activity on most, preferably all, days of the week’ (Pate et al. 1995: p. 404; for an update and clarification on 1995 recommendations, see Haskell et al. 2007). For the same period, I also present the BMI that is measured by an individual’s body weight in kilograms divided by the square of an individual’s height in meters. This trend in BMI is illustrated in Figure 25.2 in which the vertical axis indicates the BMI while the horizontal axis shows the corresponding year in which the information is estimated. I follow the same approach and linearly interpolate the values for those years that the information is not available. There are two conclusions that can be reached with data illustrated in Figure 25.2. The first is about the differences in BMI between men and women. The figure shows that men are heavier than women during this period, and the difference stays the same. The second observation, which is more striking, is related to the alarming public health implications of this issue. Figure 25.2 shows that the BMI is stable at around 25, with an upward trend over time reaching 26.6 for men and 25.1 for women in 2008. This estimate suggests that, on average, Canadian men and women have an overweight problem that is alarming given that obesity is identified as an important public health issue around the world. As presented in Figure 25.1 and Figure 25.2, participation in sports and exercise, as well as the BMI, in Canada have not changed substantially. The change in BMI or participation in sports and exercise over time that is not even substantial has not aligned with the intentions of health improvement reported by Canadians as presented in the previous section. We observe that the level of physical activity has not increased, and the BMI has not decreased over time. This aggregated information in Figure 25.1 and Figure 25.2 has been illustrated for the entire country. As we described in section 2, the data for the level of health improvement intentions come from three provinces (Ontario, Manitoba and NFL) in 2005. Therefore, to be consistent with the data period, and sub-sample, I present the sports and exercise participation as well as the BMI in 2005 and the next available year (2007) using two cycles of the CCHS. These results are presented in Table 25.3. Table 25.3 presents the participation in physical activity and BMI for those aged 20–59. The upper panel of the table provides this information for the entire country while the lower panel shows it for those living in Ontario, Manitoba or NFL. As shown in the

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Figure 25.2

Table 25.3

Body Mass Index for adult men and women in Canada

Physical activity and body mass index in Canada (aged 20–59) Average in CCHS 3.1 (1)

Average in CCHS 4.1 (2)

Difference (2) – (1)

p-value for mean difference test

All Canada Physical activity Body Mass Index (BMI)

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0.00 0.00

Note: The indicator for physical activity shows the daily kilocalories burned from all types of LTPAs measured by per kilogram of body weights of individuals. The bottom panel of the table shows the results for the residents in Ontario (ON), Manitoba (MB), and Newfoundland and Labrador (NFL). The right-hand column shows the corresponding p-values for the mean difference test for the average differences between cycles 4.1 and 3.1.

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Time inconsistent preferences in choices for physical activity and weight loss 457 table, both for the entire country and for the three provinces, the level of physical activity has decreased from 2005 to 2007 while the BMI has increased in the same period. The difference in change is comparable between the entire country and the three provinces listed above. As indicated in the last column of the table, the change from 2005 to 2007 is statistically significant. The table shows that the BMI increased (rather than decreased) by 0.25 points (0.95 percent increase), while the intensity of participation in sports and exercise decreased by 3.23 percent. As we can see, the direction of the change from 2005 to 2007 is not consistent with the large proportion of Canadians in the three provinces reporting that they have intentions to do something including physical activity, diet, and weight loss to improve their health during the next year. Our estimates for these relevant health outcomes do not support the fact that individuals keep their promises regarding health improvement intentions.4 In fact, the evidence reported in the table suggests that the individuals’ actual behavior is not consistent with their intentions about their behavior reported a year ago. This is a good example for time inconsistent preferences in which individuals deviate from their earlier decisions made for that time period when that time arrives, and have a tendency to pursue immediate gratification at the expense of long-term health benefits. There are series of questions arising from this inconsistency that cannot be explained using a standard economics models. Does this inconsistency imply that individuals are not optimizing, and therefore are not rational? Could this still be decision making consistent with the rationality assumption of economic theory but inconsistent with standard economic modeling? Are they simply anomalies that should be ignored for the field of economics or are there lessons that we can all learn from this inconsistency? These are just a subset of interesting questions that can be articulated better elsewhere. The direction that I follow in the next section is to offer explanations derived from behavioral economics that provide alternative ways to model individual choices which conforms to the observed time-inconsistent preferences.

4

ALTERNATIVE APPROACHES CONSISTENT WITH TIMEINCONSISTENT PREFERENCES

In this section, I offer explanations in which the case described above could still be rational decision making in spite of its inconsistency with standard economic models. The source of the inconsistency could be related to the simplifying assumptions continuously made in standard economic models. For instance, discount rates and individuals’ preferences are assumed to be constant when modeling intertemporal decisions. As illustrated in behavioral economics literature, the models with these assumptions do not fit well with observed behavior and experimental data. Among several alternative explanations that would explain the behavior described in the previous section, I restrict the discussion related to discounting, present-day bias and self-control and commitment issue. The purpose is to offer a set of alternative approaches in which the decision making of the individuals for the scenario described in the previous section could still be rational in spite of its inconsistency with the standard models in economic theory. The aim, however, is not to provide a comprehensive review of this literature but to derive some lessons from the field of behavioral economics for the economics of physical activity, and obesity.

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4.1

Discounting and Time Inconsistency

Discounted utility models (DMUs) have been widely used in economics to study individuals’ behavior that requires potential trade-off for costs and benefits not necessarily accrued in the same time period. In these models, individuals are assumed to maximize their present discounted sum of utilities with utility in each period depending on consumption of goods and services. In standard models on intertemporal decisions, it is common practice to use a constant discount rate and to weigh the future utilities by an exponentially declining discount factor. As also shown for other types of consumer behavior, the data presented in the previous section regarding physical activity, diet, and weight loss do not support an exponentially declining constant discount rate assumption (for more discussion on this issue see Loewenstein and Prelec 1992; Frederick et al. 2002; Rabin 2002; Nyhus and Webley 2006). Despite its lack of applicability to real-life choices requiring intertemporal decision making, this assumption has still been widely used in economics. Among others, Thaler (1981) also examined whether the discount rate is constant, using a sample of students at the University of Oregon. In his experiment, the students were instructed to imagine that they had won some money in a lottery held by their bank. They were given a choice to take the money now or to wait and earn interest. The experiment had follow-up questions asking the subjects the amount of money that they would require to make waiting just as attractive as getting the money now. Based on the responses, Thaler computed implicit discount rates for different money amounts and time delays. The results showed that implicit discount rates decrease as the length of time delay increases. Following Thaler’s contribution, others also studied the same issue and showed that discount rates decrease with an increase in time delay (see Frederick et al. 2002 for a comprehensive review of this literature). As shown in economics experiments as well as in cognitive psychology literature, the predictions regarding intertemporal choices made based on utility functions with constant discount rates are inconsistent with what we observe in real-life applications. In an earlier work, Strotz (1956) is one of many who emphasizes the time-inconsistent preferences, and incorporates alternative discount function in his model. The model developed in his paper predicts that an individual deciding now for future consumption of goods and services could deviate from her optimal plan. As a result, the individual’s future behavior becomes inconsistent with her optimal plan determined as of today. Given that the discount function in his model depends on the time distance of the future date from the present moment, it shifts due to a change in the time distance. As a result, individuals may deviate from their optimal plan decided now for the future date. As he argued ‘there is nothing patently irrational about the individual who finds that he is in an intertemporal tussle with himself except that rational behavior requires he takes the prospects of such a tussle into account’ (Strotz 1956, p. 171). As clearly indicated here, a rational individual is expected to take this conflict or ‘tussle’ into account when making intertemporal decisions. In the context of exercise and diet, this becomes especially relevant given that the individuals face a conflict between short-term temptations, and long-term health goals. We will discuss this in the context of self-control and commitment after introducing present-day bias and hyperbolic discounting as an alternative form of discount function in economic models.

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Time inconsistent preferences in choices for physical activity and weight loss 459 4.2

Present Biased Preferences and Hyperbolic Discounting

As consistently argued in the recent literature, a simple hyperbolic time discounting fits well with experimental data as opposed to standard exponential discounting (Laibson 1997; O’Donoghue and Rabin 1999). Laibson states that hyperbolic discount functions are characterized by a relatively high discount rate over short horizons and a relatively low discount rate over long horizons. It therefore creates a conflict between today’s preferences, and the preferences of the future. The discount rate between time t and t + 1 does not stay the same but it changes based on the time distance to time t. For example, from today’s perspective the discount rate between time t and t + 1 is the long-term low discount rate, but it becomes the short-term high discount rate from time t’s perspective. As a result, the change in discount rate creates contradiction in the way that the decisions are made for the same time period. The decision made for time t + 1 as of today becomes suboptimal for the individual at time t. Therefore, the individual may deviate at time t from the decision made as of today for time t + 1. Although it is not present in standard discount function, hyperbolic time discounting creates a time inconsistency in intertemporal choices owing to systematic changes in preferences. Given the same information, and the choice set, an individual with exponential discount function makes the same decisions prospectively with the one that she would make when the decision actually arrives. It is not the same with someone who has hyperbolic time-inconsistent discounting. The individual who makes a decision for the future takes far-sighted actions; but when the future arrives he or she behaves against his or her earlier decisions, and pursues immediate gratification (that is, following an unhealthy diet, and avoiding exercising) rather than long-run well-being.5 This behavioral shift fits well with individuals’ behavior related to sport and exercise, and diet. Hyperbolic discounting, therefore, is a more consistent assumption than the widely used exponential discounting assumption to study individuals’ preferences for participation in sports and activity and behavioral change to improve their health.6 4.3

Self-control and Commitment Problem

Although hyperbolic discounting would be an improvement over exponential discounting in explaining time-inconsistent intertemporal decisions discussed above, this may not be sufficient to analyze and explain individuals’ intertemporal behavior. The discussions related to present-biased preferences predict that a path of action that will take place at t + 1 seems utility enhancing as it is chosen at time t, but it becomes not so when the decision is re-analyzed at time t + 1. Then the relevant question to ask would be why would you make this type of intertemporal choices at time t given that they might know (perhaps with earlier experience) that they will not commit to the path of action that they have decided at time t for time t + 1. There is a growing theoretical literature to deal with this issue by incorporating self-control and commitment/temptation into economic models. We will provide a summary of the general approach below. Although incorporating hyperbolic discounting would be a potential way to improve economic models in studying intertemporal choices, we also need to consider self-control and temptation/commitment as additional issues to be included in economic models to explain and make predictions in decision making when it has immediate costs and only

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long-term benefits. While it might be relevant with many other intertemporal consumption decisions, it certainly is the case for health behaviors, that is, exercise and diet that have immediate costs with potential long-term health benefits. Several earlier studies have incorporated self-control and commitment issues to model economic decision making with present-biased preferences. In these models, hyperbolic discounting is assumed with exponential discounting being a special case. In addition to that, the general strategy is to model time-inconsistent preferences as if there are ‘separate agents’ choosing individual’s current behavior to maximize his or her current utility based on his or her current preferences, and letting the future selves to control his or her future behavior. In these models that incorporate self-control into economic decision making, a person is assumed to maximize lifetime utility based on his or her preferences today and predicted preferences for the future selves. As a result, a path of action decided at time t for time t + 1 could be followed if the future selves would obey the decision made today. That is, it depends on the power of the prediction for true preferences of future selves (that is, whether the present selves correctly predict what future selves might want to do), or the level of awareness about his or her self-control problem, and therefore the mechanisms developed to follow the path of action determined today. There are several alternative assumptions introduced in economic modeling to incorporate the idea of ‘separate agents’ to economic models. As illustrated in O’Donoghue and Rabin (1999), it can be modeled based on two extreme assumptions for the person’s awareness of his or her self-control problem: (1) a person fully knows his or her selfcontrol problem (that is, has perfect knowledge about preferences of his or her future selves), therefore may commit/limit himself or herself to a smaller set of future choices through binding (legally or socially) pre-commitments – in this framework, they are called sophisticated individuals; (2) at the other extreme, a person is assumed to be naive, that is, she believes that her future preferences will be identical with those he or she has now. Even after failing at time t + 1 to follow the path of action decided at time t for time t + 1, he or she does not learn from this shift in his or her future preferences, therefore still stays optimistic about his or her self-control problem that the future selves would impose on him or her. There are, of course, continuums of other types in between. In an alternative but similar approach, Shefrin and Thaler (2004) introduce a framework in which a person is assumed to have two sets of coexisting and mutually inconsistent preferences: one concerned with long-run (planner) and the other with short-run (doer) outcomes of the choices made. We could also interpret this in the framework of the ‘separate agents’ model mentioned above. In this comparison, the sophisticated individual would be the one with dominant ‘planner’ preferences while the naive individual would be the one with dominant ‘doer’ preferences.7 There are other possibilities in between these two extreme assumptions. A person could understand the self-control problem but systematically underestimate the degree of present-biased preferences, and therefore consistently fail to follow a path of action decided at time t for time t + 1. In the case of health behavior mentioned in this chapter (more exercise, quitting smoking or following a diet), people may fail to follow their intention to improve their health because of being overly optimistic about their future selves’ commitment for healthy behavior in the future. This intention to put the behavioral change off to the next period could accumulate substantial delay costs (that is, obesity and developing chronic diseases) from postponing it regularly. In order to deal with their commitment

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Time inconsistent preferences in choices for physical activity and weight loss 461 and self-control problem, individuals with sophisticated preferences may choose to use various devices to self-limit themselves for a path of action decided at time t for time t + 1. For those individuals interested in improving their health by exercising more and losing weight, there are several market products available (fat farms, all-inclusive resorts, or intense boot camps, and exercise classes) that offer a variety of packages to meet individuals’ needs. These devices simply offer pre-packaged programs with diet and exercise focus for a certain time period (from a week to a few months) at a certain location (hotel or a resort) simply to enforce a set of behavioral changes. For these types of services, individuals are ready to pay a substantial amount of money for what, we could argue, individuals could do without enrolling in these programs. It is hard to argue that such decision making is not rational, although it is a decision against the preferences of future selves. A sophisticated individual with planner preferences would make self-limiting decisions for their future selves in order to achieve long-term benefits, and there is nothing irrational about this decision making despite its inconsistency with narrowly defined understanding of economic theory. This is precisely the point stated in Strotz (1956): that a rational individual who aims to achieve long-run health benefits takes the possibility of a deviation from the path of action decided today in the future by their future selves into account.8

5

CONCLUDING REMARKS AND IMPLICATIONS

In this chapter, I provide evidence from attitudes related to physical activity and obesity in Canada to show that time-inconsistent preferences are a widespread phenomenon among Canadian adults. As presented in the chapter, about 55 percent of the people surveyed stated that they will do something to improve their health during the next year. In the follow-up question regarding the type of specific actions to be undertaken, about 65 percent of those reported that they will start to or increase exercise during the next year. This is followed by reported intention to improve their diet and lose weight (about 40 percent). Given the substantial level of reported intention to increase exercise, improve diet, and lose weight, we would expect an observable change in the level of activity and body weight of this population group during the year following the survey. However, this is not what we find in the data, suggesting that time-inconsistent preferences are more widespread phenomena than we imagined. Following this observation in the data, we offer alternative ways to incorporate this behavioral shift into economic models. As indicated in the previous section, we could learn more from models taking present-day bias, self-control and commitment issues into account when studying economics of health behavior owing to its intertemporal nature. These alternative models allow us to examine the possibility of individuals’ deviation from predicted behavior based on models with unrealistic assumptions related to preferences. They are also useful for studying the role of public policy in the context of sports and exercise and healthy lifestyle choices. As discussed in the previous section, there are continuums of consumers with preferences varying from naive to sophisticated, and the sophisticated individuals would make self-limiting decisions for their future selves in order to achieve long-term benefits. For other individuals who do not use self-limiting devices, there could be mechanisms in which healthy behavior could be encouraged through group-based approaches specifically designed to achieve behavioral changes. One strategy

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in these group-based approaches would be to explore potential effects of social interactions (social mechanism) for behavioral changes (Leibenstein 1950; Bernheim 1994). Examples are community-level organized sports and exercise activities, and amateur and community level sports leagues. There could be other effective policies that aim to help the people understand their degree of optimism, and to help them to develop strategies to achieve behavioral changes to improve their long-term health outcomes. Further research studies to uncover the effectiveness of alternative social mechanisms developed to encourage behavioral changes would certainly be needed, and have the potential to extend our understanding on this particularly relevant public health issue.

NOTES 1. For instance, in Canada, the UK and the USA about two-thirds of people are not meeting the recommended level of physical activity (World Health Organization 2010). 2. What is not known is whether individuals express their ‘true’ preferences in survey data. It is likely that there could be reporting bias in survey data regarding their preferences. 3. The equivalent energy expenditure from the corresponding activity is calculated using the corresponding metabolic rate (MET) for each activity type. The METs are multiples of the resting rates of oxygen consumption during the activity. For instance, 1 MET represents the approximate rate of oxygen consumption of a body at rest, and the equivalent energy expenditure of 1 MET is 1 kilocalorie in an hour per kilogram of individual’s body weight (kcal.hr−1.kg−1). 4. Given that the data reported here come from cross-sectional representative surveys, we do not compare the same individuals’ reported intentions and actual behavior in the next year. The samples in both years, however, are representative for the corresponding provinces; therefore, we would expect to observe a sizeable change in direction that would be consistent with such a large proportion of the population reporting that they have intentions to do something to improve their health in the next year. 5. It is likely that individuals are constrained by time owing to work- and non-work-related obligations, as well as income. The deviations from earlier decisions could be due to these constraints but not a shift in their preferences. 6. In an interesting paper, Rubinstein (2003) offers an alternative explanation to understand time inconsistent choices made by the individuals. He presents experimental evidence to show that they are incompatible with the hyperbolic discounting while compatible with the procedural approach that is defined as decision makers’ approach to identify similarity relations in determining the dominance of one option over others. 7. This idea is similar to the two-system dichotomy introduced in Kahneman (2011) in which he argues that there are two systems simultaneously operating in our mind. They can be considered as two agents with their individual abilities, limitations, and functions. As system 1 operates quickly and with little effort, system 2 operates more carefully by allocating attention to the effortful mental activities. 8. We could only take this into account if he or she can correctly predict the preferences of future selves. If we cannot make correct predictions about the future, then the possible deviations would not be considered irrational acts.

REFERENCES Bernheim, B.D. (1994), ‘A theory of conformity’, Journal of Political Economy, 102 (5), 841–77 Frederick, S., G. Loewenstein and T. O’Donoghue (2002), ‘Time discounting and time preference: a critical review’, Journal of Economic Literature, 40 (2), 351–401. Haskell, W.L., I.M. Lee, R.R. Pate, K.E. Powell, S.N. Blaire, B.A. Franklyn et al. (2007), ‘Physical activity and public health: updated recommendation for adults from the American college of sports medicine and the American heart association’, Medicine and Science in Sports and Exercise, 39 (8), 1423–34. Kahneman, D. (2011), Thinking Fast and Slow, Ottawa: Doubleday Canada. Laibson, D. (1997), ‘Golden eggs and hyperbolic discounting’, Quarterly Journal of Economics, 112 (2), 443–77 Lechner, M. (2009), ‘Long-run labour market and health effects of individual sports activities’, Journal of Health Economics, 28 (4) 839–54

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Time inconsistent preferences in choices for physical activity and weight loss 463 Lechner, M. and N. Sari (2015), ‘Labor market effects of sports and exercise: evidence from Canadian panel data’, Labor Economics, 35 (August), 1–15 Leibenstein, H. (1950), ‘Bandwagon, snob, and Veblen effects in the theory of consumers’ demand’, Quarterly Journal of Economics, 64 (2), 183–207 Loewenstein, G. and D. Prelec (1992), ‘Anomalies in intertemporal choice: evidence and an interpretation’, Quarterly Journal of Economics, 107 (2), 573–97. Nyhus, E. and P. Webley (2006), ‘Discounting, self-control, and saving’, in M. Altman (ed.), Handbook of Contemporary Behavioural Economics, New York: M.E. Sharp, pp. 297–325. O’Donoghue, T. and M. Rabin (1999), ‘Doing it now or later’, American Economic Review, 89 (1), 103–24. Pate, R.R., M. Pratt, S.N. Blair, W.L. Haskell, C.A. Macera, C. Bouchard et al. (1995), ‘Physical activity and public health: a recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine’, Journal of the American Medical Association, 273 (5), 402–7. Rabin, M. (2002), ‘A perspective on psychology and economics’, European Economic Review, 46 (4–5), 657–85. Rubinstein, A. (2003), ‘Economics and psychology? The case of hyperbolic discounting’, International Economic Review, 44 (4), 1207–16. Sari, N. (2009), ‘Physical inactivity and its impact on healthcare utilization’, Health Economics, 18 (8), 885–901. Sari, N. (2010), ‘A short walk a day shortens the hospital stay: physical activity and the demand for hospital services for older adults’, Canadian Journal of Public Health, 101 (5), 385–9. Sari, N. (2011), ‘Does physical exercise affect demand for hospital services? Evidence from Canadian panel data’, in P.R. Guerrero, S. Kesenne and B.R. Humphreys (eds), The Economics of Sport, Health and Happiness: The Promotion of Well-being through Sporting Activities, Cheltenham, UK and Northampton, MA, USA: Edward Elgar Publishing, pp. 81–100. Sari, N. (2014), ‘Sports, exercise and length of stay in hospitals: is there a differential effect for chronically ill people?’, Contemporary Economic Policy, 32 (2), 247–60. Sari, N. and M. Lechner (2015), ‘Long-run health effects of sports and exercise in Canada’, Canadian Center for Health Economics Working Paper No. 150018, University of Toronto. Shefrin, H.M. and R.H. Thaler. (2004), ‘Mental accounting, saving and self-control’, in C.F. Camerer, G. Loewenstein and M. Rabin (eds), Advances in Behavioral Economics. Princeton, NJ: Princeton University Press, pp. 395–428. Statistics Canada (2005), ‘Canadian Community Health Survey 3.1’, accessed 10 January 2016 at http://www23. statcan.gc.ca/imdb/p2SV.pl?Function5getSurvey&Id522642. Strotz, R.H. (1956), ‘Myopia and inconsistency in dynamic utility maximization’, Review of Economic Studies, 23 (3), 165–80. Thaler, R. (1981), ‘Some empirical evidence on dynamic inconsistency’, Economics Letters, 8 (3), 201–7. US Department of Health and Human Services (2004), The Health Consequences of Smoking: A Report of the Surgeon General, Atlanta, GA: US Department of Health and Human Services, Office of the Surgeon General. US Department of Health and Human Services (2010), The Surgeon General’s Vision for a Healthy and Fit Nation, Rockville, MD: US Department of Health and Human Services, Office of the Surgeon General. Warburton, D., C.W. Nicol and S.S.D. Bredin (2006), ‘Health benefits of physical activity: the evidence’, Canadian Medical Association Journal, 174 (6), 801–9. World Health Organization (WHO) (2010), Global Recommendations on Physical Activity for Health, Geneva: WHO Publications.

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26 Suicide among smart people Bijou Yang and David Lester

This chapter reviews what impact intelligence might have on suicide from the perspective of behavioral economics. We see, in this chapter, that suicide is less common in those who are smart as measured by intelligence tests. Thus, suicide does not appear to be a ‘smart’ decision, and this is in line with the predominant view of suicide as the result, in part, of psychiatric disorder and irrational thinking. The research on the association between intelligence and suicide1 is examined first, followed by a review of two behavioral economic theories of suicide, namely, Hamermesh and Soss’s utility maximization theory of suicide and Yang and Lester’s cost–benefit theory of suicide, in order to see whether intelligence would be expected to have an impact on suicide as an outcome. Next, the evidence that delay-discounting is associated with both suicidal behavior and intelligence is reviewed, followed by our conclusions.

INTELLIGENCE AND SUICIDE Intelligence evolves over an individual’s life and is affected both by the individual’s innate abilities and by their experiences from infancy on. Once suicides are identified, it is obviously too late to obtain measures of their intelligence using standardized measuring instruments, and few suicides have data available on their scores from prior intelligence tests. In order to obtain methodologically sound data on the impact of intelligence on suicide, longitudinal follow-up studies are required. Such studies are rare and typically carried out only in Scandinavia where records on citizens are centralized and open to investigators. In one of these studies, Allebeck et al. (1988) followed up 50 465 young men (born in 1949–51) who were conscripts in Sweden in 1969–1970 when aged 18–20, 247 of whom died by suicide during a 13-year follow-up, which indicates a suicide rate of about 38 per 100 000 per year.2 Low intelligence test scores predicted subsequent suicide in the univariate model and, together with 11 other variables in a multiple regression, contributed to the prediction of suicide. Using a much larger sample, which probably included those in the previous study, Gunnell et al. (2005) studied 987 308 men born in Sweden during 1950–76 who were tested during conscription procedures in 1968–94 and who had intelligence test scores recorded. The only young men excluded from conscription during this period were those who had foreign citizenship, a severe chronic medical condition, or a handicap. Four basic tests covering different aspects of intelligence were given: (1) logic, (2) identifying synonyms, (3) visuospatial and geometric perception, and (4) mechanical skills and mathematics problems. Of these men, 2811 later died by suicide.3 Better performance on each of these tests was associated with a lower rate of subsequent suicide. Among these four tests, the largest difference was on the test of logic. Dividing intelligence test scores into nine 464

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Suicide among smart people 465 groups, the suicide rate of the lowest scoring group was three times the suicide rate of the highest scoring group, clearly indicating that a higher intelligence test score was associated with a lower suicide rate. The age-adjusted hazard ratios4 for suicide were 0.88 for the logic test, 0.90 for the synonym test, 0.90 for the spatial test and 0.90 for the technical test, all statistically significant. In addition, focusing on the logic test, Gunnell et al. found that the association between intelligence and suicide remained even after controlling for education, pre-existing psychiatric illness, and the educational level of the parents of the men, and therefore the association between intelligence and suicide was robust.5 Because the association between intelligence and suicide was found after controlling for psychiatric illness, Gunnell et al. (2005) suggested that the association was most likely caused by the fact that, in times of crisis, people with lower intelligence test scores are less able to identify adaptive solutions to their problems and, therefore, suicide becomes a more viable solution. The conclusion seems to be that, in the general population, high intelligence test scores are a protective factor for suicide. Smart people make better decisions and are less likely to make the decision to die by suicide.

THE RELEVANCE OF INTELLIGENCE IN BEHAVIORAL ECONOMIC THEORIES OF SUICIDE The potential impact of intelligence on suicidal behavior might arise because smart people would be expected to have careers in which they earn more, and they would be expected to be more successful in these careers. Lifetime income has played a role in several economic theories of suicide, notably Hamermesh and Soss’s lifetime utility theory and Yang and Lester’s cost–benefit analysis of suicide. Hamermesh and Soss’s Microeconomic Theory of Suicide The economic theory of suicide developed by Hamermesh and Soss (1974) is based on a utility function which is determined by the permanent income and the current age of the individual. The permanent income is the average income expected over a person’s lifetime. In the calculation of permanent income, Hamermesh and Soss follow a formula that includes the real income of the current year plus the rest of the years of working life of an individual. Thus, this brings in a concept of opportunity cost, that is, by completing suicide,6 an individual forgoes the opportunity of earning income during the rest of his or her life. The permanent income and the current age of an individual determine the consumption level from which an individual will derive satisfaction. The current age also determines the cost of maintaining the day-to-day life of the individual, which is a negative attribute of the utility function. A third element relevant to suicide is the taste for living or distaste for suicide, which is assumed to be a parameter normally distributed with a zero mean and constant variance. When the total discounted lifetime utility (which includes the taste for living) remaining for a person reaches zero, an individual will complete suicide. This economic model of suicide contains the following assumptions. (1) The older the current age, the lower the total satisfaction, because the cost of day-to-day living increases

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with age. (2) The greater the permanent income, the higher the total satisfaction, since a higher income level warrants a higher consumption level. However, the additional satisfaction brought forward by additional income decreases with higher income. Based on the principle of utility maximization in this economic model, we can derive several theorems about the suicide rate of a society. First, the suicide rate will increase with age. Since the marginal utility of lifetime income decreases with increased permanent income, the older that individuals become, the less additional satisfaction they are going to derive from consumption. This should increase the probability that they will choose suicide. Secondly, the suicide rate will be inversely related to permanent income. If individuals receive a greater amount of lifetime income, they are expected to have a greater amount of consumption and, therefore, a greater satisfaction from life. This should decrease the probability of completing suicide. Hamermesh and Soss tested the usefulness of their theory by using an econometric approach that places more weight on the efficient prediction of the target variable. The equation used was: S(A,t) 5 a0 + a1I(A,t) + a2I2(A,t) + a3A + a4A2 + a5A3 + a6UN(A,t) + a7UN(A,t) • A + vA,t where A is the age group, t is the year, I is the discounted permanent income of age group A at time t, UN is the unemployment rate of age group A at time t, ai are constants and v is a disturbance term. Note the presence of higher order powers in some of the terms. Rather than conducting separate time series analyses for each age group, Hamermesh and Soss pooled the data for a 21-year period and for the nine age groups, giving 189 pooled observations. The empirical results presented by Hamermesh and Soss (1974) showed that permanent income was negatively related to the suicide rate, while the unemployment rate had a positive association with the suicide rate and an increasing effect on suicide as workers become older, as predicted. Yet as income rose in the postwar period, the male suicide rate fell for most age groups. Only for the youngest three age groups (those aged 20–34 years) did the suicide rate rise with rising incomes, a result that is in contrast with the theory’s prediction. Hamermesh and Soss explained this by arguing that the expansion in the number of people pursuing education into their twenties and the consequent postponement of consumption by an increased fraction of the people in these groups could affect the prediction. The decline in suicide rates for older people as their income has increased may be especially strong because of the decrease in variability of income resulting from the expansion of Social Security benefits. Indeed, Hamermesh and Soss pointed out that there was a sharp drop in the relative suicide rates of groups over age 55 in the late 1930s coincident with the introduction of Social Security benefits. A Cost–Benefit Analysis of Suicide In Yang and Lester’s cost–benefit analysis of suicide (Lester and Yang 1997; Yang and Lester 2006), completing suicide is considered to be a rational act. Individuals are acting ‘rationally’ if, given a choice between various alternatives, they select what seems to be the most desirable or the least undesirable alternative. The decision to commit suicide depends upon the benefits and costs associated with suicide and with alternative actions.

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Suicide among smart people 467 An individual will be less likely to commit suicide if the benefits from suicide decrease, the costs of suicide increase, the costs of alternative actions decrease, or the benefits from alternative activities increase. The benefits from suicide include escape from physical or psychological pain (as in the suicide of someone dying from terminal cancer), the anticipation of the impact of the suicide’s death on other people (as in someone who hopes to make the survivors feel guilty), or restoring his or her public image (as in the suicide of Antigone in Sophocles’ play of the same name). There are several costs in completing suicide. These include the money and effort spent in obtaining the information and equipment needed for the act of suicide, the fear and pain involved in preparing to kill oneself and in the process of completing suicide, the possible drawbacks as a result of dying by suicide such as the expected punishment predicted by most of the major religions of the world, and the opportunity costs (that is, the net gain to be expected if other alternative activities were chosen and life continued). An individual will choose suicide only if its benefits are greater than all of the costs mentioned above. Suicide can also be examined as if it were a commodity or a service that we buy. However, it is immediately obvious that suicide is very different from the typical objects that we purchase. For example, when we buy an object, we pay a specific price to obtain it and then we enjoy it. Suicide results in death, and as a result we have to conceptualize our enjoyment of it quite differently. Suicide is somewhat similar to the purchasing of healthcare services. In both, we pay a price to get rid of something, life in the case of suicide and sickness in the case of health care. Yet there is a basic difference between suicide and health care in that suicide leads to death, while health care (hopefully) leads to further life. Of course, for those who believe that there will be a ‘life after death,’ suicide also leads to further life, but of a different kind. Looking at matters from a demand-side perspective, when we purchase a commodity (or a service), the price we pay for the commodity (or service) reflects the benefits we expect to receive from consuming that commodity. From a demand-side perspective, beef costs more than chicken because the public desires beef more than chicken, and their stronger desire for beef reflects their expectation of greater satisfaction from eating beef than from eating chicken. In a demand-side analysis of suicide, the notion of its ‘price’ is different from the ordinary price of a commodity. The benefit expected by a suicide is the relief of unbearable distress, and so a scale of distress is used to measure the benefit expected by the suicidal individual. This benefit expected by suicidal individuals is reflected in the ‘price’ they must pay for their suicide. In essence, the demand curve for suicide is a relationship indicating the probability of completing suicide as a function of the amount of distress felt by the individual. As the amount of distress increases, the probability of completing suicide increases. The demand for suicide is, therefore, an upwardsloping curve, which is quite different from the typical downward-sloping demand curve applicable, for example, in economic analyses of commodity markets.7 On the supply side, the probability of completing suicide is related to the cost of completing suicide. The cost of completing suicide includes the cost of losing your life, collecting information about how to commit the act, purchasing the means for suicide, and so on. While the latter two items have a clear-cut scale of measurement, the cost of losing life is much harder to measure. It includes at least three components, namely, the psychological fear of death, the loss of income in the future which otherwise would have been earned by the suicide, and the loss of any enjoyment that would be experienced

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Price/cost of committing suicide

D

S

SE

P1

Figure 26.1

PE

P2

1 Probability of committing suicide

A demand and supply curve for suicide

during the rest of your ‘natural’ life. The higher the cost of completing suicide, the lower the probability that individuals will actually kill themselves. Therefore, the supply curve should be a downward-sloping curve. Both the demand for suicide and supply for suicide are shown in Figure 26.1. The vertical axis indicates the price (or the cost) of completing suicide, while the horizontal axis represents the probability of completing suicide (with a range of 0 to 1). The demand curve is an upward-sloping curve which begins from the origin8 and ends at the point when the probability of completing suicide is equal to 1. The price level for completing suicide, which corresponds to the point where the probability is equal to 1, refers to the threshold level of distress that an individual can no longer tolerate. Under these circumstances, suicide becomes inevitable. The intersection of the demand and supply curves represents an equilibrium for an individual. For that equilibrium level of distress and the corresponding costs of completing suicide, there is an equilibrium probability of completing suicide. As the supply curve might intersect any section of the demand curve, the equilibrium probability of completing suicide can, therefore, range anywhere from 0 to 1. Given the aforementioned economic framework for suicide, what needs to be determined is how to measure the psychological variables (for example, level of distress and future pleasure) in monetary units so that an equilibrium can be obtained through equating the demand and supply for suicide. For example, the level of distress can be operationalized as the cost of the psychological services required to eliminate the distress that the suicidal person is experiencing. Since there is a typical price for psychological services,

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Suicide among smart people 469 each level of distress could be converted into a monetary measure representing the cost of psychological services needed to eliminate the distress. The higher the cost of treatment, the greater the level of distress. Another issue concerns converting future pleasure for life into monetary units so that future pleasure can be made comparable to that of the level of distress, and so on. This may be accomplished by compiling a list of major categories of activities that produce pleasure for individuals. By definition, the equilibrium probability of completing suicide is determined by the intersection of the supply and demand curves. Owing to the peculiar nature of the demand and supply of suicide, the equilibrium so obtained is not a stable one. Since the demand for suicide is an upward-sloping line, the higher the distress level, the more likely the probability of completing suicide. Since the supply curve is downward sloping, the higher the cost of completing suicide, the less likely the probability of doing so. Let us label the equilibrium level of distress and the cost of completing suicide SE and the corresponding equilibrium probability PE (see Figure 26.1). Let us examine the implications of such an unorthodox combination of supply and demand curves. If the probability of completing suicide is initially at P1, which is lower than the equilibrium probability PE, this corresponds to a low level of distress from the perspective of the demand side and a high cost of completing suicide from the perspective of the supply side (see Figure 26.1). From the demand side, the amount of distress created by life is smaller than the corresponding cost of treating the distress. This will motivate the individual to further reduce the distress created by life. As a result, the situation will lead to an even lower probability of completing suicide, and the individual will eventually withdraw from the suicidal situation. On the other hand, if the probability of completing suicide initially is higher than the equilibrium probability PE, say at P2, this corresponds to a high level of distress from the demand-side perspective and a low cost of completing suicide from a supply-side perspective (see Figure 26.1). This situation refers to a group of individuals at high risk of suicide because they have high levels of distress and because the cost of completing suicide for them is manageably low, since ending their lives will relieve their continuing suffering. Thus, this situation will lead to an even higher probability of completing suicide. Both situations, whether the initial probability of completing suicide is higher or lower than the equilibrium level, result in movement away from the equilibrium. If the initial probability of completing suicide is lower than the equilibrium level, then the individual becomes less likely to choose suicide; while, if the initial probability of completing suicide is higher than the equilibrium level, the individual becomes more likely to choose suicide. In short, this economic model of suicide implies that suicidal behavior is an unstable behavior. Comment Since smart people will earn more in their lifetimes, increasing both permanent income and the cost of suicide, the prediction from the utility maximization and cost-benefit theories of suicides is that smart people should have a lower suicide rate, a prediction confirmed by the research reviewed in the first section of this chapter.

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DELAY DISCOUNTING Delay discounting is a phenomenon in which individuals decrease the value of an outcome if its receipt is delayed. There are three features related to the impact on decision making from delay discounting, two related to the ‘time’ variable and one related to the size of the ‘outcome’: 1. Now versus later in the time dimension. Individuals are more inclined to accept an outcome of lesser utility (or value) now rather than an outcome of greater utility (or value) later, for example, choosing a smaller amount of money immediately versus a larger amount later. 2. A shorter versus a longer length for the outcome. Delay discounting is often hyperbolic, that is, small delays for the receipt of the outcome have a proportionately greater impact on value than do longer delays. 3. A small versus a large outcome. Delay discounting may lead to impulsive behavior because individuals choose a smaller but quicker outcome over a larger but later outcome.9 Since delay discounting can be a stable personality trait (Odum 2011), it is likely to have many manifestations in people’s behavior. Research has shown that delay discounting is associated with a variety of problem behaviors including smoking, drug use, over-eating, failure to exercise, and taking on large amounts of credit card debt (da Matta et al. 2012). For example, research has indicated that delay discounting as a trait is associated with antisocial personality disorder (for example, Finn et al. 2009), while Kirby et al. (1999) found that heroin addicts showed delay discounting more than controls from the community matched for age, sex and education. However, measures of delay discounting do not always correlate with measures of impulsivity, perhaps because self-report scales of impulsivity measure different aspects of impulsivity (Odum 2011). Nonetheless, in a survey of over 42 000 British television viewers who were given a single delay-discounting choice of ₤45 in three days versus ₤70 in three months, Reimers et al. (2009) found that those choosing the smaller amount in three days were younger, had less income and less education, and were more likely to have engaged in impulsive behavior measured by variables such as age at first sexual activity, relationship infidelity and smoking. Is delay discounting associated with intelligence, that is, are smarter people less likely to engage in delay discounting? The answer is positive. For example, Shamosh and Gray (2008) reported a meta-analysis of 24 eligible studies on this issue and concluded that individuals with greater intelligence were indeed less likely to engage in delay discounting, particularly in studies in which the payoffs were real. Application to Depression Lempert and Pizzagalli (2010) administered undergraduate students a choice test in which they had to choose between $10 to be received after a delay of 1, 2, 40, 180 and 365 days or $2 immediately. Delay discounting was not associated with scores on a general impulsiveness scale, but students who obtained higher scores on a measure of anhedonia (a lack of

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Suicide among smart people 471 reactivity to pleasurable stimuli) were more likely to choose a larger delayed reward over an immediate smaller reward. Using a similar task, Takahashi et al. (2008) found that psychiatric patients with a diagnosis of affective disorder, with the most recent episode being depression, were more impulsive compared with controls for both gain and loss. Takahashi et al. related this result to impaired neural processing of reward and punishment. In line with this, Must et al. (2006) found that patients with a major depressive disorder showed an increased sensitivity to reward which resulted in them making poor decisions. It seems, therefore, that a tendency toward delay discounting is associated with depression. Application to Suicide Cutler et al. (2001) speculated that delay discounting might explain the rising rate of youth suicide since youths are less able to discount present pain with the possibility of future pleasure, but they did not test this empirically. Pittel and Rübbelke (2009) hypothesized that suicide bombers forgo utility from future life in order to acquire present utility from their present actions. The present utility for suicide bombers includes status for themselves, as well as material utility for their families. They may feel anticipatory feelings of pride and accomplishment, as well as the expectation of rewards from God or Allah. The intertemporal utility of their shortened lives exceeds the utility for lives lived to their natural end. Since Lankford (2013) has convincingly documented the miserable current lives of many suicide bombers, their choice makes even more sense. In an empirical study on delay discounting in suicidal individuals, Dombrowski et al. (2011) gave individuals over the age of 60 a choice between smaller, immediate monetary rewards ($25–$35) and larger delayed monetary rewards ($75–$85). Delay discounting was greatest in those who had made low-lethality suicide attempts, less in those with only suicidal ideation, and least in depressed but non-suicidal individuals and those making high-lethality suicide attempts. Delay discounting was not associated with hopelessness or depression scores or with intelligence test scores. This result is surprising, but it suggests that the more seriously suicidal elderly have a lesser tendency toward delay discounting. It may be, however, that the more serious suicide attempters (who had, therefore, required hospitalization) were making an effort to appear hypernormal in the hospital in order to obtain their release from the hospital.10 Liu et al. (2012) gave a monetary rewards task11 to patients with a diagnosed substance abuse disorder (primarily cocaine or opioid dependence) and found that those with no history of a suicide attempt discounted small delayed rewards more than large ones, whereas those with a prior suicide attempt showed no difference in discounting rates for small versus large rewards. Overall, delayed discounting was not associated with a history of attempted suicide. More speculatively, Van Heeringen et al. (2011) concluded from their review of functional and structural brain studies in suicidal individuals that suicidal individuals are more likely than non-suicidal individuals to have abnormal functioning in the orbitofrontal and dorsolateral parts of the prefrontal cortex, areas of the brain that Gray (1975) thought were the basis for sensitivity to punishment. Van Heeringen et al. (2011) concluded from their review that suicidal individuals might be overly sensitive to social disapproval and more willing to choose options with immediate reward.

