Methods And Interdisciplinarity [01] 1786304880, 9781786304889

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Methods and Interdisciplinarity

Modeling Methodologies in Social Sciences Set coordinated by Roger Waldeck

Volume 1

Methods and Interdisciplinarity

Edited by

Roger Waldeck

First published 2019 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK

John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA

www.iste.co.uk

www.wiley.com

© ISTE Ltd 2019 The rights of Roger Waldeck to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2019946191 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-488-9

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Roger WALDECK Chapter 1. Promoting and Experimenting with Interdisciplinarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pierre LIVET 1.1. Iméra project (Institut méditerranéen d’études avancées – Mediterranean Institute of Advanced Studies) . . . . . . . . . 1.2. Testing the typology of interdisciplinarity . . . . . . . . . 1.2.1. From formalisms to models and experiments . . . . 1.2.2. Interacting with cross-disciplinary learning and instrumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3. Interdisciplinarity of competing hypotheses and experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.4. Reflective intertemporal interdisciplinarity . . . . . 1.2.5. Interactions by combining disciplines . . . . . . . . . 1.2.6. Interdisciplinarity of reciprocity between contexts . 1.2.7. Transdisciplinarity between science and the reception of science . . . . . . . . . . . . . . . . . . . . 1.2.8. Transdisciplinarity between arts and sciences . . . . 1.3. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 2. Geography and Computer Science: Reasons for a Marriage, a Marriage of Reason? . . . . . . Denise PUMAIN 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Computers and numbers: quantifying geography . . 2.2.1. Diversity of practices . . . . . . . . . . . . . . . . . . 2.2.2. Epistemological changes driven by computer science rather than conceptual borrowings . . . . . . . . 2.3. Simulation in geography and algorithmic thinking . 2.3.1. A difficult path . . . . . . . . . . . . . . . . . . . . . . 2.3.2. Towards a win-win collaboration . . . . . . . . . . 2.3.3. Geography in all digital objects . . . . . . . . . . . 2.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. References. . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 3. Conceptual Modeling and Multidisciplinary Dialogue . . . . . . . . . . . . . . . . . . . . . Jean-Pierre MÜLLER

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3.1. Introduction . . . . . . . . . . . . . . . . . . . 3.2. Representation of theoretical discourses . 3.3. Disciplinary views on species . . . . . . . . 3.4. Sectors and qualities . . . . . . . . . . . . . . 3.5. Validation and communicability . . . . . . 3.6. Conclusion . . . . . . . . . . . . . . . . . . . . 3.7. References. . . . . . . . . . . . . . . . . . . . .

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Chapter 4. Network Analysis: Linking Social and Ecological Dynamics . . . . . . . . . . . . . . . . . . Vanesse LABEYRIE, Sophie CAILLON, Matthieu SALPETEUR and Mathieu THOMAS 4.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1. Societies-environment interactions, what complex systems? . . . . . . . . . . . . . . . . . . . . . . 4.1.2. Introduction to network formalism . . . . . . . . . . 4.2. Examples of applications to the study of interactions between societies and the environment . . . . . . . . . . . .

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4.2.1. Crop seed circulation and social networks 4.2.2. Circulation of knowledge and structuring of know-how . . . . . . . . . . . . . . . . 4.3. Discussion: a necessary link between the quantitative and the qualitative . . . . . . . . . . 4.4. References . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 5. Interdisciplinarity and VUCA . . . . . . . . . . . Roger WALDECK, Sophie GAULTIER LE BRIS and Siegfried ROUVRAIS

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5.1. Introduction . . . . . . . . . . . . . . . . . . 5.2. Decision theory . . . . . . . . . . . . . . . . 5.3. An interdisciplinary look at VUCA . . . 5.3.1. VUCA definitions in management . 5.3.2. Definitions from decision theory . . 5.4. Discussion . . . . . . . . . . . . . . . . . . . 5.5. References . . . . . . . . . . . . . . . . . . .