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The conclusion seems to be that intelligent people show less delayed discounting which protects them against suicide.12

OTHER CONSIDERATIONS Neuropsychiatric Considerations Neuropsychologists have explored the role of the executive function in human behavior. Executive function refers to a set of higher-order mental processes primarily governed by the frontal lobes of the cortex. These functions include initiation, planning and selfregulation of goal-directed behavior. To a large extent, therefore, smart people would be expected to have more effective executive functioning. In general, suicidal individuals have been found to have executive dysfunction as compared to non-suicidal comparison subjects. For example, Keilp et al. (2001) studied depressed, non-medicated patients and found that those who in the past had made serious attempts at suicide performed worse on tests of executive functioning (including general intellectual functioning, attention and memory). Homaifar et al. (2012) recommended, therefore, that routine assessment for suicidal potential in patients should always include measuring executive function. Creative People Although ‘smart’ typically refers to having a high level of intelligence, creative people are also smart, albeit in a different sense. Cox (1926) tried to estimate the intelligence quotient (IQ) scores of famous historical individuals based on information in their biographies, such as the age at which they talked, learned languages, and so on. For example, John Stuart Mill, a philosopher, was estimated to have an IQ of 190. He began to study Greek at the age of 3, could read Plato in the original Greek at age 7, and wrote a history of Rome at the age of 6. Cox’s estimates of average IQ scores were: Philosophers Poets, novelists Scientists

170 160 155

Musicians Artists Military leaders

145 140 125

Thus, creative individuals, such as writers, musicians and artists, are found to have high levels of intelligence. Cox also reported that psychological disturbance was most common in the poets, novelists, musicians and artists (and less common in revolutionaries, statesmen and religious leaders). Modern research has confirmed this. Andreasen (1987) found a greater incidence of psychiatric disorder in contemporary writers compared with matched controls, especially bipolar affective disorder (manic depression). This has been confirmed by Jamison (1993) who found that creative individuals were especially productive during their periods of mania, such as the Pulitzer Prize winning poet, Anne Sexton.13 Goodwin (1988) noted the high incidence of alcohol abuse in creative writers, suggesting either than alcohol use

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Suicide among smart people 473 facilitates creative writing or that creativity increases stress which is relieved by using and abusing alcohol. Affective disorders (both major depression and manic depression) and alcohol abuse are risk factors for suicide. Creative people, therefore, have an increased risk of psychiatric problems that increase their risk of suicide. Since they typically have high levels of intelligence, it appears that the protective factor of high intelligence does not outweigh the risk factors arising from their creativity. Are Richer People Happier? In studying suicidal behavior, the role of happiness and depression appear to be central, and so, from an economic viewpoint, it is relevant to ask whether higher incomes are associated with greater subjective well-being (or happiness). Forty years ago, Easterlin (1974) argued that increased income did not raise subjective well-being, but more recent research has shown that Easterlin was wrong. For example, Stevenson and Wolfers (2013) found that residents of wealthier countries have greater life satisfaction and, in addition, for the 25 most populous countries, they found the same relationship at the individual level. Furthermore, Stevenson and Wolfers failed to find a satiation effect. For example, using a sample of 1014 respondents in the United States in 2007, they found a monotonic relationship between income (from $500k) and both happiness and life satisfaction. Therefore, it appears that richer people are happier, and so we may also conclude that more smart people (who should be richer) should be happier and, therefore, less likely to be depressed and suicidal.

CONCLUSIONS This review of research has indicated that higher intelligence and its associated traits of reduced delay-discounting and better neuropsychological executive functioning are each empirically associated with reduced rates of suicide and of the risk factors for suicide (such as depression). This conclusion ties in nicely with two economic theories of suicide, proposed by Hamermesh and Soss and by Yang and Lester, which predict that more intelligent people should be less prone to suicide because they have an increased lifetime utility and because their suicides would entail increased costs.

NOTES 1. 2. 3.

4.

This chapter focuses on completed suicide (that is, acts in which the person dies) and does not consider non-fatal suicidal behavior. In Sweden in 1980, the suicide rate for men aged 25–34 in the general population was 33.3 per 100 000. Gunnell et al. (2005) did not calculate a suicide rate for this sample but, given that the follow-up period averaged 15.5 years, the estimated suicide rate is 18.4 per 100 000 per year, which is lower than the suicide rate for all Swedish males during this period (for example, 37.6 per 100 000 per year for men aged 35–44 in 1980; Lester and Yang 1998). Remember that conscripts excluded men with medical and psychiatric problems and handicaps. Hazard ratios are calculated by dividing the subjects into those above the median score and those below

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5. 6. 7. 8. 9. 10.

11. 12. 13.

Handbook of behavioural economics and smart decision-making the median score and calculating the number of suicides in the higher scoring group divided by the number of suicides in the lower scoring group. Previous studies in other countries were based on only a small number of suicides, as in an Australian study based on only 76 suicides (O’Toole and Cantor 1995). The preferred term in suicidology is ‘dying by suicide’, and ‘committing suicide’ is unacceptable. We use ‘completing suicide’ in the present chapter. In typical analyses, the quantity demanded is inversely related to the cost of the product or service, and so the typical demand curve is downward sloping. The initial point of the demand curve means a logical origin of ‘zero distress’ and ‘zero probability of suicide.’ Another way to illustrate the unique features of delay discounting is to compare it with the laws of diminishing marginal utility developed to illustrate basic consumer behavior in which the marginal satisfaction from each new object steadily declines. Interestingly, in another study, Dombrowski et al. (2010) found that elderly individuals who had made suicide attempts in the past (and who were currently psychiatric inpatients with major depressive disorders) discounted previous experiences to a higher degree, reacting more than control patients to what happened most recently in an experimental task. Such as $5 now versus $10 one week from now. There is a small subset of suicides who appear to make impulsive decisions, but impulsivity in suicidal behavior appears to be much more common in those individuals who make suicide attempts but who do not die than in completed suicides. Sexton (1928–74) refused to take the recommended medication for manic depression because it suppressed her creativity, and she died by suicide in 1974 (Lester 1993).

REFERENCES Allebeck, P., C. Allgulander and L.D. Fisher (1988), ‘Predictors of completed suicide in a cohort of 50,465 young men’, British Medical Journal, 297 (6642), 176–8. Andreasen, N.C. (1987), ‘Creativity and mental illness’, American Journal of Psychiatry, 144 (10), 1288–92. Cox, C.M. (1926), Early Mental Traits of Three Hundred Geniuses, Palo Alto, CA: Stanford University Press. Cutler, D.M., E.L. Glaeser and K.E. Norberg (2001), ‘Explaining the rise in youth suicide’, in J. Gruber (ed.), Risky Behavior among Youths, Chicago, IL: University of Chicago Press, pp. 219–69. Da Matta, A., F.L. Gonçalves and L. Bizarro (2012), ‘Delay discounting’, Psychology & Neuroscience, 5 (2), 135–46. Dombrowski, A.Y., L. Clark, G.J. Siegle, M.A. Butters, N. Ichikawa, B.J. Sahakian and K. Szanto (2010), ‘Reward/punishment reversal learning in older suicide attempters’, American Journal of Psychiatry, 167 (6), 699–707. Dombrovski, A.Y., K. Szanto, G.J. Siegle, M.L. Wallace, S.D. Forman, B. Sahakian et al. (2011), ‘Lethal forethought’, Biological Psychiatry, 70 (2), 138–44. Easterlin, R.A. (1974), ‘Does economic growth improve the human lot?’, in P.A. David and M.W. Reder (eds), Nations and Households in Economic Growth, New York: Academic, pp. 89–125. Finn, P.R., M.E. Rickert, M.A. Miller, J. Lucas, T. Bogg, L. Bobova and H. Cantrell (2009), ‘Reduced cognitive ability in alcohol dependence’, Journal of Abnormal Psychology, 118 (1), 100–116. Goodwin, D.W. (1988), Alcohol and the Writer, Kansas City, MO: Andrews McNeel. Gray, J.A. (1975), Elements of a Two Process Theory of Learning, London: Academic. Gunnell, D., P.K.E. Magnusson and F. Rasmussen (2005), ‘Low intelligence test scores in 18 year old men and risk of suicide’, British Medical Journal, 330 (7484), 167. Hamermesh, D.S. and N.M. Soss (1974), ‘An economic theory of suicide’, Journal of Political Economy, 82 (1), 83–98. Homaifar, B.Y., N. Bahraini, M.M. Silverman and L.A. Brenner (2012), ‘Executive functioning as a component of suicide risk assessment’, Journal of Mental Health Counseling, 34 (2), 110–20. Jamison, K.R. (1993), Touched with Fire, New York: Free Press. Keilp, J.G., H.A. Sackheim, B.S. Brodsky, M.A. Oquendo, K.M. Malone and J.J. Mann (2001), ‘Neuropsychological dysfunction in depressed suicide attempters’, American Journal of Psychiatry, 158 (5), 735–41. Kirby, K.N., M.N. Petry and W.K. Bickel (1999), ‘Heroin addicts have higher discount rates for delayed rewards than non-drug-using controls’, Journal of Experimental Psychology: General, 128 (1), 78–87. Lankford, A. (2013), The Myth of Martyrdom, New York: Palgrave.

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Suicide among smart people 475 Lempert, K.M. and D.A. Pizzagalli (2010), ‘Delay discounting and future-directed thinking in anhedonic individuals’, Journal of Behavior Therapy & Experimental Psychiatry, 41 (3), 258–64. Lester, D. (1993), Suicide in Creative Women, Commack, NY: Nova Science. Lester, D. and B. Yang (1997), The Economy and Suicide, Commack, NY: Nova Science. Lester, D. and B. Yang (1998), Suicide and Homicide in the 20th Century, Commack, NY: Nova Science. Liu, R.T., J. Vassileva, R. Gonzalez and E.M. Martin (2012), ‘A comparison of delay discounting among substance users with and without suicide attempt history’, Psychology of Addictive Behaviors, 26 (4), 980–85. Must, A., Z. Szabo, N. Bodi, A. Szasz, Z. Janka and S. Keri (2006), ‘Sensitivity to reward and punishment and the prefrontal cortex in major depression’, Journal of Affective Disorders, 90 (2–3), 209–15. O’Toole, B. and C. Cantor (1995), ‘Suicide risk factors among Australia Vietnam era draftees’, Suicide & LifeThreatening Behavior, 25 (4), 475–88. Odum, A.L. (2011), ‘Delay discounting’, Behavioural Processes, 87 (1), 1–9. Pittel, K. and D.T.G. Rübbelke (2009), ‘Decision processes of a suicide bomber’, CER-ETH – Center of Economic Research at ETH Zurich Economics Working Paper No. 09/106, Social Science Research Network (SSRN) accessed 6 February 2016 at http://papers.ssrn.com/sol3/papers.cfm?abstract_id51347945. Reimers, S., E.A. Maylor, N. Stewart and N. Chater (2009), ‘Associations between a one-shot delay discounting measure and age, income, education and real-world impulsive behavior’, Personality & Individual Differences, 47 (8), 973–8. Shamosh, N.A. and J.R. Gray (2008), ‘Delay discounting and intelligence’, Intelligence, 36 (4), 289–305. Stevenson, B. and J. Wolfers (2013), ‘Subjective well-being and income’, American Economic Review, 103 (3), 598–604. Takahashi, T., H. Oono, T. Inoue, S. Boku, Y. Kako, Y. Kitaichi et al. (2008), ‘Depressive patients are more impulsive and inconsistent in intertemporal choice behavior for monetary gain and loss than healthy subjects’, Neuro Endocrinology Letters, 29 (3), 351–8. Van Heeringen, C., S. Bjittebier and K. Godfrin (2011), ‘Suicidal brains’, Neuroscience & Biobehavioral Reviews, 35 (3), 688–98. Yang, B. and D. Lester. (2006), ‘A prolegomenon to behavioral economic studies of suicide’, in M. Altman (ed.), Handbook of Contemporary Behavioral Economics, Armonk, NY: M.E. Sharpe, pp. 543–59.

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PART VII SOCIOLOGICAL DIMENSIONS OF SMART DECISION-MAKING

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27 Seeing and knowing others: the impact of social ties on economic interactions* Astrid Hopfensitz

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INTRODUCTION

Most economic interactions concern situations where two or more people meet and interact. A seller is approached by a buyer either on a market, in a store or over the Internet. An employer bargains with his employee. A group of fishermen meets in the harbor before going to sea. During some of these interactions the involved parties might have a long history together and might know each other well. In other interactions the involved people might never have met before but see the face, dress and body language of their interaction partner. In other situations they might not see each other but hear the other person’s voice (over the telephone) or see their photograph on a website (over the Internet). The different pieces of information we gather about our interaction partners are used to help us build an image of him or her. Psychologists have long known that men are treated differently than women, that more beautiful and healthy-looking individuals have an advantage and that we act differently with respect to people who look similar to ourselves compared with people who look ‘foreign’. Economists often treat such effects as biases that impede rational decisionmaking. As a result, economists usually focus on situations where agents are anonymous, and a number of policy recommendations concern methods to create anonymity, for example, for job candidates. In this chapter I discuss the different ways in which information about the other might influence choices and the rationale that might lie behind some of these behaviors. I argue that many of these seeming biases might have evolutionary rationales that help humans make smarter decisions when interacting with others. In some cases the influence might be due to us ‘learning’ something about possible skills or abilities of our interaction partner (that is, statistical discrimination). In other cases the influence might be due to an impact on our own concerns for this person (that is, other regarding preferences). In yet other situations we might learn about the payoffs for the other and use this to predict how he or she will act (for example, observing the others’ emotions). Again in other situations the exchange of information will serve to create some common knowledge among the involved parties that can help to coordinate them. Disentangling these different underlying motives is crucial for a good understanding of how social interactions might influence human behavior.

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INFORMATION AND REPUTATION

Social interactions have traditionally been modeled as games by economists. Such games can involve various people and can concern problems as different as cooperation (for example, prisoners’ dilemma and the public good game), competition (for example, zerosum games) or coordination (for example, coordination games and battle of the sexes game). While a game concerns one interaction, interactions between the same group of people might occur more than once. Thus players in a game might either have information from past interaction or anticipate future interactions. Game theory has traditionally distinguished ‘one-shot’ games from ‘repeated games’. Repeated games in this context mean interactions that have no clear end-point and where after each interaction a positive probability of a future interaction exists. Since it is assumed that after the game is played, outcomes are revealed to the participating players, these future interactions will thus introduce concerns for reputation among all involved members. In contrast a one-shot interaction means that after the game no future interaction between the involved parties is going to happen and thus concerns for reputation or punishment cannot influence choices in this case. In many cases also, any series of interactions that has a clear end-point of this type is treated similarly to a one-shot interaction owing to the principle of backward induction.1 The experimental literature in economics has in this context mainly focused on situations where cooperation or helping in a game is possible (for example, prisoners’ dilemmas, and helping and public good games). Cooperative acts are generally costly for the individual but provide a benefit for the receiver. In one shot interactions, such acts are, from an individual point of view, not beneficial. However, in repeated interactions, strategies might be based on the previous choices by interaction partners and thus players who previously cooperated will be rewarded in the future by further cooperation opportunities (Trivers 1971). The superiority of such strategies was famously demonstrated by Axelrod in 1984 when matching various game strategies against each other in a tournament of repeated prisoners’ dilemma games. The clear winner of these studies was the very simple strategy of ‘tit-for-tat’ – do to your partner as he or she has done to you in the past. Experiments have confirmed that direct reciprocity is a prominent strategy in settings where small groups of individuals interact repeatedly (Trivers 1971; Binmore 1992). However, in many settings groups are large, and therefore interactions between the same two members are rare. In such settings, indirect reciprocity might start to influence players’ interactions. Indirect reciprocity concerns situations where an agent (i) has no first-hand experience about another agent’s (j) behavior, but has information about behavior by this agent (j) when interacting with a third party (k). Such information has been modeled as ‘image scores’ (Nowak and Sigmund 1998), that is, a number that summarizes an agent’s previous actions in a game. Experimental studies have confirmed the idea that humans base their choices on the image score of their interaction partner. A number of laboratory experiments have studied behavior when previous behavior by interaction partners is observable, specifically indirect reciprocity (for example, Wedekind and Milinski 2000; Milinski et al. 2002). Seinen and Schram (2006) studied the effect of observing the final six previous decisions made by an interaction partner in a helping game. In this game a ‘helper’ can give some money to a ‘recipient’ with the benefit being larger than the cost of giving. Since players were randomly re-matched each period, the

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Seeing and knowing others 481 observed information was about interactions with a third party. Giving rates were significantly higher when it was common knowledge among players that information about previous-round behavior would be available than in a control treatment without this information. Also the amount of help received increased with the number of previous helpful choices made by a participant. Thus, reputation is clearly taken into account by others; however, reputation is also used strategically by agents who know that their reputation will matter in the future. Whether such strategic motives are taken into account when seeing reputation information is another interesting question concerning indirect reciprocity. Engelmann and Fischbacher (2009) investigate this question in an experimental setting where agents had, in only half of the periods, an observable reputation score. In the remaining half of the periods strategic reputation building was thus not possible. About half of the participants can be classified as strategic, that is, helping mostly in the periods where their behavior will be visible to others. Strategic reputation building also pays off. Participants playing strategically earn significantly more than players who are categorized as ‘weakly’ strategic. While most of these experiments are structured such that either only direct or only indirect reputation is available to participants, real human interactions are usually characterized by a mix of reputation information and first-hand experience. The interaction of direct and indirect reciprocity has been studied by Molleman et al. (2013). In their setup, participants in a helping game can either use information based on direct interactions or on reputation information. Not surprisingly, first-hand experience is weighted stronger, especially when the two types of information provide conflicting evidence. The importance of image scoring for real economic interactions is nicely illustrated by reputation systems created by various online communities. Most online communities provide the possibility to give publicly visible ‘grades’ to previous interaction partners or to reward community members. A grading system is used on platforms such as eBay; other platforms (such as Wikipedia) use awards that can be given to others. The impact of receiving good grades or rewards on future interactions has been studied by a number of different researchers. For example, van de Rijt et al. (2014) studied how receiving one initial reward (controlled by the experimenters) influences the probability of receiving future rewards (from other users) for Wikipedia. To study this question, a random sample of 208 Wikipedia editors among the top 1 percent of all editors (based on edit counts) were endowed by the researchers with a customized award. Ninety days later the editor pages of the 208 treated editors were sampled and compared with the pages of 313 nontreated editors that were initially similarly ranked. While 31 percent of the control editors received another award in this period, 40 percent of the treated editors received another award. However, it is unclear whether these effects were due to an increase in motivation for the participants receiving positive feedback or solely based on the increased reputation score of these people. To control for this and to focus on situations that actually require some kind of cooperation, van Apeldoorn and Schram (2014) focused on ratings and behavior in an online community where members volunteer to host others (free of charge) who are travelling to their city. Specifically, a number of artificial profiles were created that differed solely with respect to how many times the member had previously offered the service to others from the community. Ratings were therefore not a signal of trustworthiness of the member, but a sign of previous cooperative acts of the member. The studied online community had, at the time of the study, about 5.5 million members

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worldwide and can thus be considered a great success. As theories of image scoring would suggest, the profiles that showed a history of previous contributions to the online community received more than double the number of positive responses when sending out a request themselves, compared with a neutral profile. The importance of reputation in public good situations is also illustrated by Yoeli et al. (2013) who study willingness to participate in a campaign to prevent blackouts (SmartAC). This program aims at residents of Northern California to volunteer to restrict their demand from central air conditioners on days of unusual high demand or unexpected plant failures. Participating in this program is voluntary and participants contribute at a cost of comfort to themselves to a public good of their community. The study varied whether participants were signing up for this program on sheets on which their own name and address was visible to others or where they signed up with an anonymous identifier. Observability tripled participation in the program. The effect was approximately seven times larger than offering a $25 cash incentive. The effect was particularly large among people living in apartments (compared to houses) and for owners (compared to renters). This is in line with the idea that reputation should matter especially in situations where interactions are frequent or repeated for a long time, as is the case of inhabitants of an apartment block or residents who bought their home.

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SEEING THE OTHER

Is the used car seller going to cheat me? Is the employee going to be able to perform the required tasks? Is the creditor going to pay back his loan? Many immediate questions in social interaction turn around the expected behavior of others. Behavior might be due to a specific strategy followed by the other or to constraints and abilities of the other. Collecting information about an interaction partner very often has the goal of informing us about likely behavior by the other. When no verifiable information is available, people often use statistical extrapolation to infer likely behavior. If women or foreigners are more likely to possess a specific trait, seeing a woman or hearing a foreign name might trigger beliefs that this specific person is also going to have these traits. Statistical discrimination is most likely to occur with respect to groups that have a clear reputation and where group membership can be observed easily. Not surprisingly the literature of statistical discrimination has mainly focused on gender and race. Both gender and race are easily deduced from seeing or hearing another person, and even more abstract information, such as the person’s name, handwriting or address, can be used. However, seeing or hearing another person also leads us to use ‘nonverbal’ communication transmitted by gestures, mimic or tone of voice to anticipate the interaction partner’s behavior. Studying these impacts is especially interesting for economists, who traditionally tend to consider such information to be either ‘cheap talk’ (that is, messages that are aimed at convincing the other party to do something, with no real commitment behind it) or as uninformative noise. However, people are surprisingly consistent in evaluations of others based on seeing them and there is now increasing evidence that they are also better than chance in predicting the true underlying strategies that will be used by the other player (in certain interaction settings). The most commonly investigated setting concerns the judgment of other people’s trust-

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Seeing and knowing others 483 worthiness. Specifically, if we assume that trust is increasing overall welfare but can get exploited by untrustworthy others, individuals who can detect trustworthiness will have an advantage over those that cannot. Interesting evidence for a real-world application of this ability is a study on vote-buying behavior in Paraguay (Finan and Schechter 2012). In many countries where ideological differences between parties are small, vote-buying can have a large impact on election outcomes. Vote-buying describes the exchange of small personal gifts in return for a promise to vote for a specific party. If votes are anonymous, the person ‘buying’ the vote has to believe that the promise will be kept. Thus it is in a candidate’s interest to focus on those voters that are likely to reciprocate the gift. Finan and Schechter investigate data from a household survey and a middleman survey (those who actually do the vote-buying for politicians) for the case of Paraguay. Based on the household survey, reciprocity was measured for voters based on the share they returned as second mover in a trust game. This measure of reciprocity was significantly influencing the probability that this individual was offered something in exchange for their vote. Thus middlemen seem able to detect reciprocal individuals and use this information when targeting their vote buying activities. Trustworthiness judgments based on seeing human faces are made quickly. When seeing a face for less than 100 milliseconds, ratings are essentially the same as when given unlimited time to look at the face (Willis and Todorov 2006; Todorov et al. 2009). Generally photographs of faces seem to be consistently rated concerning their trustworthiness, that is, people in general agree on what a trustworthy face looks like. Even 5- to 6-year-old children are at the same level of adults’ consistency (Cogsdill et al. 2014). Agreement can also be observed across cultures. Rule et al. (2010) asked American and Japanese participants to rate faces of US and Japanese political candidates. Ratings of faces showed large agreement, regardless of culture. Trustworthiness judgments, based on facial features also influence real choices; for example, by influencing the decision to send money to the depicted person in a trust game (Van’t Wout and Sanfey 2008). In this one-shot game, one player is endowed with a sum of money, and informed that he has the opportunity to transfer part of that money to another player. If he does so, the amount he transfers is multiplied by a factor (for example, 3) and given to the second player. The second player can now choose to transfer back some of this multiplied amount. The first player’s decision thus amounts to deciding whether he will trust the other player with his money. The effect persists to influence choices even after direct experience. In a repeated setup, initial trustworthiness ratings were observed to be updated by own experience but keep influencing decisions even after multiple rounds (Chang et al. 2010). The facial features influencing character ratings are often triggered by cues that carry some valid information about the person: for example, facial symmetry as a signal of fitness (Zebrowitz and Rhodes 2004). However, conclusions about the actual information content of such signals often have to be critically viewed. For example, facial width to height ratio is often referred to as a signal of aggressiveness (Carré and McCormick 2008; Stirrat and Perret 2010), however, recent data has been questioning this relationship (Deaner et al. 2012; Gomez-Valdes et al. 2013). Whether actual trustworthiness can be reliably detected from faces is still controversial (for example, Yamagishi et al. 2003; Verplaetse et al. 2007; Efferson and Vogt 2013). Trustworthiness detection might be based on signals about the thought process or

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preferences of an actor that are mirrored in his face while contemplating the choices. For these types of signals it is necessary to study the effect of photographs or movies taken while the interaction partner takes his decision. Indeed some studies have observed this ability (Willis and Todorov 2006; Verplaetse et al. 2007). Trustworthiness detection might also use cues that are related to the interaction partner’s personality and general disposition to be trustworthy. In this case any kind of observation about the other should carry useful information. Experimental studies have observed an ability to detect trustworthiness in photographic material that was not taken during the decision period. Thus some underlying characteristics seem to be visible in an agent’s face. However, it seems that the signal is only detected when information is limited by eliminating visual distractors such as clothing or hairstyle (Bonnefon et al. 2013). The same study shows that trustworthiness detection is a so-called ‘modular process’, since intelligence is not enhancing it nor does cognitive load decrease it. Strongly related to trustworthiness judgments are the expressed emotions in a person’s face. Specifically smiling faces (photographs and videos) elicit high trust. Scharlemann et al. (2001) used still pictures and observed that participants trust more when seeing a smiling image of their partner. Johnston et al. (2010) use video clips and observe more trust in response to enjoyment smiles. Enjoyment or ‘genuine’ smiles are not under straightforward voluntary control. Since the work of Duchenne de Bologne (1862) and Darwin (1872), many researchers have attempted to identify objective measures of ‘honest’ smiles, concluding that genuine smiles are characterized by use of the orbicularis oculi (the muscle surrounding the eyes) in combination with the zygomatic major (raising the corners of the mouth); symmetry is also an important characteristic. More recent research focuses on the importance of temporal dynamics such as smile onset, apex, and offset durations (Krumhuber et al. 2007). Mehu et al. (2007) assess which characteristics are associated with honest smiles by rating 50 faces across ten attributes. It turns out that Duchenne de Bologne smiles play a significant role in the assessment of generosity and extraversion. Smiles perceived as genuine not only influence choices, but also seem informative about the possible gains from trust. Centorrino et al. (2015) study the impact of short video messages recorded by a trustee in a trust game. Trustees were aware that this was their only opportunity to convince their interaction partner to trust them and were thus motivated to give a trustworthy ‘impression’. This is a typical situation for cheap talk, since interaction partners did not know each other and thus a breach of trust would never be punished. The text in the video message was standardized, thus trustees only had their tone and face to convince the other to send them money. As predicted, the authors observe that trustees who are perceived to have more genuine smiles are more trusted. In addition, on average, trusting those whose smiles were rated as more genuine led to higher earnings for trustors. Trustees who were rated as smiling more genuinely return more money, on average, to senders. This was mainly owing to the fact that genuine smiles are informative of the situation of the trustee (here the amount of money the trustee has available to share with the trustor). As far as situations requiring trust are concerned, non-verbal communication might thus be informative with respect to personality characteristics, the current decision process of the decision maker and to the constraints that the decision maker is facing. Trust evaluations are mainly influential for situations in which contracts cannot be enforced and thus where penalizing a norm or contract violator is not possible. Another problem arises

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Seeing and knowing others 485 in situations where punishment is possible. Indeed, punishment of norm violators who free-ride on others has been observed to be a very powerful instrument to motivate prosocial behavior (for example, Fehr and Gaechter 2000). However, punishment can also lead to welfare losses (Egas and Riedl 2005), especially if punishment is unconstrained and those who feel treated unfairly can retaliate with counter-punishment (Nikiforakis 2008). Punishment in these settings is usually costly to the individual punishing and can thus not be explained from an own material welfare-maximizing point of view. However, norm violations have been observed to very reliably trigger emotions, specifically anger (Bosman and van Winden 2002; de Quervain et al. 2004), which under certain conditions can be inhibited by feelings of guilt or shame (Hopfensitz and Reuben 2009). An ability to detect an interaction partner’s disposition to feel such emotions might thus be informative to judge the ‘probability’ of being punished. The ability to judge an interaction partner’s costs and abilities for punishment might be informative to judge the potential expected ‘damage’ from punishment. The two dimensions seem not uncorrelated. Sell et al. (2009) hypothesize that individuals with enhanced abilities to inflict costs have a better bargaining position in conflicts and, consequently, they might be more prone to anger. The face seems to carry information that enables participants to detect who will reject a low offer in an ultimatum game (van Leeuwen et al. 2014). The same study observed that own facial asymmetry is positively correlated with getting angry after receiving a low offer, as well as with the decision to reject a low offer. Furthermore, observers correctly perceive facial asymmetry as a cue for getting angry after norm violations, though they underestimate the magnitude of the correlation.

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TALKING TO THE OTHER

When agents talk many things happen. Most obviously information is transmitted from the speaker to the listener. However, this information might consist of much more than the meaning of the words that are exchanged. The tone of the voice might carry many additional signals. That these signals are informative and detected by others has been observed for the voice of women over the menstrual cycle. Pipitone et al. (2008) recorded voice samples by women at different moments of their menstrual cycle. Results showed a significant increase in voice attractiveness ratings as the estimated risk of conception increased across the menstrual cycle in naturally cycling women. In line with this, there was no effect observed for women using hormonal contraceptives. While men find higher pitch voices in women to be more attractive, feminine and healthier (for example, Feinberg et al. 2008), women have been observed to find men with low pitch to be more attractive (for example, Collins 2000). Lower voice pitch in men has even been observed to be related to higher reproductive success in a population of hunter gatherers (Apicella et al. 2007). Preference for low-pitched voices in men has also been observed in respect of political candidates (Tigue et al. 2012) and might influence behavior in many other circumstances. In addition to the explicit and implicit information transmitted by speech, public announcements might even be informative when all implied parties already had the announced information. Specifically, speech is very well suited to generating common knowledge. Not only does each agent possess some information, but also all agents know that all the others have this information and that these others know that others also have

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the information, and so on. Common knowledge is especially useful in coordination games. In a coordination game Alice will try to act the same way as Bob, but at the same time she knows that Bob will try to act the same way as Alice. To break through this cycle, information that is common to all players might act as a coordination device. Many real-life situations involve coordination. Also, what might otherwise be considered as not-informative communication (for example, advertising) might serve to create common knowledge. Advertising broadcast during the Super Bowl (that is, advertising that many people will see and that people know that many others will see) comes especially often from products that would profit from common knowledge (for example, a new technology that is only interesting if many others also use it; see Chwe 2013). Hearing someone speak something out loud further informs us about how committed that person will be in the future to do what he or she said. Most people are much more likely to stick with a decision once it has been announced loudly. Reasons for this are a motivation to keep a reputation of being consistent and reliable, but also an avoidance of situations generating cognitive dissonance. This has been modeled by economists as ‘guilt aversion’; the anticipation of feeling bad after breaking a promise and the subsequent tendency to stick with what was said during cheap talk (for example, Charness and Grosskopf 2004; Charness and Dufwenberg 2006). Ideally the study of cheap talk concerns ‘how people skeptically, but reasonably and mostly conventionally, interpret language. It is the study of rational people who know how to communicate in the ordinary way’ (Farrel and Rabin 1996). This implies that messages that are ‘cheap’ (that is, by not influencing the players’ payoffs) can nevertheless influence players’ choices in what they communicate and how they react to it. This, in turn, can also influence payoffs.

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KNOWING THE OTHER

Besides hearing gossip, seeing and talking with others, most importantly we ‘know’ many of the agents we interact with. This knowing implies that we have information about preferences, skills and habits of the other with much less error proneness than from the previously discussed indirect channels. Note that these translate into information about the other’s payoffs for different outcomes (preferences), his or her strategy set (skills) and the to-be-expected strategy (habits). However, knowing the other player will also influence our own preferences, such as when we play with someone we really care about (and thus his or her utility will positively influence our utility) versus interacting with someone we dislike (with an inverse relationship between utilities). We thus have to differentiate between interactions between unknown players and between players who have a history of previous interactions. Having such ‘social ties’ does not necessarily imply that the players will cooperate more or trust each other more. Depending on the previous interaction history, a novel interaction will be influenced in one or the other way (for example, van Dijk and van Winden 1997). The valuation of others has been observed to be positively influenced, for example, by a history of successful interactions in a public good environment (van Dijk et al. 2002). Harm done to someone we care about can feel like harm to ourselves. So-called ‘mirror neurons’ for people with strong social ties can lead to emotional experiences very similar

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Seeing and knowing others 487 to the actual experience of an observed person (Singer et al. 2004; Singer and Frith 2005). For example, when one partner of a couple received slight electric shocks, neuroimaging confirmed that actual pain was experienced by the observing partner. Thus what economists call ‘other regarding preferences’ might be reinforced by biological factors making the other’s pain our own. Although we are influenced when our interaction partner is a friend, colleague or acquaintance, similar mechanisms are at work when we interact with a person from a specific group that we know. This ‘in-group’ versus ‘out-group’ bias has received much attention in economics and psychology. The observation that such biases exist even in situations where grouping is more or less arbitrary led to the development of the so-called ‘minimal-group paradigm’ (Tajfel et al. 1971). The work by Tajfel et al. was initially studying behavior between ‘meaningless’ groups to be able to sequentially add relevant group information in order to find which characteristics actually lead to discrimination between groups. The surprising finding was that even when group identity is based on quasi-arbitrary allocation (for example, picture preferences or a coin flip), group discrimination was observed. Since then, the paradigm has been used in a multitude of studies to investigate the mechanisms of group discrimination (for example, Brewer 1979, 1999; Tajfel and Turner 1979; Yamagishi and Kiyonari 2000). Evidence now converges to the insight that biases mainly lead to in-group favoritism and less often to out-group punishment, and such behavior has been linked to the presence of the neuropeptide oxytocin. Oxytocin plays an important role during and after childbirth, facilitating birth, maternal bonding and lactation. Oxytocin has been observed to play a role in pair bonding and situations requiring mutual trust, and is therefore sometimes referred to as the ‘bonding hormone’. In studies on minimal groups, oxytocin has been observed to lead to ‘a “tend and defend” response in that it promoted in-group trust and cooperation, and defensive, but not offensive, aggression toward competing out-groups’ (De Dreu et al. 2010, p. 1408). Thus a biological mechanism linked to bonding is, through its impact on in-group favoritism, also a cause of intergroup biases. Favoritism easily triggers negative emotions and protest in those that feel disfavored and thus oxytocin might trigger a chain reaction leading to between-group conflict. Ethnocentrism is thus to a certain degree the result of an adaptive process that helps individuals and their groups, but that also has negative consequences by generating intergroup bias and conflict (De Dreu et al. 2011). A very specific group of interaction partners are members of our own family. Biology and anthropology have long studied the theoretical and actual interactions between genetically related individuals, but also family economics has in recent years departed from the traditional view that a household is simply ‘one’ economic decision maker. Economic models are traditionally much less concerned with the actual genetic relatedness but see the household as a social structure based on institutions such as marriage and legal contracts. The biological approach is mainly based on the concept of kin selection, that is, that individually costly behavior might benefit the individual’s genes if beneficiaries are genetically related. Hamilton’s rule popularized this concept in mathematical terms and it specifies that genes can increase in frequency when the genetic relatedness of a recipient to an agent multiplied by the benefit to the recipient is greater than the reproductive cost to the agent. Kin selection is often cited when explaining individual costly altruistic behavior. Helping others at a cost or fighting to protect the life of others might be costly for the

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individual but the cost is outweighed by the benefits to the individual’s genes carried in those that receive the help. If ‘genetically related’ is thus generating one sort of group, the corresponding outgroup can be seen as those that are ‘not genetically related’. While this concept is obviously less clearly defined, since even with individuals who are not direct relatives we do share a majority of our genes, it nevertheless can influence behavior. Specifically reproduction partners should ideally not be close relatives, as so-called ‘inbreeding’ increases the chances of offspring being affected by recessive or deleterious traits. Also, portraits that are modified to seem ‘less’ related to the individual are generally rated as more attractive (DeBruine et al. 2011), and the body smell of individuals with dissimilar genotypes has been observed to be preferred. This mechanism is linked to a set of genes (the major histocompatibility complex, MHC) that plays an important role in the human immune system. Humans have been shown to prefer the body odor of potential partners that have a different MHC, which will thus result in offspring benefiting from two different types of immune systems (Wedekind et al. 1995). The impact of this preference can be also observed with respect to artificial smells, such as perfumes. Notably large individual differences concerning perfume preferences exist among humans. These preferences are also remarkably stable over the lifetime. When participants are asked to report perfumes they would like to use them themselves, ingredients in the preferred perfumes have been shown to imitate and enhance signals from the person’s natural smell (Milinski and Wedekind 2001). Family members are a combination of people who are closely related (parents, siblings and children) but who are also unrelated individuals (our partner and the partners of our siblings and children). That nevertheless all family members are part of a specific group is related to legal and cultural constraints that apply to it. Family members have specific rights but also obligations with respect to each other. Consequently, economists have long modeled the family as one decision-making unit, without investigating how preferences or choices of a family come about. Specifically, most models assume that a household is able to reach a Pareto efficient allocation, that is, that it is not possible to find another allocation that could increase simultaneously the welfare of all household members. Relatedly many models assume that households do ‘income-pooling’, that is, the income of all household members is pooled and then allocated to its members according to a household specific sharing rule. Experimental evidence is now suggesting that households do not always reach efficient outcomes and that they do not always aim at maximizing the household income pool. Relatedly, sociological studies often confirm that family members frequently have conflicting preferences. Contributions in a public good game, for example, are observed to be higher among family members than among strangers, but not at the full level of cooperation (for example, Peters et al. 2004). Spouses were more willing to invest in a public good when they considered themselves as having more control over the final allocation across partners (Mani 2010). Whether spouses aim at maximizing the household’s total income seems to be related to possible fairness issues. Specifically some spouses are observed to prefer equal but less efficient allocations when this means avoiding a very unequal outcome (Beblo et al. 2015). That is, spouses seem to be willing to pay a cost to avoid situations that are likely to lead to conflict and disagreements. A strategy that might in the long run help the couple, even if it comes at a monetary cost.

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6

CONCLUSION

Gossip, seeing, hearing and knowing our interaction partner are often dismissed by economists as uninformative noise or cheap talk. In this chapter I have tried to give an idea of why many of the influences and ‘biases’ that have been observed in respect of our interaction partners might be actually more than that. Owing to its importance in almost all interactions, we need to take into account what agents ‘assume’ they know about their interaction partner and what they ‘do’ know (through previous interactions). Doing so might turn many seemingly irrational biases into behaviors adapted to make us interact with the right people at the right time (in the right way).

NOTES *

Support from the ANR–Labex IAST and funding through the ANR-TIES (ANR 2010 JCJC 1803 01) is gratefully acknowledged. For helpful comments and discussions, I also thank the participants in three workshops on social ties in Toulouse and members of the Institute for Advanced Study in Toulouse: especially Ernesto Reuben, Eva van den Broek, Aljaz Ule, Francois Cochard, Giuseppe Attanasi, Frederic Moisan, Emiliano Lorini and Boris van Leeuwen. 1. Exceptions are games where the Nash prediction for the stage game (that is, the one-shot game) leads to a payoff superior to the minimax payoff.

REFERENCES Apicella, C.L., D.R. Feinberg and F.W. Marlowe (2007), ‘Voice pitch predicts reproductive success in male hunter-gatherers’, Biology Letters, 3 (6), 682–4, doi:10.1098/rsbl.2007.0410. Axelrod, R. (1984), The Evolution of Cooperation, New York: Basic Books. Beblo, M., D. Beninger, F. Cochard, H. Couprie and A. Hopfensitz (2015), ‘Efficiency-equality trade-off within French and German couples – a comparative experimental study’, Annals of Economics and Statistics, 117–18 (January–June), 233–52. Binmore, K.G. (1992), ‘Evolutionary stability in repeated games played by finite automata’, Journal of Economic Theory, 57 (2), 278–305. Bonnefon, J.-F., A. Hopfensitz and W. De Neys (2013), ‘The modular nature of trustworthiness detection’, Journal of Experimental Psychology: General, 142 (1), 143–50. Bosman, R. and F. van Winden (2002), ‘Emotional hazard in a power to take experiment’, Economic Journal, 112 (476), 147–69. Brewer, M.B. (1979), ‘In-group bias in the minimal intergroup situation: a cognitive-motivational analysis’, Psychological Bulletin, 86 (2), 307–24. Brewer, M.B. (1999), ‘The psychology of prejudice: ingroup love and outgroup hate?’, Journal of Social Issues, 55 (3), 429–44. Carré, J.M. and C.M. McCormick (2008), ‘In your face: facial metrics predict aggressive behaviour in the laboratory and in varsity and professional hockey players’, Proceedings of The Royal Society B, 275 (1651), 2651–6. Centorrino, S., E. Djemai, A. Hopfensitz, M. Milinski and P. Seabright (2015), ‘Honest signalling in trust interactions: smiles rated as genuine induce trust and signal higher earnings opportunities’, Evolution and Human Behavior, 36 (1), 8–16. Chang, L.J., B.B. Doll, M. van’t Wout, M.J. Frank and A.G. Sanfey (2010), ‘Seeing is believing: trustworthiness as a dynamic belief’, Cognitive Psychology, 61 (2), 87–105. Charness, G. and M. Dufwenberg (2006), ‘Promises and partnership’, Econometrica. 74 (6), 1579–601. Charness, G. and B. Grosskopf (2004), ‘What makes cheap talk effective? Experimental evidence’, Economics Letters, 83 (3), 383–9. Chwe, M.S.-Y. (2013), Rational Rituals: Culture, Coordination and Common Knowledge, Princeton, NJ: Princeton University Press. Cogsdill, E.J., A.T. Todorov, E.S. Spelke and M.R. Banaji (2014), ‘Infering character from faces: a developmental study’, Psychological Science, 25 (5), 1132–9.

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Quist (2011), ‘Oppositesex siblings decrease attraction, but not prosocial attributions, to self-resembling opposite-sex faces’, Proceedings of the National Academy of Sciences, 108 (28), 11710–14. Deaner, R.O., S.M.M. Goetz, K. Shattuck and T. Schnotala (2012), ‘Body weight, not facial width-to-height ratio, predicts aggression in pro hockey players’, Journal of Research in Personality, 46 (2), 235–8. Duchenne de Boulogne, G. (1862), The Mechanism of Human Facial Expression, Paris: Jules Renard. Efferson, C. and S. Vogt (2013), ‘Viewing men’s faces does not lead to accurate predictions of trustworthiness’, Scientific Reports, 3 (10 January), art. 1047, doi:10.1038/srep1047. Egas, M. and A. Riedl (2008), ‘The economics of altruistic punishment and the maintenance of cooperation’, Proceedings of the Royal Society B – Biological Sciences, 275 (1637), 871–8. Engelmann, D. and U. Fischbacher (2009), ‘Indirect reciprocity and strategic reputation building in an experimental helping game’, Games and Economic Behavior, 67, 399–407. Farrell, J. and M. Rabin (1996), ‘Cheap talk’, Journal of Economic Perspectives, 10 (3), 103–18. Fehr, E. and S. Gaechter (2000), ‘Cooperation and punishment in public goods experiments’, American Economic Review, 90 (4), 980–94. Feinberg, D.R., L.M. DeBruine, B.C. Jones and D.I. Perrett (2008), ‘The role of femininity and averageness of voice pitch in aesthetic judgments of women’s voices’, Perception, 37 (4), 615–23. Finan, F. and L. Schechter (2012), ‘Vote-buying and reciprocity’, Econometrica, 80 (2), 863–81. Gomez-Valdes, J., T. Hunemeier, M. Quinto-Sanchez, C. Paschetta, S. de Azevedo, M.F. Gonzalez et al. (2013), ‘Lack of support for the association between facial shape and aggression: a reappraisal based on a worldwide population genetics perspective’, PLoS ONE, 8 (1), e52317, doi:10.1371/journal.pone.0052317. 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Milinski, M., D. Semmann and H.J. Krambeck (2002), ‘Donors to charity gain in both indirect reciprocity and political reputation’, Proceedings of the Royal Society, 269 (1494), 881–3. Molleman, L., E. van den Broek and M. Egas (2013), ‘Personal experience and reputation interact in human decisions to help reciprocally’, Proceedings of The Royal Society B, 280 (1757), 20123044, doi:10.1098/ rspb.2012.3044. Nikiforakis, N. (2008), ‘Punishment and counter-punishment in public good games: can we really govern ourselves’, Journal of Public Economics, 92 (1–2), 91–112. Nowak, M. and K. Sigmund (2005), ‘Evolution of indirect reciprocity’, Nature, 437 (7063), 1291–8. Peters, E.H., A.N. Ünür, J. Clark and W. D. Schulze (2004), ‘Free-riding and the provision of public goods in the family: a laboratory experiment’, International Economic Review, 45 (1), 283–99. Pipitone, R.N. and G.G. Gallup (2008), ‘Women’s voice attractiveness varies across the menstrual cycle’, Evolution and Human Behavior, 29 (4), 268–74. Rule, N.O., N. Ambady, R.B. Adams, H. Ozono, S. Nakashima, S. Yoshikawa and M. Watabe (2010), ‘Polling the face: prediction and consensus across cultures’, Journal of Personality and Social Psychology, 98 (1), 1–15. Scharlemann, J.P.W, C.C. Eckel, A. Kacelnik and R.K. Wilson (2001), ‘The value of a smile: game theory with a human face’, Journal of Economic Psychology, 22 (5), 617–40. Seinen, I. and A. Schram (2006), ‘Social status and group norms: indirect reciprocity in a repeated helping experiment’, European Economic Review, 50 (3), 581–602. Sell, A., J. Tooby and L. Cosmides (2009), ‘Formidability and the logic of human anger’, Proceedings of the National Academy of Sciences, 106 (35), 15073–8.

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Seeing and knowing others 491 Singer, T. and C. Frith (2005), ‘The painful side of empathy’, News & Views. Nature Neuroscience, 8 (7), 845–6. Singer, T., B. Seymour, J.P. O’Doherty, K.E. Stephan, R.J. Dolan and C. Frith (2004), ‘Empathy for pain involves the affective but not sensory component of pain’, Science, 303 (5661), 1157–62. Stirrat, M. and D.I. Perrett (2010), ‘Valid facial cues to cooperation and trust: male facial width and trustworthiness’, Psychological Science, 21 (3), 349–54. Tajfel, H. and J. Turner (1979), ‘An integrative theory of intergroup conflict’, in W. Austin and S. Worchel (eds), The Social Psychology of Intergroup Relations, Pacific Grove, CA: Brooks/Cole, pp. 33–47. Tajfel, H., M. Billig, R. Bundy and C. Flament (1971), ‘Social categorization and intergroup behaviour’, European Journal of Social Psychology, 1 (2),149–78. Tigue, C.C., D.J. Borak, J.J.M. O’Connor, C. Schandl and D.R. Feinberg (2012), ‘Voice pitch influences voting behavior’, Evolution and Human Behavior, 33 (3), 210–16. Todorov, A., M. Pakrashi and N.N. Oosterhof (2009), ‘Evaluating faces on trustworthiness after minimal time exposure’, Social Cognition, 27 (6), 813–33. Trivers, R.L. (1971), ‘The evolution of reciprocal altruism’, Quarterly Review of Biology, 46 (1), 35–57. Van Apeldoorn, J. and A. Schram (2014), ‘Indirect reciprocity: a field experiment’, working paper, CREED and Tinbergen Institute, Amsterdam School of Economics. Van de Rijt, A., S.M. Kang, M. Restivo and A. Patil (2014), ‘Field experiments of success-breeds-success dynamics’, Proceedings of the National Academy of Sciences, 111 (19), 6934–9. Van Dijk, F. and F. van Winden (1997), ‘Dynamics of social ties and local public good provision’, Journal of Public Economics, 64 (3), 323–41. Van Dijk, F., J, Sonnemans and F. van Winden (2002), ‘Social ties in a public good experiment’, Journal of Public Economics, 85 (2), 275–99. Van Leeuwen, B., C.N. Noussair, T. Offerman, S. Suetens, M. van Veelen and J. van de Ven (2014), ‘Predictably angry: facial cues provide a credible signal of destructive behavior’, working paper, Tilburg University. Van’t Wout, M. and A. Sanfey (2008), ‘Friend or foe: the effect of implicit trustworthiness judgments in social decision-making’, Cognition, 108 (3), 796–803. Verplaetse, J., S. Vanneste and J. Braeckman (2007), ‘You can judge a book by its cover: the sequel. A kernel of truth in predictive cheating detection’, Evolution and Human Behavior, 28 (4), 260–71. Wedekind, C. and M. Milinski (2000), ‘Cooperation through image scoring in humans’, Science, 288 (5467), 850–52. Wedekind, C., T. Seebeck, F. Bettens and A.J. Paepke (1995), ‘MHC-dependent mate preferences in humans’, Proceedings of the Royal Society B, 282 (1801), 245–9. Willis, J. and A. Todorov (2006), ‘First impressions: making up your mind after a 100-ms exposure to a face’, Psychological Science, 17 (7), 592–8. Yamagishi, T. and T. Kiyonari (2000), ‘The group as the container of generalized reciprocity’, Social Psychology Quarterly, 63 (2), 116–32. Yamagishi, T., S. Tanida, R. Mashima, E. Shimoma and S. Kanazawa (2003), ‘You can judge a book by its cover: evidence that cheaters may look different from cooperators’, Evolution and Human Behavior, 24 (4), 290–301. Yoeli, E., M. Hoffman, D.G. Rand and M.A. Nowak (2013), ‘Powering up with indirect reciprocity in a largescale field experiment’, Proceedings of the National Academy of Sciences, 110 (2), 10424–9. Zebrowitz, L.A. and G. Rhodes (2004), ‘Sensitivity to “bad genes” and the anomalous face overgeneralization effect: cue validity, cue utilization, and accuracy in judging intelligence and health’, Journal of Nonverbal Behavior, 28 (3), 167–85.

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28 Weakness of will and stiffness of will: how far are shirking, slackening, favoritism, spoiling of children, and pornography from obsessivecompulsive behavior?* Elias L. Khalil

1

INTRODUCTION

This chapter studies phenomena as diverse as malaise, favoritism, the spoiling of children, pornography-imaging, and shirking. The chapter argues that these phenomena, despite their diversity, are different instances of weakness of will. However, this argument can be easily made once we depart from the standard view of weakness of will. The standard view traces weakness of will to biased preferences – as if the main issue is a conflict between supposed present and future preferences or putative present and future selves (for example, Strotz 1956; Laibson 1997; O’Donoghue and Rabin 1999, 2001). By tracing it to supposed biased preferences, the economics literature has mistakenly coupled weakness of will with time preferences per se, where the agent ‘naturally’ favors one apple today over one apple tomorrow. Also, with weakness of will, the favoring is presumed to be more excessive, that is, the product of extra-favoring of present preferences. In contrast, this chapter aims to provide a different view. The chapter couples weakness of will with impulsivity – a proclivity that has little to do with time preferences (see DeYoung 2010). As defined here, impulsivity arises from whatever the reason that seduces the agent to deviate from the optimal decision. Some of these reasons can be whimsical or erratic, which are ignored here since they cannot be analyzed systematically. Other reasons, the focus here, can be grounded systematically on beliefs on the riskiness of choice, where such beliefs are biased beliefs. As defined here, the term ‘biased beliefs’ denotes suboptimal beliefs where the agent misrepresents to the self. So, if we ignore the importance of erratic or whimsical causes, we can identify weakness of will with biased beliefs. Such identification affords a broad platform to analyze diverse phenomena such as malaise, favoritism, pornography-imaging, and so on. The proposed coupling of weakness of will with biased beliefs has a more important payoff. It sheds light on biased beliefs in the other direction, towards what is called here ‘obsessive-compulsive behavior’ or, in short, ‘compulsivity.’ Compulsivity differs from its pathological excess, known as ‘obsessive-compulsive disorder,’ as explained below. Examples of compulsivity includes over-saving, over-checking locked door, overchecking turned off stoves, over-washing the hands, and so on. Compulsivity amounts to excessive caution, where the person is over-afraid of falling into weakness of will. Parallel to how impulsivity arises from weakness of will, compulsivity stems from what can be coined as ‘stiffness of will’.1 While stiffness of will involves anxiety-ridden safety procedures and security-enhancement actions, weakness of will involves slipshod 492

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Weakness of will and stiffness of will 493 procedures and actions, such as under-saving, under-washing of hands, under-checking stoves and doors, and so on. Despite the difference in behavior, impulsivity and compulsivity are analytically alike. Weakness of will (impulsivity) entails recklessness based, at a deeper level, on higherthan-optimal prior beliefs about success in the face of risk. In contrast, stiffness of will (compulsivity) entails excessive precautionary actions based, at a deeper level, on lowerthan-optimal prior beliefs about success in the face of risk. So, the difference between weakness of will and stiffness of will is about the direction of the biased belief – whether it is towards pessimism or optimism. Aside from the two promised payoffs, how could economists, accustomed for so long to commencing analysis of weakness of will with biased preferences, change their ways and commence analysis instead with biased beliefs? The next section facilitates such a transition on the basis of three claims: 1. 2.

3.

Shirking has little to do with principal–agent conflict – and, at a deeper level, it has more to do with intra-individual conflict. Intra-individual conflict, in turn, has little to do with present-biased preferences – and, at a deeper level, it has more to do with impulsive–calm conflict – where the impulsive (over-confident) self operates according to a higher probability of success than the probability used by the calm (impartial) self. Impulsive–calm conflict, in turn, has little to do with bio-psychological givens – and, at a deeper level, it has more to do with endogenous formation of beliefs as heuristics. While people, in general, adopt heuristics as a result of bounded rationality, the heuristics are usually smart in the sense of being the best or the optimal. However, in some situations, the heuristics may become non-smart, stray away from the optimal heuristics. These situations can be categorized into two kinds. There is the situation when the heuristics stray away in the direction of over-confidence, giving rise to impulsivity (weakness of will). There is the situation when the heuristics stray away in the direction of under-confidence, giving rise to compulsivity (stiffness of will).

We need to be clear about the terms of ‘bounded rationality’ and ‘smart decisionmaking’. This chapter uses the terms interchangeably. Herbert Simon (1957) coined the term ‘bounded rationality’ and Gerd Gigerenzer (2000; see Gigerenzer and Selten, 2001) developed it further. The term, as used by Simon and Gigerenzer, differs from the neoclassical sense. This chapter follows the neoclassical sense, as opposed to Simon and Gigerenzer’s sense, of the term ‘bounded rationality’ and, correspondingly, the term ‘smart decision-making’. Simon and Gigerenzer’s notion of bounded rationality is unrelated to rationality in the neoclassical sense of responding to changing incentives (Khalil 2011b). For Simon and Gigerenzer, people do not respond to incentives not because of some critical threshold that some actions need as proposed by the neoclassical sense. For them, bounded rationality is about the priority of habits and routines, which is unrelated to any neoclassical account of critical threshold. Simon and Gigerenzer take habits and routines as the entry-point of any explanation of behavior. For them, humans are, by default, attached to the status quo as long as the habits and routines are functional. Humans do not have a concept of what is optimal because, to start with, the optimum cannot be theoretically

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defined. Humans rather accept the status quo because it is the nature of how humans, and all living organisms, conduct their behavior. Such organisms accept the status quo, and whatever routines and habit they entail, as long as the status quo ‘satisfices’ a minimum level of needs. Humans start to question the habits and routines, and reject the status quo, when the routines and habits become dysfunctional, that is, fail to satisfice the minimum level of needs. For Simon and Gigerenzer, humans are smart decision-makers (that is, boundedly rational) in the sense of adopting routines and heuristics that are functional in the habituation sense, not in the optimal sense. This chapter, to the contrary, uses the term ‘smart decision-making’ and ‘bounded rationality’ in the neoclassical sense. According to the neoclassical sense, people adopt habits, heuristics, and beliefs as generalizations. Such generalizations are needed since people do not have the time or the resources to undertake deep and scientific assessment of every risk or opportunity that they encounter. It is always true that generalizations are costly when they turn out to be wrong. But they are optimal, in the neoclassical sense, when the cost exceeds the benefit when they succeed. When a generalization succeeds, the person, in effect, avoids the enormous cost of coming up with a more accurate belief. So, a belief is smart as long as its expected benefit exceeds its expected cost. Given such neoclassical-based boundedly rational or smart belief, the rational agent would resist updating the belief with each minor change of incentives. The agent may uphold a belief, contrary to incentive or factual evidence, because the incentive or evidence can be the product of noise or temporary, and it would be more efficient to stay the course with the habitual belief. But if the change of incentives is no longer minor, or the factual evidence is no longer the outcome of noise, the person would change the habit or the routinized belief. Put differently, the rational agent upholds a belief within reason, where such reason is defined by the expected costs and benefits of changing one’s belief. Otherwise, if the agent updates a belief with every mistake arising from minor change of incentives or facts, it would be a ‘perfectly Bayesian belief’, a belief that is updated constantly. However, the perfectly Bayesian belief would not be a smart (optimal) belief. The rational agent would rather avoid constant updating, and hence incur the cost of some mistakes, because constant updating would involve even greater cost than the incurred cost of mistakes. Given such neoclassical understanding of smart decision-making, it is now possible to define over-confidence and under-confidence. They are dispositions that give rise to non-smart beliefs, that is, to beliefs that the person should not adopt. However, people do adopt them and, consequently, succumb to weakness of will (in the case of overconfidence) or succumb to stiffness of will (in the case of under-confidence). The following section reviews the literature. Then the chapter analyzes how shirking, the canonical case, is better understood as weakness of will, rather than via the principalagent framework. This is possible once weakness of will is modeled via biased beliefs, that is, the outcome of impulsive–calm conflict. It also shows the condition of how such conflict can take the opposite route, a compulsive–calm conflict that originates stiffness of will. The chapter proceeds to show how slackness (malaise), favoritism, the spoiling of children, and pornography-imaging are, similar to shirking, instances of impulsive–calm conflict. In each case, the chapter identifies the opposite case, the compulsive–calm conflict that occasions stiffness of will. So, the task here is the exposition of the isomorphic structure that underpin diverse phenomena of weakness of will and stiffness of will. Given the task, this chapter differs

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Weakness of will and stiffness of will 495 broadly from papers in the economics literature. This chapter, unlike such literature, does not aim to expose the necessary conditions under which shirking as opposed to cooperation takes place. The task is rather conceptual, namely, to expose the common structure of weakness of will or stiffness of will that underpin varied and seemingly unrelated phenomena.

2

THE CORE ARGUMENT

The proposed confidence-biased belief approach can be summed up in three propositions: Proposition 1: Shirking as weakness of will It is usually supposed by the principal-agent framework that agents shirk in order to maximize benefit at the expense of the principal. This chapter argues the contrary: the conflict is an intra-individual conflict rather than a principal-agent conflict – even in the case of shirking. Proposition 2: Weakness of will as over-confidence It is usually supposed by the standard present-biased preferences framework that weakness of will is the outcome of quasihyperbolic discounting. This chapter proposes an alternative framework: the issue is an intra-individual conflict not in the sense of present-biased preferences – but rather in the sense of confidence-biased beliefs – specifically in the direction of optimism, that is, over-confidence. The confidence-biased beliefs can take an opposite direction, namely, the formation of under-confidence beliefs that underpin compulsivity (stiffness of will). Such compulsivity is behind obsessions and excessive care – the apparent opposite of slackening, under-saving and other instances of weakness of will. Either over- or underconfident beliefs trump the beliefs of impartial (calm) self. The impartial self is ‘calm’ in the sense that the self updates its beliefs – namely, the probability of success in risky behavior – in a Bayesian fashion, which is neither the case with the impulsive self nor with the compulsive self. Proposition 3: Confidence-biased belief as sub-rational representativeness heuristic It is commonly supposed that over-confidence (impulsivity or weakness of will) and underconfidence (compulsivity or stiffness of will) are given traits of the bio-psychological profile. Such explanation is unsatisfactory. Confidence-biased beliefs can arise endogenously: the agent experiences or selects, in a biased way, a sample of events that exaggerates the incidences of either success or failure in risky behavior. The sub-rational agent (the impulsive and the compulsive) generalizes from the biased sample to form subrational representativeness heuristic. While a generalization or a representative heuristic, given bounded rationality, is normally rational (smart), it becomes sub-rational (nonsmart) when one propounds it when one should have ditched the heuristic. The proposed analytical similarity between weakness of will (impulsivity) and stiffness of will (compulsivity) is unexpected. As stated above, the two phenomena, as observed by others (for example, Kalis et al. 2008, p. 410), are opposite ends of the pole: While both are suboptimal behavior, weakness of will amounts to excessive carelessness and obsessivecompulsive behavior amounts to excessive carefulness.

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Obsessive-compulsive behavior, as studied here, is ubiquitous, almost every human being exhibits stiffness of will on some occasions as often as he or she exhibits weakness of will. Many anthropologists regard a great deal of rituals linked usually to religious practices – ranging from fasting, self-deprivation, self-mutilation, excessive hygienic rituals – as disproportionate acts of precaution to fend against the risks of disease, natural disasters, and famine. Such cultural rituals correspond to hard-wired psychological mechanisms at the individual level, which they call ‘hazard-precaution systems’ (see Dulaney and Fiske 1994; Fiske and Haslam 1997; Liénard and Boyer 2006; Boyer and Liénard 2006). So, stiffness of will (compulsivity) should not be confused with pathological compulsivity, what psychologists diagnose as obsessive-compulsive disorder (OCD) or obsessive-compulsive personality disorder (OCPD). This chapter is not about pathological compulsivity – nor about how to differentiate OCD and OCPD (see Pinto et al. 2014). Even when mild, we can judge that obsessivecompulsive behavior – ranging from over-washing of hands, certainty effect, to overavoidance of air travel – involves excessive or suboptimal actions of carefulness, that is, responses to exaggerated perception of risks (see Davis 2008). As Anthony Pinto et al. (2014) document, at least with respect to OCPD, people with compulsive behavior have great capacity to delay reward, the extreme opposite of impulsive behavior (weakness of will). They maintain that impulsivity and compulsivity are extreme ends of the same continuum.

3

REVIEW OF LITERATURE

It is appropriate for economists, then, to advance a single model that can analyze simultaneously stiffness of will (compulsivity) and weakness of will (impulsivity). Economists have so far, unfortunately, focused on weakness of will. This could be for two reasons. First, weakness of will has, more than stiffness of will, debilitating effects on future welfare in terms of under-saving for retirement (Laibson 1997). Second, given the entrypoint of biased preferences, which invokes the issue of time preferences, it is hard to explain stiffness of will. It just does not seem intuitive or natural to suppose that people have a biased preference towards future consumption when they have been teaching about discounted future revenues. So, as expected, the literature has ignored stiffness of will – and does not provide a single theory that links it to weakness of will. The focus on preferences has led researchers to pursue a misleading path of questions: why do agents have present-biased preferences? This led them to distinguish between two supposedly different kinds of utility: the reasonable or optimal preferences and the myopic or suboptimal preferences. The proposition that we have two kinds of utility has taken different forms. At the hand of psychologists such as Daniel Kahneman et al. (1997; Kahneman, 1994), it took the form of distinguishing between putatively ‘experienced utility’, which captures hedonic sensation of consumption, and ‘decision utility’, which captures anxiety and anticipation prior to consumption as well as rejoice and regret after consumption. So, while experienced utility captures the myopic or suboptimal preferences, decision utility approximates the reasonable or optimal preferences. Such a dualism is not a necessary conclusion for every advocate of the biased-preferences approach. For instance, George Ainslie (2012), who subscribes to the biased-preferences

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Weakness of will and stiffness of will 497 approach, does not support the dualism conclusion. However, the dualism conclusion is gaining ground. For instance, George Loewenstein (1996) attributes experienced utility to visceral factors that enslave the present self and make it deviate from what is rational (see also Bernheim and Rangel 2004; McClure et al. 2004; Ditto et al. 2006). In particular, Loewenstein and O’Donoghue (2004) suppose a disjunction between two different systems in the brain, where the ‘affective system’ is triggered by hedonic sensations while the ‘deliberative system’ takes into consideration future interest. Colin Camerer (2006), and before him Berridge (2003), argues that there are two kinds of utility, which he calls ‘wanting’ and ‘liking’, respectively. He traces them back to two separate neural systems. However, neuroscientists (for example, Kable and Glimcher 2007) find that the known reward valuation centers (ventral striatum, medial prefrontal cortex, and posterior cingulate) all respond in tandem at the point of consumption. That is, there is no conflict of two separate systems of utility. Of more importance, the appeal to two systems actually begs more questions than it solves. For instance, how does one system of utility relate to the other? This question raises the old ‘body–mind’ problem that has risen in light of the Cartesian dichotomy between the emotions and reason. New discoveries in neuroscience dispute the Cartesian dichotomy and, hence, avoid the problematic issue of how to relate one system of utility to the other (for example, Damasio 1994). Long ago, Adam Smith (1759 [1976]) argued that rational decision is nothing other than confronting one sentiment, which arises from the myopic heat of the moment, with a calm sentiment that takes into consideration the emotion or interest of the future self (see Khalil 2010, 2015a). Smith’s view, adopted here, portrays the individual as involved in an internal dialogue. The dialogue is between a calm or rational self, what Smith called the ‘impartial spectator’ that resides within the breast, and the partial or sub-rational self. The sub-rational self can be impulsive, which gives rise to weakness of will, or compulsive, which gives rise to stiffness of will. The dialogue between the two selves concerns the probability of succeeding in the face of risk. George Ainslie (2001, 2012), with little reference to Smith, marches along with Smith’s approach, namely, the self must be seen as unified, in his analysis of weakness of will. Ainslie is a psychologist whose early work (Ainslie 1975) alerted economists about hyperbolic discounting (see also Ainslie 1992). For Ainslie, the person can be aware of how the impulsive self can be over-confident, which prompts the calm or rational self to set up internal rules, what Smith called ‘self-command’, about behavior. The proposed confidence-biased preferences open up a new vista that supersedes the well-entrenched divide between positive and normative theories of rationality. For positive theory, as expressed in present-biased preferences view, what matters is the description of behavior. Such methodological agenda neglects the fact that what is hidden is that behavior is the outcome of an inner dialogue, or conflict, and hence misses what lies deeply below apparent phenomena. For normative theory, as in recent developments of decision theory (Gul and Pesendorfer 2001, 2004a, 2004b), the deviations from rationality are not deviations if we construct new axioms. This chapter proposes an alternative framework, which can simultaneously explain diverse instances of weakness of will as well as account for its antithesis, stiffness of will. As such, this chapter does not offer a mathematical model – nor an axiomatic one. It focuses instead on outlining the alternative that relies on confidence-biased beliefs, instead of present-biased preferences. The payoff of the proposed framework consists of

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shedding light on the unifying structure underpinning diverse phenomena ranging from shirking and slackness to pornography-imaging. As important, the proposed framework shows the analytical similarity, despite being opposite ends of the pole, between weakness of will (impulsive behavior), at one extreme, and obsessions and excessive care (compulsive behavior), at the other.

4

TO SHIRK OR TO COOPERATE?

It is shown here that shirking beyond an optimal level would amount to an example of succumbing to weakness of will. Let us suppose a partnership made up of two individuals. For instance, two roommates agree on how to divide the tasks of cleaning their shared apartment, where one cleans the kitchen while the other cleans the shower room. 4.1

Assumptions

Let us assume the following, which should apply to all cases of weakness of will: 1.

Each actor sacrifices the same amount of leisure, which they value equally, in performing the task. 2. Each actor values his or her own leisure more than a half-cleaned apartment, but less than a fully cleaned apartment. 3. This is an indefinitely repeated game, where both have the incentive to cooperate. 4. At each round of the game, though, only the partner under focus has a sub-rational (or irrational) impulse to shirk, while the partner is free of such impulse – that is, the partner acts as the ‘principal’ who can only cooperate, that is, the partner can never shirk. 5. The partner (principal) decides to monitor the actor under focus (agent), but such monitoring cannot be complete since it is costly – but not too costly to the point that partnership is made impossible. Such cost is common knowledge to both players. 6. At the start of each round of the game, prior to actor’s expenditure of the effort, a shock strikes, which is binary and can only be totally obstructive or totally benign in reference to output. For example, if obstructive, such as bad weather or sickness, the shock totally nullifies the fruit of the actor’s effort. 7. Both the actor and the partner do not know the frequency of the shock. Both cannot learn the frequency because of ‘irresolvable ignorance’, that is, the shock is pulled from an unknown distribution that is a part of a set of infinite kinds of distributions. But say that q denotes the unknown frequency of benign shocks, that is, (1 – q) the frequency of the unknown obstructive shocks. 8. If otherwise, that is, the two players commonly know the frequency, there would be no need for monitoring because, in an indefinitely repeated game, it would not be possible for the actor to shirk. On average the actor would have to claim obstructive shocks as many as allowed by the known frequency. 9. While the actor under focus knows whether the shock is benign or obstructive, the partner does not know. 10. At the start of the stage, the shock occurs. If obstructive, the actor would not

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Weakness of will and stiffness of will 499 Timeline of actor under focus: T0 Shock

T1 Actor: produce/shirk

Timeline of partner: T1 Partner: monitor/not monitor

Figure 28.1

T2 Output revealed to monitor

T3 Partner: cooperate/retaliate

Asymmetric timelines of choices

produce – which would not be shirking. If benign, the actor decides whether to produce or shirk. The partner decides, without being cognizant yet of the product, to monitor in the sense of checking out the nature of the shock. After the partner sends the monitor to check the shock, the partner becomes cognizant of the product. That is the monitoring decision is prior to the revelation of the product. Given the last assumption, Figure 28.1 provides a sketch of the timeline of the actor under focus as well as the timeline of the partner. The discrete time (T) proceeds sequentially in each round from T0 to T3. The player is only aware of his or her own timeline. At T1, both the actor and partner take simultaneous decisions. That is, when the actor decides at T1 whether to produce or shirk, he or she is unaware of the monitoring decision of the partner. Likewise, the partner at T1 is unaware of the actor’s decision – and also unaware of the nature of the shock that is only revealed in the actor’s timeline. The mere fact that output of the actor at T2 (O2) is zero, that is, half of the apartment that falls under the responsibility of the actor is unclean, does not necessarily indicate to the partner that the actor had shirked, O2 5 f(D1, s0) where D 5 {0,1} and where s 5 {0,1} where D is binary action of either work (D 5 1) or abstain (D 5 0), and s is binary shock of either obstruction (s 5 0) or benign (s 5 1) with, respectively, the unknown frequency of (1 − q) and q. The unclean half can be the outcome of an obstructive shock (s 5 0), irrespective of whether D 5 1 or D 5 0. In this case, there could not have been shirking. If for some reason the partner decides not to monitor, and then finds a half-unclean apartment, the partner could not rescind the decision and decide to monitor. The partner can only, given the strong assumption that the partner does not shirk even without monitoring or punishment, fulfill his or her commitment and clean the other half. The partner would retaliate only under three conditions: (1) if there was monitoring; (2) if the product turns out half unclean apartment; and (3) monitoring reveals that the shock is benign. It is only then possible for the partner to conclude that the actor has shirked. In this model, the actor can get away with shirking on some occasions because, first, the asymmetry of information about the shock and, second, the partner’s monitoring is costly. The cost entails that the partner, acting rationally, cannot monitor every instance

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– that is, monitoring is incomplete. So, shirking is possible – and this should accomplish the task. The task, again, is not to define the conditions under which shirking is necessary. The task is, rather, to lay out the common structure that underpins diverse and apparently unrelated phenomena of weakness of will. 4.2

The Model

In equilibrium, given the cost of monitoring and expected benefit, the partner decides on the frequency of monitoring. The actor under focus, knowing the same information about cost and benefit of monitoring, decides on the optimum amount of shirking. Such shirking, at a deeper level, is not a conflict between two persons, an agent (actor) and a principal (partner). It is, rather, an intra-individual conflict if we view the actor as the myopic self or agent who is sub-rational, while the partner as the calm self or principal who is rational. Such a principal–agent framework departs from the standard principal–agent approach (Schelling 1960; Thaler and Shefrin 1981; Fudenberg and Levine 2006). The standard approach consists basically of dual-self theories: the conflict is between the actual present self and the actual future self – where both stand symmetrical to each other. In contrast, the proposed approach presents the conflict as between the myopic self and the calm self. Both are constructs of the present moment. While the calm self is rational, and can be obligated and committed to a course of action, the myopic self tries to direct an action contrary to the command of the rational self. The calm self, again, is not the future self. It, rather, expresses the optimal decision taken at present. The calm self is rational insofar as it presents an optimal plan in light of ex ante decisions. Further, the proposed approach of intra-individual conflict is the result of asymmetry of information concerning beliefs – as well as the opportunity for shirking afforded by a reasonable cost of monitoring. The intra-individual conflict has nothing to do with preferences. In any case, let us denote (1 − p) probability as the likelihood of the partner, under the above three conditions, to catch the actor under focus shirking. That is, the actor under focus can get away with lying or shirking with p probability. If the actor abides by such probability, the shirking frequency would be optimal: shirking would be limited to a certain number of occasions such that the expected utility from leisure equals at the margin the expected loss of unclean apartment in the case of getting caught. So, shirking is rational as long as it maximizes utility – that is, shirking is not necessarily indicative of succumbing to weakness of will. For the shirking to become weakness of will, the frequency of shirking must exceed the optimal level. In this case, we must suppose that the actor under focus (present self) is impulsive – and impulsive in the direction of over-estimating the probability of getting away with shirking as opposed to the direction of under-estimating such a probability. While the calm self (partner) is always impartial, the myopic self (actor), insofar as it succumbs to weakness of will, must be partial in the direction of shirking above the optimum. This can also be characterized as acting according to optimistic beliefs of getting away with cheating, which would be contrary to the beliefs held by the impartial calm self (partner). The beliefs of the impartial calm self, even when the impartial calm self faces an accidental series of successes, are not swayed or prejudiced by the small sample

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Weakness of will and stiffness of will 501 of successes. The impartial self rather forms a probability (pp) of successes that is based on a large sample and, hence, rational. This does not mean pp 5 p., that is, the impartial self may not recommend a frequency of shirking according to the objective probability of success of shirking (p). But, as a result of Bayesian updating, pp must be optimal given the information and, with greater information, must approach p. In contrast, the assessment of the probability of success of the impulsive agent (pa) is based on a biased, small sample. While the sample is not necessarily in the direction of optimism (belief in the success of shirking), it must be in such a direction in the case that the consequent behavior involves weakness of will. Thus, the case of weakness of will dictates the following condition: pa > pp, where the (pa − pp) gap can be called the ‘degree of over-confidence’ or ‘degree of optimism,’ which is defined here as sub-rational. 4.3

Sub-rational Representativeness Heuristic

To recap, the first claim stated at the outset, namely, shirking is intra-individual conflict, is shown. The second claim, namely, intra-individual conflict concerns a conflict of beliefs between partial and impartial selves concerning the probability of success, is also shown. The third claim, namely, the over-confidence belief of the present self (pa) is endogenous, cannot be fully discussed here (see Khalil 2015b, 2017). To state it briefly, though, the belief of the impulsive self, which can be over- or under-confidence, need not be ingrained, that is, given by immovable biological or psychological factors. It rather arises from a small sample that is non-representative – which, in addition, is non-representative in a biased or selected manner in a particular direction. If the bias of the non-representative sample is in the direction of including exaggerated instances of successes, the partial present self that selects the sample wants to be over-confident or optimistic. If the bias is in the direction of including mostly instances of failures, the partial present self wants to be under-confident or pessimistic. In the case of optimism, the actor’s belief over-estimates the probability of success. In the case of pessimism, the actor’s belief under-estimates the probability of success of getting away with cheating. Either case is the outcome of how people generalize. However, we have to be careful and distinguish between two types of generalization. The first, which cannot give rise to weakness of will, can be a rational generalization, sometimes called in the literature ‘representativeness heuristic’ (Tversky and Kahneman 1973, 1974; Plous, 1993). The second, which gives rise to weakness of will, is a sub-rational generalization, where the representativeness heuristic is selective or biased in the direction of optimism. To elaborate on the first type of generalization, the rational agent finds it useful to form smart (rational) heuristics as a result of bounded rationality. As explained previously, smart heuristics differ from how Simon (1957) and others conceive heuristics (for example, Gigerenzer 2000; Gigerenzer and Selten 2001). As conceived here, the heuristics are smart in the neoclassical sense: They arise in order to economize on the costly (bounded) operations of cognitive process. The rational agent should economize on examining all the population of experiences and deliberate frugally. That is, the rational agent should avoid examining a representative and large sample when the expected benefit from such examination is lower, at the margin, than the expected cost. In this case, the agent is justified in forming smart beliefs on the basis of a non-representative sample. That is, the agent, to optimize benefits, should be ready to generalize from a biased number of cases.

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This inevitably leads to making mistakes on some occasions, but the cost of such mistakes is less than the benefit derived from using rough and smart generalizations. Some of these mistakes include mis-steps in logical deduction. As discussed above with regard to bounded rationality, the actor would find it inefficient to employ a certain cognitive capacity to undertake flawless logical deduction or to examine non-biased samples if not justified by the expected benefit. So, the agent may rationally ignore a series of a small number of successes as indicative of a higher probability of success than is the case, or ignore a series of a small number of failures as indicative of a lower probability of success than is the case. If the agent is rational, we face an anomaly: given that the representative heuristic is rational (smart), any deviation of the heuristic from smartness should be an aberration that can go in either direction with equal probability. That is, the frequency of overconfidence should equal the frequency of under-confidence for the same actor. So, on average, the two kinds of erroneous generalizations should offset each other. Therefore, on average, the actor need not suffer from a chronic case of weakness of will or a chronic case of stiffness of will. However, empirically, the frequency of over-confidence (optimism) is greater than the frequency of under-confidence (pessimism) (see Baron 2008, ch. 6). That is, human actions tend to exhibit sub-rational behavior in the direction of weakness of will because of imbalance: there is a deficiency of under-confidence that may rectify our behavior. The preponderance of weakness of will stems from a biased or selective representativeness heuristic in the direction of over-confidence, that is, where optimistic beliefs dominate pessimistic beliefs. Such biased direction in favor of optimism cannot be merely the product of bounded rationality, the simple formation of generalizations. The dominance of over-confidence might be related to ambition, that is, the innate desire to succeed. The analysis of ambition or the desire to succeed falls outside the scope of this chapter. Stated briefly, though, the desire to succeed is probably the main motive of entrepreneurship, as analyzed by William Baumol (2010). However, we need to distinguish the desire to succeed from ‘wishful thinking’ in the sense of over-confidence. While wishful thinking might be the product of the desire to succeed, they are different. The desire to succeed can be seen as a preference or a taste and, hence, cannot be judged whether it is rational or subrational. The desire to succeed would be judged as rational only if it operates within reasonable cognition of one’s ability. The desire would become irrational if the desire starts to manipulate one’s assessment of ability. This usually involves hubris and self-delusions. It is important to avoid the conflation of hubris or delusion, on the one hand, with wishful thinking (over-confidence), on the other. While hubris and over-confidence are suboptimal and could be traced to the desire to succeed, they differ with regard to the object of the manipulated or sub-optimal belief. Hubris or delusion involve beliefs concerning self-ability, while over-confidence involves beliefs concerning one’s environment, that is, the chances of certain environmental shocks. To say that over-confidence arises from wishful thinking would amount to tautology because both involve the manipulation of belief concerning our environment. To say that wishful thinking (over-confidence) arises from the desire to succeed is insufficient. The desire, in itself, cannot fully explain wishful thinking. One possible way to explain wishful thinking (over-confidence) is evolutionary. Natural selection may favor organisms with a trait of strong wishful thinking more than with a

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Weakness of will and stiffness of will 503 trait that favors caution. However, for the wishful thinking trait (impulsivity) to survive, we must presume that natural selection is not operating efficiently. In an efficient environment, impulsivity is destructive on average. So, for carriers of such a trait to survive and reproduce successfully, the natural environment, which is undertaking the editing and selection, must have crevices and niches that protect the organisms from elimination. This evolutionary reasoning need not concern us here. It is sufficient to add, though, the same evolutionary reasoning a priori can explain compulsivity, that is, the evolution of traits that nurture pessimistic, sub-rational beliefs. If the environment is efficient, optimistic beliefs should not survive because they are, on average, destructive. So, the environment must not be inefficient to allow for such a trait to survive. However, to examine simultaneously the frequency of both traits in a population, an efficient environment allows for the rise of both pessimism and optimism as long as they balance each other. Such balancing may also explain extreme cases on both ends: where excessively impulsive people are equal in number to excessively compulsive people. As for the case of compulsivity, we may hypothesize that OCD is merely a pathological or an exaggerated form of stiffness of will – where OCD arises when under-confident, pessimistic beliefs go awry into anxiety, fear, and worries about safety and health. This is not the place to discuss theories of OCD, why some people suffer from it or how widespread it is (see Schwartz and Beyette 1997; Emily 1998; Osborn 1999; Baer 2002; Veale and Wilson 2005; Davis 2008). This is also not the place to explain how daily, non-pathological stiffness of will may or may not evolve into OCD. It is sufficient to note that the analytical structure of the impulsive self, which is behind weakness of will, can shed light in understanding the compulsive self, which is behind non-pathological obsessions. The only difference is the direction of the biasness of sub-rational representative heuristics – whether it is towards impulsive over-confidence (optimism) or compulsive under-confidence (pessimism). 4.4

Three Moments of Shirking

To recapitulate, in the first moment, shirking is rational when it is recommended by marginal calculation in light of (1) asymmetric information about whether the shock is benign or obstructive; (2) costly monitoring. In the second moment, shirking is rational as a result of bounded rationality that occasions rational representative heuristics, that is, when deliberation over a representative sample is cognitively costly. In the third moment, shirking is sub-rational – and sub-rational in the direction of optimism that encourages us to succumb to weakness of will – when the actor becomes impulsive. Such sub-rational behavior must be, for weakness of will to arise, impulsive, that is, in the direction of over-confidence rather than in the direction of under-confidence. To provide a precise definition of shirking at the third moment, let us define first shirking at the first moment. Let us specify a critical threshold where the expected net benefit (ENB) of shirking, or any kind of cheating, equals zero. At such a threshold, the individual finds the expected benefit (EB) from cooperation to equal the EB from shirking, that is, ENB 5 0. Let us take shirking as the default option. If an individual, acting as the calm principal, finds the ENB from shirking to be positive, ENBp ≥ 0, it would be optimal for the individual to shirk. Otherwise, it would be suboptimal to shirk. If it is suboptimal

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to shirk for the principal, that is, ENBp < 0, but it is optimal for the impulsive agent, that is, ENBa ≥ 0, the opportunity for weakness of will (suboptimal shirking) arises. We can presume that the discount rate of future utility is unitary. However, even if it is less than one, it would have a nil effect on the analysis. The impulsive agent would not shirk if ENBa < 0, that is, even fools are not absolutely foolish. So, the necessary condition for shirking as sub-rational weakness of will is ENBa > 0 > ENBp. Summed up, weakness of will entails that (1) the impulsive self (agent) finds additional leisure optimal; and (2) the calm self (principal) finds the same choice suboptimal. On the other hand, in the case of stiffness of will, the opposite of shirking is over-working to the point of allowing oneself to be exploited, which includes self-exploitation. The necessary condition for the antithesis of shirking, such as over-working, as sub-rational stiffness of will is ENBa < 0 < ENBp. Stated tersely, obsessive behavior entails that (1) the compulsive present self (agent) finds additional leisure suboptimal; and (2) the calm self (principal) finds the same choice optimal. That is, the compulsive agent would continue to be compelled to work harder or be excessively diligent. However, the above set of inequalities should not mean that the individual would definitely succumb to the suboptimal choice – whether weakness of will or obsession. The inequality that recommends weakness of will, whose extent is a function of the over-confidence gap (pa − pp), rather defines the range through which the succumbing to weakness of will is possible. Symmetrically, the inequality that recommends obsessive behavior, whose extent is a function of what can be called the ‘under-confidence gap’ (pp − pa), rather defines the range through which the compelling act of obsession is possible. There is the possibility that the agent, as a result of internal constraints as well as external institutions, may not succumb to weakness of will. As a result of education and reflection, the individual may develop internal constraints, what Adam Smith calls ‘self-command,’ to prevent the individual from undertaking reckless or weakness of will actions (Khalil 2010). In case of the failure of self-command, the individual may adopt external institutions or constraints, known as precommitments (Elster 2000), to deflate or restrain beliefs that are formed out of a limited sample and, hence, misrepresent the true Bayesian process of updating. In summary, shirking is not, ultimately, a conflict between two separate individuals. Insofar as the roommate or partner can punish the actor under focus, the conflict is intraindividual. Second, while the conflict is intra-individual, it is not about present-biased preferences. Rather, it is about two different assessments of the probability of success; one held by the calm (impartial) self and the other held by the sub-rational (partial) self. Third, the gap between the beliefs of the calm and impulsive selves is not the outcome of some ingrained or biologically immovable traits. Rather, the gap is partially the outcome of bounded rationality that give rise to representativeness heuristics – where such heuristics move in a selective direction that favors optimism or over-confidence. Thus, weakness of will is never fixed concerning some goods as opposed to others. But rather it varies with the variation of the over-confidence gap (pa − pp), as slackening (malaise) and other phenomena illustrate.

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5

TO SLACKEN OR TO WORK?