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Chapter 6. Learning Methodology for VUCA Situations . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Sophie GAULTIER LE BRIS, Siegfried ROUVRAIS and Roger WALDECK 6.1. Engineering education & training and highly reliable organizations . . . . . . . . . . . . . . . . . . 6.2. Issues at stake . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1. VUCA phenomenon classes . . . . . . . . . . . . . . . . 6.3. Theoretical framework of organizational reliability . . . 6.3.1. Running highly reliable and actionist organizations 6.3.2. Selected models . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Cross-disciplinary decision-making skills: design-oriented research . . . . . . . . . . . . . . . . . . . . . . . 6.4.1. Research methodology for learning . . . . . . . . . . . 6.4.2. From model to reality . . . . . . . . . . . . . . . . . . . . 6.4.3. Learning outcomes . . . . . . . . . . . . . . . . . . . . . . 6.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6. Appendix: level of experience and feedback from IMTA students . . . . . . . . . . . . . . . . . . . . 6.7. References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 7. Approaches to and Applications of Graphemics . . . . . . . . . . . . . . . . . . . . . Yannis HARALAMBOUS

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7.1. Writing and linguistics . . . . . . . . . . . . . . . . . . . 7.2. Spectral decomposition to the rescue of linguistics . 7.3. Application in biometrics . . . . . . . . . . . . . . . . . . 7.4. Application in steganography . . . . . . . . . . . . . . . 7.4.1. Steganographic approach to Greeklish . . . . . . 7.4.2. Steganographic method: evaluation . . . . . . . . 7.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6. References. . . . . . . . . . . . . . . . . . . . . . . . . . . .

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List of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Preface

Preamble This book is a follow-up to the 2018 winter school’s presentations entitled “Rencontres interdisciplinaires sur les systèmes complexes naturels et artificiels” (Interdisciplinary Meetings on Natural and Artificial Complex Systems) held each year in Rochebrune, France. Interdisciplinarity is at the heart of the Rochebrune meetings and participants from all disciplines gather for five days in a unique place in the heart of the Alps to discuss a theme that changes every year. The result is a framework conducive to interdisciplinary exchange and questioning, whether on practices and methods or on objects. The disciplines present at Rochebrune generally agree that their object of study has the characteristics of complex systems, with the implication that the points of view between different disciplines on the same object may be interdependent or conducive to mutual enrichment. Rochebrune is therefore a privileged place for interdisciplinary dialogue, which makes it possible to shift disciplinary boundaries in a mutual enrichment of perspectives. The theme of Rochebrune 2018 was “Methods and Interdisciplinarity” and questioned a vision of disciplines trying to establish relationships between objects,

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methods, and finally points of view. This book includes a selection of the school’s presentations. Methods and interdisciplinarity The theme of the Rochebrune thematic course on the study of artificial and natural complex systems was “Methods and Interdisciplinarity” in 2018. The school’s stated aim was to understand the mechanisms of cooperation and hybridization between disciplines, and in particular the mechanisms for transferring methods or knowledge from one discipline to another. These are indeed central questions of interdisciplinarity. This growing need for new methods and cross-fertilized and multi-perspective knowledge on the same object of study and the hybridization process of one discipline with another are not independent, on the one hand, of the emergence of complex problems related to multiform objects of study and, on the other hand, on a stronger demand coming from society. This requires more cooperation between scientists than in the past. The complexity of an object of study comes precisely from the fact that the phenomena associated with it are not independent of each other. Interactions between different facets of the subject under study are central and make interdisciplinarity necessary as a corollary of complexity. For example, economists have long admitted that research on price formation can be done without considerations on the functioning of markets. The separation between sociologists involved in the study of interactions and economists involved in the study of price formation has prevailed for some time. However, the two phenomena, namely market structure and pricing, are not independent. To define interdisciplinarity, it is already a matter of defining what a discipline is. Edgard Morin (1994) characterizes a scientific discipline by (translated from French):