When we cheat, but the injured party is none other than our future self, we would be involved in self-cheating. Self-cheating is usually called indolence, sloth, malaise, or slackening. Slackening, in itself, need not be an example of succumbing to weakness of will. Only after an optimal level does slackening become such an example. There are many examples of slackness: over-eating, indulging, under-saving, underexercising, under-investing in our education, and under-diligence. In these examples, the person takes the easy road, over-consumes leisure and food, or simply enjoys present consumption that undermines the optimal plan of consumption. The shirking model, useful to study inter-individual conflict, can be imported in toto to capture the intra-individual conflict of slackening. This chapter suggests, with respect to shirking, that the impulsive actor under focus (agent) is the present self, while the calm partner (principal) is the future self. (This should not suggest the opposite: the present self is necessarily impulsive and the future self is calm.) However, such importation faces two apparent difficulties. First, in shirking, the punishment is leveled by the injured partner (the principal) by withholding cooperation. In slackening, while the injured party is the future self, it cannot level punishment in the same manner. At a deeper level of examination, though, the future self (principal) can retaliate by leveling penalties on the present self in the form of psychological pain such as self-blame, stressful worries, or the feeling of low self-esteem. The intensity of such punishment, as in the shirking model, can be supposed to be high enough to offset the present self’s pleasure arising from slackening. The second difficulty concerns the shocks. To be clear, the shocks, as in the shirking model, are generated from ‘irresolvable ignorance’. Also, as in the shirking model, the obstructive shock can be fatigue or sickness that fully negates the productive action of the present self. Such a shock would justify if we over-eat or relax because any disciplinary work would not benefit the individual. If the obstructive shock were hunger, if we refrain from over-eating, we would be too distracted to produce output. The difficulty arises because of one critical difference: in shirking, it is feasible to suppose that the timelines of the shocks in Figure 28.1 can be asymmetric. The actor and the partner can have different knowledge of whether the shock is benign or obstructive. We cannot maintain such asymmetry in slackening because the future self and the present self make up the same individual. However, if we assume self-deception, we can overcome the second difficulty, that is, maintain the asymmetry of information in slackening. The present self or, generally, the myopic self can keep the future self or, generally, the calm self in the dark concerning the nature of the shock (Khalil 2011a, 2016). The future self could not know, if it does not initiate monitoring, whether the shock is benign or obstructive. So, we can import in toto the asymmetric timelines of Figure 28.1, where the present or myopic self (agent) and the future or calm self (principal) have different information about the nature of the shock. As in shirking, if the future self chooses at the start of his or her timeline, in the indefinitely repeated game, to withhold monitoring, it has to cooperate. It cannot level punishment in the form of self-blame or stressful worries even when the product is zero. The future self can retaliate, as in shirking, under three conditions: (1) the product is zero; (2) the shock is benign; and (3) there is monitoring. The future self may monitor the nature of the shock in many forms: meditation,

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self-reflection, and even spiritual exercises such as prayers. Such monitoring devices would expose the misleading fibs or lies put forward by the present self. Such devices would uncover whether the zero output is the outcome of slackening or the outcome of obstructive shocks such as sickness. However, given that such monitoring is costly, it is optimal for the future self to conduct occasional monitoring, capturing only (1 − p) of all the cases of self-deception. Also, it is optimal for the present self to self-cheat, such as to under-save, to over-eat, or to under-exercise, insofar as the expected benefit from slackening exceeds, at the margin, the expected cost if the present self gets exposed. As in the shirking model, for slackening to be suboptimal, that is, involving weakness of will, the present or myopic self must be sub-rational – and sub-rational in the direction of forming impulsive beliefs amounting to over-confidence of getting away with cheating. Here, the belief of the present or myopic self or agent (pa) is higher than the belief of the future or calm self or principal (pp). As in the shirking model, the necessary condition for slackening as sub-rational weakness of will is ENBa > 0 > ENBp. That is, the expected net benefit from slackening (additional leisure) would be tempting only if it is suboptimal for the principal (0 > ENBp), while optimal for the agent (ENBa > 0). In the case of stiffness of will, the opposite of slackening is over-working, overdiligence, and over-alertness. The necessary condition for the antithesis of slackening, such as miser-like saving, to be an example of sub-rational obsessive-compulsive behavior is ENBa < 0 < ENBp. That is, the expected net benefit from additional leisure or relaxation would be compelling only if ENB is optimal for the principal (0 < ENBp), while suboptimal for the compulsive agent (ENBa < 0). That is, the compulsive agent would continue to be compelled to be ever careful and over-save as a miser. Under the above range of inequality behind slackness, the impulsive agent would take reckless risks, under-saves, under-invests, over-sleeps, and so on, relying on the overconfident belief of success, that is, getting away with self-cheating. Also, under the above range of inequality behind over-working, the compulsive agent would check a protective measure many times, forgo relaxation and vacations, and under-sleeps, and so on, relying on the under-confident belief of success, that is, getting away with self-cheating. Again, individuals usually resort to internal and, if need be, external constraints to re-align the over- or under-confident belief with the impartial belief.

6

TO PLAY FAVORITES OR TO ACT FAIR?

If favoritism exceeds an optimal level, it becomes an instance of weakness of will. In a team that consists of a director and subordinates, the director may desire to shower favors on a particular subordinate, and no other. Such favoritism would be definitely unfair only if we assume that property rights are well established and, hence, fairness is clearly defined. Under such an assumption, the director’s unfair action could potentially draw retaliation from the rest of the subordinates, that is, who did not receive the same treatment. The director here would be an agent who has duties towards the principal, that is, the rest of the subordinates. As in the shirking case, if the principal (that is, the rest of the subordinates) finds out, via monitoring, that the director has favored a subordinate, the principal would suffer from withheld cooperation.

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Weakness of will and stiffness of will 507 We can import the assumptions of the shirking model in toto to explain favoritism. Let us assume that the director gains ‘altruistic utility’ upon knowing that the favored subordinate is enjoying a day off, a perk, or an unmerited promotion. The director’s altruistic utility would be similar to the actor’s leisure utility gained from shirking. For cooperation to continue in an indefinitely repeated game, the director’s altruistic utility must exceed the director’s loss as a result of the subordinate’s forgone productivity. However, the retaliation by the rest of the subordinates must be painful enough to more than offset the gained altruistic utility from favoritism. As in the shirking model, we have shocks that inflict the favored subordinate. They can be of two kinds: benign and obstructive. The benign shock means a fake sickness or a fake fatigue is claimed to have inflicted the favored subordinate. The obstructive shock can be true sickness/fatigue that fully negates the favored subordinate’s productive action. With obstructive shock, the director would be justified to shower leisure or another favor on the favored subordinate. Following the asymmetric timelines of Figure 28.1, the favored subordinate and the director know the nature of the shock. It is useful to think of both as a single ‘agent’ – who can potentially be sub-rational. On the other hand, the principal (that is, the rest of the team), who is rational according to the shirking model, can only become aware of the nature of the shock if the principal had chosen already to monitor the shock in that round. If the principal, at the start of his or her timeline, chose to withhold monitoring, he has to cooperate. That is, he cannot level punishment even when the product is zero. The principal, to specify again, can retaliate under three conditions: (1) the product is zero; (2) the shock is benign; and (3) there is monitoring. As in shirking, monitoring is costly. So, it is optimal for the principal to conduct occasional monitoring, capturing only (1 − p) of all the cases of deception. Also, it is optimal for the actor (the favored subordinate and director) to cheat, that is, to claim that the shock is obstructive when it is not, insofar as the expected benefit from cheating exceeds, at the margin, the expected cost if the actor gets exposed. As in the above models, for the cheating to be suboptimal, that is, involves weakness of will, the actor must be sub-rational – and sub-rational in the direction of forming impulsive, over-confident beliefs. Here, the belief of the actor or agent (pa) is higher than the belief of the principal (pp). Again, the same necessary condition holds for favoritism as sub-rational weakness of will is ENBa > 0 > ENBp. That is, the expected net benefit from deception would be tempting only if it is suboptimal for the principal (0 > ENBp), while optimal for the agent (ENBa > 0). In the case of stiffness of will, the opposite of favoritism is excessive harshness towards relatives, acquaintances, and other ones that provide altruistic utility to the director. The necessary condition for the antithesis of favoritism, such as excessive harshness, as sub-rational obsessive-compulsive behavior is ENBa < 0 < ENBp. That is, the expected net benefit from additional leniency towards relatives or favored subordinates would be compelling only if it is optimal for the principal (0 < ENBp), while suboptimal for the compulsive agent (ENBa < 0). That is, the compulsive agent would continue to be compelled to treat favored subordinates with excessive harshness. Under the above range of inequality behind favoritism, the impulsive director would take reckless risks and shower undeserved favors to ones from whom the director can derive altruistic utility. The director would be relying on the sub-rational, over-confident

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belief of getting away with cheating. Under the above range of inequality behind excessive harshness, the compulsive director would level additional deprivations and punishment on the favored subordinates. The director would be relying on the sub-rational, under-confident belief of getting away with deception. Also, individuals usually resort to internal and, if need be, external constraints to re-align the over- or under-confident belief with the impartial belief.

7

TO SPOIL OR TO DISCIPLINE CHILDREN?

The spoiling of children, if it goes beyond an optimal level, would be another instance of succumbing to weakness of will. The parent or caretaker would be involved in spoiling – which is not necessarily sub-rational – when the parent or caretaker decides to shower unmerited benefits – in the sense of excessive attention – on the child when the situation actually calls for disciplinarian ‘tough love’. If the adult showers the child with undeserved attention, it could undermine the productive human capital of the child, which would hurt the future income of the child. In this case, the caretaker and the child are better considered as a single actor, the sub-rational agent. The future child, that is, the future grown-up, is the future or calm principal. So, the favoritism case resembles the slackening model because the present child and the caretaker would enjoy spoiling the child. However, excessive, sub-rational spoiling would increase the chance of lowering the future income or health of the future grown-up. The spoiling case presented here differs from Gary Becker’s (1976) model of the ‘rotten kid’. Becker argues that it is in the self-interest of the rotten kid, insofar the kid is rational, not to extract excessive benefits from the parent. Such extraction would rob the parent of the ability to produce more goods and, hence, undermine the future income of the rotten kid. The difference lies with the question of who suffers from the spoiling of the rotten kid. In Becker’s model, the future grown-up suffers, but only because the spoiling undermines the production ability of the parent. In the following analysis, the future grown-up suffers directly, that is, because the spoiling undermines the production ability of the future child. We can import the assumptions of the slackening model in toto to explain spoiling as weakness of will. The caretaker must derive some utility, altruistic or otherwise, from spoiling the child. However, given that such spoiling cannot be in the optimal interest of the child, it is difficult to suppose that altruistic utility is the motive of the caretaker. More likely, the caretaker derives ‘reflexive utility’ upon knowing that the child is enjoying the attention. Reflexive utility amounts to vicarious utility, that is, we enjoy the enjoyment of the other by assuming that such enjoyment is happening to ourselves (see Khalil 2004, 2005). In this case, the caretaker would be using the child as a medium to actually spoil our own self. This hypothesis is plausible because, from rational calculation, the caretaker’s responsibility includes not only the present state of the child but also the long-term wellbeing of the child. The caretaker should discipline the child in some cases if he or she is altruistic, that is, care about the child’s full human capital development. The spoiling, as such, may hinder the flourishing of the child’s full human capital potential. As in the case of slackening, in the case of the caretaker is spoiling, that is, cheating,

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Weakness of will and stiffness of will 509 the future grown-up (principal) cannot retaliate in the usual sense of punishment. The principal can retaliate through self-blame, sense of shame, and remorse. The caretaker’s reflexive utility would be similar to the actor’s leisure utility gained from slackening. For cooperation between the caretaker and the future grown-up to continue in an indefinitely repeated game, the caretaker’s reflexive utility must exceed the caretaker’s sense of self-blame for the loss of income for the future grown-up. However, the self-blame leveled by the future grown-up must be painful enough to more than offset the gained reflexive utility from spoiling. As in the slackening model, we have shocks that inflict the child. They can be of two kinds: benign and obstructive. Let us say that the child misbehaves and the caretaker spoils rather than disciplines the child. It would be a benign shock if the caretaker appeals to a fake shock – such as hunger or exhaustion that has supposedly befallen the child – as an excuse for the spoiling. Here the spoiling resembles the stolen leisure in the shirking and slackening models. On the other hand, it would be an obstructive shock if the shock inflicting the child were non-fake. Thus, in light of the misbehavior of the child, the caretaker justly accords the child attention and affection. Following Figure 28.1 concerning the asymmetric timelines, the sub-rational caretaker (actor or agent) knows the nature of the shock. On the other hand, the future grown-up (principal), who is rational according to the shirking model, can only become aware of the nature of the shock if the principal had chosen already to monitor the shock. In case there is no monitoring, though, how could the future grown-up, who actually resides in the mental fabric of the caretaker, be kept unaware of the shock, given that the caretaker knows? As in the case of slackening, the future grown-up is kept in the dark via self-deception. Namely, the actor invents fibs and excuses that block the future grown-up, that is, the principal within the breast of the caretaker, from knowing about the nature of the shock. If the principal, at the start of his or her timeline, had chosen to withhold monitoring, he has to cooperate in the form of congratulating the caretaker for attention showered on the child. That is, he cannot level self-blame even when the consequent attitude of the child, that is, the child’s incremental human capital in the form of disciplined behavior, is zero. The principal, to specify again, can retaliate under three conditions: (1) the product is zero; (2) the shock is benign; and (3) there is monitoring. But monitoring is costly. So, it is optimal for the future grown-up (principal) to conduct occasional monitoring, capturing only (1 − p) of all the cases of self-cheating by the caretaker. It is optimal for the actor (the caretaker) to cheat, that is, to claim that the shock is obstructive when it is not, insofar as the expected benefit from spoiling exceeds, at the margin, the expected cost if the actor suffers from self-blame. As in the above models, for the spoiling to be suboptimal, that is, involving weakness of will, the actor must be sub-rational – and sub-rational in the direction of forming impulsive beliefs in the sense of optimism or over-confidence. Here, the belief of the actor or the agent (pa) is higher than the belief of the future grown-up or principal (pp). Again, the same necessary condition holds for the spoiling of children as sub-rational weakness of will is ENBa > 0 > ENBp. That is, the expected net benefit from spoiling would be tempting only if it is suboptimal for the principal (0 > ENBp), while optimal for the agent (ENBa > 0). In the case of stiffness of will, the opposite of spoiling is excessive harshness or abusive

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behavior towards the child. The necessary condition for the harshness to be sub-rational, obsessive-compulsive behavior is ENBa < 0 < ENBp. That is, the expected net benefit from additional affection towards the child would be compelling only if it is optimal for the principal (0 < ENBp), while suboptimal for the compulsive agent (ENBa < 0). That is, the compulsive agent would continue to be compelled to inflict harsh punishment on the child, motivated to mold a well-disciplined future grown-up. Under the above range of inequality behind the spoiling of children, the impulsive caretaker would shower undeserved attention on the child. The caretaker would be relying on the sub-rational, over-confident belief of getting away with spoiling. Under the above range of inequality behind excessive harshness, the compulsive caretaker would inflict extra deprivation, which would amount to child abuse. The caretaker would be relying on the sub-rational, under-confident belief of getting away with the excessive harshness. Also, individuals usually resort to internal and, if need be, external constraints to re-align the over- or under-confident belief with the impartial belief.

8

TO CONSUME PORNOGRAPHY-IMAGING OR TO REFRAIN?

Pornography-imaging, beyond an optimal level, could become another example of succumbing to weakness of will. The proposed confidence-biased belief model can shed light on such an example. It can provide a precise differentiation between erotica-imaging and pornography-imaging, a long elusive distinction. It has been elusive for a good reason. Once researchers start with present-biased preferences approach, such as Dan Ariely and George Loewenstein (2006), what is erotica-imaging and what is pornography-imaging would be a matter of preferences, that is, in the eyes of the beholder. In both imagings, the actor under focus (agent) seeks sexual utility with a partner (principal) with the aid of an imaginative set-up. As before, let us assume that the partner (principal) is rational and would not cheat, that is, would reciprocate justly and invest in erotica-imaging during the sexual interaction. The partner, though, would retaliate, by withholding effort in erotica-imaging, in case the partner finds out that the actor under focus is cheating, that is, employing only pornography-imaging. Either player can invest in erotica-imaging, defined as the investment in a rousing romantic atmosphere, the presentation of flowers, the use of special aromas, or the selection and the watching of a well-executed sex film. Otherwise, the player would be investing in pornography-imaging, defined as hasty construction of an atmosphere, little effort to arouse the other, and the use of short and cheap sex film. Such little effort, to normalize, can be assumed to be ‘zero effort’ as the case of the actor’s shirking in the shirking model. We can import the assumptions of the shirking model in toto to explain pornographyimaging as cheating in effort expenditure. When the actor cheats with pornographyimaging, the actor steals leisure utility. For cooperation to continue nonetheless in an indefinitely repeated game, the actor’s leisure utility in each round must exceed the actor’s partial loss of sexual pleasure as a result of failing to set-up erotica-imaging. However, the retaliation by the sexual partner, which would make such loss total, must be painful enough to more than offset the actor’s leisure utility from pornography-imaging. As in the shirking model, the actor suffers from shocks of two kinds: benign and

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Weakness of will and stiffness of will 511 obstructive. If the shock is benign, the actor’s ability to set-up erotica-imaging is not impeded, but if the shock is obstructive, such as true sickness or fatigue, such ability is hindered. That is, irrespective of the choice of the actor, the actor’s output would be zero as if produced by cheap, pornography-imaging. With obstructive shock, the actor under focus would be justified to avoid wasteful investment in erotica-imaging. Following the asymmetric timelines of Figure 28.1, the actor knows the nature of the shock, while the sexual partner (principal), who is rational and cannot cheat, can only become aware of the nature of the shock if the principal had chosen already to monitor the shock in that round. If the principal, at the start of the round, had chosen to withhold monitoring, it has to cooperate, that is, invest in erotica-imaging. That is, it cannot level punishment in the form of pornography-imaging even when the actor’s product is zero. The principal, to specify again, can only provide pornography-imaging under three conditions: (1) the product is zero; (2) the shock is benign; and (3) there is monitoring. As in shirking, monitoring of whether the actor is suffering from genuine shock is costly. So, it is optimal for the principal to conduct occasional monitoring, capturing only (1 − p) of all the cases of deception, and it is optimal for the actor to deceive, that is, to claim that he or she is inflicted with an obstructive shock when it is not, insofar as the expected benefit from deceiving exceeds, at the margin, the expected cost if the actor gets caught. As in the above models, for the pornography-imaging to exceed the optimal level, that is, to succumb to weakness of will, the actor must be sub-rational – and sub-rational in the direction of forming impulsive, over-confident beliefs. Here, the belief of the actor or agent (pa) that he can get away with deception is higher than the belief of the rational calm self. Such rational calm self of the actor actually holds the same rational belief as the principal (pp). Again, the same necessary condition holds for the appearance of pornography-imaging as sub-rational weakness of will, ENBa > 0 > ENBp. That is, the expected net benefit from pretending that the shock is obstructive would be tempting only if ENB is suboptimal for the principal (0 > ENBp), while optimal for the agent (ENBa > 0). In the case of stiffness of will, the opposite of pornography-imaging is excessive investment in erotica-imaging, even when the actor is struck by genuine fatigue. It is possible to characterize sexual fetishism as an example of sub-rational obsessive behavior. The necessary condition for sexual fetishism or other antithesis of pornographic-imaging as sub-rational obsessive-compulsive behavior is ENBa < 0 < ENBp. That is, the expected net benefit from freeing ourselves from investment in erotica-imaging would be compelling only if it is optimal for the principal (0 < ENBp), while suboptimal for the compulsive agent (ENBa < 0). That is, the compulsive agent would continue to be compelled to invest feverishly in erotica-imaging. Under the above range of inequality behind pornography-imaging, the impulsive actor would become careless, investing less in erotic approach to his or her partner. The actor would be relying on the sub-rational, over-confident belief of getting away with cheating. Under the above range of inequality behind obsessive behavior, the compulsive actor would become obsessed with erotica-imaging, which is probably behind sexual fetishism. The actor would be relying on the sub-rational, under-confident belief of getting away with deception. Again, individuals usually resort to internal, and if need, external constraints to re-align the over- or under-confident belief with the impartial belief.

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CONCLUSION

This chapter grounds the confidence-biased beliefs approach on three propositions. It first argues that conflicts between two individuals, as demonstrated in the case of shirking, are ultimately an intra-individual conflict. Second, it argues that intra-individual conflicts concern the different assessments of the probability of success in the face of risk. They need not express conflicts between present-biased and non-myopic preferences. Third, it shows that the intra-individual conflict concerning probabilities of success arises endogenously, from a small sample of experiences. Such conflict would die out if the small sample were prompted only by bounded rationality. The small sample, in the case of weakness of will, seems to be prompted, additionally, by the desire to succeed (ambition). Note, as mentioned above, the desire to succeed need not lead to wishful thinking, over-confidence, or the impulsivity behind weakness of will. That is, the dominance of impulsivity over compulsivity may arise from the fact that people do not have a desire to fail, but have a desire to succeed. If desire is the reason for the rise of weakness of will (impulsivity), we should witness fewer cases of stiffness of will (compulsivity). The fact that stiffness of will is ubiquitous may force us to conceive the desire to succeed in a broader light: in some cases it might propel the agent to take excessive risks (impulsivity), but in other cases, when the person is frustrated with many disappointments, it might propel the agent towards excessive cautiousness (compulsivity). So, there is a need for a more general theory of sub-rationality, a theory that can account for the endogenous development of weakness of will vis-à-vis stiffness of will. The goal of this chapter is more preliminary: We need to commence with biased beliefs, rather than biased preferences, if we want to understand how weakness of will and stiffness of will are analytically similar phenomena.

NOTE *

Earlier drafts benefited from the comments of Harold Demsetz, Joel Sobel, Eric Schliesser, Leonidas Montes, James Buchanan, Steven Gardner, participants of a seminar at Monash University, and especially Ian McDonald and Haiou Zhou. The current version benefited from the comments of Alain Marciano, George Ainslie, and the editor, Morris Altman. The chapter benefited from a grant from Monash University’s Faculty of Business and Economics. The usual caveat applies. 1. I prefer to use the term ‘stiffness of will’ rather than the ‘strength of will’ because the term ‘strength of will’ connotes rational choice. The term ‘stiffness of will’, on the other hand, connotes sub-rational choice in the sense that the will is not flexible enough. I want to use a term that is opposite to ‘weakness of will’ while still suggesting that the will enacts sub-rational choices.

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Weakness of will and stiffness of will 513 Ariely, D. and G. Loewenstein (2006), ‘The heat of the moment: the effect of sexual arousal on sexual decision making’, Journal of Behavioral Decision Making, 19 (2), 87–98, doi:10.1002/bdm.501. Baer, L. (2002), The Imp of the Mind: Exploring the Silent Epidemic of Obsessive Bad Thoughts, New York: Plume Books. Baron, J. (2008), Thinking and Deciding, 4th edn, Cambridge: Cambridge University Press. Baumol, W.J. (2010), The Microtheory of Innovative Entrepreneurship, Princeton, NJ: Princeton University Press. Becker, G.S (1976), ‘Altruism, egoism, and genetic fitness: economics and sociobiology’, Journal of Economic Literature, 4 (3), 817–26. Bernheim, B.D. and A. Rangel (2004), ‘Addiction and cue-triggered decision processes’, American Economic Review, 94 (5), 1558–90. Berridge, K.C. (2003), ‘Pleasures of the brain’, Brain and Cognition, 52 (1), 106–28. Boyer, P. and P. Liénard (2006), ‘Why ritualized behaviour? Precaution systems and action parsing in developmental, pathological, and cultural rituals’, Behavioral and Brain Sciences, 29 (6), 595–613. Camerer, C.F. (2006), ‘Wanting, liking and learning: neuroscience and paternalism’, University of Chicago Law Review, 73 (1), 87–110. Damasio, A.R. (1994), Descartes’ Error: Emotion, Reason, and the Human Brain, New York: G.P. Putnam. Davis, L.J. (2008), Obsession: A History, Chicago, IL: University of Chicago Press. DeYoung, C.G. (2010), ‘Impulsivity as a personality trait’, in K.D. Vohs and R.F. Baumeister (eds), Handbook of Self-Regulation: Research, Theory, and Applications, 2nd edn, New York: Guilford Press, pp. 485–502. Ditto, P.H., D.A. Pizarro, E.B. Epstein, J.A. Jacobson and T.K. Macdonald (2006), ‘Visceral influences on risk-taking behavior’, Journal of Behavioral Decision Making, 19 (2), 99–113, doi:10.1002/bdm.520. Dulaney, S. and A.P. Fiske (1994), ‘Cultural rituals and obsessive-compulsive disorder: is there a common psychological mechanism?’, Ethos, 22 (3), 243–83. Elster, J. (ed.) (2000), Ulysses Unbound: Studies in Rationality, Precommitment, and Constraints, Cambridge: Cambridge University Press. Emily, C. (1998), Just Checking: Scenes from the Life of an Obsessive-Compulsive, New York: Pocket Books. Fiske, A.P. and N. Haslam (1997), ‘Is obsessive-compulsive disorder a pathology of the human disposition to perform socially meaningful rituals?’, Journal of Nervous and Mental Disease, 185 (4), 211–22. Fudenberg, D. and D.K. Levine (2006), ‘A dual-self model of impulse control’, American Economic Review, 96 (5), 1449–76. Gigerenzer, G. (2000), Adaptive Thinking: Rationality in the Real World, Oxford: Oxford University Press. Gigerenzer, G. and R. Selten (eds) (2001), Bounded Rationality: The Adaptive Toolbox, Cambridge, MA: MIT Press. Gul, F. and W. Pesendorfer (2001), ‘Temptation and self control’, Econometrica, 69 (6), 1403–35. Gul, F. and W. Pesendorfer (2004a), ‘Self control and the theory of consumption’, Econometrica, 72 (1), 110–58. Gul, F. and W. Pesendorfer (2004b), ‘Self control, revealed preference and consumption choice’, Review of Economic Dynamics, 7 (2), 243–64. Kable, J.W. and P.W. Glimcher (2007), ‘The neural correlates of subjective value during intertemporal choice’, Nature Neuroscience, 10 (12), 1625–33. Kahneman, D. (1994), ‘New challenges to the rationality assumption’, Journal of Institutional and Theoretical Economics, 150 (1), 18–36. Kahneman, D., P.P. Wakker and R. Sarin (1997), ‘Back to Bentham? Explorations of experienced utility’, Quarterly Journal of Economics, 112 (2), 375–406. Kalis, A., A. Mojzisch, T.S. Schweizer and S. Kaiser (2008), ‘Weakness of will, akrasia, and the neuropsychiatry of decision making: an interdisciplinary perspective’, Cognitive, Affective, & Behavioral Neuroscience, 8 (4), 402–17, doi:10.3758/CABN.8.4.402. Khalil, E.L. (2004), ‘What is altruism?’, Journal of Economic Psychology, 25 (1), 97–123. Khalil, E.L. (2005), ‘An anatomy of authority: Adam Smith as political theorist’, Cambridge Journal of Economics, 29 (1), 57–71. Khalil, E.L. (2010), ‘Adam Smith’s concept of self-command as a solution to dynamic inconsistency and the commitment problem’, Economic Inquiry, 48 (1), 177–91, doi:10.1111/j.1465-7295.2008.00182.x. Khalil, E.L. (2011a), ‘The weightless hat: is self-deception optimal?’, Behavioral and Brain Sciences, 34 (1), 30–31. Khalil, E.L. (2011b), ‘Rational, normative and procedural theories of beliefs: can they explain internal motivations?’, Journal of Economic Issues, 45 (3), 641–64. Khalil, E.L. (2015a), ‘The fellow-feeling paradox: Hume, Smith and the moral order’, Philosophy, 90 (4), 653–78. Khalil, E.L. (2015b), ‘Temptations as impulsivity: how far are regret and the Allais paradox from shoplifting?’, Economic Modelling, 51 (December), 551–9. Khalil, E.L. (2016), ‘Self-deception as weightless mask’, Facta Universitatis, Series: Philosophy, Sociology, Psychology, and History, 15 (1), 1–11.

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Khalil, E.L. (2017), ‘Dynamic inconsistency as overconfidence: slippery slopes, ethical rules and holiday license’, working paper. Laibson, D. (1997), ‘Golden eggs and hyperbolic discounting’, Quarterly Journal of Economics, 112 (2), 443–77. Liénard, P. and P. Boyer (2006), ‘Whence collective rituals? A cultural selection model of ritualized behavior’, American Anthropologist, 108 (4), 814–27. Loewenstein, G. (1996), ‘Out of control: visceral influences on behavior’, Organizational Behavior & Human Decision Processes, 65 (3), 272–92. Loewenstein, G. and T. O’Donoghue (2004), ‘Animal spirits: affective and deliberative processes in economic behavior’, paper, May, accessed 23 April 2015 at http://papers.ssrn.com/sol3/papers.cfm?abstract_id5539843. McClure, S.M., D. Laibson, G. Loewenstein and J.D. Cohen (2004), ‘Separate neural systems value immediate and delayed monetary rewards’, Science, 306 (5695), 503–7. O’Donoghue, T. and M. Rabin (1999), ‘Doing it now or later’, American Economic Review, 89 (1), 103–24. O’Donoghue, T. and M. Rabin (2001), ‘Choice and procrastination’, Quarterly Journal of Economics, 116 (1), 121–60. Osborn, I. (1999), Tormenting Thoughts and Secret Rituals: The Hidden Epidemic of Obsessive-Compulsive Disorder, New York: Dell. Pinto, A., J.E. Steinglass, A.L. Greene, E.U. Weber and H.B. Simpson (2014), ‘Capacity to delay reward differentiates obsessive-compulsive disorder and obsessive-compulsive personality disorder’, Biological Psychiatry, 75 (8), 653–9. Plous, S. (1993), The Psychology of Judgment and Decision Making, New York: McGraw-Hill. Schelling, T.C. (1960), The Strategy of Conflict, London: Oxford University Press. Schwartz, J.M. and B. Beyette (1997), Brain Lock: Free Yourself from Obsessive-Compulsive Behavior, New York: Harper Perennial. Simon, H.A. (1957), Models of Man, New York: John Wiley. Smith, A. (1759), The Theory of Moral Sentiments, reprinted in A.L. Macfie and D.D. Raphael (eds) (1976), The Glasgow Edition of the Works and Correspondence of Adam Smith, vol. 1, Oxford: Oxford University Press. Strotz, R.H. (1956), ‘Myopia and inconsistency in dynamic utility maximization’, Review of Economic Studies, 23 (3), 165–80. Thaler, R.H. and H.M. Shefrin (1981), ‘An economic theory of self-control’, Journal of Political Economy, 89 (2), 392–406. Tversky, A. and D. Kahneman (1973), ‘Availability: a heuristic for judging frequency and probability’, Cognitive Psychology, 5 (2), 207–32. Tversky, A. and D. Kahneman (1974), ‘Judgment under uncertainty: heuristics and biases’, Science, 185 (4157), 1124–30. Veale, D. and R. Wilson (2005), Overcoming Obsessive-Compulsive Disorder: A Self-Help Guide Using Cognitive Behavioral Techniques, London: Constable & Robinson.

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29 The role of identity, personal and social capital in community crime prevention Ambrose Leung and Brandon Harrison

1

INTRODUCTION

Community crime prevention (CCP) is based on collective action by citizens to reduce criminal opportunities through surveillance and vigilance of their community (Schneider 2007). The success of CCP hinges critically on a community’s characteristics that form unique opportunities and constraints faced by members of the community. A smart decision that concerns the level of CCP participation for residents in crime-ridden areas is likely different from that of residents in low-crime areas. This chapter shows that the type of smart decisions made by individuals can vary between communities with different characteristics that determine the success or failure of CCP. From the 1920s to 1960s, police practice followed mainly the ‘professional model of policing’, which emphasizes the role of police as crime fighters and citizens as passive recipients of police services (Smith et al. 1997). Changing economic and social conditions during the 1960s proved such police practice ineffective as crime rates continued to rise. Alternative solutions were called for and crime prevention entered the era of community crime prevention (Vallée 2010). Since the 1970s, the professional model of policing has received various criticisms. First, it is an expensive practice that appeared ineffective as crime rates increased during the 1960s. Second, a ‘gap’ had been created between police and citizens as citizens took a passive role in crime prevention. Encouraging active citizen participation in crime fighting became the natural solution for two reasons: (1) to help lessen the burden on police as fully responsible for fighting crime, and (2) to improve police–citizen relations as citizens become partners with police in crime prevention. Community-based policing first began in the United States in the early 1970s and was swiftly introduced to other countries such as England and Canada by the early 1980s (Solicitor General Canada 1984; Hourihan 1987; Vallée 2010). Community crime prevention programmes such as Neighbourhood Watch has continued to be a popular strategy advocated by governments to complement formal law enforcement efforts by police (Garofalo and McLeod 1989; Lewis 1996). Since the mid-1980s, a large body of literature has emerged to discuss the effectiveness of CCP programmes in reducing crime as well as the extent of citizen involvement in such programmes. Evidence on CCP effectiveness has been mixed at best. The consensus is that various community-based crime prevention programmes have either no, or only limited, effects to reduce crime at local levels (Garofalo and McLeod 1989; Lewis 1996; Smith et al. 1997; Schneider 2007). With regard to CCP participation, evidence generally suggests that high-crime neighbourhoods that are most in need of CCP efforts often experience the least success in implementing and maintaining programmes such as Neighbourhood Watch, of which typical participants tend to be long-term residents who are home owners 515

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with high income, education, and socioeconomic status in affluent suburban areas (Skogan 1989; Lewis 1996; Kang 2011; Becker 2013). Demographic factors such as age, gender and ethnicity appear to have no consistent relationship with participation in community crime prevention (Schneider 2007). The rest of this chapter begins with a description of the theoretical explanations that have been used to motivate the discussion of CCP. The role of identity, personal and social capital in CCP is then examined from a behavioural economics perspective, followed by an application of a game theoretical approach to integrate the various theories used to explain CCP. The chapter concludes with some policy implications.

2

COMMUNITY CRIME PREVENTION: THE SOCIOLOGICAL TRADITION

During the 1960s, crime rates surged in countries such as the United States, the United Kingdom and Canada. By the 1970s, police started to recognize the need to modify existing police practices by paying more attention to community relations. Community crime prevention was formulated as a response to the changing social conditions such that citizens began to take a more active role in crime prevention (Schneider 2007; Vallée 2010). Community crime prevention programmes, such as Neighbourhood Watch, that are considered examples of situational crime prevention intervention, began to develop in the 1980s. Situational crime prevention is predicated upon the idea that opportunities for criminal activity can be reduced or eliminated by manipulating the environment, such as through improving the built environment or social relationships among residents (Clarke 1983). The basis for this form of crime prevention is founded on the rational choice model, which assumes that individuals will weigh both the costs and benefits of criminality, and proceed if they perceive the benefits of engaging in crime as high and the costs as low (Clarke 1995). Another prominent criminological theory tied to CCP is routine activity theory, which posits that the convergence of a motivated offender, suitable target and absence of capable guardians will lead to an increased likelihood of a criminal event occurring (Cohen and Felson 1979). The formation of social groups and structures such as Neighbourhood Watch thus act to deter individuals from criminality owing to collective action against crime which raises the costs of engaging in delinquent and criminal activities and ensures that capable guardians are in place to aid in deterring crime. A main purpose of CCP is to mobilize collective action by citizens to complement police efforts in reducing opportunities for crime within a community. A natural starting point for CCP analysis is the concept of community. Within the sociological tradition, the theory of social disorganization explains communities (for example, Chicago in the 1920s) that experience rapid urban growth as characterized by unstable social conditions such as high residential mobility, racial diversity, broken homes and low socioeconomic status (Shaw and McKay 1942). Under such chaotic conditions, traditional social institutions such as family, school, and church start to fail in their functions to uphold social norms and informal social control, giving rise to higher rates of crime and delinquency (Leung and Brittain 2009). The theory of social disorganization serves as the background for the emergence of CCP because social institutions play an important role in determining the strength of informal social control within a community.

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Role of identity, personal and social capital in community crime prevention 517 Collective efficacy, defined as the combination of trust, shared values and expectations of informal social control (Pattavina et al. 2006; Schneider 2007; Sampson and Graif 2009), is a concept often cited in the literature to explain the emergence and maintenance of CCP. An important element in CCP analysis is the common values shared among members within a community. Social cohesion along with shared social norms are considered necessary requirements to mobilize joint social action such as CCP. Effective communications and network connections as well as mutual trust between members of the community are essential components to realize the joint activities for crime prevention. Once the joint activities have materialized, for CCP to be successful it will still require an effective system of informal social control such as citizens’ willingness to intervene and punish deviant behaviour (Becker 2013). Community crime prevention will remain ineffective to reduce crime if the level of willingness to tolerate crime by citizens is too high. For example, some communities with abundant gang activities and high crime rates may be otherwise socially cohesive. In this case, many gang members are local residents who commit crime in their own neighbourhood; money made from crime is often injected back into the same community as financial resources to support their own families or even the larger community. Various forms of investment made by the criminals to the community enhance the social ties between the criminals and the community, which in turn decreases the willingness of residents to punish criminal activities. This kind of positive social interaction between criminals and law-abiding citizens is known as ‘negotiated coexistence’ between the two groups, which serves as a possible explanation for why some socially cohesive communities experience high crime rates (Browning 2009). Community crime prevention effort such as Neighbourhood Watch has been shown to have only modest impacts in reducing crime. The maintenance of any CCP programme often faces difficulties at both the organizational and the participation levels. At the organizational level, programmes such as Neighbourhood Watch often emphasize the importance of reporting criminal activities to police instead of confronting the criminals directly, thus encouraging a passive role of citizens in crime prevention. Furthermore, some CCP programmes are initiated in response to certain isolated criminal events in a neighbourhood and participants lose interest after a while, causing many CCP programmes to become inactive or dormant (Garofalo and McLeod 1989). A given level of crime might be necessary for a Neighbourhood Watch programme to remain active (Huck and Kosfeld 2007). At the participation level, opportunities to engage in CCP activities are unequally distributed across neighbourhoods and individuals. Socially cohesive neighbourhoods with low crime rates are often in a better position to experience successful CCP than crime-ridden neighbourhoods with weak social ties (Skogan 1989). For some CCP programmes that enjoy active participation, bias exists regarding individual participation, especially among communities with high degrees of racial and cultural diversity. In such communities, residents from different backgrounds tend to behave and live according to different cultural norms. For example, Becker (2013) shows that white males can become the dominant force that limits CCP participation of females and non-white members in a community. Such differential opportunity structures often create an insiders–outsiders phenomenon that has a detrimental impact on social cohesion within the community. As suggested by Schneider (2007), social interaction, cohesion and informal control are three elements that are invoked by successful CCP, but at the same time are essential for successful implementation of CCP.

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In sum, the CCP analysis from the sociological tradition is built upon the idea of common values on crime prevention shared by members of a socially cohesive community with an effective system of informal social control that community members are willing to exercise. In the next section, the CCP analysis is extended to incorporate a behavioural economics perspective.

3

EXTENSION TOWARDS A BEHAVIOURAL ECONOMICS PERSPECTIVE

The study of economics examines how rational individuals maximize their own wellbeing. Behavioural economics further considers the role of psychological and social factors in affecting rational decision-making. A behavioural economic approach therefore fits well with the CCP analysis as CCP is a type of situational crime prevention intervention that is formulated as a rational response shaped by social and psychological factors to deal with the problem of crime. Consider the conditions and characteristics under which CCP is most likely to be effective that include social cohesion, common values, trust, social networking, and effective informal social control. Each of these characteristics can be thought of as a type of useful resource for successful CCP operations. However, the simultaneous presence of the various characteristics is required to realize the goal. For example, a high level of trust on its own is not sufficient to guarantee successful CCP without effective informal social control such as willingness of residents to sanction deviant behaviour. The various CCP requirements in this case can be considered different forms of social capital, defined as productive resources that people can build through social interactions (Leung 2002). For instance, a person’s active participation in CCP increases the social capital stock of both the person and the community as stronger social ties and higher levels of social cohesion are being developed (Ren et al., 2006). In the seminal paper of Coleman (1988), three forms of social capital were suggested: (1) trust and expected obligations, (2) information channels, and (3) effective norms and sanctions. All three forms of social capital are useful resources to facilitate social cohesion and collective action. Trust built between individuals enhances social cohesion within a community, thus providing an environment conducive for the formation of social relations and networks between people. Effective social norms are developed and thrive in a community where members share common values and goals, and fulfil the expected obligation of sanctioning norm-violating behaviour. The literature further distinguishes two types of social capital. Bridging social capital refers to personal relationships built upon direct interaction with other people, while bonding social capital is based on identifying oneself with a specific group (Putnam 2000; Brisson and Usher 2007). For example, interacting and networking with a fellow member of an organization is considered a form of bridging social capital; identifying oneself with other members of the organization strictly by membership without personal interaction is considered a form of bonding social capital. In addition to social capital, another type of capital discussed in the literature is personal capital, defined as productive resources gathered by a person through his or her own activities without interpersonal interaction (Becker and Murphy 2000; Leung and Brittain 2015). The main difference between personal capital and social capital lies in the

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Role of identity, personal and social capital in community crime prevention 519 fact that personal capital can be gathered strictly based on one’s own activity, while social capital can only be gathered through interactions with other people. Both types of social capital as well as personal capital all contribute to the shaping of an individual’s personal identity as well as social identity (Davis 2014; Jiang and Carroll 2009). As people continue to establish their identities, labels are attached. The distinction between members (or insiders) and non-members (or outsiders) is often an inevitable product of identity formation (Akerlof and Kranton 2010). Social identity theory suggests that people’s choice to identify with certain groups defines who they are (personal identity) and what social category they belong to (social identity). People’s behaviour is then shaped by the social norms and sanctions of the social groups they belong to (Jiang and Carroll 2009; Akerlof and Kranton 2010). Hence, people who belong to different social groups are expected to behave differently. By continuing to identify with certain groups and observing the same set of group norms and sanctions over time, the personal and social identities of people as well as the distinction between insiders and outsiders are further reinforced. In the traditional microeconomic analysis of consumer behaviour, consumers derive satisfaction or utility from the consumption of goods and services. Identity utility can be similarly derived when a person establishes personal and social identity. Akerlof and Kranton (2010) explain the relationship between identity and utility with a three-step process. First, personal and social identities place a person in a certain social category. Second, norms associated with the social category are defined. Third, a person gains or loses utility from each decision and action they make based on the defined identity and norms. A person’s identity and social category are important determinants of behavioural patterns. Insiders and outsiders, as defined by different groups or social categories, behave differently to increase their respective utility. Consider a population of high school students that is made up of different peer groups such as ‘brains’ and ‘partyers’, where ‘brains’ spend most of their free time studying and ‘partyers’ spend most of their free time partying (Erskine et al. 2006). Suppose the main purpose of school is designed for increasing individual productivity through the consumption of education (Becker 1964). A student who is identified with the peer group ‘brains’ gains identity utility by spending time studying as individual productivity increases at the same time. A student identified as a ‘partyer’ gains identity utility by spending time partying as individual productivity remains low or decreases owing to limited or no time devoted to studying and practising skills relevant for improving productivity. The formation of identity often interacts with the capital accumulation process to affect a person’s utility. For example, a person who joins a religious group can experience a change in the process of personal and social capital accumulation through identifying himself or herself with the group. The group identity may allow the person to realize a better purpose for life (an increase of personal capital stock), and at the same time to develop new relationships and networks through interaction with other members in the group (an increase of social capital stock) – all of which bring the person a higher level of utility. Furthermore, group identity can solidify the stock of social capital such as trust and cooperation within the group and increase the utility of all group members, but can also at the same time create conflict between groups with different norms or between insiders and outsiders within a same group (Davis 2014). The various concepts of capital as well as identity formation are important building

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blocks for understanding CCP from the behavioural economics perspective. Consider a community that attempts to engage in community crime prevention through the initiation of a Neighbourhood Watch programme. Whether such an attempt will be successful or not depends on the structural characteristics of the community and the individual characteristics of its residents, of which the two characteristics often impact each other in determining the outcome. For example, a neighbourhood with mostly homeowners who are long-term residents is more likely to be socially cohesive than a neighbourhood with mostly transient renters. Social identities of neighbourhoods often develop in relation to the personal identities of residents, both of which interact with the process of capital accumulation to determine the outcome. Residents of a neighbourhood with strong positive social identity tend to have more incentive to identify with their community and to become socially cohesive as residents develop trust and relations with each other. As a result, the stock of personal and social capital within the neighbourhood continues to grow, which will further reinforce the positive social identity of the area. A strong negative social identity acquired, like the label of a ‘ghetto’, provides incentives for residents not to identify with the community and to move elsewhere that has a more positive social identity (Temkin and Rohe 1998), which in turn has a detrimental effect on the stock of personal and social capital within the neighbourhood as residents fail to develop trust and social ties with each other.