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the division and specialization of labor [...]. Although encompassed within a broader scientific framework, a discipline naturally tends towards autonomy, through the delimitation of its boundaries, the language it constitutes, the techniques it is led to develop or use, and possibly through its own specific theories. The discipline therefore creates its object of study and designs its methods. Disciplinary confinement can be detrimental both because the object of study cannot be contained in the frame and concerns of a single discipline, but also because in setting its conceptual framework, a discipline neglects facets or solutions outside that framework. Nevertheless, despite an institutional segmentation of disciplines, their knowledge and methods are, alike biological mechanisms subject to perpetual evolution under the mechanisms of selection, hybridization, and mutation: less efficient theories are replaced by better ones and hybridization between disciplines enriches existing theories in successive steps without challenging however the boundaries of the discipline field; the novelty appears through mutation, that is a reinterpretation and adaptation in the new discipline of observations, methods or knowledge from another discipline. In this sense, interdisciplinarity is permanent and occurs mainly at the margins, in particular due to conformism and preservation instinct from the scientific community. Economics is a good example of hybridization. Homo economicus, a seminal concept of microeconomics, is slowly adapting to integrate criticism coming from experimental psychology or Simon’s concept of bounded rationality. Neoclassical theory is becoming more flexible by integrating the contribution coming from game theory or institutional economics. However, other radically different theories are sometimes proposed and there is then a coexistence without

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one theory taking precedence over the other. For example, the evolutionary theory of the firm, the philosophy of which is derived from Darwinian theory, proposes an alternative model to the rational theory of the firm. Interdisciplinarity is therefore not simply a matter of a scientific research context comprising several disciplines. It is in contrast to the concept of multidisciplinarity, where each discipline operates in isolation, whereas in interdisciplinarity the desirable objective would be to answer a question by transcending disciplines in order to produce integrated knowledge. Barry et al. (2008) propose the following synthetic formulation on how to consider the relationships between disciplines: Commonly, a distinction is made between multidisciplinarity – several disciplines cooperating but without altering their standard disciplinary frameworks – and interdisciplinarity – for which an effort is made to integrate or synthesize perspectives from different disciplines. In the case of interdisciplinarity, it is indeed a question of considering a cross-fertilization between disciplines that is mutually enriching. Multidisciplinarity is a rapprochement of disciplines around an object either by complementarity of approaches or by complementary points of view on the object, but each one keeping its methods and frameworks. Interdisciplinarity would therefore consist of bringing together knowledge, methods, and experiments between disciplines, with the aim of making them coherent and articulating knowledge around the study of an object or question (Ramadier 2004). In return, each of the disciplines is enriched by this experience, either in its methods, theories or experiments. Chapter 1 by Livet analyzes precisely the different forms that interdisciplinarity can take. He presents a variety of

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interdisciplinary modes of interaction through examples from the Mediterranean Institute for Advanced Studies and proposes a typology of these modes of interaction. Notably, he shows cases of interdisciplinarity: by transfer of methods, with cross fertilization of points of view, with confrontation of theories through experimentation, with recursive loops (one discipline being the subject of studies of the other), with a combination of experimental methods, and finally interdisciplinarity by sharing fields/objects, but without sharing methods. Livet speaks, in this case, of interdisciplinarity by convergence on interrelated phenomena, which is a form of multidisciplinarity, each discipline keeping a certain autonomy from the others except that the results of one discipline affect the perspectives of the other disciplines. In Chapter 2 Pumain analyzes the evolution of geography following the emergence of new processes related to computer science and digitization in her discipline. The main question concerns the epistemological transformations of geography due to the new practices associated with digitization. Pumain considers three main steps in this digitization: an instrumental use of computational tools by geography, particularly for data analysis and processing, then a partial assimilation of computer concepts adapted to geography, and finally a feedback interaction where dimensions of geography are used in computer productions. In Livet’s categorization, it would be a question of interdisciplinarity through the interpenetration of methods. The following two contributions (Chapters 3 and 4) show the importance of knowledge representation formalisms in the interdisciplinary dialogue. Going back to the categories of Pierre Livet, they involve both interactions between researchers by combining disciplines and the application of existing formalisms coming from one discipline to other fields of study.

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Labeyrie, Caillon, Salpeteur and Thomas mobilize network analysis for the study of socio-ecological systems. Three examples are presented and discussed, showing how network analysis can articulate qualitative and quantitative approaches. The first case concerns the link between seed genetics and social anthropology with the aim of testing the effect of ethnolinguistic organization on seed circulation. In the second case, social network analysis allows food plants with different biocultural status within a community in Vanuatu to be monitored through trade networks. The third example examines how formal (migration groups, gender) or informal (friendships) relationships impact the transmission of local naturalistic knowledge within nomadic herder groups in India. Müller dissects the process of building knowledge on the same object from several points of view. To do this, he uses the UML (Unified Modeling Language) formalism to represent visions of several disciplines (botany, phylogenetics, organoleptics) on Madagascar pepper. Müller shows how the confrontation of different ontologies allows for: – first, clarifying the debates on concepts shared by different disciplines, illustrated here by the notion of species; – second, articulating complementary points of view to facilitate dialogue, illustrated here by the articulation of the notion of sector with the notion of quality chain. The classifications made by each of the disciplines do not lead to a unified vision of the notion of species, which is above all a social construct. Müller also discusses the dialogue between modelers and scientists, particularly on the construction of an ontology in an approach involving the participants. Müller’s interdisciplinary discourse emphasizes both the contribution of a methodology of knowledge

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representation coming from the computer sciences that allows dialogue between different stakeholders, but also shows how different disciplinary points of view on the same subject challenge the points of view of other disciplines, that is an interdisciplinarity by convergence on interrelated phenomena. In Chapter 5 Waldeck, Gaultier Le Bris, and Rouvrais compare the points of view of different disciplines on the same subject: decision-making in VUCA environments (for volatile, uncertain, complex, and ambiguous). They aim to show how different disciplines whose object of study is decision-making define differently the same VUCA concepts. A formalism already developed by the decision sciences is used to define the VUCA concepts and comparison with the definition used in the management sciences is made. The stake of the comparison is a possible convergence to a future common theoretical corpus on VUCA decision-making. In Chapter 6 Gaultier Le Bris, Rouvrais, and Waldeck discuss how VUCA concepts can be used in an educational environment. The interaction goes in both directions: VUCA concepts give rise to the implementation of pedagogical experiments and pedagogical experiments lead the experimenter to specify how VUCA concepts should be instantiated. We can speak of interdisciplinarity through the interpenetration of methods, here between methods relating to the “Design Research” approach in educational pedagogies with formalisms drawn from the management sciences and applied to VUCA. In Chapter 7 Haralambous deals with a little-known discipline, which has been neglected by Saussurian linguists, in favor of phonology: graphematics. After a short introduction to this discipline and its tribulations, he proposes three approaches to graphematics from three different disciplines: mathematics with a spectral

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decomposition of graphemes that allows for, without any information other than their combinations, finding the “consonants” and “vowels”, biometrics which, based on precise measurements of time differences between keyboard keys pressed when writing a German word, makes it possible to find its morphological structure, and steganography, where the variability of the transcription from the Greek to the Latin alphabet can be used to hide information in it. The authors Sophie Caillon Sophie Caillon, an ethno-ecologist, questions the interactions between humans and non-humans, particularly in agricultural environments between Vanuatu and the Gaillac wine-growing area. She integrates a diversity of qualitative and quantitative approaches to address topics as diverse as biocultural diversity, seed circulation, indicators of well-being or attachment to place. Sophie Gaultier Le Bris Sophie Gaultier Le Bris is a lecturer in management sciences at the École Navale where she has been teaching future officers since 2005 as head of academic leadership training. Her research focuses on decision-making in complex environments with a focus on increased reliability and resilience. She has founded a “Resilience and Leadership” Research Chair – in partnership with UBO, Université de Rennes 1, and with the support of Naxicap, Safran, and the Banque Française Mutualiste – from where she graduated, and which was launched at the beginning of the 2018 academic year.