4

A GAME THEORETICAL ILLUSTRATION

Community crime prevention is a form of collective action that requires cooperative behaviour from members to be successful which is largely determined by community characteristics and social relationship among residents (Clarke 1983). In such a situation, which entails people behaving strategically and making smart decisions to maximize their own interest, the potential free-rider problem can arise at both the organization and the participation levels. For instance, who should initiate the Neighbourhood Watch programme in a community? Who should monitor the daily operation of the programme? How can it be assured that residents actively participate in the programme? Game theory is a useful tool for analysing strategic interactions between people as well as gaining insights on the free-rider problem (Nash 1953). Empirical evidence suggests that certain types of communities are more likely than others to have success in CCP. Consider two communities, Eastville and Westville, with the following characteristics: 1.

2.

Eastville is a high-crime area that is made up of a large number of transient rental residents with diverse cultural backgrounds. Other characteristics of Eastville include crowded living conditions, a high proportion of single-parent households, and a high unemployment rate. Westville is a low-crime neighbourhood with residents who are professionals and own their homes. The residents, for the most part, share the same ethnic and cultural backgrounds and are active participants in community activities. Suppose the police propose a certain sum of start-up funds to initiate Neighbourhood

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Role of identity, personal and social capital in community crime prevention 521 Table 29.1

Strategic interaction in Eastville Lee (L)

Jones (J)

Cooperate (c) Deviate (d)

Cooperate (c)

Deviate (d)

J 5 10, L 5 10 J 5 15, L 5 −5

J 5 −5, L 5 15 J 5 0, L 5 0

Watch in both Eastville and Westville. In the following analysis, game theory is applied to analyse how self-interested residents make smart decisions and respond to Neighbourhood Watch participation from strategically interacting with each other. To keep the analysis simple and clear, a two-household game is used to analyse the interaction. The results can be generalized to the case with n-household. Let Jones and Lee be two households in Eastville. Both Jones and Lee choose one of two strategies regarding participation in Neighbourhood Watch: 1. 2.

Cooperate – active participation in Neighbourhood Watch, or Deviate – free-ride with no active participation in Neighbourhood Watch.

With the simple case of two households and two possible strategies for each, there exist four possible outcomes as a result of the strategic interaction between the two households. Each outcome will yield each household a certain level of satisfaction or utility. First consider the case of Eastville as summarized in Table 29.1. Table 29.1 shows that the best possible outcome is for the two households to cooperate so that joint utility is maximized at 10 units for each (for a joint utility maximization of 20, which yields the highest joint values among the four possible outcomes). To achieve this outcome, the two households must share at least some common interest in crime prevention and trust each other. Unfortunately, the characteristics of Eastville are not conducive to realizing this cooperative and potential best outcome. The transient nature of the neighbourhood implies that residents are not likely to identify themselves with Eastville, especially with its social identity of being a high-crime area. For the same reason, it is also difficult for Eastville residents to identify, let alone to develop relations and networks, with each other. That is, Eastville residents are likely to identify themselves as ‘outsiders’ and will not gain positive identity utility from cooperation. In such a chaotic living environment with broken homes and high unemployment, traditional social institutions such as family, school and church are not likely to maintain conventional social norms and informal social control for personal and social capital to develop. If the cooperative outcome cannot be realized for Eastville residents, what other outcome can be reached through strategic interaction between the residents when they do not trust each other? Consider the decision strategy of the Lee household. Lee knows that Jones has two choices, cooperate or deviate. The Lee family will ask themselves the following question: ‘If the Jones family chooses to cooperate, is it the best strategy for us to cooperate or deviate?’ Given the information on utility from Table 29.1, Lee will get 10 units of utility from choosing to cooperate and 15 units of utility from choosing to deviate by assuming the Jones family chooses to cooperate. As such, the smart decision is for the Lee family to deviate. Then Lee will also consider the case if the Jones family

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Table 29.2

Strategic interaction in Westville Lee (L)

Jones (J)

Cooperate (c) Deviate (d)

Cooperate (c)

Deviate (d)

J 5 16, L 5 16 J 5 9, L 5 1

J 5 1, L 5 9 J 5 −6, L 5 −6

chooses to deviate, in which case Lee will get –5 from choosing to cooperate and 0 from choosing to deviate. The smart decision is for the Lee family again to deviate such as to minimize potential loss. Therefore, ‘deviate’ is the dominant strategy for Lee as ‘deviate’ is the smart decision for Lee regardless of Jones’s choice. The Jones family can similarly rationalize their smart decision to ‘deviate’. Hence the final equilibrium outcome derived from strategic interaction, which is referred to as the Nash equilibrium in the game theory literature, is for both households to deviate from involvement in Neighbourhood Watch. In the case of Eastville, both households end up choosing to deviate as their best strategy when each settles with 0 units of utility, which is a much worse outcome than the best possible outcome of both families choosing to cooperate. This means that Neighbourhood Watch will not be successful in Eastville when there is lack of both trust and cooperation between residents. There also exists the free-rider problem in Eastville when residents try to rely on other residents to participate in Neighbourhood Watch without their own participation as their smart responses. We now proceed to Westville. The values of utility associated with the cooperation and deviation strategies in Westville (as shown in Table 29.2) are different from those of Eastville (as shown in Table 29.1). The positive social identity of Westville as a low-crime neighbourhood along with its homogeneous ethnic and cultural background provides a socially cohesive environment for Westville residents to share social norms and to develop trust with each other. The presence of a stable home environment (long-term homeowners) coupled with active community participation allows residents to easily build personal and social capital that further reinforces the positive social identity of Westville. The characteristics of Westville provide an environment that is conducive for residents to cooperate with each other and identify themselves as ‘insiders’, implying that the utility value associated with cooperation is high in Westville as Westville residents gain positive identity utility from cooperation. The common norms and trust serve as informal social controls to penalize deviating behaviour, causing the utility value associated with deviation in Westville to be low as Westville residents lose identity utility from deviation. Consider again the simple case with two households, Jones and Lee in Westville. Based on the utility values shown in Table 29.2 for Westville, a similar strategic interaction analysis can be performed as shown for the case of Eastville. In the case of Westville, the dominant strategy for each household is to cooperate and the Nash equilibrium is where both households choose to cooperate as their smart responses with a utility value of 16 for each. Neighbourhood Watch is therefore a successful operation in Westville. For CCP to be successful in a place such as Westville, a basic criterion is that residents have crime prevention as the collective goal. In addition, similar preferences for CCP organization of and participation must be at least similar, if not identical, among residents. For instance, when different residents have vastly different ideas on how Neighbourhood

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Role of identity, personal and social capital in community crime prevention 523 Watch should be run, the programme will not succeed even if residents share the common goal of crime prevention. Smart decision-makers in a crime-ridden area will cooperate and succeed in CCP only when residents are ready to collectively identify with their own community by sharing a set of common social norms and values.

5

CONCLUSION

Community crime prevention efforts are based on the assumption that a collective social energy emphasizing pro-social norms and values will serve to reduce crime in a given area. While CCP may appear to be a straightforward crime prevention initiative, social capital, personal capital and identity structures all converge to either hinder or advance CCP efforts. Thus, individual and community characteristics play a role in whether or not effective CCP initiatives will be recognized. As addressed previously, communities with a strong social identity and which seek to uphold positive norms and values are more likely to succeed in implementing effective CCP than communities with weak social relations and support of law-abiding behaviours and attitudes. To illustrate how individuals may strategically interact in relation to their CCP input in different communities, the framework of game theory was utilized. In communities characterized by weak social relations, high unemployment and a disordered environment, the dominant strategy for individuals is to deviate from cooperating in CCP activities regardless of whether or not they believe that other individuals will either cooperate or deviate from CCP efforts. As such, disorganized and chaotic communities will, in theory, fail to initiate and carry out appropriate CCP programmes such as Neighbourhood Watch. On the other hand, communities with strong social ties which emphasize and promote positive norms and values will likely be successful in terms of CCP implementation. Individuals in these tight-knit communities are likely to cooperate in CCP activities regardless of their beliefs about whether or not their neighbours will cooperate or deviate from such activities. Taken as a whole, community crime prevention efforts are highly dependent on the community nature and structure in which they are being implemented. Thus, communities with strong social bonds will likely embrace CCP to a significant extent, while communities with weak social bonds will likely experience difficulty in implementing and carrying out such activities. Understanding which environments will and will not embrace CCP is crucial in determining which crime prevention efforts should be implemented in certain areas. Knowing this, efficient and effective crime prevention initiatives can be instituted according to community characteristics. An important consideration to have a more complete understanding of CCP analysis is the size of the community. Communication between residents is made easier in a small community where common values and goals are more likely to be shared by residents. In larger communities, cultural diversity with different value systems is more likely the reality where collective activities such as community crime prevention become more difficult to implement and maintain. For example, the theory of clubs from the public choice literature (Buchanan 1965) suggests that the chances of individuals cooperating with each other decrease as the number of members in a group increases. The optimal size of a group (or community in our case) is determined by the benefit compared with the cost of adding a new member. Further research can therefore extend the behavioural economic

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analysis of community crime prevention presented here by incorporating ideas from the theory of clubs. The framework presented in this chapter has shown that concepts such as identity, personal capital and social capital play an important role in the application of the behavioural economic approach to explain community crime prevention. Smart decisions of individuals vary in response to a community’s unique characteristics. For residents of a crime-ridden area, a set of common values and shared goals are necessary for any kind of crime prevention effort to be successful. In addition, a sense of belonging and positive identity towards their own community is also an important element for any kind of cooperative CCP effort to exist among citizens.

REFERENCES Akerlof, G.A. and R.E. Kranton (2010), Identity Economics: How Our Identities Shape Our Work, Wages, and Well-Being, Princeton, NJ and Oxford: Prince University Press. Becker, G.S. (1964), Human Capital, New York: Columbia University Press. Becker, G.S. and K.M. Murphy (2000), Social Economics: Market Behavior in a Social Environment, Cambridge, MA: Belknap Press of Harvard University Press. Becker, S. (2013), ‘An intersectional analysis of differential opportunity structures for community-based anticrime efforts’, Race and Justice, 3 (1), 31–57. Brisson, D. and C.L. Usher (2007), ‘The effects of informal neighborhood bonding social capital and neighborhood context on home ownership for families living in poverty’, Journal of Urban Affairs, 29 (1), 65–75. Browning, C.R. (2009), ‘Illuminating the downside of social capital: negotiated coexistence, property crime, and disorder in urban neighborhoods’, American Behavioral Scientist, 52 (11), 1556–78. Buchanan, J.M. (1965), ‘An economic theory of clubs’, Economica, 32 (125), 1–14. Clarke, R.V. (1983), ‘Situational crime prevention: Its theoretical basis and practical scope’, Crime and Justice, 4 (1), 225–56. Clarke, R.V. (1995), ‘Situational crime prevention’, Crime and Justice, 19 (1), 91–150. Cohen, L.E. and M. Felson (1979), ‘Social change and crime rate trends: a routine activity approach’, American Sociological Review, 44 (4), 588–608. Coleman, J.M. (1988), ‘Social capital in the creation of human capital’, American Journal of Sociology, 94 (supplement), S95–S120. Davis, J.B. (2014), ‘Social capital and social identity: trust and conflict’, in A. Christoforou and J. Davis (eds), Social Capital and Economics: Social Values, Power, and Identity, London: Routledge, pp. 98–112. Erskine, M., C. Kier, A. Leung and R. Sproule (2006), ‘Peer crowds, work experience, and financial saving behaviour of young Canadians’, Journal of Economic Psychology, 27 (2), 262–84. Garofalo, J. and M. McLeod (1989), ‘The structure and operations of neighborhood watch programs in the United States’, Crime & Delinquency, 35 (3), 326–44. Hourihan, K. (1987), ‘Local community involvement and participation in neighborhood watch: a case-study in Cork, Ireland’, Urban Studies, 24 (2), 129–36. Huck, S. and M. Kosfeld (2007), ‘The dynamics of neighbourhood watch and norm enforcement’, Economic Journal, 117 (516), 270–86. Jiang, H. and J.M. Carroll (2009), ‘Social capital, social network and identity bonds: a reconceptualization’, C&T’09 Communities and Technologies, Proceedings of the Fourth International Conference, New York: Association for Computing Machinery (ACM), pp. 51–60. Kang, J.H. (2011), ‘Participation in the community social control, the neighborhood watch groups: individualand neighborhood-related factors’, Crime & Delinquency, 20 (10), 1–25. Leung, A. (2002), ‘Delinquency, social institutions, and capital accumulation’, Journal of Institutional and Theoretical Economics, 158 (3), 420–40. Leung, A. and I. Brittain (2009), ‘An integrated rational choice framework of juvenile delinquency’, in A. Kakanowski and M. Marusevich (eds), Handbook of Social Justice, New York: Nova Science, pp. 149–65. Leung, A. and I. Brittain (2015), ‘Social capital and personal capital (Gary Becker)’, in M. Altman (ed.), Real World Decision Making: An Encyclopedia of Behavioral Economics, Santa Barbara, CA: Praeger, pp. 399–401. Lewis, D.A. (1996), ‘Crime and community: continuities, contradictions, and complexities’, Cityscape: A Journal of Policy Development and Research, 2 (2), 95–119. Nash, J.F. Jr (1953), ‘Two-person cooperative games’, Econometrica, 21 (1), 128–40.

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Role of identity, personal and social capital in community crime prevention 525 Pattavina, A., J.M. Byrne and L. Garcia (2006), ‘An examination of citizen involvement in crime prevention in high-risk versus low- to moderate-risk neighborhoods’, Crime & Delinquency, 52 (2), 203–31. Putnam, R.D. (2000), Bowling Alone: The Collapse and Revival of American Community, New York: Simon and Schuster. Ren, L., J.S. Zhao, N.P. Lovrich and M.J. Gaffney (2006), ‘Participation community crime prevention: who volunteers for police work?’, An International Journal of Police Strategies & Management, 29 (3), 464–81. Sampson, R.J. and C. Graif (2009), ‘Neighborhood social capital as differential social organization: resident and leadership dimensions’, American Behavioral Scientist, 52 (11), 1579–605. Schneider, S. (2007), Refocusing Crime Prevention: Collective Action and the Quest for Community, Toronto: University of Toronto Press. Shaw, C. and H. McKay (1942), Juvenile Delinquency and Urban Areas, Chicago, IL: University of Chicago Press. Skogan, W.G. (1989), ‘Communities, crime and neighborhood organization’, Crime & Delinquency, 35 (3), 437–57. Smith, B.W., K.J. Novak and D.C. Hurley (1997), ‘Neighborhood crime prevention: the influences of community-based organizations and neighborhood watch’, Journal of Crime and Justice, 20 (2), 69–86. Solicitor General Canada (1984), Crime Prevention: Awareness and Practice, Canadian Urban Victimization Survey Bulletin No. 3, Ottawa: Research and Statistics Group Programs Branch. Temkin, K. and W.M. Rohe (1998), ‘Social capital and neighborhood stability: an empirical investigation’, Housing Policy Debate, 9 (1), 61–88. Vallée, M. (2010), ‘An historical overview of crime prevention initiatives in Canada: a federal perspective’, International Journal of Child, Youth, and Family Studies, 1 (1), 21–52.

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30 Norms, culture, and cognition Shinji Teraji

1

DECISION-MAKING IS INSTITUTIONALLY CONSTRAINED

People follow rules of behavior in society. These rules indicate what people should or should not do under some circumstances. Douglass North once defined institutions simply as ‘humanly devised constraints that shape human interaction’ (North 1990, p. 3). The existence of rules implies constraints. However, such a constraint may enable choices that otherwise would not exist. Rules include social norms as well as legal rules. There is a distinction between formal institutions (constitutions, laws, property rights, and so on) and informal institutions (sanctions, taboos, customs, traditions, codes of conduct, and so on). Formal rules are made explicit or written down, particularly if they are enforced by the state, whereas informal rules are implicit and enforced endogenously by the members of the relevant group. Social norms enforced by the community can be viewed as informal constraints. ‘While formal institutions can be changed by fiat, informal institutions evolve in ways that are still far from completely understood and therefore are not typically amenable to deliberate human manipulation’ (North 2005, p. 50). An understanding of social norms is critical to predict and explain human behavior. In the 1990s, North initiated the cognitive approach to the study of institutions. It is the study of how humans use mental models to explain and interpret the world and how learning in response to new experiences can produce an incremental process of change in beliefs and preferences. For Denzau and North (1994) and North (2005), institutions are the external (to the mind) mechanisms that individuals create to structure and order the environment. Individuals with common backgrounds and experiences will share reasonably convergent mental models. Agents who belong to the same group are exposed to the same external representation of knowledge, which produces shared mental models. Radical reforms are often constrained by societies’ inherited belief systems. There are individuals who are resistant to altering their belief systems and hence the resulting behavior. The sticky nature of beliefs helps explain why imported rules, laws, and constitutions have been so unsuccessful. However, shared beliefs sometimes change. In The Sensory Order (1952), Friedrich A. Hayek provided a theory of the process by which the mind perceives the world around it. Hayek’s The Sensory Order is in no way a direct economic or social analysis, but it can be looked at in the context of the cognitive problems related to the conceptualization of institutions.1 For Hayek, ‘psychology must start from stimuli defined in physical terms and proceed to show why and how the senses classify similar physical stimuli sometimes as alike and sometimes as different, and why different physical stimuli will sometimes appear as similar and sometimes as different’ (Hayek 1952, pp. 7–8). According to Hayek, knowing the world is a classification of sensory qualities by the mind: ‘the classification of the stimuli performed by our senses will be based on a system of acquired connections which reproduce, in a partial and imperfect manner, relations existing between the corresponding physical stimuli’ (Hayek 1952, p. 145). 526

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Norms, culture, and cognition 527 What we know at any moment about the external world is determined by the order of the apparatus of classification which has been built up by previous sensory linkages. The qualitative differences in perceptions that people experience depend on the specific pattern of neuron firings that a given stimulus produces within various neural networks. The experiences of individuals will differ according to the pattern of neuron firings that each develops. Every perception of external data depends on the subjective characteristics, which in turn depends on original paths of interpretation. Hence, human perceptions emerge according to self-reinforcing processes that are likely to accumulate in a specific path of action. That is, individuals’ decision processes continue to reproduce one particular choice pattern, not switch to some previously plausible alternatives. Hayek’s cognitive theory explains how different magnitudes of different pieces of cognitive information cause different perceptions and therefore actions. New linkages are established, depending on the pattern of ongoing neural activity. The structure of linkages governs our cognitive processes. For Hayek, the central element in the cognitive process is the feedback between individual and environment. The apparatus by means of which we learn about the external world ‘is shaped by the conditions prevailing in the environment in which we live, and it represents a kind of generic reproduction of the relations between the elements of this environment which we have experienced in the past; and we interpret any new event in the environment in the light of that experience’ (Hayek 1952, p. 165). Hayek’s concept of perception as classification has a counterpart in his concepts of rules and rule-following behavior. Homo sapiens is ‘a rule-following animal’ (Hayek 1973, p. 11). Relying on rules is a device we have learned to use because our reason is insufficient to master the detail of complex reality. Therefore, ‘what we refer to as knowledge is mainly a system for rules of action supported and modified by rules indicating similarities and differences between combinations of stimuli’ (Hayek 1978, p. 41). For Hayek, rules make it possible for individuals to classify stimuli. The order of a group can be generated by the rules of conduct adhered to by its members. How the mind classifies stimuli determines how individuals act on the external world. Much of our knowledge is embedded in institutions. The mind is shaped not only by experience but also by custom. This feature relates to an interpretation of rules as behavioral patterns or regularities of conduct. If rules are recognized as recurrent patterns of behavior, individuals act according to rules of conduct. The diffusion of shared behavioral patterns is necessary to obtain the social order. Shared rules facilitate the decision-making in complex situations by limiting the range of circumstances to which individuals have to pay attention. This chapter considers a relationship between the sensory order and the social order. Hayek’s theory of mind sheds light on the process of choice. The sensory order is fundamental in the sense that the explanation of social order begins with the human mind. The central element in the cognitive process is the feedback between individual and environment. The chapter considers cultural evolution as an endogenous phenomenon from a cognitive viewpoint. The remainder of the chapter is organized as follows. Section 2 presents some reasons why people comply with social norms. Section 3 is a brief overview of Hayek’s cognitive theory. Section 4 focuses on culture from a cognitive viewpoint. Section 5 presents a perspective on the relationship between cognition and cultural evolution. Section 6 is the conclusion.

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NORMS

Social norms are informal rules, as opposed to formal rules promulgated by a court or a legislature. There are widespread expectations of proper and acceptable behavior within a group. A social norm is a behavior characterized by its being prevalent among group members. Social norms often direct individuals to undertake actions that are inconsistent with selfish actions. For example, in the dictator game, 50–50 division is generally viewed as norm-compliant (Andreoni and Bernheim 2009). However, people may deviate from such norms. In the case of legally compliance, individual incentives most often refer to deterrence (Becker 1968). That is, individuals are deterred from criminal activities by a higher fine and by a higher probability of conviction. In the rational choice approach, legal compliance is accounted for by standard economic incentives of self-utility maximization; compliance is in a person’s self-interest. Thus, the rational choice approach predicts that the frequency of crimes will decrease if the perceived benefits associated with offending decisions are reduced, and the perceived cost associated with offending decisions are increased. Unlike legal rules, social norms are not supported by formal sanctions. Why do people obey norms? There are some reasons why people comply with norms: 1. Individuals hold a preference for conformity to a behavioral pattern through socialization (Teraji 2007). The internalization of norms can guide a certain form of behavior. Once a norm has been internalized through socialization, many people tend to follow it routinely. Then, without checking the soundness of reasons of internalized norms, people may comply with norms. Simon (1993) supposes that human choice is driven by a number of motives, not limited to economic gain. Cheap adequate solutions are often preferred to costly perfect ones. Rationally limited agents cope with a problem by adopting simplifying strategies for its solution. Individuals have a tendency to act on advice and respect social norms. Social norms are standards of behavior that indicate what people should or should not do under some circumstances. As Simon (1993) points out, in large measure, people do what they do because they have learned from those who surround them, not from their own experience, what is good for them and what is not. Behaving in this fashion contributes heavily to the fitness of human beings in evolutionary competition. As a consequence, people exhibit a very large measure of ‘docility’. Here, docility means the tendency to depend on suggestions, recommendations, persuasion, and information obtained through social channels as a major basis for choice. Docile persons often make choices under social advice to do so. Certain kinds of common understandings among individuals are required to produce the common associations of ideas that allow conventions to emerge and to reproduce themselves. Social interaction will lead individuals to behave in a more similar manner. The society is sustained by processes favorable to individuals endowed with some docility in following rules. Smith (2003) develops the concept of an order as an undesigned ecological system that emerges out of economic, cultural, and biological evolutionary processes. He introduces the distinction between ‘constructivist’ rationality and ‘ecological’ rationality to capture this point. According to constructivism, institutions have been deliberately designed in order to accomplish human purposes. In Hayek’s view,

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Norms, culture, and cognition 529 this is an erroneous conception and has dangerous effects as a guide for political, social, and economic interventions. Deliberately organized forces of society may destroy spontaneous forces which have made advance possible. On the other hand, ecological rationality uses reason to discover the possible intelligence embodied in the rules and norms that are created from human interactions but not by deliberate human design. Ecological rationality is an evolutionary-oriented notion of rationality. Through a process of social evolution, institutional arrangements emerge from human interactions that enable individuals to better coordinate their behavior. This evolutionary process takes place despite their imperfect knowledge of the structure of their environment. 2. Norms that involve pecuniary disadvantages for people adhering to them do not necessarily disappear. Akerlof (1980) develops an economic model to show that disobeying a norm may involve a loss of reputation. Certain groups of individuals can maintain a strong reputation over time. People want to achieve the reputation of being fair. People are fair because they care about their reputation. They may not be genuinely fair. They have to be rewarded for good reputation, and they have to be willing to comply with the norm. Individuals are influenced in their convictions by what they think others will do. Conformity to the norm is conditional on expectations about other people’s behavior. Norms are constituted by expectations shared by members in a population and are jointly recognized among them. Social norms can be sustained if the pecuniary advantage from breaking norms is not sufficient to offset the forgone reputation effect. This is related to indirect reciprocity. According to Alexander (1987), indirect reciprocity is arranged in the form of a chain; a person is eventually helped by someone else who may not have been directly helped by him or her. Altruistic actions can be sustained if people who support others receive support in turn. To achieve such indirect reciprocity, building up a positive reputation is needed. 3. People comply with norms because the threat of punishment makes it in their interest to do so. The importance of decentralized punishments (that is, punishments carried out by individuals without the intervention of a central authority) is documented in experimental studies. Ostrom et al. (1992) show the existence of such punishment opportunities in a common-pool resource use game. The fear of punishment has a positive effect on cooperation. In public goods experiments, subjects begin by contributing on average about half of their endowments to the public account. However, the level of contribution decays over the course of multiple rounds. When costly punishment is permitted, cooperation does not deteriorate. Fehr and Gächter (2000) indicate that many individuals are willing to punish unfair behavior at a personal cost in public goods games. Potential punishers are not themselves the victims but have merely witnessed unfair behavior. This is called altruistic punishment as individuals sacrifice for no direct benefits. It suggests that cooperation has evolved through the sacrifice of altruistic punishers who are ready to incur some costs to prevent unfair behavior. The existence of such altruistic punishers constitutes a threat that acts as a deterrent against norm violations. A norm is regarded as a rule governing an individual’s behavior. Norms are enforced due to the expectations that norm violations will be punished. 4. Norms are represented as Nash equilibria of games played by rational agents, and as

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such they are self-enforcing (Lewis 1969). The equilibrium account of norms must be supplemented with a story of how agents learn to recognize a behavioral pattern, how they settle on a stable pattern, and what sort of behavior is more likely to be sustainable. Norms are supported by shared expectations about what should or should not be done in a population (Bicchieri 2006). Let R be a behavioral regularity in a population P. Each member of P knows that a certain behavioral regularity R exists. Then, R is a norm if and only if R depends on the beliefs and preferences of the members of P in the following way: (1) almost every member of P prefers to conform to R on the condition that almost everyone else conforms to R, too; and (2) almost every member of P believes that almost every other member of P conforms to R. A social norm is a rule of action that exists whenever an agent expects others to follow it and believes that others expect him or her to follow it as well. Thus, a norm is an equilibrium in the game-theoretic sense of being a combination of strategies, one for each player, such that each player’s strategy is a best response to others’ strategies. 5. Correlated equilibrium allows players’ actions to be statistically dependent on some random signals external to the model (Aumann 1987). Correlated equilibrium only requires rationality and common priors, while Nash equilibrium requires stronger premises. Different players can potentially take different actions. However, their actions are conditional on some external signal. Nature first gives a publicly observable signal. Players’ strategies assign an action to every possible observation. If no player has an incentive to deviate from the recommended strategy, the distribution is a correlated equilibrium. Social norms play the role of a ‘choreographer’ who leads people to take actions according to some commonly known probability distribution (Gintis 2009). A social norm prescribes which strategy every player should choose depending on the observed signal, in such a way that it is rational for him or her to follow the suggestion knowing that others are following it, too.

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COGNITION

A choice is a selection among numerous possible behavioral alternatives. A decision is a process through which this selection is performed. Conventional economic theory is about choices actually made, not about decision-making processes leading to the choices. That is, conventional economic models include only variables that condition ‘what an agent chooses’ and none that condition ‘how an agent chooses’. This entails a ‘black box’ view on the individual, meaning that it does not matter analytically how that behavior is actually generated. It is no doubt to exclude a need of psychological inquiry from economics. Following Hayek’s theory of mind, however, economics needs to build on the characterization of processes that contribute to decision-making. Hayek’s theory of mind sheds light on the process of choice; it describes the human mind as an adaptive classification system by which individual behavior is shaped. Furthermore: What we call mind is not something that the individual is born with, as he is born with his brain, or something that the brain produces, but something that his genetic equipment . . . helps him to acquire, as he grows up, from his family and adult fellows by absorbing the results of a tradition that is not genetically transmitted. (Hayek 1988, p. 22)

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Norms, culture, and cognition 531 In The Sensory Order (1952), Hayek provided a theory of the process by which the mind perceives the world around it. The brain must digest information in a way to simplify data and stimuli that would otherwise be infinitely complex and confusing. The basic structure of the system is in the form of a network of components which interact via the transmission of electrical impulses. The sensory order is a classification that takes place via a network of impulse connections. The essence of Hayek’s attempt in theoretical psychology is to show how a structure can be formed which discriminates between different physical stimuli and generates the sensory order that we actually experience.2 The sensory order is a system of qualities that do not simply represent physical properties of the external world. The sensory order is even an incomplete and imperfect representation of the physical world. The subjectivity of individual knowledge finds its foundation in the construction of the mind. Through learning and updating, the sensory order evolves into a gradual approximation of the physical order. The mind operates by assembling new sensory data into associations with our accumulated inventory of knowledge. Knowledge is forged by the connection of new sensory information to previous sensory experiences. People form expectations of future events by recalling what has followed in the past after the type of events they now perceive around them. After the processes of interpreting their perceptions, individuals have to think of alternative courses of action and of probable consequences in each of the alternatives. Individuals then try to do the same in a given situation by acting in a way that has proved to produce satisfying outcomes in similar situations of the past. Hayek’s (1952) ‘map’ provides a framework for evaluating impulses; it is an apparatus by means of which we learn about the external world. Experience shapes the map. The map represents the individual’s past. The map ‘is formed by connections capable of transmitting impulses from neuron to neuron’ (Hayek 1952, p. 115). Operating within the map is a dynamic and changeable ‘model’. The model is ‘the pattern of impulses which is traced at any moment within the given network of semi-permanent channels’ (Hayek 1952, p. 114). The map is similar to a set of implications waiting to happen, and the model pulls out the implications relevant to the current environment from this set. The model refers to the current interpretation of the environment. The map–model structure is subject to continuous change and can be updated. As a person learns, a new interpretation of reality takes over the old interpretation through re-classification. The brain is an adaptive system interacting with and adapting to its environment by performing a multi-level classification on the stimuli it receives from the environment. Individuals in the real world are often ignorant of the consequences of their choices. However, they may form expectations of future events and make good decisions on the basis of beliefs in the correctness of their expectations. An agent makes a decision by assessing past situations that resemble those in question right now and by recalling the consequences of acting in each of them. The agent compares any particular instance with a large number of different sets of cases that he or she has experience of, each having some qualities of relevance to the current case. Each case can produce a tendency to a kind of action. As Hayek (1978) suggests: The important point is that only very rarely if ever will a single signal sent out from the highest levels of the nervous system evoke an invariable action pattern, and that normally the particular sequence of movements of particular muscles will be the joint result of many super-imposed dispositions. A disposition will thus, strictly speaking, not be directed towards a particular action,

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but towards an action possessing certain properties, and it will be the concurrent effect of many such dispositions which will determine the various attributes of a particular action. A disposition to act will be directed towards a particular pattern of movements only in the abstract sense of pattern, and the execution of the movement will take one of many different possible concrete forms adjusted to the situation taken into account by the joint effect of many other dispositions existing at the moment. (Hayek 1978, p. 40)

The mind is an order of relations. At the level of neurons in the brain, the classification of primary sensory impulses, and further impulses they evoke, can take place on many successive levels. As a result of the multiple classification process, the human mind produces a highly complex mental order. There are three parallels between Hayek’s cognitive and institutional theories (Wenzel 2010). First, knowledge is limited. The sensory order is an imperfect representation of the physical order. Second, learning at the cognitive level and generation of knowledge are important. The mind updates its understanding of the environment. Third, knowledge has a social dimension. Much of our understanding of the world comes through the human mind. In addition, the mind learns from its environment. Much of our knowledge is thus embedded in institutions, norms, and custom. According to Hayek (1948), the knowledge that an individual can possess is dispersed, and each person can just possess a little piece of all the knowledge available in society. The dissemination of knowledge is crucial in society. Interactions of people in society make adaptation possible. At an individual level, actions are based on subjective perceptions of what exists. However, a correspondence between individual actions and an overall order is inherently problematic. People live in a world of expectations about interactions with others’ actions. Interactions between people in society make adaptation possible. If the fragments of knowledge existing in different minds were justified true beliefs, there could be no need for their resolution via coordination. Dispersed knowledge required for coordination can be facilitated by a set of inter-subjectively shared rules. Individuals learn and act upon the information that is dispersed throughout institutional environments. Social coordination requires institutional structures that encourage the use of dispersed knowledge. Human action is purposeful in the sense that individuals attempt to reach their goals. It is meaningful to discuss the social order only when all agents share the same perception of existing reality which includes others’ actions. North (2005) interprets Hayek’s approach to institutions as closely connected to his theory of mind. For Denzau and North (1994) and North (2005), individuals construct mental models to interpret and produce expectations about their social environment. That is, Hayek’s mental ‘map’ is the ‘mental models’ as used by Denzau and North (1994) and North (2005) to justify the importance of institutions. In order to avoid confusion, we use ‘mental models’ to refer to Hayek’s ‘map’. All experiences are organized from particular points of view. Owing to the variety of mental models, there are multiple possible framings of any given situation. There are different consequences depending on the way people frame the situation. Mental models might be hypothetical constructs of a certain set of experiences, through which individuals process information.

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CULTURE

Human actions are imprinted by their history in a way. Culture refers to the set of learned traditions and living styles, shared by the members of a group. Conceptualization of culture varies across disciplines. A broad definition of the term culture is provided as follows: ‘Cultures are patterns of behavior, thought and feeling that are acquired or influenced through learning and that are characteristics of groups of people rather than individuals’ (Harris 1971, p. 136). Culture includes the specific ways of thinking, feeling, and behaving of a group. Culture, as a system of shared beliefs, provides collective understandings in forming people’s choices. According to Richerson and Boyd (2005), culture is ‘information’ capable of affecting individuals’ behavior that they acquire from others through teaching, imitation, and other forms of social transmission. The term ‘information’ means any kind of mental state, conscious or not, that is acquired or modified by social learning, and affects behavior. Social learning is the mechanism whereby information is transmitted. For Richerson and Boyd (2005), the most basic type of micro-event in cultural evolution is the adoption by an individual of some cultural variant. The collective phenomenon of culture is taken to be the evolving outcome of the aggregation of these micro-events. The existence of culture presupposes a population capable of mental representations. People are likely to perceive bits of information that are germane to existing schemata ‒ knowledge structures that represent objects or events and provide default assumptions about their characteristics, relationships, and entailments under conditions of incomplete information (DiMaggio 1997). People experience culture as schematic structures that organize that information. As suggested by Denzau and North (1994, p. 15), culture may be regarded as ‘encapsulating the experiences of past generations of any particular cultural group’. Culture collectively accumulates partial solutions to frequently encountered problems of the past and works as a filter for processing and interpreting new sensory data. Culture can be understood as heterogeneous and changeable. There is cultural variation in the way people think about themselves and about other agents. These differences are responsible for differences in the way people behave. Cultural differences are, to a large extent, due to environmental differences. Therefore, patterns of social interactions affect the structure of cultural systems. Axelrod (1997) argues that culture is taken to be what social influence influences. In Axelrod (1997), two groups that are already culturally similar are more likely to interact and, therefore, to be even more culturally similar. Alternatively, two neighboring groups with zero cultural similarity are unlikely to interact and, therefore, will have no tendency to be more culturally similar. The emergence of conventions can be accelerated if the population has a neighborhood structure, and individuals adjust their behavior over time by imitation within their own neighborhood. Humans acquire large parts of their behavioral repertoire via forms of social learning (basically imitation). Understanding how people learn from others is important not only for understanding individual decisions, but also for comprehending patterns of change and variation among human groups. Differences among individuals can exist because they acquire different behavior as a result of some form of social learning. Henrich (2004) proposes culture as the mechanism reducing ‘intra-group’ differences and maintaining ‘inter-group’ differences, by biasing individuals in favor of copying the common beliefs. As Henrich (2004) suggests, cultural

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evolution is likely to proceed much more rapidly than genetic evolution because cultural transmission can spread novel behavior, ideas, and practices among populations within a single generation. The transmission of culture involves learning from others. Once a cultural trait is acquired and internalized, it tends to direct actions without significant cognitive effort or reflection. Individual actors’ expectations and actions somehow seem to achieve the quite remarkable level of coordination regularity witnessed in many aspects of social life. Culture coordinates the expectations of many agents about the actions, and it shapes and structures our daily patterns of behavior, guiding much of what we should do by prescribing what behavior is acceptable. Agents who belong to the same cultural group are exposed to the same external representation of knowledge, which produces shared mental models. Culturally shared mental models expedite the process by which people learn directly from experiences, and facilitate communication between people. Culture thus provides shared collective understandings in shaping individuals’ actions. Path dependence includes features such as persistency and lock-in. Arthur (1989) explains increasing returns in technology by using a model of path dependence.3 North (1990) applies increasing returns arguments to institutions more broadly. Established institutions generate powerful inducements that reinforce their own stability. Path dependence in the evolution of belief systems results from a ‘common cultural heritage’ which ‘provides a means of reducing the divergent mental models that people in a society possess and constitutes the means for the intergenerational transfer of unifying perceptions’ (North 2005, p. 27). Cognition may have more subjective aspects, while culture enables individuals to develop inter-subjectively shared mental models. Societies that are more complex than tribal communities are characterized by different and competing interpretations of the social environment. Because culture reflects habituation which individuals acquire in their group, it is slow to change. Some individuals are resistant to altering their belief systems and hence the resulting behavior. Belief systems are the ideas and thoughts common to individuals that govern social interaction. Thus, different cultures may imply different belief systems. Traditions and customs have evolved and are upheld in social interactions. Individuals in the real world are often ignorant of the consequences of their choices. However, they may form expectations of future events and make good decisions on the basis of beliefs in the correctness of their expectations. For Hayek (1973), one of the main characteristics of human behavior consists of following rules of conduct. We can understand the actions of others who are equipped with similar systems for producing action patterns: The question which is of central importance as much for social theory as for social policy is thus what properties the rules must possess so that the separate actions of the individuals will produce an overall order. Some such rules all individuals of a society will obey because of the similar manner in which their environment represents itself to their minds. Others they will follow spontaneously because they will be part of their common cultural tradition. But there will be still others which they may have to be made to obey, since, although it would be in the interest of each to disregard them, the overall order on which the success of their actions depends will arise only if these rules are generally followed. (Hayek 1973, p. 45)

Rules of conduct are shared by individuals having a common cultural tradition. ‘This matching of the intentions and expectations that determine the actions of different indi-

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Norms, culture, and cognition 535 viduals is the form in which order manifests itself in social life’ (Hayek 1973, p. 36). If people have widely divergent expectations, some of their actions will invariably fail and need to be revised. Culture limits the range of actions that people are likely to take in a particular situation, making their conduct more predictable and thereby facilitating the formation of reliable expectations. Shared mental models can give rise to behavioral regularities, to the extent that they can be observed in the population. As a consequence, following rules of conduct mutually reinforces sets of expectations to maintain a degree of social order. Patterns emerge endogenously, reflecting a socially constructed reality. Given the human need for rules, there is a tendency to repeat those patterns as a guideline for action in future instances of similar behavior. In real life, people are skilled at coordinating their actions. This is because there are attractors for their strategies. Schelling (1960) has developed the study of mechanisms allowing people to coordinate. Schelling’s focal-point idea is important as an explanation of how players coordinate. Each member of the population expects every other member to behave in accordance with the relevant regularity. A shared vision of what should be obvious to each player leads to the emergence of a focal point. Individuals may be aware of convergent expectations indicating where the focal points are settled. By harmonizing expected responses, focal points reduce uncertainty despite the presence of imperfect information, enabling individuals to coordinate their activities towards the achievement of their goals (Leeson et al. 2006). Lewis (1969) has given more formal structure to characterize coordination problems. In pure coordination games, individuals’ interests roughly coincide. In general, in order to have a sufficient reason for choosing a particular action, an agent needs to have a sufficient degree of beliefs that the other agent will choose a certain action. For Lewis (1969, p. 36), coordination may be achieved with the aid of ‘concordant mutual expectations’ about action. Past experience of a convention, or ‘precedent,’ is the source of such expectations.