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Yannis Haralambous Yannis Haralambous is a professor-researcher in the computer sciences department of IMT Atlantique and a member of the DECIDE team of the Lab-STICC laboratory of the CNRS. His research area covers automatic language processing, text mining, knowledge management as well as graphics and digital typography. Vanesse Labeyrie Vanesse Labeyrie is an agro-ecologist at CIRAD, attached to the UR GREEN “Management of Renewable Resources and Environment”. She studies plant diversity management practices in agricultural landscapes in West Africa and Madagascar, as well as their transformations in response to global changes. Her research aims in particular to characterize the networks of plant material and agroecological knowledge circulation in rural societies practicing family farming, and to analyze their role in these agrarian transformations. Pierre Livet Pierre Livet is Professor Emeritus of Philosophy and a member of the CNRS Centre Gilles Gaston Granger team. His research focuses on the epistemology and ontology of the social sciences (sociology, economics) and cognitive sciences, the theory of action, emotions, and interactions. Jean-Pierre Müller Jean-Pierre Müller is a scientific officer at the Centre de Coopération internationale pour la recherche en agronomie pour le développement (CIRAD) in the Unité propre de

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recherche (UPR) “gestion des ressources naturelles et environnement” GREEN (management of natural and environmental resources), and a specialist in artificial intelligence and multi-agent systems. His research focuses on the modeling of complex systems, more specifically socioenvironmental systems, and on the modeling process itself, from disciplinary discourse to simulations and multi-agent models. Denise Pumain Denise Pumain, Professor Emeritus of Geography, is cofounder of the Geography-Cités laboratory and creator of the electronic journal Cybergeo in 1996. Author of an evolutionary theory of city systems, she transfers concepts and models of complex systems (self-organization, laws of scale, spatio-temporal dynamics) to geography. Siegfried Rouvrais Siegfried Rouvrais holds a doctorate in computer science from the Université de Rennes. He is a researcher in systems and software engineering, member of AFNOR, member of the IEEE Senior, member of the research lab Lab-Sticc and expert with the Commission des titres d’ingénieur. He has devoted many years to teaching engineering at a leading general engineering school, IMT Atlantique. More recently, he has been interested in models and methods for the improvement of higher education training, at the crossroads of many points of view, particularly through European and international projects. Matthieu Salpeteur Matthieu Salpeteur is an anthropologist at the IRD, attached to the UMR PALOC “Local Heritage, Environment

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and Globalization” (IRD-MNHN). He mobilizes a mixed methodology to study contemporary transformations of pastoral systems in India, facing a range of social and environmental changes. Mathieu Thomas Mathieu Thomas is a geneticist at CIRAD, part of the AGAP research unit “genetic improvement and adaptation of Mediterranean and tropical plants” (INRA-CIRAD-SupAgro). He studies the impact of individual and collective agricultural practices on the evolution of cultivated diversity in Western Europe and West Africa. He uses modeling by combining population genetics approaches (meta populations) and statistics (network analysis) and collaborates with ethnologists and anthropologists specializing in agricultural seed circulation. Roger Waldeck Roger Waldeck is an associate professor at IMT Atlantique and a research member of the Laboratoire d'Économie et de Gestion de l’Ouest (LEGO). His current research focuses on methods for modeling and analysing complex social systems. Acknowledgements Sophie Gaultier Le Bris, Siegfried Rouvrais, and Roger Waldeck received support from the European Union’s Erasmus+ program1. Their chapters only reflect the authors’ point of view. The Commission is not responsible for any use

1 DAhoy Project, DecisionShip Ahoy, number 2017-1-FR01-KA203-037301, www.dahoy project.eu.

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that may be made of the information contained in these chapters. References Barry, A., Born, G., Weszkalnys, G. (2008). Logics interdisciplinarity. Economy and Society, 37(1), 2049.

of

Lawrence, R.J., Després, C. (2004). Futures of Transdisciplinarity. Futures, 36(4), 397–405. Lyall, C., Bruce, A., Marsden, W., Meagher, L. (2013). The role of funding agencies in creating interdisciplinary knowledge. Science and Public Policy, 40(1), 62–71. [Online]. Available at: https://doi.org/10.1093/scipol/scs121. Morin, E. (1994). Sur l’interdisciplinarité. Ciret. [Online]. Available at: http://ciret-transdisciplinarity.org/bulletin/b2c2.php#. Ramadier, T. (2004). Transdisciplinary and its challenge: The case of urban studies. Futures, 36(126), 423–439. Roger WALDECK August 2019