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CULTURAL EVOLUTION

Many traditional rules suppress our behavior, and may well seem irrational to us. A rule suppressing some behavior will be effective only if people generally expect others to act in a way which makes it effective. ‘Most of the steps in the evolution of cultures were made possible by some individuals breaking some traditional rules and practicing new forms of conduct – not because they understood them to be better, but because the groups which acted upon them prospered more than others and grew’ (Hayek 1979, p. 161). As Hayek (1976) put it: There are, undoubtedly, many forms of tribal or closed societies which rest on very different systems of rules. All that we are here maintaining is that we know only of one kind of such systems of rules, undoubtedly still very imperfect and capable of much improvement, which makes the kind of open or ‘humanistic’ society possible where each individual counts as an individual and not only as a member of a particular group, and where therefore universal rules of conduct can exist which are equally applicable to all responsible human beings. (Hayek 1976, p. 27)

Hayek’s (1988) concept of the ‘extended order’ refers to the process by which a human society develops the capacity to cope with increasing degrees of complexity. The growing

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complexity is built on the foundations of social rules that coordinate the disparate interests, actions, and knowledge of individuals across society. Coordination of groups larger than hunting and gathering bands requires cultural evolution of learnt rules of conduct. In cultural evolution, the things getting varied or retained are not genes, but ways of acting. Rules cannot be constructed in a discretionary manner outside a particular historically grown order. They emerge in evolutionary processes which are not guided by explicit reason. According to Hayek (1988, p. 25), ‘cultural evolution operates largely through group selection’. The concept of group selection is a weak point in Hayek’s system of ideas (Sugden 1993). It is the idea that cultural traits and behavioral features are naturally selected on the basis of advantages and disadvantages for the groups of people who practice them. The theory of group selection implicitly assumes that specific conventions are tied to specific social groups. Thus, a convention can spread only as a consequence of the expansion of the social group to which it belongs. The main criticism is that Hayek’s analysis of cultural evolution that operates at the group level is inconsistent with his methodological individualism (Vanberg 1986). Vanberg (1986) defines methodological individualism as the guiding principle that aggregate social phenomena can be and should be explained in terms of individual actions, their inter-relations, and their (largely unintended) combined effects. In Hayek’s theory of cultural evolution, however, societies are not only subject to group selection but have developed through a process in which individuals choose the rules that form the social order (Gick and Gick 2001).4 ‘Individual selection refers to the perception of rules that are slightly different from already existing ones and hence leads to the creation of new rules’ (Gick and Gick 2001, p. 156, original emphasis). In the evolutionary process, there is no central mechanism which coordinates a shift in the rules perceived by individuals. New rules undergo some kind of decentralized selection process, as a consequence of which some spread through the population. The role of individuals is necessary to innovate practices. That is, individual minds conceive of problems and new ways to solve those problems, and individuals choose whether or not to follow a new rule. Cognition takes place by structuring individual minds in social context, and systems of rules literally ‘epitomize’ the cognitive capabilities of humans that evolve socially. ‘Individual action and social emergence of rules appear to be two sides of the same coin: the mind’ (Rizzello and Turvani 2002, p. 199). As individuals interact with members of the other group, they may learn how to behave from those other cultural members. Then, individuals become connected and integrated into larger social networks. Alternatively, a particular form of culture may lead to social connections that are sustained by a restricted form of social ties. Institutional change is an endogenous phenomenon that starts in the mind of individuals. Individuals may coordinate to new behavioral patterns, realizing an inconsistency between following original rules and the expected results from their action plans. Individual behavior in the same group shows a high degree of similarity because of common perception of the environment. Human action obeys action plans made by individuals in accordance with their mental models. To trade with agents in the other group needs the establishment of new sets of rules. While people want to follow their original conventions, new sets of rules may enhance social interactions and facilitate economic exchanges. Once the different courses of actions have been desirable to their goals, people select some of them.

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Norms, culture, and cognition 537 Changes are caused by the violations of original practices and the development of new ones. In the beginning, individuals face the choice of whether or not to support a change in existing rules. The expected costs and benefits associated with institutional change (change in rules) play an important role. The original conventions constrain the repertoire of possible reactions to changes in the environment. Information on expected benefits from alternative sets of rules and the costs of changing existing rules affect the likelihood of institutional change. Individuals may modify their mental models and alter their perceptions of the effects of alternative rules. Shifts in mental models change individuals’ action plans, which in turn leads to cultural evolution. Cultural evolution is an endogenous phenomenon with a cognitive dimension. In the first instance, the sensory representation of the environment, and of the possible goal to be achieved in his environment, will evoke a movement pattern generally aimed at the achievement of the goal. But at first the pattern of movement initiated will not be fully successful. The current sensory reports about what is happening will be checked against expectations, and the difference between the two will act as a further stimulus indicating the required corrections. The result of every step in the course of action will, as it were, be evaluated against the expected results, and any difference will serve as an indicator of the corrections required. (Hayek 1952, p. 95)

Hayek’s cognitive theory provides an account of a particular adaptive classifier system that produces a classification over a field of sensory inputs. Individual knowledge is the adaptive response based on the classification the brain has generated. Hayek explicitly points to the mind–institution link. Perception involves the capacity to identify regularities or patterns. Cognitive activity functions as a mechanism of adaptation. Expectations are formed endogenously by virtue of an individual adapting behavior to achieve a closer fit with external reality. Therefore, expectations will ordinarily correspond with rules of conduct geared toward an individual’s successful adaptation to the environment. Even though some rules of conduct are given and known, they may not be followed in the dual processes of cultural and individual changes that take place as a result of contact between different cultural groups. Coordination of these groups requires individual selections of rules of conduct. The evolution of the order of actions, which can yield new rules, can lead to corresponding changes in the mind. Then the mind will rearrange sensory experiences into new configurations that allow better predictions to be made about social reality. The criterion of fitness is confirmation of expectations as indicated by the success of individual actions. Expectations more consistent with social reality give an advantage to the individuals holding them. Players’ new action choices based on such expectations generate satisfactory payoffs, and a new pattern of plays of the game becomes collectively recognized as the way the game is now being played. Thus, the key story is as follows: perceived social reality S mental models S actions S payoffs S altered perceived social reality. It is a discovery procedure where there are unexploited opportunities in the form of new rules. Cultural evolution is the result of an interaction between individuals’ perceptions of alternative rules and their action plans according to these rules. The coordination acts as an inter-subjective learning procedure in which various ideas dispersed among individuals are constantly tested against one another. New rules are discovered and disseminated through decentralized adjustment processes. Any agent will discover only opportunities related to his or her own prior knowledge. To some extent, the concept of ignorance accounts for the gap between the opportunities available in society and the

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opportunities perceived by individual agents. The gap exists because of the dispersion of knowledge between agents who are considered to be subjective. The discovery of opportunities can be considered as the perception of a new framework about how to adapt in particular contexts. The agent’s ability to identify opportunities depends upon his or her subjective understanding of environment conditions. As individuals gain new experiences and update their stock of knowledge, those ways of doing things will crystalize over time into institutions where new specific meanings are derived from the interactions between the parties involved. Mental models can be modified by feedback from altered perceived reality as a consequence of people’s altered actions. A key to understanding cultural evolution is an understanding of how individuals modify their mental models.

6

CONCLUSION

This chapter has argued that there is a relationship between the sensory order and the social order. Hayek’s theory of mind can shed light on the process of choice. Individuals build up an understanding of the world based on their views of that world. The sensory order is fundamental in the sense that the explanation of social order begins with the human mind (Teraji 2014). This is illustrated through ideas relating to understanding culture from a cognitive viewpoint. For Hayek, the mind is a weave of old and new sensory data in a network of connections. A classification takes place via a network of impulse connections. There are limits to what individuals can know, and the sensory order within the mind is an imperfect representation of the physical order. The central element in the cognitive process is the feedback between individual and environment. The mind updates its understanding of the environment. In Hayek’s social theory, the single individual, without rules and ties, would lose common understanding. Coordination of groups requires cultural evolution of learnt rules of conduct. This chapter draws on ideas of co-evolution of individuals’ mental models and their actions. Shifts in mental models change individuals’ action plans, which in turn leads to cultural evolution. Thus, a key to understanding cultural evolution is an understanding of how individuals modify their mental models. Cultural evolution is an endogenous phenomenon with a cognitive dimension.

NOTES 1. Some researchers (Butos and Koppl 1993; Streit 1993; Rizzello 1999; Caldwell 2000, 2004; Horwitz 2000) argue that Hayek’s cognitive theory spilled over to his later work on political and social theory. 2. For further discussions of Hayek’s cognitive theory, see his essay ‘Rules, perception and intelligibility’ (Hayek 1967, pp. 43–65). 3. In a world of increasing returns to scale, initial and trivial circumstances can have important and irreversible influences on the ultimate market allocation of resources. An inefficient outcome can persist. The form of path dependence conflicts with conventional economics, where efficient outcomes are attained. Switching cost must include the uncertainty concerning the cost of adopting the superior technology. It would be more efficient to adhere to the current technology than switch to the superior one. 4. Vromen (1995) presents a reinterpretation of Hayek’s statements on cultural evolution in individualistic

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Norms, culture, and cognition 539 terms. When Hayek uses the term ‘group,’ it should really be read as ‘order’. This reinterpretation allows for individual processes of not only between-group migration but also between-group imitation and withingroup imitation.

REFERENCES Akerlof, G. (1980), ‘A theory of social custom, of which unemployment may be one consequence’, Quarterly Journal of Economics, 94 (4), 749–75. Alexander, R.D. (1987), The Biology of Moral Systems, New York: Aldine De Gruyter. Andreoni, J. and B.D. Bernheim (2009), ‘Social image and the 50–50 norm: a theoretical and experimental analysis of audience effects’, Econometrica, 77 (5), 1607–36. Arthur, W.B. (1989), ‘Competing technologies, increasing returns, and lock-in by historical events’, Economic Journal, 99 (March), 116–31. Aumann, R.J. (1987), ‘Correlated equilibrium as an expression of Bayesian rationality’, Econometrica, 55 (1), 1–18. Axelrod, R. (1997), ‘The dissemination of culture: a model with local convergence and global polarization’, Journal of Conflict Resolution, 41 (2), 203–26. Becker, G.S. (1968), ‘Crime and punishment: an economic approach’, Journal of Political Economy, 76 (2), 169–217. Bicchieri, C. (2006), The Grammar of Society: The Nature and Dynamics of Social Norms, Cambridge: Cambridge University Press. Butos, W.N. and R.G. Koppl (1993), ‘Hayekian expectations: theory and empirical applications’, Constitutional Political Economy, 4 (3), 303–29. Caldwell, B. (2000), ‘The emergence of Hayek’s ideas on cultural evolution’, Review of Austrian Economics, 13 (1), 5–22. Caldwell, B. (2004), Hayek’s Challenge: An Intellectual Biography of F.A. Hayek, Chicago, IL: University of Chicago Press. Denzau, A.T. and D.C. North (1994), ‘Shared mental models: ideologies and institutions’, Kyklos, 47 (1), 3–31. DiMaggio, P. (1997), ‘Culture and cognition’, Annual Review of Sociology, 23 (August), 263–87. Fehr, E. and S. Gächter (2000), ‘Cooperation and punishment in public goods experiments’, American Economic Review, 90 (4), 980–94. Gick, E and W. Gick (2001), ‘F.A. Hayek’s theory of mind and theory of cultural evolution revisited: toward an integrated perspective’, Mind and Society, 2 (1), 149–62. Gintis, H. (2009), The Bounds of Reason: Game Theory and the Unification of the Social Sciences, Princeton, NJ: Princeton University Press. Harris, M. (1971), Culture, Man and Nature, New York: Crowell. Hayek, F.A. (1948), Individualism and Economic Order, Chicago, IL: University of Chicago Press. Hayek, F.A. (1952), The Sensory Order, Chicago, IL: University of Chicago Press. Hayek, F.A. (1967), Studies in Philosophy, Politics, and Economics, Chicago, IL: University of Chicago Press. Hayek, F.A. (1973), Law, Legislation and Liberty, vol. 1, Rules and Order, Chicago, IL: University of Chicago Press. Hayek, F.A. (1976), Law, Legislation and Liberty, vol. 2, The Mirage of Social Justice, Chicago, IL: University of Chicago Press. Hayek, F.A. (1978), New Studies in Philosophy, Politics, Economics and the History of Ideas, London: Routledge. Hayek, F.A. (1979), Law, Legislation and Liberty, vol. 3, The Political Order of a Free People, Chicago, IL: University of Chicago Press. Hayek, F.A. (1988), The Fatal Conceit: The Errors of Socialism, Chicago, IL: University of Chicago Press. Henrich, J. (2004), ‘Cultural group selection, coevolutionary processes and large-scale cooperation’, Journal of Economic Behavior and Organization, 53 (1), 3–35. Horwitz, S. (2000), ‘From The Sensory Order to the liberal order: Hayek’s non-rationalist liberalism’, Review of Austrian Economics, 13 (1), 23–40. Leeson, P.T., C.J. Coyne and P.J. Boettke (2006), ‘Converting social conflict: focal points and the evolution of cooperation’, Review of Austrian Economics, 19 (2–3), 137–47. Lewis, D. (1969), Convention: A Philosophical Study, Cambridge, MA: Harvard University Press. North, D.C. (1990), Institutions, Institutional Change and Economic Performance, Cambridge: Cambridge University Press. North, D.C. (2005), Understanding the Process of Economic Change, Princeton, NJ: Princeton University Press.

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Ostrom, E., J. Walker and R. Gardner (1992), ‘Covenants with and without a sword: self-governance is possible’, American Political Science Review, 86 (2), 404–17. Richerson, P.J. and R. Boyd (2005), Not by Genes Alone: How Culture Transformed Human Evolution, Chicago, IL: University of Chicago Press. Rizzello, S. (1999), The Economics of the Mind, Cheltenham, UK and Northampton, MA, USA: Edward Elgar. Rizzello, S. and M. Turvani (2002), ‘Subjective diversity and social learning: a cognitive perspective for understanding institutional behavior’, Constitutional Political Economy, 13 (2), 197–210. Schelling, T.C. (1960), The Strategy of Conflict, Cambridge, MA: Harvard University Press. Simon, H.A. (1993), ‘Altruism and economics’, American Economic Review, 83 (2), 156–61. Smith, V.L. (2003), ‘Constructivist and ecological rationality in economics’, American Economic Review, 93 (3), 465–508. Streit, M.E. (1993), ‘Cognition, competition, and catallaxy: in memory of Friedrich August von Hayek’, Constitutional Political Economy, 4 (2), 223–62. Sugden, R. (1993), ‘Normative judgments and spontaneous order: the contractarian element in Hayek’s thought’, Constitutional Political Economy, 4 (3), 393–424. Teraji, S. (2007), ‘Morale and the evolution of norms’, Journal of Socio-Economics, 36 (1), 48–57. Teraji, S. (2014), ‘On cognition and cultural evolution’, Mind and Society, 13 (2), 167–82. Vanberg, V.J (1986), ‘Spontaneous market order and social rules: a critical examination of F.A. Hayek’s theory of cultural evolution’, Economics and Philosophy, 2 (1), 75–100. Vromen, J.J. (1995), Economic Evolution: An Inquiry into the Foundations of the New Institutional Economics, London: Routledge. Wenzel, N. (2010), ‘From contract to mental models: constitutional culture as a fact of the social science’, Review of Austrian Economics, 23 (1), 55–78.

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PART VIII MORALS AND ETHICS

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31 Rational choice in public and private spheres Herbert Gintis

1

INTRODUCTION

Behavioral economics has doubtless been one of the most important innovations in economic theory ever. One strand of this theory, however, has gone seriously beyond the pale in interpreting the results of experiments, arguing that human subjects are systematically irrational (Ariely 2010). In virtually every case, however, it is not people who are deficient in rationality but rather experimenters who apply inappropriate models of human decision-making (Gintis 2009). Our theoretical task is to develop new models of personal and social rationality that explain the observed behavior. In this chapter I approach this task by developing a rational model of voting behavior in democratic elections.

2

THE IRRATIONALITY OF THE CLASSICAL RATIONAL ACTOR VOTING MODEL

Estimates of the probability that a single voter’s decision will determine the outcome of a large election are between 1 in 10 million and 1 in 100 million (Gelman et al. 1998). In a compendium of close election results in Canada, Great Britain, Australia, and the United States, no election in which more than 40 000 votes were cast has ever been decided by a single vote. In the Massachusetts gubernatorial election of 1839, Marcus Morton won by two votes out of 102 066 votes cast. In the Winchester, UK, general election of 1997, Mike Oaten won by two votes out of 62 054 votes cast. The result was annulled and in a later by-election, Oaten won by 21 000 votes. In smaller elections, a victory by a very small margin is routinely followed by a recount where the margin is rarely less than 25. There is thus virtually no loss in accuracy in modeling voting behavior in large elections as purely non-consequential in the sense that a single individual’s decision to vote or abstain, or for whom to vote, has no effect on the outcome of the election (Downs 1957a; Riker and Ordeshook 1968). By a canonical participant in a decision process I mean an individual whose choice is non-consequential: his or her behavior affects the outcomes infinitesimally or not at all. According to the data presented above, voters in a large election are canonical participants. Individuals who participate in a large collective action are similarly canonical participants, as are those who volunteer to fight or otherwise contribute to one side in a war between nations. There are some public-sphere activities that are potentially consequential and hence non-canonical, such as running for office, organizing a voter registration drive, or contributing considerable amounts of money to a particular party or candidate, but most activities in the public sphere are virtually non-consequential, and hence canonical. Ignoring the infinitesimal probabilities that canonical participants affect outcomes is a useful and harmless simplification, akin to ignoring the force of gravity in 543

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analyzing the electronic circuitry of a computer or ignoring the light from distant stars in calculating the effectiveness of a solar panel. The private sphere is the locus of everyday transactions in civil society. The public sphere is the locus of canonical and consequential political activities that create, maintain, and transform the rules of the game that define society itself. The private and public spheres are interrelated in individual decision-making. A public-sphere transaction may have private-sphere costs and benefits that a canonical participant in the public sphere may take into account in deciding how to act. For instance, an individual may not vote if queues at the polling station are very long, or may decide to skip a collective action in which the probability of physical harm is very high. Non-consequential public sphere activities are at the center of the structure and dynamics of modern societies. If citizens did not vote, or voted in an uninformed and random manner, liberal democratic societies could not operate. Moreover, modern liberal democracy was achieved through collective actions over centuries. These collective actions have been successful because of the collective impact of canonical participants who incurred significant personal costs, often death, in opposing illegitimate authority. Canonical participants consider their behavior as rational goal-oriented behavior. If you ask people in a queue at the polling booth why they are standing there, or if you ask a group protesting political corruption why they are chanting and holding signs, they will think the question absurd. They are there, of course, to register their support for various candidates for office, or to help topple a corrupt government. If you point out to canonical participant Alice that her personal contribution will make no difference to the outcome, Alice will likely respond that you are guilty of faulty reasoning because if everyone followed your reasoning, no one would vote and no one would fight to topple a corrupt regime. If you persist in asking why Alice personally votes, noting that the other participants do not follow your (faulty) reasoning, and Alice’s abstention will not affect the decision of others, Alice may well judge you mentally ill, unless she realizes that your concept of rationality conforms to the classical axioms of rational choice theory (Savage 1954), while the behavior of canonical participants in the public sphere does not. Indeed, canonical participants are rational, pace the Savage axioms, in the sense their behavior does determine who is elected, and may determine whether a corrupt regime is or is not toppled. How, then, might we account for rational, non-consequential behavior? One possible answer is that people believe their public sphere behaviors are consequential even when they are not, so they act as though their actions effectively determine the outcome, at least with some substantially positive probability. From my reading of the literature on canonical political behavior, this is the most common, though rarely explicitly stated, assumption. For instance, Duncan Black’s famous median voter theorem (Black 1948) implicitly assumes that a self-interested citizen will vote and this vote will register his personal preferences. Similarly, Anthony Downs, a pioneer in the application of the rational actor model to political behavior (Downs 1957a) describes his model as follows: ‘Every agent in the model—whether an individual, a party or a private coalition, behaves rationally at all times; that is, it proceeds toward its goals with a minimal use of scarce resources and undertakes only those actions for which marginal return exceeds marginal cost’ (Downs 1957b, p. 137). Yet, almost immediately after stating this assumption, he writes: ‘[We assume that] voters actually vote according to (a) changes in their utility incomes from

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Rational choice in public and private spheres 545 government activity and (b) the alternatives offered by the opposition’ (Downs 1957b, p. 138).These two assumptions are compatible only if agents believe that their votes are consequential. However, canonical participants generally do not believe that their behavior is consequential. For instance, Enos and Fowler (2010) report a study in which the median respondent to the question as to the chance their vote will change the outcome of a presidential election gave the answer 1 in 1000, which although small, is in fact too large by a factor of at least 10 000. The authors write: ‘However . . . over 40% of regular voters know that the chances of a pivotal vote are less than one in a million. . ..[Moreover], the less likely you are to think your vote will actually matter, the more likely you are to vote’ (Enos and Fowler 2010). Thus, although voters behave strategically (Fedderson and Sandroni 2006), they know that their behavior is non-consequential (Edlin et al. 2007; Hamlin and Jennings 2011). Another possible answer is that people consider voting a social obligation, or they consider themselves part of a ‘team’ dedicated to a particular cause, and not voting is an unethical act of free-riding on the altruism of others. Doubtless some individuals are so motivated, but it then must also be a social obligation to vote intelligently and nonrandomly. This notion would be perhaps plausible if one vote made a difference, but it is surely a bizarre social obligation for a non-consequential action. Moreover, many individuals consider being politically literate and voting as a positive contribution to their well-being rather than an onerous duty, and it is these individuals that render a liberal democracy possible and effective. Finally, committed canonical participants often become angry with or annoyed by friends who vote differently from themselves, even though they realize that their friends’ behavior is non-consequential. A third possibility is that many voters are altruistic and vote out of concern for the wellbeing of others who will be affected by the outcome of the electoral process. Although if voting is non-consequential, a single voter cannot affect the well-being of others, in this case, where there may be many millions of others, the 1 in 10 million or 1 in 100 million chance of changing the outcome of the election, when multiplied by the number of people thereby affected, becomes a significant quantity. Certainly, however, no voter thinks in this bizarre way, and many canonical participants have interests that are far narrower than the citizenry as a whole, and often act to promote the interests of one group of citizens at the expense of another. Indeed, it is common to hear a small group of voters deemed ‘selfish’ because they promote their own parochial interest above the good of society as a whole. To model the rationality of the canonical participant in the political sphere, we must revise the standard axioms of rational choice (Savage 1954). In a related paper, I have explored the implications of replacing Savage’s assumption that beliefs are purely personal ‘subjective probabilities’ with the notion that the individual is generally embedded in a network of social actors over which information and experience concerning the relationship between actions and outcomes is spread. The rational actor thus draws on a network of beliefs and experiences distributed among the social actors to which he is informationally and socially connected. By the sociological principle of homophily, social actors are likely to structure their network of personal associates according to principles of social similarity, and to alter personal tastes in the direction of increasing compatibility with networked associates (McPherson et al. 2001; Durrett and Levin 2005; Fischer et al. 2013).

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To this principle of distributed cognition we may add a principle of distributed intentionality in which canonical participants consider their actions as effective contributions to social outcomes when they do their part as a member of a network of loosely linked individuals with consonant objectives distributed across the network. In this framework, the behavior of a canonical participant is not a costly choice with no benefits, but rather a voluntary, costly but personally enriching, participation in a collective effort. With this notion in place, a theory of canonical participant behavior must elucidate how individual preferences over political outcomes are formed, and how agents trade off between public and private sphere objectives. The principle of distributed intentionality appears closely related to the notions of team reasoning and team intentionality as developed in an extensive philosophical and economics literature (Bacharach 1987, 1992, 2006; Gilbert 1987, 1989; Bratman 1993; Searle 1995; Tuomela 1995; Hurley 2002; Sugden 2003; Colman et al. 2008). However, the behavior explored in these contributions is generally socially structured cooperation and collaboration, in which actions are highly consequential. We approach the problem of distributed intentionality by considering canonical participation as a form of moral behavior.

3

BEHAVIORAL MORALITY

Behavioral morality is the set of moral rules we attribute to people by virtue of their actions. We contrast this with normative morality, the set of rules that philosophers consider that moral individuals are obliged to obey. The content of both behavioral and normative morality are contested, and the appropriate relationship between the two is complex. I deal with behavioral morality alone. Traditional social science embraces a rather straightforward understanding of the relationship between human biological evolution and behavioral morality. This is the venerable notion of tabula rasa (Tooby and Cosmides 1992; Pinker 2002), according to which the brain is empty at birth but filled with moral principles through social learning. This idea is famously expressed by Thomas Hobbes (1651 [1968]), who writes: ‘The state of men without civil society (which may be called the state of nature) is nothing but a war of all against all . . . Where every man is enemy to every man, the life of man is solitary, poor, nasty, brutish, and short.’ We find the same sentiment some three centuries later in the prominent biologist Richard Dawkins (1976), who writes: ‘We are survival machines–robot vehicles blindly programmed to preserve the selfish molecules known as genes . . . Let us try to teach generosity and altruism, because we are born selfish’ (original emphasis). Morality, then, is an elaborate veneer hiding our basically selfish natures. A more plausible approach to behavioral morality is based on evolutionary biology, the rational actor model and experimental game theory. The basic principles are: ●

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Behavioral morality is the product of an evolutionary dynamic extending over hundreds of thousands of years in the hominin line involving the interaction of genes and culture. In this dynamic, hominin societies transformed culture, and the new culture made

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●

●

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4

new behaviors fitness-enhancing, transforming the gene pool of the hominin line itself. Thus, gene-culture coevolution: in humans, genes are the product of culture and culture is the product of genes. Behavioral morality, in particular, is predicated upon a set of human, on balance prosocial, evolved predispositions inherited from our experience in small-scale hunter-gatherer societies. At some point our ancestors began to devise games and play according to their agreed-upon rules. It then became possible to conceive of society itself as a social game, the rules of which are determined in a new arena of social life, which we have called the public sphere. Humans thus evolved two modes of social behavior, a private persona of personal preferences, located within social networks of distributed cognition and intentionality, regulating everyday life in civil society, and a public persona of personal preferences, again rooted in social networks of distributed cognition and intentionality, regulating their behavior in the public sphere. At the heart of our moral capacities, both as private and public persona, is the capacity to conceptualize a higher moral realm that leads us to protect social values, to feel satisfaction at ‘doing the right thing,’ and to feel degraded when we have not done the right thing.

SELF-REGARDING, OTHER-REGARDING, AND UNIVERSALIST RATIONAL ACTION

Rational actors exhibit three types of motives in their daily lives: self-regarding, otherregarding, and universalist. Self-regarding motives include seeking personal wealth, consumption, leisure, social reputation, status, esteem, and other markers of personal advantage. Other-regarding motives include valuing reciprocity and fairness, and contributing to the well-being of others. Universalist motives are those that are followed for their own sake rather than for their effects. Chief among universalist goals are character virtues, including honesty, loyalty, courage, trustworthiness, and considerateness. In the private sphere such universalist goals have consequences for those with whom we interact, and for society as a whole. However, we undertake universalist actions for their own sake, beyond any consideration of their effects. Agents will generally trade off among these various motives. For instance, being honest may be personally costly or reputationally rewarding, and may either hurt or benefit others whose well-being we value. Universalist motives thus do not reduce to self- or other-regarding motives, but they do trade off against these other motives. Self- and other-regarding behavior is well documented in the literature, but universalist behavior is far less so. I present an example, as revealed by laboratory experiments using experimental game theory. 4.1

The Universalist Principle of Honesty

Universalist moral actions are performed, at least in part, because it is virtuous to do so, apart from any effects these actions have on yourself, others, or society in general. For

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instance, we can be honest in dealing with another agent without caring at all about the effect on the other agent, or even caring about the impact of honest behavior on society at large. Similarly, we can be courageous in battle because it is the right thing to do, independent from the effect of one’s actions on winning or losing the battle. Of course, the value of honesty in a transaction may be lessened or turned negative if it is personally costly or it harms others about whom one cares. Many studies have shown that people exhibit considerable degrees of honesty even when this is costly and there is no chance that they could be discovered cheating (Mazar et al. 2008; Bucciol and Piovesan 2011; Houser et al. 2012; Shalvi et al. 2012; Cohn et al. 2014; Fischbacher and Follmi-Heusi 2014). A particularly clear example of the value of honesty is reported by Gneezy (2005), who studied 450 undergraduate participants paired off to play three games of the following form, all payoffs to which are of the form a; b, where player 1 (Alice) receives a and player 2 (Bob) receives b. In all games, Alice was shown two pairs of payoffs, A:(x; y) and B:(z; w) where x, y, z, and w are amounts of money with x < z and y > w, so in all cases, B is better for Bob and A is better for Alice. Alice could then say to Bob, who could not see the amounts of money, either ‘Option A will earn you more money than option B,’ or ‘Option B will earn you more money than option A.’ The first game was A:(5,6) versus B:(6,5) so Alice could gain 1 by lying and being believed, while imposing a cost of 1 on Bob. The second game was A:(5,15) versus B:(6,5) so Alice could gain 10 by lying and being believed, while still imposing a cost of 1 on Bob. The third game was A:(5,15) versus B:(15,5), so Alice could gain 10 by lying and being believed, while imposing a cost of 10 on Bob. Before starting play, the experimenter asked each Alice whether she expected her advice to be followed, inducing honest responses by promising to reward her if her guesses were correct. He found that 82 percent of Alices expected their advice to be followed (the actual result was that 78 percent of Bobs followed their Alice’s advice). It follows that if Alices were self-regarding, they would always lie and recommend B to their Bob. The experimenters found that, in game two, where lying was very costly to Bob and the gain to lying for Alice was small, only 17 percent of subjects lied. In game one, where the cost of lying to Bob was only one but the gain to Alice was the same as in game two, 36 percent lied. That is, subjects were loath to lie, but considerably more so when it was costly to their partner. In game three, where the gain from lying was large for Alice, and equal to the loss to Bob, 52 percent lied. This shows that many subjects are willing to sacrifice material gain to avoid lying in a one-shot, anonymous interaction, their willingness to lie increasing with an increased cost of truth-telling to themselves, and decreasing with an increase in their partner’s cost of being deceived. Similar results were found by Boles et al. (2000) and Charness and Dufwenberg (2006). Gunnthorsdottir et al. (2002) and Burks et al. (2003) have shown that a social-psychological measure of ‘Machiavellianism’ predicts which subjects are likely to be trustworthy and trusting.

5

THE PUBLIC SPHERE

The social life of most species, including mating practices, symbolic communication, and power relations, is expressed in genetically grounded stereotypical form (Alcock 1993, Krebs and Davies 1997). Homo sapiens is unique in adapting its social life in highly flex-

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Rational choice in public and private spheres 549 ible ways to environmental and social challenges and opportunities (Richerson and Boyd 2004). This flexibility is based on two aspects of our mental powers. The first is our ability to devise new rules of social life, and to base our social interactions on these new rules. This capacity, absent in other species, makes us Homo ludens, Man the game player. This capacity is possessed even by very young children who invent, understand, and play games for fun. In adult life, this same capacity is exercised when people come together to erect, maintain, and transform the social rules that govern their daily transactions. Broadly speaking, we can define the public sphere as the arena in which society-wide rules of the game are created, evaluated, and transformed, and politics as the cooperative, conflictual, and competitive behaviors through which rules are established and individuals are assigned to particular public positions. Humans evolved in hunter–gatherer societies consisting of a dozen families or so (Kelly 1995), in which political behavior was a part of daily life, involving the sorts of self-regarding, other-regarding, and universalistic motivations described above (Gintis et al. 2015). In particular, political activity was strongly consequentialist: a single individual could expect to make a difference to the outcome of a deliberation, a conflict, or a collaboration, so that our political morality developed intimately entwined with material interests and everyday consequentialist moral sentiments (Boehm 1999). In the transition from small-scale hunter–gatherer societies to modern mass societies with millions of members, the public sphere passed from being closely embedded in daily life to being a largely detached institutional arena, governed by complex institutions controlled by a small set of individuals, and over which most members have at best formal influence through the ballot box, and at worst no formal influence at all. Political activity in modern societies has thus become predominately non-consequentialist. Canonical participants in the public sphere appear to follow a non-consequentialist logic that may be summarized as rule-consequentialism: in public life, choose a rule that like-minded people might plausibly choose, and, if followed by all such like-minded people, it will lead to the most desirable outcome (Harsanyi 1977; Roemer 2010; Hooker 2011). Rule-consequentialism explains why people are perfectly reasonable in assenting to such assertions as ‘I am helping my candidate win by voting’ and ‘I am helping promote democracy by demonstrating against the dictator’. Because rule-consequentialism is so ingrained in our public persona, canonical participants untrained in traditional rational decision theory simply cannot understand the argument that it is irrational to vote or to participate in collective actions, even when they can easily be persuaded that their actions are non-consequential. Rule-consequentialism can also explain many stylized facts of voter behavior. First, when the cost of voting increases, fewer people vote. The rule here is something like: ‘My unusual personal situation means voting would be very costly to me today. I would not expect anyone in my position to vote, so I am comfortable with not voting.’ Second, it explains why voter turnout is higher when the issues to be decided have greater social impact. Third, it explains why turnout is higher when the election is expected to be close. Finally, it explains why, in a two-party election, turnout is likely to be higher among voters for the side that is not expected to win. Indeed, it is reasonable to speculate that rule-consequentialism leads voters to act in very large elections in much the same way they would in very small elections, although in very small elections consequentialist issues (for example, self-interested) may trump the non-consequentialist rule.

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We conclude that the individual immersed in consequentialist everyday life expresses his or her private persona, while his or her behavior in the public sphere reveals his or her public persona. Individuals acting in the public sphere are, then, a different sort of animal, which Aristotle called ‘zoon politikon’ in his Nicomachean Ethics.

6

PRIVATE AND PUBLIC PERSONA

The concept of a non-consequentialist public persona suggests a two by three categorization of human motivations, as presented in Figure 31.1. In this figure, the three columns represent three modes of social interaction. The self-regarding mode represents the individual whose social behavior is purely instrumental to meeting his or her personal needs, while the other-regarding represents the individual who is embedded in a network of significant social interactions with valued others, and the universal represents the individual who values moral behavior for its own sake. The two rows represent the agent’s private persona of social relations in civil society, and the agent’s public persona of political relationships in the public sphere. Homo economicus is the venerable rational selfish maximizer of traditional economic theory, Homo socialis is the other-regarding agent who cares about fairness, reciprocity, and the well-being of others, and Homo vertus is the Aristotelian bearer of noninstrumental character virtues. The new types of public persona are Homo politicus who behaves publicly just as Homo economicus does privately, while Homo parochialis votes and engages in collective action reflecting the narrow interests of the demographic, ethnic and/or social status groups with which he identifies. Finally, Homo universalis acts politically to achieve what he considers the best state for the larger society, perhaps reflecting John Rawls’s (1971) veil of ignorance, John Harsanyi’s (1977) criterion of universality, or John Roemer’s (2010) Kantian equilibrium. Homo politicus is the political entrepreneur who acts purely to enhance his personal stature and wealth. Curiously, the individual whose private persona is other-regarding is generally considered altruistic, whereas the individual whose public persona is otherregarding is often considered selfish and narrow-minded, acting in a partisan manner on behalf of the specific interests of the social networks to which he belongs. Of course Homo parochialis is in fact altruistic, sacrificing on behalf of these social networks. Self-regarding

Figure 31.1

Other-regarding

Universalist

Private persona

Homo economicus

Homo socialis

Homo vertus

Public persona

Homo politicus

Homo parochialis

Homo universalis

A typology of human motivations

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7

THE EVOLUTIONARY EMERGENCE OF PRIVATE MORALITY

By cooperation we mean engaging with others in a mutually beneficial activity. Cooperative behavior may confer net benefits on the individual cooperator, and thus can be motivated entirely by self-interest. In this case, cooperation is a form of mutualism. Cooperation may also be a net cost to the individual but the benefits may accrue to a close relative. We call this kin altruism. Cooperation can also take the form of one individual’s costly contribution to the welfare of another individual being reliably reciprocated at a future date. This is often called reciprocal altruism (Trivers 1971), although it is really just tit-for-tat mutualism. However, important forms of cooperation impose net costs upon individuals, the beneficiaries many not be close kin, and the benefit to others may not be expected to be repaid in the future. This cooperative behavior is true altruism. The evolution of mutualistic cooperation and kin altruism is easily explained. Cooperation among close family members evolves by natural selection because the benefits of cooperative actions are conferred on the close genetic relatives of the cooperator, thereby helping to proliferate genes associated with the cooperative behavior. Kin altruism and mutualism explain many forms of human cooperation, particularly those occurring in families or in frequently repeated two-person interactions. But these scenarios fail to explain two facts about human cooperation: that it takes place in groups far larger than the immediate family, and that both in real life and in laboratory experiments, it occurs in interactions that are unlikely to be repeated, and where it is impossible to obtain reputational gains from cooperating. These forms of behavior are regulated by moral sentiments. The most parsimonious proximal explanation of altruistic cooperation that is supported by extensive experimental and everyday-life evidence is that people gain pleasure from cooperating and feel morally obligated to cooperate with likeminded people. People also enjoy punishing those who exploit the cooperation of others. Free-riders frequently feel guilty, and if they are sanctioned by others, they may feel ashamed. We term these feelings social preferences. Social preferences include a concern, positive or negative, for the well-being of others, as well as a desire to uphold ethical norms (Bowles and Gintis 2011). 7.1

The Roots of Social Preferences

Why are the social preferences that sustain altruistic cooperation in daily life so common? Early human environments are part of the answer. Our Late Pleistocene ancestors inhabited the large-mammal-rich African savannah and other environments in which cooperation in acquiring and sharing food yielded substantial benefits at relatively low cost (Boyd and Silk 2002). Human longevity, including an extended period of dependency of the young, also made the cooperation of non-kin in child rearing and provisioning beneficial (Hrdy 1999). As a result, members of groups that sustained cooperative strategies for provisioning, child-rearing, sanctioning non-cooperators, defending against hostile neighbors, and truthfully sharing information had significant advantages over members of non-cooperative groups. There are several reasons why these altruistic social preferences supporting cooperation outcompeted amoral self-interest. First, human groups devised ways to protect their altruistic members from exploitation by the selfish. Prominent among these is the collective

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punishment of miscreants (Boyd et al. 2010), including the public-spirited shunning, ostracism, and even execution of free-riders and others who violate cooperative norms. Second, humans adopted elaborate systems of socialization that led individuals to internalize the norms that induce cooperation, so that contributing to common projects and punishing defectors became objectives in their own right rather than constraints on behavior. Together, the internalization of norms and the protection of the altruists from exploitation served to offset, at least partially, the competitive handicaps born by those who were motivated to bear personal costs to benefit others (Gintis 2003). Third, between-group competition for resources and survival was and remains a decisive force in human evolutionary dynamics. Groups with many cooperative members tended to survive these challenges and to encroach upon the territory of the less cooperative groups, thereby both gaining reproductive advantages and proliferating cooperative behaviors through cultural transmission. The extraordinarily high stakes of intergroup competition and the contribution of altruistic cooperators to success in these contests meant that sacrifice on behalf of others, extending beyond the immediate family and even to virtual strangers, could proliferate (Turchin and Korotayev 2006; Choi and Bowles 2007; Bowles 2009). Between-group competition accounts for the fact that humans are extraordinarily group minded, favoring cooperation with insiders and often expressing hostility toward outsiders. Boundary maintenance supported within-group cooperation and exchange by limiting group size and within-group linguistic, normative and other forms of heterogeneity. Insider favoritism also sustained the between-group conflicts and differences in behavior that made group competition a powerful evolutionary force (Choi and Bowles 2007). In summary, humans have social preferences because in the course of our evolution as a species, cooperation was highly beneficial to the members of groups provided they were able to construct social institutions that compensated for the disadvantages of those with prosocial preferences regarding fellow group members, while heightening the group-level advantages associated with the high levels of cooperation that these prosocial preferences generated. These institutions proliferated because the groups that adopted them secured high levels of within-group cooperation, which in turn favored the groups’ survival as biological and cultural entities in the face of environmental, military and other challenges.

8

THE EVOLUTIONARY EMERGENCE OF THE PUBLIC PERSONA

Non-human species, even if highly social, do not engage in activities that structure the social rules that regulate their lives. Therefore there is no politics and no public sphere in these species, and hence its members have no public persona. How, then, might a public persona with a prominent position for canonical participation have arisen in the hominin line leading up to Homo sapiens? In a related paper, Carel van Schaik, Christopher Boehm, and I (Gintis et al. 2015) supply an answer grounded in the information available to us from a variety of fields, including paleontology, primatology, the anthropology of contemporary hunter–gatherer groups, animal behavior theory, and genetics. We propose that the emergence of bipedalism, cooperative breeding, and lethal weapons (stones and wooden spears) in the

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Rational choice in public and private spheres 553 hominin line, together with favorable climate change, made the collaborative hunting and scavenging of large game fitness enhancing. Lethal weapons are the most unique of these innovations, for other predators, such as lions, tigers and other big cats, wolves, foxes and other canines, use only their natural weapons – sharp claws and teeth, powerful jaws and great speed – in hunting, while none of these endowments was available to early hominins. Lethal hunting weapons, moreover, transformed human sociopolitical life because they could be applied to humans just as easily as to other animals. The combination of the need for collaboration and the availability of lethal weapons in early hominin society undermined the social dominance hierarchy characteristic of primate and earlier hominin groups, which was based on pure physical prowess. The successful sociopolitical structure that ultimately replaced the ancestral social dominance hierarchy was an egalitarian political system in which lethal weapons made possible group control of leaders – group success depended on the ability of leaders to persuade and motivate – and of followers to contribute to a consensual decision process. The heightened social value of non-authoritarian leadership entailed enhanced biological fitness for such leadership traits as linguistic facility, ability to form and influence coalitions, and indeed for hypercognition in general. This egalitarian political system persisted until some 10 000 years ago when cultural changes in the Holocene involving settle trade and agriculture entailed the accumulation of material wealth, through which it became possible once again to sustain a social dominance hierarchy with strong authoritarian leaders who could buy a modicum of protection and allegiance from well-rewarded professional soldiers and clansmen (Richerson and Boyd 2001). Yet, despite the power of authoritarian states, the zoon politikon that social evolution had nourished over tens of thousands of years was not erased by a few thousand years of Holocene history. Indeed, the extremely high level of tribal and clan warfare prevalent until recent centuries doubtless favored groups whose members conserved the hunter–gatherer mentality of political commitment and the desire for personal political efficacy (Pinker 2011).