1 Promoting and Experimenting with Interdisciplinarity

1.1. Iméra project (Institut méditerranéen d’études avancées – Mediterranean Institute of Advanced Studies) Exploring the possibilities of interdisciplinarity in the human and social sciences, and trying to give the maximum openness to interdisciplinary interactions, by extending them to the relations between the arts and sciences and also between formal and experimental sciences and humanities, giving university teams opportunities to develop these perspectives by inviting foreign researchers who have already produced interesting work in these inter-domains, this is Iméra’s ambitious, risky but exciting project. Iméra was founded by Robert Ilbert, historian and director of the Maison méditerranéenne des sciences de l'homme (Mediterranean House of Human Sciences). His goal was to strengthen and develop interdisciplinary research, first in the field of human and social sciences (HSS) and particularly in research on fields related to the Mediterranean, in collaboration with researchers from its various shores. Chapter written by Pierre LIVET.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Iméra is part of the network of institutes of advanced studies in HSS, with Nantes, Paris, and Lyon. After a period of project preparation and seeking premises, it started operating in 2008 and recently celebrated its tenth anniversary. It welcomes residents for a stay of five or ten months. Initially, these residents were from France; now they are mostly foreigners. This institute is backed by the University of AixMarseille, and its “excellence investment” AMidex, which has made it an “explorer of interdisciplinarity”1. Here, exploration means: opening up research avenues that are effectively interdisciplinary while making a useful contribution for researchers from different disciplines; feedback on interdisciplinary work already carried out in different laboratories at the university; analysis of the conditions that have enabled these links between disciplines to be established; study of the difficulties encountered, which may be problems in learning different methods, in adjusting these different methods reciprocally, in redefining the problems, but which may also be due to difficulties in misunderstanding by colleagues who are more focused on their discipline, and to disparate evaluations of grant applications due in particular to differences between evaluators who are each focused on their discipline. This exploration also takes into account the problems of disseminating interdisciplinary results to audiences open to the different sciences and who, unlike previous evaluators, expect decisive breakthroughs from them, and are disappointed when these studies simply make plausible new avenues still to be explored in depth. In the first period, our call was “open” to all researchers, with quite varied successes. The field of the history of science

1 It is also integrated into the network of “University based” institutes of advanced studies (UBIAS).

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has been productive and in line with the Iméra project, because it requires researchers to have a dual competence, in history and philosophy of science and in a particular science, which ensures an interdisciplinarity that is not superficial. Our goal of ensuring fruitful interactions between residents and laboratories has not always been successful. Sometimes high-level residents have not elicited any interaction, in particular a good quantum physics philosopher and supporter of Everett’s many-worlds theory who was met with a lack of interest or even resistance from university physicists – one of whom is nevertheless very open to philosophy. Interfaces between history, sociology, and political science have been possible. We also welcomed a physicist who had switched to network analysis (formal neurons and deep learning). Many of the artists hosted were sonifiers; sonification is a method that does not necessarily imply knowledge of the scientific discipline whose sounds on various instruments are supposed to mimic the recording curves, EEGs, seismograph recordings, etc. One success, however, was a duo between a fluid physicist and a videographer, both of whom were studying the waves. The physicist made the videographer more sensitive to differences in wave dynamics and the videographer revealed phenomena to the physicist that had not been noticed before. The relationship between the arts and sciences can sometimes be surprising. Thus, in a supposedly interdisciplinary session between art and mathematics, art was represented by Bernard Venet, who told us very honestly that when he started displaying enlarged reproductions of pages of math books – those with figures – like paintings, he had no knowledge of mathematics, nor indeed any interest in them. He only wanted to show that even math could be produced as art, without any real intervention by the artist, other than selecting the page.