9

CONCLUSION

This chapter has provided evidence for a model of human behavior based on the rational actor model, in which individuals have both private and public persona, and their preferences range over self-regarding, other-regarding, and universalist modes in both the private and the public sphere. Morality in this model is defined in behavioral terms: moral choices are those made in social and universalist modes. The public sphere in this model is an arena where preferences and actions are primarily non-consequentialist. The other-regarding preferences of Homo Socialis and the character virtues of Homo Vertus are underpinnings of civil society, while Homo Parochialis and Homo Universalis make possible the varieties of political life characteristic of our species. This taxonomy of human motives has several important implications for a theory of political behavior: ●

Despite the ubiquity of the assumption that rational individuals have personal interests which they register through electoral processes and collective actions, the

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Handbook of behavioural economics and smart decision-making notion is incoherent, ineluctably entailing faulty reasoning. Private persona individuals, whether Homo economicus, Homo socialis, or Homo vertus, will simply not participate in such processes, and those who do are canonical participants whose political preferences are constituted by the social networks in which they are embedded as Homo parochialis, and the higher-level moral principles to which they adhere as Homo universalis. Private sphere costs and benefits may play a large role in whether an individual participates in electoral processes or collective actions, but they have little or no effect on his electoral preferences or which collective actions he supports. Thus we should not be at all surprised when abstract moral principles appear to trump economic interests in individual economic decisions. The fact that the canonical participants are a mix of Homo parochialis and Homo universalis explains why political movements are sensitive to issues of justice and fairness and insensitive to issues of social efficiency when the latter conflict with the former. For instance, voters typically care about corruption, workers’ rights, graft, and unemployment but not rates of economic growth or measures of wealth dispersion.

REFERENCES Alcock, J. (2002), Animal Behavior: An Evolutionary Approach, Sunderland, MA: Sinauer. Ariely, D. (2010), Predictably Irrational: The Hidden Forces That Shape Our Decisions, New York: Harper. Aristotle (2002), Nicomachean Ethic, Newburyport, MA: Focus. First published 350 BC. Bacharach, M. (1987), ‘A theory of rational decision in games’, Erkentness, 27 (1), 17–56. Bacharach, M. (1992), ‘The acquisition of common knowledge’, in C. Bicchieri and M.L.D. Chiara (eds), Knowledge, Belief, and Strategic Interaction, Cambridge: Cambridge University Press, pp. 285–316. Bacharach, M. (2006), Beyond Individual Choice: Teams and Frames in Game Theory, N. Gold and R. Sugden (eds), Princeton, NJ: Princeton University Press. Black, D. (1948), ‘On the rationale of group decision-making’, Journal of Political Economy, 56 (1), 23–34. Boehm, C. (1999), Hierarchy in the Forest: The Evolution of Egalitarian Behavior, Cambridge, MA: Harvard University Press. Boles, T.L., R.T.A. Croson and J.K. Murnighan (2000), ‘Deception and retribution in repeated ultimatum bargaining’, Organizational Behavior and Human Decision Processes, 83 (2), 235–59. Bowles, S. (2009), ‘Did warfare among ancestral hunter-gatherer groups affect the evolution of human social behaviors’, Science, 324 (5932), 1293–8. Bowles, S. and H. Gintis (2011), A Cooperative Species: Human Reciprocity and its Evolution, Princeton, NJ: Princeton University Press. Boyd, R. and J. Silk (2002), How Humans Evolved, 3rd edn, New York: W.W. Norton. Boyd, R., H. Gintis and S. Bowles (2010), ‘Coordinated punishment of defectors sustains cooperation and can proliferate when rare’, Science, 328 (5978), 617–20. Bratman, M.E. (1993), ‘Shared intention’, Ethics, 104 (1), 97–113. Bucciol, A. and M. Piovesan (2011), ‘Luck or cheating? A field experiment on honesty with children’, Journal of Economic Psychology, 32 (1), 73–8. Burks, S.V., J.P. Carpenter and E. Verhoogen (2003), ‘Playing both roles in the trust game’, Journal of Economic Behavior and Organization, 51 (2), 195–216. Charness, G. and M. Dufwenberg (2006), ‘Promises and partnership’, Econometrica, 74 (6), 1579–601. Choi, J.-K. and S. Bowles (2007), ‘The coevolution of parochial altruism and war’, Science, 318 (26), 636–40. Cohn, A., E. Fehr and M.A. Maréchal (2014), ‘Business culture and dishonesty in the banking industry’, Nature, 516 (7529), 86–9. Colman, A.M., B.D. Pulford and J. Rose (2008), ‘Collective rationality in interactive decisions: evidence for team reasoning’, Acta Psychologica, 128 (2), 387–97. Dawkins, R. (1976), The Selfish Gene, Oxford: Oxford University Press. Downs, A. (1957a), An Economic Theory of Democracy, Boston, MA: Harper & Row.

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Rational choice in public and private spheres 555 Downs, A. (1957b), ‘An economic theory of political action in a democracy’, Journal of Political Economy, 65 (2), 135–50. Durrett, R. and S.A. Levin (2005), ‘Can stable social groups be maintained by homophilous imitation alone?’, Journal of Economic Behavior and Organization, 57 (3), 267–86. Edlin, A., A. Gelman and N. Kaplan (2007), ‘Voting as a rational choice: why and how people vote to improve the well-being of others’, Rationality and Society, 19 (3), 293–314. Enos, R. and A. Fowler (2010), ‘Why did you vote’, Daily Dish, 11 November. Fedderson, T. and A. Sandroni (2006), ‘A theory of participation in elections’, American Economic Review, 96 (4), 1271–82. Fischbacher, U. and F. Follmi-Heusi (2014), ‘Lies in disguise – an experimental study on cheating’, Journal of the European Economics Association, 11 (3), 525–47. Fischer, I., A. Frid, S.J. Goerg, S.A. Levin, D.I. Rubenstein and R. Selten (2013), ‘Fusing enacted and expected mimicry generates a winning strategy that promotes the evolution of cooperation’, Proceedings of the National Academy of Sciences, 110 (25), 10229–33. Gelman, A., G. King and J. Boscardin (1998), ‘Estimating the probability of events that have never occurred: when is your vote decisive?’, Journal of the American Statistical Association, 93 (441), 1–9. Gilbert, M. (1987), ‘Modeling collective belief’, Synthese, 73 (1), 185–204. Gilbert, M. (1989), On Social Facts, New York: Routledge. Gintis, H. (2009), The Bounds of Reason, Princeton, NJ: Princeton University Press. Gintis, H. (2003), ‘The hitchhiker’s guide to altruism: genes, culture, and the internalization of norms’, Journal of Theoretical Biology, 220 (4), 407–18. Gintis, H., C. van Schaik and C. Boehm (2015), ‘The evolutionary origins of human political systems’, Current Anthropology, 56 (3), 327–53. Gneezy, U. (2005), ‘Deception: the role of consequences’, American Economic Review, 95 (1), 384–94. Gunnthorsdottir, A., K. McCabe and V.L. Smith (2002), ‘Using the Machiavellianism instrument to predict trustworthiness in a bargaining game’, Journal of Economic Psychology, 23 (1), 49–66. Hamlin, A. and C. Jennings (2011), ‘Expressive political behavior: foundations, scope and implications’, British Journal of Political Science, 41 (3), 645–70. Harsanyi, J.C. (1977), ‘Morality and the theory of rational behavior’, Social Research, 44 (4), 623–56. Hobbes, T. (1651), Leviathan, reprinted 1968, C.B. MacPherson (ed.), New York: Penguin. Hooker, B. (2011), ‘Rule consequentialism’, in E.N. Zalta (prin. ed.), Stanford Encyclopedia of Philosophy, accessed 8 December 2016 at https://plato.stanford.edu/entries/consequentialism-rule/. Houser, D., S. Vetter and J Winter (2012), ‘Fairness and cheating’, European Economic Review, 56 (8), 1645–55. Hrdy, S.B. (1999), Mother Nature: A History of Mothers, Infants, and Natural Selection, New York: Pantheon Books. Hurley, S.L. (2002), Consciousness in Action, Cambridge, MA: Harvard University Press. Kelly, R.L. (1995), The Foraging Spectrum: Diversity in Hunter-Gatherer Lifeways, Washington, DC: Smithsonian Institution. Krebs, J.R. and N.B. Davies (1997), Behavioral Ecology: An Evolutionary Approach, 4th edn, Oxford: Blackwell Science. Mazar, N., O Amir and D. Ariely (2008), ‘The dishonesty of honest people: a theory of self-concept maintenance’, Journal of Market Research, 45 (6), 633–44. McPherson, M., L. Smith-Lovin and J. Cook (2001), ‘Birds of a feather: homophily in social networks’, Annual Review of Sociology, 27 (August), 415–44. Pinker, S. (2002), The Blank Slate: The Modern Denial of Human Nature, New York: Viking. Pinker, S. (2011), Our Better Angels: Why Violence Has Declined, New York: Penguin Books. Rawls, J. (1971), A Theory of Justice, Cambridge, MA: Harvard University Press. Richerson, P.J. and R. Boyd (2001), ‘Institutional evolution in the Holocene: the rise of complex societies’, in W.G. Runciman (ed.), Proceedings of the British Academy: The Origin of Human Social Institutions, vol. 110, London: British Academy, pp. 197–204. Richerson, P.J. and R. Boyd (2004), Not by Genes Alone, Chicago, IL: University of Chicago Press. Riker, W.H. and P.C. Ordeshook (1968), ‘A theory of the calculus of voting’, American Political Science Review, 62 (1), 25–42. Roemer, J. (2010), ‘Kantian equilibrium’, Scandinavian Journal of Economics, 112 (1), 1–24. Savage, L.J. (1954), The Foundations of Statistics, New York: John Wiley & Sons. Searle, J. (1995), The Construction of Social Reality, New York: Free Press. Shalvi, S., O. Eldar and Y. Bereby-Meyer (2012), ‘Honesty requires time (and lack of justifications)’, Psychological Science, 23 (10), 1264–70. Sugden, R. (2003), ‘The logic of team reasoning’, Philosophical Explorations, 6 (3), 165–81. Tooby, J. and L. Cosmides (1992), ‘The psychological foundations of culture’, in J.H. Barkow, L. Cosmides and

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J. Tooby (eds), The Adapted Mind: Evolutionary Psychology and the Generation of Culture, New York: Oxford University Press, pp. 19–136. Trivers, R.L. (1971), ‘The evolution of reciprocal altruism’, Quarterly Review of Biology, 46 (1), 35–57. Tuomela, R. (1995), The Importance of Us, Stanford, CA: Stanford University Press. Turchin, P. and A. Korotayev (2006), ‘Population dynamics and internal warfare: a reconsideration’, Social Evolution & History, 5 (2), 112–47.

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32 Ethics and simple games Mark Pingle

INTRODUCTION Among the roughly 6100 words which George Washington left to American posterity in his farewell address were these: ‘Of all the dispositions and habits which lead to political prosperity, religion and morality are indispensable supports.’ How does morality support prosperity? This is an interesting general question, but not one receiving much attention in economics, even though understanding the causes of prosperity is an important aspect of economics. This chapter provides some understanding of how morality supports prosperity, illustrating that the impacts of morality on decision making can be considered using the most simple and standard games of game theory. The prosperity of interest here is general well-being, not the political prosperity of interest to George Washington. A key insight is that well-being not only depends upon outcomes but also upon how outcomes are obtained. In many decision contexts, ethics are not an issue because the ‘way of behaving’ is not an issue. However, instances do arise when different decision alternatives are associated with different ways of behaving, and the different ways of behaving have different moral connotations. In these instances, moral considerations may influence choice. Because game theory seeks to explain the decisions of interacting individuals, and because much of morality involves how to behave when interacting with others, game theory provides a useful framework for considering morality. In a Nash equilibrium, no individual can benefit by changing strategies, so the Nash equilibrium implicitly assumes decision makers maximize their own interest. This assumption is maintained in the modeling below, which includes various ethical concerns. The approach, which is now common in behavioral economics, is to recognize that interests providing individual satisfaction may extend beyond own material self-interest. A few simple games have been the focus of much theoretical and experimental interest, both because they model the potential cooperation and conflict people face in the real world and because players often do not play as the Nash equilibrium predicts. Many ethical considerations relate to how we should behave in situations with the potential for cooperation or conflict. Thus, it is reasonable to think these considerations may influence decision makers as they play these simple games. The material payoffs of the games translate into Nash equilibrium predictions, but they will not fully characterize the game actually being played if the decision maker is also being influenced by moral considerations. The approach taken here is to model the transformation of payoffs that moral considerations might cause, and then apply the Nash equilibrium concept to identify the predicted choice inclusive of moral considerations. One theme of this book is that behavioral economics is not just a set of social theories that arise because people make mistakes when they make decisions. In the models presented in this chapter, decision makers do not make mistakes. However, people without 557

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ethics who make rational decisions in their own interest are not smart in that they can achieve better outcomes. With ethics, reason and self-interest lead people to the better outcomes. That is, in a real sense, ethics make people smarter because they rule out inferior outcomes that will otherwise tend to occur as people interact. The chapter is organized as follows. The next section reviews literature on morality, virtues, and ethics, including a focus on their definitions, a focus on how they arise, and a focus on how they may influence the games people play. The third section examines many standard two-player games (coordination game, stag hunt game, prisoner’s dilemma, battle of the sexes game, and hawk–dove game), illustrating how ethical considerations might reasonably influence behavior by transforming these games. The concluding section summarizes what is learned.

DEFINING MORALITY, VIRTUES, AND ETHICS Moral principles distinguish right behavior from wrong, good behavior from bad. A virtue is a good way of behaving, while a vice is a bad way. Ethics, or moral philosophy, is a branch of social science which studies morality and seeks to construct sets of moral principles, principles distinguishing virtue from vice. In his Theory of Moral Sentiments, Adam Smith (1759 [2000], pp. 393–4) contends ‘virtue . . . must either be ascribed indifferently to all our affections . . . or it must be confined to some one class or division of them’. Explaining himself further he said: ‘If virtue . . . does not consist in propriety, it must consist either in prudence or in benevolence.’ Among moral philosophies, Adam Smith’s idea that virtue is propriety is relatively unique. He speaks of both ‘the man in the breast’ and the ‘impartial spectator’ as important determinants of what is appropriate (that is, virtuous) behavior. The man in the breast is your own perspective, your conscience, deciding what is proper. ‘We approve of another man’s judgment, not as something useful, but as right: . . . and it is evident we attribute those qualities to it for no other reason but because we find that it agrees with our own’ (Smith 1759 [2000], p. 21). The impartial spectator is your belief about what other people tend to think. The ‘passions of human nature seem proper and are approved of when the heart of every impartial spectator entirely sympathizes with them, when every indifferent by-stander entirely enters into, and goes along with them’ (Smith 1759 [2000], p. 97). Adam Smith’s alternative, that virtue amounts to exhibiting a particular type of behavior, is standard, though other thinkers have included more classes of behavior than just prudence and benevolence. Plato, in his Republic, introduced what have become most commonly known as the four ‘cardinal virtues’: courage, prudence, temperance, and justice. Plato identified fortitude (courage), wisdom (prudence), and temperance as good behaviors respectively associated with three classes of people: soldiers, rulers, and producers. Temperance was a bit special to Plato because he recognized it as a virtue important to all classes, even though he saw it as a distinguishing feature of the producing class. Justice was also a special and separate virtue to Plato because he did not view it as being associated with a particular class. Rather, justice, Plato contended, regulates how the different classes should treat each other. In its catechism (Catholic Catechism 2015), the Roman Catholic Church recognizes

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Ethics and simple games 559 the four cardinal virtues, labeling them ‘human virtues’, but it also recognizes three additional ‘theological virtues’ (also known as the ‘Christian virtues’): faith, hope, and love. Theologian James Stalker combined the seven virtues and labelled all seven cardinal because ‘among the countless excellences with which human character may be adorned [these are the] seven which overtop the rest, and from which all the rest are derivable’ (Stalker 1902, p. 2). McCloskey (2006, ch. 26) also presents an ethical system which consists of these seven virtues, contending, like Stalker, that any other virtue one might identify is some combination of the seven. Historically, thinkers have most commonly viewed morality as being primarily concerned with how our own actions affect others. McCloskey (2006, p. 308) notes that Immanuel Kant viewed morality this way. Stigler (1981, p. 189) described ethics as ‘a set of rules with respect to dealings with other persons’. Love, as virtuous behavior, is the fountain of this moral perspective, out of which flows Adam Smith’s beneficence, and other attractive behaviors, such as charity, kindness, generosity, and friendliness. Baumard et al. (2013, p. 60) conclude that recent work on the evolution of human altruistic cooperation suggests ‘human morality is first and foremost altruistic’. However, McCloskey (2006, p. 255) stresses virtue also includes attending ‘to the self and the transcendent’, so it goes beyond just being good to others. Being wholly altruistic, and disregarding the claims of that person also in the room called Self, about whose needs the very Self is ordinarily best informed, is making the same mistake as being wholly selfish, disregarding the claims of that person called Other. In both cases, the mistake is to ignore someone. (McCloskey 2006, p. 255)

Aristotle identified a ‘good life’ as an end goal. You are being imprudent and cannot attain a good life if you opt for the extreme of disregarding yourself. McCloskey also contends you cannot attain the good life if you ignore the transcendent. ‘We humans cannot get along without transcendence – faith in a past, hope for a future, justified by larger considerations. If we don’t have religious hope and faith, we’ll substitute hope and faith in art or science or national learning. It’s a consequence of the human ability to symbolize’ (McCloskey, 2006, p. 183). People are motivated by the hope of making a difference. There is satisfaction in sacrificing self for something that is bigger than self, something transcendent. A number of writers emphasize temperance as a virtue, in one form or another. Smith (1759 [2000], p. 214) describes ‘the man of the most perfect virtue’ as possessing ‘the most perfect command of his own original and selfish feelings’. Stigler (1981, p. 189) notes that the rules in ethical systems ‘in general prohibit behavior which is only myopically selfserving, or which imposes large costs on others with small gains to oneself’. Atran (2013) summarized Darwin’s view as of virtue as being self-sacrifice of the type that occurs in war which allows for the survival of the group. Amartya Sen identifies commitment as a particularly important form of self-sacrifice. ‘A committed man,’ as Sen (1977, p. 327) defines him, ‘[chooses] an act that he believes will yield a lower level of personal welfare to him than an alternative that is also available to him’. Sen (1977, p. 342) connects commitment with morals, concluding ‘commitment sometimes relates to a sense of obligation going beyond the consequences [so] . . . the lack of personal gain in particular acts is accepted by considering the value of rules of behavior’.

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Ordinary goodness is not virtue in one sense, yet virtue taken to an extreme can also be vice. Adam Smith (1759 [2000], p. 28) proposed that ‘virtue is excellence, something uncommonly great and beautiful, which rises far above what is vulgar and necessary’. Conversely, Aristotle (350 BC [2015], ch. 9), in his Nicomachean Ethics, described ‘moral virtue’ as ‘a mean between two vices, the one involving excess, the other deficiency’. Aristotle describes confidence as the virtue between cowardice and rashness, for example. The mean, Aristotle said, is praiseworthy, while the extremes are blameworthy. Adam Smith did not necessarily disagree, for Smith felt the man in the breast and impartial spectator would determine what is praiseworthy and blameworthy, and praiseworthiness might well lie between extremes. McCloskey (2006) repeatedly makes the point that the seven different fundamental virtues check and balance each other, meaning a person living the good life will not tend to specialize in any one virtue but rather will exhibit all seven. Nonetheless, virtue is also often associated with exceptionalism, for example, exceptional courage, exceptional self-control, or exceptional self-sacrificing love for another. Fairness is a particular type of morality, regarding how we should share, reasonably considered a combination of love, temperance, and justice. Rawls (1971) characterizes society as a cooperative venture undertaken for mutual interest, but notes conflict is inevitable because the benefits of cooperation can be distributed in varying shares to individuals and groups. Baumard et al. (2013) see morality, fairness in particular, as emerging to guide the distribution of gains resulting from cooperative interactions so there will not be so much conflict. The next section more generally considers how virtues arise.

HOW MORALITY, VIRTUES, AND ETHICS ARISE To Adam Smith, the foundation of morality is the empathy we naturally feel for others. ‘By the imagination we place ourselves in his situation, . . . we enter as it were into his body, and become in some measure the same person with him, and thence form some idea of his sensations . . ..[This] is the source of our fellow-feeling for . . . others’ (Smith, 1759 [2000], p. 4). Recognizing people empathize, we obtain satisfaction when others feel what we feel and experience dissatisfaction when they do not: ‘[The] correspondence of the sentiments of others with our own appears to be a cause of pleasure, and the want of it a cause of pain’ Smith (1759 [2000], p. 11). The pleasure obtained from a corresponding sentiment is positive reinforcement, helping to solidify a particular notion of propriety as virtuous in the mind of the man in the breast. Alternatively, the pain of contrasting sentiments is punishment, an indication that the impartial spectator has a different notion of propriety, an encouragement to evolve toward what others think is proper. Smith (1759 [2000], p. 224) describes in some detail how an ethical standard is developed: Our continual observations upon the conduct of others, insensibly lead us to form to ourselves certain general rules concerning what is fit and proper either to be done or to be avoided . . . It is thus that the general rules of morality are formed. They are ultimately founded upon experience of what, in particular instances, our moral faculties, our natural sense of merit and propriety, approve, or disapprove of. We do not originally approve or condemn particular actions; because, upon examination, they appear to be agreeable or inconsistent with a certain general rule. The general rule, on the contrary, is formed, by finding from experience, that all actions of a certain kind, or circumstanced in a certain manner, are approved or disapproved of.

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Ethics and simple games 561 To Adam Smith, a morality is sustained by the satisfaction people derive from others when virtuous behavior is rewarded with approbation: Humanity does not desire to be great, but to be beloved. It is not in being rich that truth and justice would rejoice, but in being trusted and believed, recompenses which those virtues must almost always acquire . . . the practice of truth, justice, and humanity is a certain and almost infallible method of acquiring what those virtues chiefly aim at, the confidence and love of those we live with. (Smith 1759 [2000], p. 236)

In his book, Invariances, Robert Nozick (2001, p. 244) proposes, ‘Ethics exist because at least sometimes it is possible to coordinate actions to mutual benefit’. Similarly, Baumard et al. (2013, pp. 60–61) propose that ‘morality . . . emerged to guide the distribution of gains resulting from cooperative interactions’. They suggest morality has co-evolved to support an evolution of cooperation that could not have occurred absent the morality. In many contexts morality is selfishly instrumental, being chosen because other people favor individuals who are cooperative. However, Baumard et al. (2013, p. 65) contend that a ‘genuine concern for fairness’ can evolve as competition for partners selects the instrumentally selfish for extinction. Kenneth Binmore (1998, p. 367) similarly proposes that, ‘Morality evolved in the human race to coordinate human behavior on Pareto improving equilibria in the game of life’, but his perspective is somewhat unique. In Binmore’s game of life, life generates games with multiple Nash equilibria, and morality evolves as a means of selecting one of these. Morality does not pay directly, nor does it pay by facilitating cooperation when it would not otherwise arise. Morality, from this perspective, plays a pure coordination role, providing a ‘focal point’ that leads to the choice of one Nash equilibrium over another. Relative to capturing the benefits of cooperation, Amartya Sen (1977, p. 332) quotes Leif Johansen: ‘No society would be viable without some norms and rules of conduct. Such norms and rules are necessary for viability exactly in fields where strictly economic incentives are absent and cannot be created.’ Sen (1977, p. 336) contends ‘the purely economic man is indeed close to being a social moron’. He notes that it is difficult to explain why the purely economic man would vote, or contribute to any public goods game. Yet, real humans do vote and do not always free-ride, and they are better off for it because the benefits of cooperation are captured. Sen proposes that people recognize the value of following rules. Specifically, he explains why commitment is a form of morality with fitness. He proposes that we can move beyond the purely economic man by assuming people not only have preferences over outcomes, but they also have a ‘meta-ranking’ of preferences such that moral considerations are evaluated. McCloskey (2006, p. 395) proposes that virtues (that is, ways of behaving) are merely preferences if they cannot be connected to self-interest, and she contends virtues are meaningless if they are merely preferences. Suppose, for example, I prefer to keep my commitments as a matter of pure preference, as I might prefer vanilla ice cream. How is it meaningful to say keeping a commitment is good? It is good because I say it is good, just like I say vanilla ice cream is good. To meaningfully say a particular way of behaving is good, I must have a reason, and to have a reason indicates the virtuous behavior is instrumental, accomplishing something. Yet, as Gauthier (1986, p. 1) says: ‘Were duty no more than interest, morals would be

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superfluous.’ Evolutionary theory offers means of explaining morality that is between preference and choice. From this perspective, moral behavior is a cultural preference, roughly speaking, but one which has evolved because it has fitness (Camerer 2003, p. 268). Gintis (2003) identifies morality as being important for fitness, noting that once humans evolved the capacity to internalize self-regarding virtues the same psychological mechanisms could be ‘hijacked’ for other purposes, including inculcating social preferences. Binmore (1998, p. 354) notes that ‘moral naturalism’ is an evolutionary perspective, proposing that what is moral in a given society depends upon the history the society has experienced. McCloskey (2006, p. 255) suggests ethical systems arise so people can intentionally pursue a good life. Humans are distinguishable from other animals by the fact that humans can act with intention, setting goals and pursuing them. You cannot pursue a good life, unless you first define it. One way to think about an ethical system is a definition of a good life. Such a system can offer the opportunity to live for self, live for others, live for something that transcends self and others, or some combination of these. McCloskey (2006, p. 272) indicates ethics come from stories, characters, and experience, not from reasoning.

HOW MORAL CONSIDERATIONS MAY MODIFY THE GAMES PEOPLE PLAY If people carry ethical considerations into a decision situation, which is likely, then models of decision making which ignore ethical considerations may be inaccurate predictors of behavior, or the model may be wrongly attributing causations to factors which should be attributed to ethical motivations. This section reviews some relevant literature, especially Adam Smith’s Theory of Moral Sentiments, presenting how moral considerations might modify the games people play. People care about what others think. Adam Smith (1759 [2000], p. 311) proposed ‘obtaining . . . credit and rank among our equals, is, perhaps, the strongest of all our desires’. Smith (1759 [2000], p. 171) contended that nature has endowed mankind ‘with an original desire to please and an original aversion to offend’. We love praise or the ‘favourable sentiments of our brethren’, and we love praise-worthiness which is being ‘the proper objects of those sentiments’ (Smith 1759 [2000], p. 183). Conversely, we hate blame and blameworthiness. People are self-interested, or as Adam Smith (1759 [2000], p. 120) put it, ‘every individual, in his own breast, naturally prefers himself to all mankind’. However, Smith went on to say no man dares to ‘look mankind in the face, and avow that he acts according to this principle’. When a man acts, ‘He is conscious that others will view him, [and] he sees that to them he is but one of the multitude in no respect better than any other in it’ (Smith 1759 [2000], p. 120). What prompts us to sacrifice [our] own interests to the greater interests of others . . . [is] . . . the inhabitant of the breast, the man within, the great judge and arbiter of our conduct . . . who . . . calls to us . . . that we are but one of the multitude, in no respect better than any other in it; and that when we prefer ourselves so shamefully and so blindly to others, we become the proper objects of resentment. (Smith 1759 [2000], pp. 193–4)

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Ethics and simple games 563 Virtuous ‘rules of conduct, when they have been fixed in our mind by habitual reflection, are of great use in correcting the misrepresentations of self-love concerning what is fit and proper to be done in our particular situation (Smith 1759 [2000], p. 226). Sen (1977, p. 329) describes the commitment to such rules a ‘fact that it drives a wedge between personal choice and personal welfare’. Rochat and Robins (2013, p. 99) find that, by age 5, children ‘start inhibiting their inclination to self-maximize resources’, adopt ethical stances toward others, ‘resist conforming to a partner’s way of sharing, and engage in costly punishment, what can be equated to strong reciprocity’. Because we recognize it is not easy to inhibit self-interest, we esteem those who are generous, benevolent, or beneficent. ‘To feel much for others, and little for ourselves, that to restrain our selfish, and to indulge our benevolent, affections, constitutes the perfection of human nature’ (Smith 1759 [2000], p. 27). Recognizing people generally admire beneficence and desiring the favorable sentiments of others, we learn to love others. ‘If to be beloved by our brethren be the great object of our ambition, the surest way of obtaining it is by our conduct to show that we really love them’ (Smith 1759 [2000], p. 27). People do not value cheap praise as much as they value praise that is deserved. (Smith 1759 [2000], p. 123) notes that praise feels especially good when we are confident that we are praiseworthy, when we are ‘conscious of merit, or of deserved reward’. We can become the ‘natural object of love and gratitude’, of ‘esteem and approbation’ by performing a ‘generous action’ . . . ‘from proper motives’. Smith (1759 [2000], p. 154) helped identify proper motives when he noted: ‘Benevolent affections seem to deserve most praise, when they do not wait till it becomes almost a crime for them not to exert themselves.’ Adam Smith (1759 [2000], p. 125) contends ‘beneficence . . . is less essential to the existence of society than justice’, and he identifies ‘resentment . . . [as] the safeguard of justice’ (Smith 1759 [2000], p. 113). The resentment of injustice prompts us to beat off the mischief which is attempted to be done to us, and to retaliate that which is already done; that the offender may be made to repent of his injustice, and that others, through fear of the like punishment, may be terrified from being guilty of the like offence. (Smith 1759 [2000], p. 113)

Gintis (2006, p. 21) notes: ‘Individuals who have rewarded those who help them, or hurt those who have hurt them, do not later regret having fallen prey to irrational emotionality. Rather, they generally affirm the morality of their behavior.’ It is the satisfaction obtained from enforcing justice which is at work here. Adam Smith (1759 [2000], p. 95) notes we feel obligated to those who help us and ‘it does not content our gratitude . . . till we ourselves have been instrumental in promoting his happiness’. Conversely, Smith (1759 [2000], p. 95) notes ‘hatred and dislike . . . often lead us to take a malicious pleasure in the misfortune of the man whose conduct and character excite so painful a passion’. Not only will unjust actions of others cause us moral disapprobation, but also inappropriate intentions. Adam Smith (1759 [2000], p. 17) contends the ‘whole virtue or vice [of an action] must ultimately depend [upon] the sentiment or affection of the heart’ and the ‘effect which [the action] tends to produce’ matters, but so does ‘the motive which gives occasion to it’. Our natural love and admiration for some virtues is such, that we should wish to bestow on them all sorts of honours and rewards, . . . [while] our detestation, on the contrary, for some vices is

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such, that we should desire to heap upon them every sort of disgrace and disaster. (Smith 1759 [2000], p. 237)

Smith identifies ‘Magnanimity, generosity, and justice’ as possessing an especially ‘high degree of admiration’, while he identifies ‘fraud, falsehood, brutality, and violence’ as especially deserving ‘scorn and abhorrence’. Gratitude for good treatment ‘most immediately and directly prompts us to reward’, while ‘resentment’ for bad treatment ‘most immediately and directly prompts us to punish’ (Smith 1759 [2000], p. 94). Self-interest and ethics may conflict, so that a decision maker may gain personally by being less ethical or lose personally by being more ethical. Smith (1759 [2000], p. 88) noted: ‘Candidates for fortune too frequently abandon the paths of virtue; for unhappily, the road which leads to the one, and that which leads to the other, lie sometimes in very opposite directions.’ Smith (1759 [2000], p. 84) identified ‘wealth and greatness as one ‘road’ leading to ‘respect’ and ‘being respectable’, while he viewed ‘the study of wisdom and the practice of virtue’ as the primary alternative road. Smith (1759 [2000], pp. 221–2) perceived that ‘our passions magnify our self-love’ at the moment of action, while ‘we can more ably enter the perspective of the impartial spectator’ once our actions are over. That is, it is easier to say we should sacrifice personal wealth for virtue than to do it when the opportunity arises. Stigler (1981, p. 176) agrees, contending: ‘Where self-interest and ethical values . . . are in conflict . . ., most of the time . . . self-interest theory . . . will win.’ Yet, Sen’s (1977) ‘commitment’ involves choosing an ethical rule over self-interest. What Sen labelled commitment Adam Smith (1759 [2000], p. 229) labelled duty: ‘Duty is a regard to a general rule of conduct.’ Smith (1759 [2000], pp. 230–31) describes ‘adherence to duty’ as being ‘what makes a man dependable’, distinguishing ‘an honorable man of principle from a worthless fellow’. Immanuel Kant associated character with doing one’s duty and described the ultimate desire as ‘having a good character’, where having a good character means being ‘in need of no other incentive to recognize a duty except the representation of duty itself’ (Thorpe 2010, p. 10). Honor is traditionally associated with doing what a society has come to view as one’s duty, and maintaining one’s honor can be a tremendous motivator. Kant’s famous quote is, ‘In the kingdom of ends everything has either a price or a dignity. What has a price can be replaced by something else as its equivalent; what on the other hand is raised above all price and therefore admits of no equivalent has a dignity’ (McCloskey 2006, p. 410). In some situations, a person might increasingly sacrifice virtue as the price that must be paid increases. However, the notion that there is dignity which will not be sacrificed indicates there are situations where some principles, some virtues, some rules that are so firmly attached to honor and the maintenance of good character that the rule will be followed regardless of cost. In such instances, ‘Human virtue is superior to pain, to poverty, to danger, and to death’ (Smith 1759 [2000], p. 83).

A SIMPLE MODEL INCORPORATING ETHICS Camerer (2003, p. 23) notes: ‘When behavior does not conform to analytical game theory [a common reaction is to claim] subjects were playing a different game than the administrator created.’ The general claim made here is that ethical considerations often influence

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Ethics and simple games 565 decisions, so the incentives explicitly present in the game are not the only incentives present. Camerer (2003, p. 23) evaluates ‘such explanations’ contending they ‘are useful if they can be tested and falsified’. Space limitations preclude any test of theory here. However, an important aspect of this work is that it does suggest hypotheses about ethical considerations can be tested in future work using modifications of simple games. Consider a game played by players A and B. Let pA(ai, bj) denote the material payoff of player A when player A chooses strategy ai, i 5 1, 2, . . ., NA and player B chooses strategy bj, i 5 1, 2, . . ., NB,. Let pB(ai, bj) for player B be defined analogously. Assume ethical considerations can be related to the strategies chosen, so that the payoff experienced by player A is the sum of the material payoff and an ethical transformation payoff. Letting fA(ai, bj) denote the ethical transformation payoff of player A, it follows we can present the payoff of player A as uA(ai, bj) 5 pA(ai, bj) + fA(ai, bj). Analogously, the payoff of player B is uB(ai, bj) 5 pB(ai, bj) + fB(ai, bj). This representation of ethics is comparable to the representation of intentions used by Rabin (1993). Rabin introduces intentions into the game by postulating that intentions transform the payoffs. Here, we similarly introduce ethical considerations by assuming they transform the payoffs. The notion that it is good to follow tradition is an ethical consideration. In America, it is tradition to drive on the right, while in England it is to drive on the left. As Binmore (2004, p. 6) notes, the pure coordination game is sometimes called the ‘driving game’ because coordination yield benefits relative to non-coordination. The pure coordination game, displayed in Figure 32.1, has two Nash equilibria: (a1, b1) and (a2, b2). Schelling (1960) first suggested people will look for a ‘focal point’ in such situations, which would suggest coordinating on one choice rather than the other. To illustrate, Mehta et al. (1994) found more than 75 percent of participants chose heads over tails as a focal point when seeking to match what others would choose on a coin flip. Here, we are interested in the idea that a moral code will transform this game so no focal point is necessary. Figure 32.2 is a transformation matrix which translates ethical considerations into strategy rewards and penalties. The simple assumption employed here is strategy 1 is rewarded with one extra payoff unit, while strategy 2 is penalized one payoff unit. In terms Player B

Player A

Figure 32.1

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Figure 32.2

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A transformation matrix capturing ethical considerations

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Figure 32.3

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Player A

Figure 32.4

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A coordination game with a Pareto dominant equilibrium

of our modeling notation this implies fA(a1, b1) 5 fB(a1, b1) 5 1, fA(a1, b2) 5 fB(a2, b1) 5 1, fA(a1, b1) 5 fB(a1, b1) 5 −1, and fA(a2, b1) 5 fB(a1, b2) 5 −1. For our coordination game, strategy 1 can be considered the choice supported by tradition, where society rewards following tradition and penalizes not following tradition. Adjusting the Figure 32.1 coordination game payoffs by adding the Figure 32.2 transformation matrix payoffs, we obtain the game presented in Figure 32.3. No longer are there two Nash equilibria. Rather, strategy 1 is now payoff dominant for each player, so the ‘traditional choice’ (a1, b1) is the unique Nash equilibrium. The coordination game in Figure 32.4 is impure in that Nash equilibrium (a1, b1) Pareto dominates the Nash equilibrium (a2, b2). The Pareto dominance of strategy 1 might well provide a sufficient focal point that would lead people to choose strategy 1 over strategy 2. However, we are again interested in the idea that an ethic might transform the game, so no focal point is necessary. The Stanford Encyclopedia of Philosophy (2015) defines ‘utilitarianism’ as an ethic with ‘the view that the morally right action is the action that produces the most good’. It is one form of the broader consequentialism ethic, which contends the right action is understood entirely in terms of consequences produced. Egoism is a more focused form of consequentialism. Whereas egoism restricts the scope to the consequences for self, utilitarianism’s broader scope includes the consequences for self and others. Egoism contends it is right to put self above others, while utilitarianism contends it is right to be impartial between self and others. Jeremy Bentham’s utilitarian mantra was ‘the greatest good for the greatest number’, with no preference for self. Suppose culture reinforces a consequentialist ethic with the Figure 32.2 transformation matrix, positively reinforcing the pursuit of the most good and negatively reinforcing the alternative. The result is the Figure 32.5 game, which again has a single Nash Equilibrium, the outcome (a1, b1). What have we learned? The imposition by culture of a consequentialist ethic can motivate people to coordinate in a way that brings about the greater good. The stag hunt game, shown in Figure 32.6, is just a slight modification of the Pareto dominant coordination game shown in Figure 32.4, but it is fundamentally different. The stag hunt dilemma, attributed to Jean-Jacques Rousseau, can be described as follows. If

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Ethics and simple games 567 Player B

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Figure 32.5

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Figure 32.6

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Stag hunt game

two hunters work together, they can kill a stag. If they do not cooperate, the stag will get away. Any hunter who abandons the stag hunt to hunt a hare will be successful. If both abandon the hunt, both eat hare, which is something but not as good as eating stag. If a hunter does not abandon the stag hunt but the other does, then the hunter not abandoning the stag hunt does not eat. Thus, any hunter engaging in the stag hunt risks not eating, hoping the other will not abandon the stag hunt. The stag hunt game is interesting because, unlike the pure coordination game, its two Nash equilibria imply a risk–return tradeoff. The Nash equilibrium (a1, b1) is payoff dominant and is supported by a max–max decision criterion. Alternatively, the Nash equilibrium (a2, b2) is risk dominant and is supported by a max–min decision criterion. Seeking a high return and seeking less risk are two fundamental types of prudence which can be in conflict. Real world outcomes, as outcomes in the stag hunt game, will depend upon individual morality regarding how much risk one should take in order to pursue a higher rate of return. The stag hunt game also informs us about courage. Courage is required to pursue the higher return because there is risk. Were there no potential gain, taking the risk would not be courageous but rather would be merely imprudence, a vice. Courage involves facing a fear, generated by the potential for loss, so a gain can be obtained. The stag hunt game also informs us about hope. In standard analysis, hope is not recognized. However, in reality, people might get some satisfaction from aspiring for the higher payoff for self and for another. In the stag hunt game this would imply an extra payoff would be associated with strategy 1. The stag hunt game also contains a particular form of trust, a type of hope, in that the risk a player experiences when choosing strategy 1 is dependent upon the other player. For example, the choice of strategy 1 may indicate the player trusts that the other is courageous. Suppose society positively reinforces courage and hope, and negatively reinforces the opposite in stag hunt game situations. A transition matrix like Figure 32.2 might capture such cultural reinforcements, which implies the stag hunt game of Figure 32.6 becomes the game shown in Figure 32.7. The transformation just eliminates the risk associated with choosing strategy 1, and strategy 1 weakly dominates strategy 2.