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More elaborate seminars were held for artists on themes related to the field of experimental sciences, representations of dynamics of life, study of the relationships between plants, soils, winds and sunlight, and in relation to the field of SHS, problems of representation of social dynamics, in particular the renewal of the relationships between migration and borders. We had also tried with Robert Ilbert to propose themes that seemed to us to be conducive to interdisciplinary research with mathematicians and physicists. A good example is network research (physicists, urban planners, transport analysts, neuromimeticians, sociologists, etc.). This theme was very interesting, starting with two residents, one physicist and one artist who drew the pedestrian routes in a square. As research on networks is conducted in the university by physicists, neural modelers, and sociologists, this theme has given rise to a regular seminar in recent years that welcomes doctoral candidates from a wide variety of disciplines. 1.2. Testing the typology of interdisciplinarity I will analyze (by taking up cases simply mentioned above, and developing new cases) some examples of this interdisciplinary research, those that have produced successes, and those that have highlighted problems, which may be linked to conflicts of methods (particularly in the field of digital humanities), but also to distortions between researchers’ requirements and social expectations. The institute itself is a place to observe these conflicts, distortions, and successes. My goal is to differentiate between different types of interdisciplinary research, and also to show what they have in common. It is customary to distinguish interdisciplinarity from multidisciplinarity. In the second case, different disciplines

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each make their disciplinary contribution to the study of an object that lends itself to research in different disciplines. In the first, disciplines must combine their methods and modes of questioning and instrumentation to study an object whose functioning also combines processes that seem to require contributions from several disciplines for their study. In our reflections on the Iméra project, I also proposed to describe “transdisciplinarity” as a collaboration between disciplines that would require each discipline to consider its own objects in a modified perspective by taking into account the relevance of another discipline; this collaboration would also require these considerations from the perspective of another discipline to be reciprocal. We will find an example of this type – between biology and physics – in what follows, but we must admit that the reciprocal exchange is a very strong condition. For this study, I therefore prefer to speak more broadly about interdisciplinarity and multidisciplinarity. However, to take into account research on science-society and arts-science relationships, it is useful to reintroduce the term “transdisciplinarity”, but in a very different sense, since it will involve thinking about work on possible transfers between the two fields. Let us put it bluntly: all the types of interdisciplinarity, even transdisciplinarity that we have observed at Iméra, have one thing in common: interdisciplinarity is required when the phenomena studied from one discipline are not totally detachable from phenomena studied from another discipline, and, therefore, when these phenomena are interrelated. But we have observed many varieties of interdisciplinarity, and I propose to differentiate them by considering the type of interactions they involve and require. 1.2.1. From formalisms to models and experiments The first type of interdisciplinarity consists of trying to use models whose formalism has already been developed to

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apply them to other fields, which often leads to modifying models, asking new questions that sometimes lead to the construction of new formalisms. We will name it: interdisciplinarity through interdisciplinary models. The use of networks, ranging from physics to sociology, neuroscience, and economics, is an example. Its development was first based on the combination of the mathematical theory of graphs and the study of dynamics on a network by physicists. Then organizational theorists and economists studied network forms (especially their heterogeneity), and researchers proposed measures to define the various relationships between a node and its links with other nodes. Dynamics of diffusion on networks, but also of network development and construction have made it possible to highlight the properties specific to a whole class of networks (for example, the “small world” property, whereby a network with highly connected nodes and others less connected makes it possible to go in a few steps from one end of a very rich network to the other; or the property of scale invariance). It has been possible to study how similarities made connections more likely, and we can now study the evolution of networks and their temporal forms. Models as well as applications have been enriched, based on an initial interaction between mathematics, physics (classical interaction), and market and social relations analysis. This type of interaction requires the sociologist, for example, to master the formalisms used, and the physicist to imagine new modes of experimentation to obtain a very large amount of data on interactions, their partners, and the temporal distribution of these interactions. 1.2.2. Interacting with cross-disciplinary learning and instrumentation Another type of interaction that is just as demanding requires the ability to handle formalisms and to associate them with possible conditions for experimentation, and we