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Figure 32.7

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A transformed stag hunt game Column player b1 Row player

Figure 32.8

b2

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A general game

Consider now the game shown in Figure 32.8, where the strategy 2 payoff to the other is the variable e. When e 5 0, we have the Figure 32.4 coordination game with a Pareto dominant equilibrium. When e > 2, we have a prisoner’s dilemma. With regard to ethics, it is interesting to compare the case 0 < e < 1 to the case 1 < e < 2. As the payoff increases from 0 to 2, the Nash equilibria for the game remain the same, that is, (a1, b1) and (a2, b2), but the ethical issues do not remain the same. Consider a pair of players coordinating on the Pareto inferior equilibrium (a2, b2). If only one player deviates, the deviating player is worse off, explaining why (a2, b2) is a Nash equilibrium. However, whether the other player is worse off or better off depends upon whether 0 < e < 1 or 1 < e < 2. The stag hunt game, with e 5 1, is special in that there is no change in the wellbeing of the other player as only one player deviates from the Pareto inferior equilibrium. When e ≠ 1, there is a new ethical consideration in the game, the well-being of the other player. This game helps us define love or benevolence. When 0 < e < 1, a deviation from strategy 2 for either play not only hurts self, but it also hurts the other. Thus, while approbation for courage or hope may elicit a deviation from strategy 2, love for the other does not play a role. However, when e > 1, a unilateral move from strategy 2 involves sacrificing self while ensuring a gain for the other, so love may also play a role. The Figure 32.8 game becomes a prisoner’s dilemma when e > 2. The single Nash equilibrium is (a2, b2), which is Pareto inferior to the outcome (a1, b1). A Nash equilibrium only recognizes a single incentive, the desire to maximize our own material outcome, given the choice of the other. This love for self is not unethical, but the ethical considerations beyond a love for self might transform the prisoner’s dilemma into another game. In addition to the courage or hope that might transform the stag hunt game, love for the other might transform the prisoner’s dilemma. That is, when mutual cooperation is being observed in a prisoner’s dilemma, it may be courage, hope, love, or some combination of these that is sustaining the cooperation. The prisoner’s dilemma also helps us think about faith, which can be defined as belief which is grounded in an identity. The Nash equilibrium is supported by the belief that all

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Ethics and simple games 569 Column player

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Figure 32.9

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A transformed prisoner’s dilemma game Column player

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Figure 32.10

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A battle of the sexes game

players are materially self-interested. There may be identities which support this belief. However, a player may choose the cooperative strategy 1 because society has enculturated an identity with which is associated the belief that all in society are courageous, hopeful, and/or loving. In summary, the payoffs of a prisoner’s dilemma might be transformed because a culture reinforces courage, hope, or love. The degree to which these ethical considerations influence the perceived payoffs of the game will depend upon the situation and backgrounds of the people playing the game. Figure 32.9 presents a game where the Figure 32.8 game is made a prisoner’s dilemma with e 5 3, but then transformed with the transition matrix of Figure 32.2. The result is a game where strategy 1 is Pareto dominant and the outcome (a1, b1) is a unique Nash equilibrium. The battle of the sexes game, an illustration of which is presented in Figure 32.10, is a coordination game, but the payoffs to the coordinating players are not the same. The outcomes (a1, b1) and (a2, b2) are Nash equilibria. Row player receives a higher outcome if the two players coordinate on strategy 1, while column player receives a higher outcome when the coordination is on strategy 2. When this game is played in the laboratory setting, a lack of coordination is more common than coordination (Cooper et al. 1990). Camerer (2003, p. 356) interprets this lack of coordination as representing a situation where ‘the players crave any tie-breaking feature that distinguishes one player from the other’. Camerer (2003) reviews the literature and finds coordination can be improved with numerous mechanisms, including communication, an outside option, the external suggestion or ‘assignment’ of a strategy, and allowing one player to choose first. Of interest here is the idea that an ethic might transform this game so that the coordination problem is resolved. Tradition again can provide a coordination device, this time by providing a traditional pecking order. If two arrive at a door at the same time, who should go first? In golf, who should hit their shot first? When two cars arrive at an intersection, which car should go first? Everyone loses in these situations if there is no coordination. Tradition provides a pecking order that benefits one over the other, though both benefit relative to no

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Figure 32.11

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Figure 32.12

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–1, –1

A hawk–dove game

coordination. If culture favors the row player in the situation, it would point each player to the strategy that is of relative benefit to the row player, which in the Figure 32.10 game is strategy 1. Adding the transition matrix of Figure 32.2 to the Figure 32.10 game yields the transformed game in Figure 32.11, in which the unique Nash equilibrium is strategy 1. It is worth noting that ‘taking turns’ can also be thought of as a tradition, feasible when the battle of the sexes game is played repeatedly and the two have memory. The prisoner’s dilemma game becomes the hawk–dove game when the mutual defection profile (a2, b2) payoffs are less than the payoffs for either player in the uncoordinated conditions (Figure 32.12). The hawk–dove game is called an anti-coordination game because the two uncoordinated profiles (a1, b2) and (a2, b1) are Nash equilibria. The hawk–dove game is interesting ethically because it contains the ethical elements of the Prisoner’s dilemma game and the battle of the sexes game. As in the prisoner’s dilemma, the ‘cooperative’ outcome (a1, b1) is not a Nash equilibrium because either player can obtain a higher individual outcome by deviating. However, this ‘hawk’ strategy involves seeking to benefit self at the expense of the other, the opposite of love. Thus, as in the prisoner’s dilemma, a love ethic can transform the hawk–dove game. Because the choice of strategy 2 in the hawk–dove game reduces the outcome which can be achieved by the other more so than in the prisoner’s dilemma game, it is more clear in the hawk–dove game that strategy 2 is an especially unloving choice. Another difference in the hawk–dove game, compared with the prisoner’s dilemma and stag hunt games, is that the cooperative strategy 1 in the hawk–dove game is the max–min strategy or risk dominant strategy. Thus, prudence in the form of seeking less risk may influence a player to choose strategy 1 over strategy 2. That is, strategy 2 in the hawk–dove game is not only less loving, it is also more risky. Faith and hope are interesting to consider in the hawk–dove game. Suppose a father (row player) and daughter (column player) are playing this game. If we relate faith to the identities we expect each to bring to the game, if we expect the identity of father to be associated with a willingness to sacrifice self for daughter, and if we expect the identity of daughter to be associated with the belief that father sacrifices for daughter, then we can confidently predict the outcome (a1, b2). Here, father might be expected to gain

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Ethics and simple games 571 Player B

Player A

Figure 32.13

b1

b2

a1

3, 3

1, 2

a2

2, 1

0, 0

A transformed hawk–dove game

satisfaction in hoping for daughter, while daughter might be expected to gain satisfaction by hoping for self. Figure 32.13 presents a transformed hawk–dove game, where the transition matrix of Figure 32.2 has been added to the Figure 32.12 hawk–dove game. The transition matrix negatively reinforces the more risky and more self-interested strategy 2, while it positively reinforces the more loving and more hopeful strategy 1. In the father–daughter example of the previous paragraph, the transition matrix recognizes the identities of each and the satisfaction each will obtain from acting under the faith and hope associated with these identities. In the transformed game, the mutual cooperation strategy 1 is again the single Nash equilibrium, again illustrating that the ethical considerations inculcated in a population by culture can resolve dilemmas and promote well-being, in this case a hawk–dove game situation.

CONCLUSION The morality literature indicates that moral considerations influence behavior, not only because people gain satisfaction from receiving the praise of others and from being praiseworthy but also because people lose satisfaction from being blamed by others and from being blameworthy. The literature also indicates morality contributes to prosperity in society by facilitating coordination and cooperation. Support for these theories has been illustrated here using a series of simple games. Tradition facilitates coordination by encouraging a particular behavior and discouraging others. Courage and hope can facilitate cooperation when cooperation is risky. Beneficence, or love, can facilitate cooperation by offsetting self-interest that may discourage cooperation. In the simple examples considered here, it has been shown that moral considerations can also resolve dilemmas and transform games with multiple equilibria into a game with a single Pareto dominant equilibrium. Space limitations have prevented the examination of some games and some virtues. In particular, the ultimate game, dictator game, and trust game are promising for examining justice, which was not addressed here in the modeling section. However, it is well understood that concerns about ‘fairness’, clearly a form of justice, help explain deviations from Nash equilibrium conditions in these games. The modeling illustrations presented here make it clear that more creative experimental work could help enhance the understanding of the morality plays in decision making. The prisoner’s dilemma is interesting because its structure spawns multiple moral considerations, and a particular morality can resolve the dilemma. However, multiple moral considerations imply confounding when people play. To identify and gauge the impacts

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of particular distinct virtues, new and specially designed experiments are needed. The illustrations presented here provide some insights that should be helpful in that regard.

REFERENCES Aristotle (350 BC) [2015], Nicomachean Ethics, trans. W.D. Ross, 2015, accessed 6 May 2015 at http://classics. mit.edu/Aristotle/nicomachaen.html. Atran, S. (2013), ‘From mutualism to moral transcendence’, Behavioral and Brain Sciences, 36 (1), 81–2. Baumard, N., J.B. André and D. Sperber (2013), ‘A mutualistic approach to morality: the evolution of fairness by partner choice’, Behavioral and Brain Sciences, 36 (1), 59–78. Binmore, K. (1998), ‘Egalitarianism versus utilitarianism’, Utilitas, 10 (3), 353–67. Binmore, K (2004), ‘Reciprocity and the social contract’, Politics, Philosophy & Economics, 3 (1), 5–35. Camerer, C.F. (2003), Behavioral Game Theory, Princeton, NJ: Princeton University Press. Catholic Catechism (2015), ‘Part three: Life in Christ, Section one: Man’s vocation life in the Spirity, Article 7: The Virtues’, accessed 6 May 2015 at http://www.vatican.va/archive/ccc_css/archive/catechism/ p3s1c1a7.htm. Cooper, R., D. DeJong, B. Forsythe and T. Ross (1990), ‘Selection criteria in coordination games: some experimental results’, American Economic Review, 80 (1) 218–33. Gauthier, D. (1986), Morals by Agreement, Oxford: Clarendon Press. Gintis, H. (2003), ‘The hitchhiker’s guide to altruism: genes, culture, and the internalization of norms’, Journal of Theoretical Biology, 220 (4), 407–18. Gintis, H. (2006), ‘Behavioral ethics meets natural justice’, Politics, Philosophy & Economics, 5 (1), 5–32. McCloskey, D.N. (2006), The Bourgeois Virtues: Ethics for an Age of Commerce, Chicago, IL: University of Chicago Press. Mehta, J., C. Starmer and R. Sugden (1994), ‘The nature of salience: an experimental examination of coordination games’, American Economic Review, 84 (3), 658–73. Nozick, R. (2001), Invariances: The Structure of the Objective World, Cambridge, MA: Belknap Press of Harvard University Press. Rabin, M. (1993), ‘Incorporating fairness into game theory and economics’, American Economic Review, 83 (5), 1281–302. Rawls, J. (1971), A Theory of Justice, Cambridge, MA: Belknap Press of Harvard University Press. Rochat, P. and E. Robbins (2013), ‘Ego function of morality and developing tensions that are within’, Behavioral and Brain Sciences, 36 (1) 98–9. Schelling, T. (1960), The Strategy of Conflict, Cambridge, MA: Harvard University Press. Sen, A. (1977), ‘A critique of the behavioral foundations of economic theory’, Philosophy & Public Affairs, 6 (4), 317–44. Smith, A. (1759), The Theory of Moral Sentiments, reprinted 2000, Amherst, NY: Prometheus Books. Stalker, J. (1902), The Seven Cardinal Virtues, London: Hodder and Stoughton. Stanford Encyclopedia of Philosophy (2015), accessed 6 May 2015 at http://plato.stanford.edu/entries/ utilitarianism-history. Stigler, G. (1981), ‘Economics or ethics?’, in S. McMurrin (ed.), Tanner Lectures on Human Values, Cambridge: Cambridge University Press. Thorpe, L. (2011), ‘In the realm of ends as a community of spirits: Kant and Swedenborg on the kingdom of heaven and the cleansing of the doors of perception’, Heythrope Journal, 52 (1) 52–75.

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Index

1/N heuristic 108 ABC research program 122, 124, 125 Ackert, L.F. 385 activity and weight loss, see physical activity and weight loss, intertemporal choices adaptation decisions, farmers and climate change 251–2, 254, 264–8 addiction, food consumption 436 adult development 147–9 advertising, and obesity 442–3 Aftab, M. 286–7 age, and taxation compliance 334–5 agents, typical 15–16 aggregate demand 35–6, 37 aggregation bias, ecological correlations 87–8 agricultural decision-making case study, see climate change case study (farmers’ decision-making) Ainslie, G. 496–7 Akerlof, G.A. 35, 36, 111, 238, 519, 529 Akhtar, M. 287 Alchian, A.A. 13, 15, 74–5 Alexander, R.D. 529 Allais paradox 193–4, 195 Altman, Morris 23, 24–5, 29–30 altruism 551–2 and gender 181–4 punishment 238–9, 529 and voting 545 altruistic utility 507 An, J. 286 Anderson, C.L. 254 Anderson, E. 177 Andreoni, J. 182 anthropology, behavioral economic 233–5 Kula exchange system 235–44 Apeldoorn, J. van 481–2 Ariely, D. 144 Aristotle 560 asset markets, see experimental asset markets Atran, S. 559 Auld, M.C. 436 Aumann, R.J. 125–6 Axelrod, R. 533 axiomatization program 61–6

Backhouse, R.E. 61, 64, 65 bad jokes model (rational mistakes) 43–52 banks, studies on x-efficiency 281–7 Baron, J.D. 214, 224 bartering 239 Bates, R.H. 82–3 Baum, C.L. 440 Baum, S. 181 Baumard, N. 561 Beach, L.R. 112 Bechara, A. 366 Becker, G.S. 3, 127, 436, 508 Beek, J. van 421 behavioral economic anthropology, see anthropology, behavioral economic behavioral economics frequently referenced topics 321, 322 not a paradigm shift 64–6 principles of 68 smart decision-making, summary 1–7 Behavioral Insights Team (BIT) 324 behavioral morality 546–7 behavioral strategy 157–63, 170–71 innovation and entrepreneurship 162–3, 165–6, 167 organizational identification 158–9, 161–2, 164–5, 168 organizational routinization 159 organizational transformation 159, 166–7, 169 uncertainty 168 US Marine Corps 163–9 beliefs 494, 568–9 and culture 534 loyalty models 220–21, 226–30 overconfidence 58 and punishment 226–30 see also will, weakness and stiffness of Bell, D.E. 196 Bellon, M. 253–4 Berg, N. 55, 56, 57, 58, 59, 65 Bewley, T.F. 35 bias-variance 55, 108, 123 biased-preferences approach 496–7 biases behavioral strategy 160–61, 162, 163 gender 176–7, 180–81 in-group versus out-group 487

573

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574

Handbook of behavioural economics and smart decision-making

unconscious 366 see also heuristics and biases approach; social interactions; will, weakness and stiffness of Binmore, K. 561, 562 biological development 139–40 birth rates, and unemployment 86–7, 88–9 Black, D. 544 Boehm, C. 552–3 Boettke, P.J. 295 bonding 487 Borghans, L. 437 borrowing decisions, individuals and households 352–3, 356, 359–60 bounded rationality 1, 2–3, 19–21, 145, 493–4 Boyd, J.N. 416 Boyd, R. 533 brain, development of 140, 141, 145–6 Braithwaite, V. 336–7 Breaban, A. 378, 381 Brooks, D. 151 Brumberg, R.H. 394 Buchanan, J. 77–8, 79–80, 302, 306, 307 Budig, M. 178 Burke, M. 264 business and finance banks, studies on x-efficiency 280–87 fast and frugal heuristics 107–9 see also experimental asset markets business cycles and asset values 381 theory 34–5 Callender, G.S. 318 Camerer, C.F. 321, 564–5, 569 cancer screening (nudging) 110 Cantor, George 62, 63 catallactics 71, 234 Centorrino, S. 484 central banks, and asset markets 384 centrally planned economies 78–80 Chan, S. 285 Chandon, P. 442 Chen, A. 284 Chen, T. 285–6 Chen, X. 284 children, spoiling (weakness of will) 508–10 China, banking system 280–85 Chiu, Y. 285 Choi, S. 286 choice, and cognitive theory 530 choice architecture 31–2, 323 choice x-efficiency 29–31 choice x-inefficiency 30 Chou, S.Y. 440, 443

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climate change case study (farmers’ decisionmaking) 251–5, 268–9 methods and data 255–8 results 258–68 Coase, R. 70–71, 74, 79, 83 cognitive bounds, and behavior 127 cognitive development 138, 139, 146–7 cognitive illusions 122 cognitive reflection test (CRT), traders 378–9 cognitive theory, Hayek 526–7, 530–32, 537–8 Cohn, A. 367 Coleman, J.M. 518 colors, and priming 367 commitment 559, 561, 564 and self-control 459–61 community crime prevention (CCP) 515–16, 523–4 behavioral economics perspective 518–20 game theoretic illustration 520–23 sociological tradition 516–18 competency traps 162 COMPLIANCE (mnemonic) 327–8 compulsivity, and stiffness of will 492–3, 495–6, 503, 504 favoritism 507 pornography 511 shirking 504 slackening 506 spoiling children 509–10 confidence-biased beliefs, see will, weakness and stiffness of consequentialist ethic 566 Consideration of Future Consequences (CFC) scale 416–17, 421, 422–4 consumption 392–5 behavioral economics elements 395–6 consumption-savings model 396–405 inefficiencies 29–34 norms, and obesity 443–4 rationality and efficiency 14–15 simple rule-following 392–3 conventional economics 2, 3, 6–7, 70 core assumptions 4, 5, 18–19 economic man 68–9, 142–3 efficiency 12–16 institutions 16–18 macroeconomic choices 34–8 preferences 29 rationality 12–16, 18–19, 20–21 x-efficiency 23 cooperation 551–2 coordination devices 199–200, 385–6, 535, 569–70 Coricelli, G. 193, 338–9

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Index correlated equilibrium, coordination devices 199–200 correlations, ecological, see ecological correlations cost–benefit analysis, of suicide 466–9 courage 567 Courtemanche, C. 440 Cox, C.M. 472 Cox, D. 335–6 creativity, and suicide 472–3 credit use decisions, individuals and households 352–3, 356, 359–60 crime prevention, see community crime prevention (CCP) Croson, R. 179, 180 cross-validation 126 cultural evolution 535–8 culture 533–5 Cutler, D.M. 439 Damasio, A.R. 191, 192 Damon, F.H. 239–40 Davis, H. 355, 357, 359 Dawkins, R. 546 decisions-from-description (DfD) 124 decisions-from-experience (DfE) 124 defaults 33, 325–6 delay discounting, and suicide 470–72 depression, and delay discounting 470–71 development, human, see human development discounting 414–15, 417, 419–21, 422–3, 437–8 delay, and suicide 470–72 hyperbolic 437–8, 459 and time inconsistency 458 discrimination, statistical 482 distributed intentionality 546 distributional conflicts 355–6 docility 528 Downs, A. 544–5 Dreu, C. De 487 Drewnowski, A. 440 duty 564 Earle, T. 235, 240–41, 244 Eckel, C. 179, 378 ecological correlations 86–7, 97–8 aggregation bias 87–8 smart potential behind 93–7 use by lay people 88–92 ecological rationality 15, 20, 56, 60 applied lessons 109–14 and heuristics 103, 104 ecological rationality program (ERP) 122–3, 131 accomplishments of 123–4

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575

compatibility with economics 124–8 development of 122–3 open questions and challenges 128–30 economic crises 28 economic growth, institutional path dependence 80–81 economic man compared to smart person 151 and conventional economics 68–9, 142–3 and nudging 323 psychological 143–4, 151 and social norms 561 and tax compliance 325 and time 211 see also human decision-making Eddy, D.M. 112 educational development 139 Edwards, W. 112 efficiency, see rationality and efficiency; x-efficiency efficiency wage theory 35 effort discretion 23–6, 27 egoism 566 El Farol bar problem 126 Ellickson, R.C. 240 emotions 188–9, 190, 485 and consumption 396 mirror neurons 486–7 neurobiology 191–4 and priming 367 and risk 367 and social choice 201–2 and tax compliance 338–40 traders 379 endogenous quality jurisdictions model 294–302 income effects 309–10 scale of public services 302–8 voice and co-production 308–9 Engelmann, D. 481 England, P. 174, 178, 183 Enos, R. 545 entrepreneurship 162–3 environment and human development 139 and simple decision rules 127 Erikson, E.H. 148, 149 errors 43, 143–4; see also mistakes, rational estimates (Knight) 106–7 ethics 557–8, 559, 562–4 game theoretic model 564–71 ethnicity, and asset trading 378 Etzioni, A. 144 Euler equations, consumption 396 exchange, Kula system 235–44

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executive functioning, and suicide 472 exercise and weight loss, see physical activity and weight loss, intertemporal choices expectations, and cognitive theory (Hayek) 537 expenditure decisions, individuals and households 350–52, 354–6, 359 experimental asset markets 375–6 asset properties 379–82 characteristics, traders 376–8 cognitive abilities, traders 378–9 emotions, traders 379 future research 387 market structure 382–6 mispricing, measuring 386–7 trading strategies 379 experimental economics 4 fairness 560 taxation 321–2, 339 family 487–8, 551 farmers’ decision-making case study, see climate change case study (farmers’ decision-making) fast and frugal heuristics 5, 20, 101–2, 114–15 as adaptive tools 105–7, 125–6, 128–9 applied lessons from study of ecological rationality 109–14 development of 123–4, 125 economic rationality and psychology, overlaps between 102–5 in finance and business 107–9 nudging and uncertainty 109–11 open questions and challenges 128–30 risk literacy 111–14 favoritism (weakness of will) 506–8 Fehr, E. 238–9, 529 Feller, W. 296, 298 feminist economics 173–4, 176–7, 185–6 attitudes to risk, gender differences in 179–81 institutional analysis and development framework 173, 174–7 preferences and behavior, observed gender differences in 177–8 and studies of altruism 181–4 Ferber, M. 173, 177 Ferejohn, J.A. 200 Fiedler, K. 90, 92 finance and business banks, studies on x-efficiency 280–87 fast and frugal heuristics 107–9 see also experimental asset markets financial decision-making, household 349–50, 354–6, 360, 362, 488 individual decisions 350, 352–4

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joint decisions, empirical investigation 357–60 financial decision-making, priming 366–74 Fiorina, M.P. 200 firms and allocative inefficiency 279 economic efficiency, requirements for 27 production inefficiency 23–9, 276 profits 13 rationality and efficiency 13, 276 survival 13, 14, 20 see also x-efficiency; x-inefficiency Fischbacher, U. 238–9, 384, 481 Fisher, I. 394 Folbre, N. 174, 178, 184 Foster, G. 213, 223, 224, 229, 230 Fowler, A. 545 framing 32–3, 54 freedom of choice 30–31, 111 French, S.A. 440 Freytag, P. 90 Friedman, Milton 12–13, 35–6, 394, 395 Frijters, P. 213, 214, 223, 224, 229, 230 Frisch, Ragnar 63 Fu, X. 283–4 Füllbrunn, S.C. 378, 385 functionalism 240 fundamental value, assets 380–81, 382, 383, 384, 385, 386–7 Fung, M. 284 futures markets 384 Gächter, S. 529 García-Herrero, A. 283 gaze heuristic 55, 107 gender 176–7 and altruism 181–4 and asset trading 378 and attitudes to risk 179–81 BMI and participation in sports and exercise 454–5 and financial decision-making 356, 357–62 and labor market 176, 178, 184 and obesity 432, 437, 440, 442 preferences and behavior, observed differences in 177–8 and taxation compliance 334 generalizations 494, 501–2 gifts, traditional societies 237–8, 239 Gigerenzer, Gerd 5, 15, 20, 65, 103–5 disagreement with Kahneman and Tversky 122 errors 43 habits and routines 493–4

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Index memory 54–5 perception 54 probabilistic reasoning 112 repair program 62 social planning 57 Gilad, D. 367 Gintis, H. 552–3, 562, 563 Glanz, K. 441 Gneezy, U. 179, 180, 333–4, 548 Gode, D.K. 127 Goldstein, J. 86 Golsteyn, B.H.H. 437 governance structures, polycentric system 290–94 endogenous quality jurisdictions, model of 294–302 income effects 309–10 scale of public services 302–8 voice and co-production 308–9 government policy, and smart decision-making approach 6 Gray, A. 163, 166–7, 168, 169 ‘greedy rationality’ 207–11, 224 Grootendorst, P. 436 Grossman, P. 179 group selection 536 groups cooperation 551–2 and culture 533–4 and identity 519 negotiated coexistence 517 size of 523 and social interactions 487–8 Gunnell, D. 464–5 Güth, Werner 62 habits 493–4 Hall, P.A. 421 Hall, R.E. 396 Hallsworth, M. 338 Hamermesh, D.S. 465–6 happiness ecological correlations 93–7 and income 473 ‘Harberger triangle’ 279 Harris, M. 533 Harsanyi, J. 29, 30 Hart, S. 199 Haruvy, E. 126, 385 Hasan, I. 286 Hasseldine, J. 327 Hayek, F.A. 73, 77, 82, 526–7, 530–32, 534–8 health expenditure 432

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577

and time orientation 413, 422–4 see also obesity; physical activity and weight loss, intertemporal choices; suicide, and intelligence Hefferman, S. 283–4 Henrich, J. 244, 533–4 heuristics 1/N 108 gaze 55, 107 hiatus 108 priority 107 regret as 200 representative 501–2 see also fast and frugal heuristics heuristics and biases approach 3, 6, 21–2, 31, 119–20 choice architecture 32 development of 121–2 probabilistic reasoning 112 Hewstone, M. 91 hiatus heuristic 108 Hilbert, David 62, 63–4, 65–6 Hobbes, Thomas 546 Hoffrage, U. 56, 112 Hofmann, E. 342 Hogarth, R.M. 121 Homo economicus, see economic man; human decision-making homophily 160, 162 honesty, universalist principle of 547–8 Hong Kong, x-efficiency of banks 284–5 honor 564 hope 567, 570–71 house money effect 380 households, see financial decision-making, household human decision-making 68–70, 82–3 imperfect institutions and path dependency 78–82 rational choice proponents 70–76 rule level of analysis 76–8 human development 137–41 adult developmental stages 147–9 economic man 142–4 failures 146 non-cognitive versus cognitive 146–7 smart person 144–7, 149–52 in transitional periods 147, 148 virtues 149–51 Humean fallacy 12–13 Huy, Q.N. 160 hyperbolic discounting 437–8, 459 identity 519, 520–23 identity utility 519

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impulsivity, and delay discounting 470–71 impulsivity, and weakness of will 492–3, 495, 496–7 favoritism 506–8 pornography 510–11 shirking 498–504 slackening 505–6 spoiling of children 508–10 incentives asset markets 383–4 tax compliance 325 income effects, endogenous quality jurisdictions model 309–10 and happiness 473 and obesity 440–41 and suicide 465–6, 473 and taxation compliance 335 income and consumption 393–6 consumption-savings model 396–405 income tax, compliance 317–18, 327–8, 331–2, 343–4 behavioral economics 321–2 differential effects 334–7 neoclassical approach 332–4 nudging 317–18, 322–8 responsive regulation approach 337 social norms 326–7, 338–40 tax authorities 320–21, 337, 340–42 inconsistency, elimination of 63 inefficiency allocative 279 consumption 29–34 firms 23–9, 276, 279 production 23–9 rational 12–16, 17–18 see also x-inefficiency inertia (organizations) 162 information and asset markets 381–3 cognitive bounds 127 culture as 533 more not necessarily better 56–7 natural frequencies 114 and nudging 111 and obesity 441–3 innovation 162–3, 165–6, 169 institutional analysis and development framework 173, 174–7 institutions 16–18, 73–4, 75, 76 cognitive approach 526, 532 path dependency 78–82, 534 rule level of analysis 76–8 intertemporal choices, see physical activity and weight loss, intertemporal choices

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investment decisions 353, 356, 360, 367–70 Iowa gambling task 191, 192 James, S. 317, 318, 321, 322 Jiang, C. 283 Johnson, A.W. 235, 240–41, 244 jokes model (rational mistakes) 43–52 justice 563 Kahneman, D. 3, 21–2, 112 and development of heuristics and biases approach 121, 122 errors 144 slow and fast thinking 5–6 two-system decision-making process 370 utility, forms of 496 kaiko (ceremonial practice) 243–4 Kamas, L. 181–2 Kameda, T. 58–9 Kant, I. 564 Karlsson, N. 197 Keynes, J.M. 34–5, 36–7, 69 classical theory 277 consumption 393–5 rationality 277–8 Keynesian school 36 Kheirandish, R. 105 Kim, B. 286 Kim, J.Y. 59 kin altruism 551 Kinsey, J. 436 Kirchler, E. 340–41, 355 Kirzner, I. 72 Kliger, D. 367 Knight, F.H. 73–4, 106–7 knowledge 532 common 377, 485–6 and decision-making 105–7 and rules 527 Komlos, J. 437 Kranton, R.E. 519 Kula exchange system 235–44 Kutzner, F. 91, 92 Kwan, S. 284–5 labor market, and gender 176, 178, 184 Laibson, David 68, 83, 459 laissez-faire 69 Lakdawalla, D. 439–40 Landa, J.T. 241 Larkin, J.H. 114 lateness model (rational mistakes) 52–4 leadership, and social evolution 553 learning indirect 377

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Index simple heuristics of 130 social 533–4 see also human development Leavitt, H. 162 Leibenstein, H. 23–4, 35, 275–6, 278, 279–80 Lester, D. 466 Lester, Richard 72–3 Levinson, D.J. 147, 148–9 Levy, A. 433–4 Lewis, D. 535 Lien, D. 58 life-cycle hypothesis 394–5, 398, 401 List, John 68, 83 Lobell, D. 264 Loewenstein, G. 321, 497 Loomes, G. 197 Loureiro, M.L. 441–2 love 568, 570–71 models of 212–25 loyalty 205–7 and abstraction 225–6, 228–9 models of 207–25, 227–30 organizational identification 158–9, 161–2, 164–5, 168 Luo, D. 283 Machlup, Fritz 73 macroeconomic choices 34–8 Malaysia, x-efficiency of banks 285 Malinowski, B. 235–8, 239, 240, 241, 242, 243 Malmendier, U. 321 ‘mammography problem’ 112, 113, 114 management, and behavioral strategy 157–71 management science, and ecological rationality program 129 managerial slack 24–5 Mancino, L. 436 Mandler, M. 125 Manzini, P. 125 March, J.G. 19, 162 marginal decision-making 72–3 Marginal Revolution 70–71 Marine Corps (US), behavioral strategy 163–9 Mariotti, M. 125 market failure, and true preferences 30 markets, experimental, see experimental asset markets Markowitz, H.M. 108 Maslow, A. 139 mathematization of economics 63 Mauss, M. 238, 239 McCloskey, D.N. 149–50, 559, 560, 561, 562 Meiser, T. 91 memory, and rational mistakes 54–5 mental accounting 353–4

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Mervin, M. 224 Milner, B. 192 MINDSPACE (mnemonic) 324 minimal-group paradigm 487 Mintzberg, H. 169 Mises, Ludwig von 68, 70, 71, 72, 82–3, 277 mistakes, rational 68 absence of paradigm shift in behavioral economics 64–6 axiomatization program in mathematics 61–6 bad jokes model 43–52 best organ in the human body example 60–61 bias-variance trade-off 55 games and nature 54–5, 56–7 lateness model 52–4 lexicographic preferences 56 markets and social systems 57–9 and memory 54–5 and perception 54 singular versus plural norms 59–60 strategic games against self-interested competitors 57 Modigliani, F. 394 money illusion 36–7 money management, individuals and households 353–4, 356, 360 morality 546–7, 551–2, 557, 559, 560–62 Mousavi, S. 103, 105 Moyano-Diaz, E. 196 Moyo, M. 252 Mundell, R. 279 Murphy, K.M. 436 Nash equilibrium and morality 557, 561 public goods games 58–9 and social norms 529–30 see also ethics, game theoretic model National Tax Advocate 320 negotiated coexistence 517 Neighborhood Watch 516, 517, 520 game theoretic illustration 520–23 Nelson, J. 173, 177, 178, 179–81 neurodevelopment 140, 141, 145–6 Newcomb problem 197–9 norms, social, see social norms North, Douglas 16–17, 78, 80–81, 526, 532, 533, 534 Noussair, C.N. 378, 381 Nozick, R. 198 NUDGE (mnemonic) 323, 324 nudging 31–2, 322–4 versus boosting 129

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and taxation 317–18, 322–8 and uncertainty 109–11 Nussbaum, M. 31, 34 obesity 429–30 behavioral aspects 432–44 causes 432–3 economic consequences 432 health consequences 430, 432 prevalence 430 see also physical activity and weight loss, intertemporal choices obsessive-compulsive behavior 496, 503, 504; see also compulsivity, and stiffness of will occupation, and taxation compliance 335 O’Donoghue, T. 460 Offer, A. 441 oligopoly 293 Oliviola, C.Y. 331–2 Olson, Mancur 14, 17–18 optimality, decision-making 1–2 organization science, and ecological rationality program 129 organizational identification 158–9, 161–2, 164–5, 168 organizational routinization 159 organizational transformation 159, 166–7, 169 organizations, behavioral strategy 157–71 Ostrom, Elinor 75–6, 77, 78, 81, 174–5 administrative rigidity problem 290 common pool resources 302–8 efficient public administration 292 public goods, preferences for 293 punishment 529 Ostrom, V. 292, 293 overconfidence 58; see also impulsivity, and weakness of will oxytocin 487 Pakistan, x-efficiency of banks 286–7 path dependency, institutions 78–82, 534 Payne, J.L. 325 Pejovich, S. 80 perception 54, 526–7, 531 permanent income hypothesis 394, 395 and suicide 465–6 Perry, B. 145–6 personal capital 518–19 Peterson, C.R. 112 Philipson, T.J. 438–9 Phillips, L.D. 112 physical activity and weight loss, intertemporal choices 449–50, 461–2 BMI and participation in sports and exercise 453–7

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intentions to improve health 450–53 time inconsistent preferences 450, 457–61 Plato 558 Plumley, A. 335–6 Polanyi, K. 234–5 policing, and crime prevention 515 polycentricity, see governance structures, polycentric system pornography (weakness of will) 510–11 Posner, R.A. 28, 438–9 poverty, and obesity 440–41 Powell, H.A. 239 Powell, O. 381, 386–7 Powell, T. 160, 161, 170 power and beliefs 227–30 and the mind 223–4 and tax authorities 339–42 see also loyalty, models of praise 562, 563 Prasch, Robert 72, 73 preference ordering, axiomatization program 62, 63 preferences biased-preferences approach 496–7 and gender 177–8 revealed 29–30, 32 social 551–2 time, and obesity 436–7 time inconsistent 450, 457–61 true 29–30, 31, 32 Preis, T. 88 Preston, A. 181 price bubbles, see experimental asset markets prices, and obesity 440 priming, financial decision-making 366–74 priority heuristic 107 private persona 550, 554 private sphere (rational actor model) 544, 547, 554 probabilistic reasoning 111–14 probability conflicts 355 procedural models, ecological rationality program 127–8 process rationality 21 production inefficiency 23–9 profits, assumptions 13, 74–5 property rights 81 prospect theory axiomatization program 62–3 taxation compliance 335–6 psychosocial development 139–40 public administration, see governance structures, polycentric system

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Index public economics competition, models of 292–3, 295 markets, concept of 291–2 scale of public services 302–8 public goods 58–9 public messages, as coordination device 385–6 public persona 550, 552–3 public sphere (rational actor model) 543–4, 547, 548–50 punishment 485, 529 altruistic 238–9, 529 and beliefs 226–30 slackening versus shirking 505 purchasing decisions, individuals and households 350–52, 354–6, 359 Rabin, M. 460 randomization 58 Rashad, I. 437, 440 rational actor model motives 547, 550 private and public persona 550 public persona, evolutionary emergence of 552–3 public sphere 543–4, 547, 548–50 social preferences 551–2 universalist principle of honesty 547–8 voting 543–6, 553–4 rational choice, human element, see human decision-making rational inefficiency 12–16, 17–18 rational lifetime planning, obesity 433–4, 437 rational mistakes, see mistakes, rational rationality 3 bounded 1, 2–3, 19–21, 145, 493–4 calculation of deviations from 126–7 core assumptions 18–19 ‘greedy rationality’ 207–11, 224 rhytonian 216–21, 223, 224–5 Smith, Adam, on 497 types of 18–22, 39–40, 216–18 see also ecological rationality; ecological rationality program (ERP) rationality and efficiency 11–16, 38–40, 275–6 consumption inefficiency 29–34 institutions 16–18 macroeconomic choices 34–8 narrow and broad rationality 277–9 production inefficiency 23–9 Rawls, J. 560 reasoning by similarity 128 reciprocity 238–9, 480–81 Reder, M.W. 121 reflexive utility 508–9

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regret 188–91, 202 as coordination device and social mechanism 199–202 in decision-theory 194–9 neurobiological basis of 191–4 relationships bad jokes model 43–52 lateness model 52–4 religion 496, 559 and loyalty 226–30 representative heuristic 501–2 reputation 480–82, 529 Reskin, B. 178 revealed preferences 29–30, 32 Rezvanian, R. 282 rhytonian rationality 216–21, 223, 224–5 Richerson, P.J. 533 Rigaux, B. 355, 357, 359 Rijt, A. van de 481 risk and adaptation decisions 252, 265, 266–7 experimental asset markets 378 gender-based differences in attitudes to 179–81 and investment decisions 353, 356, 367–70 literacy 111–14 and priming 367–70 rituals 496 Roberts, R.C. 149, 150 Robinson, W. 87 Rosin, O. 438 routines 493–4 Ruhm, C.J. 440 rule-consequentialism 549 rules 76–8, 526, 527, 528–9 and culture 534–5 evolution of 535–8 simple rule-following 392–3 see also social norms Russell, Bertrand 62, 63 Rustichini, A. 333–4 Sallis, J.F. 441 sampling, and mistakes 58 satisficing 19, 20 and regret 196–7 social welfare improvements 57 Savage, L.J. 195 savings and consumption 393–4, 395 consumption-savings model 396–405 decisions, individuals and households 352–3, 356, 359–60 scale, public services 302–8 Schaik, C. van 552–3

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Handbook of behavioural economics and smart decision-making

Schelling, T.C. 535 Schmölders, G. 325 Schram, A. 480–81, 481–2 Schwartz, B. 196–7 Scoditti, G. 240 seed selection, see climate change case study (farmers’ decision-making) Seinen, I. 480–81 Self, notion of 210–11, 559 changing 212 dual-self theories 500 and models of love 212–13, 221 and types of rationality 216–18 self-control, and commitment problem 459–61 self-interest 71–2, 74, 142, 235, 562–3, 564 self-sacrifice 559 Selten, Reinhardt 62, 65, 130 Sen, A. 31, 34, 559, 561, 564 Sensory Order (Hayek) 526–7, 531, 537 Sent, E.-M. 121 Shafir, E. 198 shares 385 Sheehy, G. 147 Shefrin, H.M. 460 Shiller, R.J. 28, 111 Shin, D. 286 shirking (weakness of will) 498–504 shocks (weakness of will) 505–6, 507, 509, 510–11 similarity, reasoning by 128 Simon, H.A. behavioral strategy 161–2 bounded rationality 19, 21 economic man 142 habits and routines 493–4 institutions 16 psychology 101, 115 rationality 3, 20–21, 277, 278 smart person 145 social norms 528 Simpson’s paradox 90–91, 93 slackening (weakness of will) 505–6 smart decision-making, summary 1–7 Smith, Adam duty 564 Hayek on 82 injustice 563 morality 560–61 praise 562, 563 rationality 277, 497 self-interest 562–3, 564 value 242–3 ‘view of man’ 83 virtue 558, 559, 560, 561, 563–4 Smith, P.K. 437

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Smith, T.G. 441, 442 Smith, Vernon 19–20, 76, 102–3, 104, 105, 121, 528 smoking, and discounting 415 social capital 518–19 social cohesion 517, 518 social control 74 social dilemmas 338–9 social evolution 552–3 social identity 519, 520 game theoretic illustration, community crime prevention (CCP) 520–23 social interactions 479–80, 548–50 knowing others 486–8 modes of 550 reciprocity 480–81 reputation 480–82 seeing others 482–5 speech 485–6 trustworthiness 482–4 social learning 533–4 social norms 526, 528–30 and community crime prevention (CCP) 516–17, 520–23 and economic man 561 and identity 519 and obesity 443–4 tax compliance 326–7, 338–40 traditional societies 243–4 social order 71–2, 77 social preferences 551–2 sociological variables, and choice behavior 15 Soss, N.M. 465–6 South Korea, x-efficiency of banks 286 Specter, S.E. 440 speech 485–6 spending decisions, individuals and households 350–52, 354–6, 359 Spinoza, B. 188 stag hunt game 566–8 Stahl, D.O. 126 Stalker, J. 559 statistical discrimination 482 stiffness of will, see will, weakness and stiffness of Stigler, G. 559 Stöckl, T. 386–7 Strassels, P.N. 325 strategy, behavioral, see behavioral strategy Strathman, A. 416 Strotz, R.H. 458 Strümpel, B. 325 subjective expected utility (SEU) theory 142, 277

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Index success, desire for 502 Sugden, R. 197 Suhonen, N. 195 suicide, and intelligence 464–5 cost–benefit analysis 466–9 creativity 472–3 delay discounting 470–72 executive functioning 472 income 473 microeconomic theory of 465–6 Sunder, S. 127 Sunstein, C. 31, 32, 323, 326 survival 14, 20 Sussman, A.B. 331–2 Taiwan, x-efficiency of banks 285–6 Tajfel, H. 487 taxation fairness 321–2, 339 and nudging 317–18, 322–8 and obesity 435 size and extent of 318–20 see also endogenous quality jurisdictions model; income tax, compliance technological change, and obesity 438–40 tee (ceremonial practice) 244 Thailand, x-efficiency of banks 285 Thaler, R.H. 31, 32, 121–2, 323, 325, 353–4, 458, 460 Tideman, T.N. 201 time hyperbolic discounting, obesity 437–8 and investment diversification 353 preference, obesity 436–7 rational lifetime planning, obesity 433–4, 437 treatment of 211 utility maximization, intertemporal 394, 395–6, 397–8 see also love, models of time inconsistent preferences, physical activity and weight loss 450, 457–61 time orientation 413–17, 424–5 consideration of future consequences 416–17, 421, 422–4 discounting 414–15, 417, 419–21, 422–3 domain differences in 419–21 and health behavior 413, 422–4 relationship between measures 417 time perspective 415–16, 421 Todd, P.M. 20 Torgler, B. 322 trading, asset markets, see experimental asset markets Treatise on Human Nature (Hume) 12–13

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true preferences 29–30, 31, 32 trust as social capital 518 and social interactions 482–4 universalist principle of honesty 547–8 Tullock, G. 306, 307 Tversky, A. 3, 21–2, 112, 121, 122, 198 Uberoi, J.S. 241 uncertainty and behavioral strategy 168 and decision-making 105–7 and nudging 109–11 unemployment 35–8 and birth rates 86–7, 88–9 US Marine Corps, behavioral strategy 163–9 utilitarianism 566 utility altruistic 507 early representation theorems 63 gift-giving 214 identity 519 reflexive 508–9 and regret theory 195–7 subjective expected utility (SEU) 142, 277 types of 496–7 utility maximization and core assumptions, conventional economics 19 discounting 458 and freedom of choice 30–31 heuristics 125 intertemporal 394, 395–6, 397–8 loyalty, models of 207–25 and obesity 433–9 and social inefficiency 18 and suicide 465–6 value conflicts 355 Veblen, T. 69, 72, 74 Vesterland, L. 182 virtues 149–51, 558–60, 561, 563–4 Vogel, T. 89, 92 vote-buying 483 voting classical rational actor model 543–6 motives 550, 553–4 and regret 200–202 rule-consequentialism 549 wages, and unemployment 35–8 Wansink, B. 442, 443–4 Wason selection task 103 weakness of will, see will, weakness and stiffness of

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wealth, consumption-savings model 396–405 Weatherly, J.N. 419, 420–21 Whitehead, A.N. 62, 63 Wilber, K. 138, 140–41, 143, 151 will, weakness and stiffness of 492–6 favoritism 506–8 literature review 496–8 pornography 510–11 shirking 498–504 slackening 505–6 spoiling of children 508–10 wishful thinking 502–3 Wood, W.J. 149, 150 Wu, H. 282–3 x-efficiency 37, 275–6 choice 29–31

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definition 279 reason for existence 278–9 studies on 280–87 x-inefficiency 23–6, 275, 276 choice 30 extent of 279 reason for existence 278–9 Yang, B. 466 Yaniv, G. 434–5 Yao, S. 281–2, 283 Yoeli, E. 482 Young, H.P. 190 Zhang, L. 437 Zimbardo Time Perspective Inventory (ZTPI) 416, 417, 422–3

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