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may add the construction of sophisticated devices to study phenomena. We will talk about interdisciplinarity through the interpenetration of methods. Let’s take the example of Rigneault and Marguet (both at our university). The former is a physicist, the latter a biochemist. In their two fields of research, the challenge was to detect by imaging, at a nano-level, the evolution of groups of molecules that would maintain their dynamics (while cryogenics freezes them). This implies combining spatial resolution and temporal resolution, but without limiting the development of phenomena too much, since we want to observe the movements of molecules. The physicist has a knowledge of the effects of his lasers, and one of the problems of using them in imaging is ensuring a correct signal-to-noise ratio. The biochemist knows how to maintain cell lines and keep molecules interacting. Collaboration between them has involved cross-fertilization of their skills, but also interactions between their ways of imagining processes to overcome problems. As is often the case, the physicist’s reference background is a random scattering dynamic – which he uses to detect behaviors that differ from it. The biochemist finds products in pharmacology that make it possible to disrupt the functioning of molecules without blocking them. The physicist proposes combining images at different spatial scales, to connect the surfaces explored by different imaging modes, and this according to the temporality of the developments. The use of fluorescent molecules must then be combined with the intertemporal consistency of image stacks. It was also necessary to model the variation in signal intensity of a particle, which occurs when another particle approaches or moves away from it, to properly interpret image variations. In these studies, each method has its advantages, but also its imperfections, and it is necessary to compensate for these imperfections and to succeed in crossing the methods while keeping in line the same object of study.

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As soon as phenomena are intertwined, interactions between disciplines that are both modeling and experimental are due to the complexity of the phenomena, which is not simply that of their structures, but that of their dynamics and the combinations of the various temporalities involved by these dynamics. And it is this complexity that must be modelled. Miglietta (in collaboration with CEREGE and Iméra’s Mediterranean program) has studied how the underside of the leaves of olive trees diffract the reflected light more than their upper sides, which makes it possible to evacuate heat more easily in windy weather, as the wind turns the leaves around. The different subspecies of olive trees present more or less high contrasts between these two faces. The fact that wind conditions, which themselves depend on climatic variations, but also on the slopes chosen for planting, must be taken into account is another example of how the complexity of the relationships between living systems and their environment is largely due to differences in dynamics, and not simply to properties that could be studied in a static way. This project can be compared to Cook’s project, which modelled the various temporalities and physical forms (steam, etc.) of water transfers circulating at different depths in the soil (Crau). In both cases, these are complex problems of relationships between different dynamics. Cook’s complexity is due, among other things, to the relationships between long or short temporalities, and that studied by Miglietta to the relationships between stationary and accelerated dynamics. The instrumentations and modes of experimentation must therefore differ according to these varieties of dynamics. This is likely to lead to interdisciplinarity, and it is entirely due here to the interrelationships between the different dynamics of

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phenomena and the implications of these interrelationships in methods. 1.2.3. Interdisciplinarity of competing hypotheses and experiments Interaction is also demanding, when we are at a stage where competing hypotheses are developing, but which are inspired by different disciplines, and whose testing requires the construction of different modes of experimentation and the use of different formalisms, to validate or revise each of them. We can speak of interdisciplinarity by combinations and competition of hypotheses (implied: from different disciplines). For example, one of our residents, Vanessa Redford, brought a project to test a hypothesis that could explain how we can speak so quickly, while mapping the different components of the phonetic-syntaxico-semantic system is very complex, and requires great finesse and complexity in the motor control of the phonatory organs, as well as a fine control of its productions by our hearing. She thought it was the motor system that imposed its tempo and production conditions. But other experiments have shown that it is more complicated and that the references given by syntax and semantics, as well as acoustic information, contribute to this relative fluidity. Language production is obviously a field of research where interdisciplinarity between linguistics, biophysics, neurosciences, acoustics, informatics, cognitive psychology is essential. The difficulty is that a hypothesis coming from one of these fields must be confronted with those coming from the other fields, and that for each of these confrontations, it is necessary to define the functioning of the different models in such a way that they can be tested, at least according to two of the disciplines, in the same experimental system, and it is necessary to develop experimental protocols capable of giving a precise scope to the results of these confrontations.

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1.2.4. Reflective intertemporal interdisciplinarity Interdisciplinarity does not always require that different research projects be directly confronted, or even within the same generation. In the history and philosophy of science, the increasing complexity of formalisms and experiments and the increase in the speed of production of new knowledge mean that historians and philosophers most often work on scientific research that took place a few decades before, or even a few centuries ago. However, the history and philosophy of science require technical skills equivalent, if not to those of past geniuses, then at least to those of their contemporaries. Moreover, this interdisciplinarity takes place in an asymmetrical dependent relationship. Science historians depend on scientists, not the other way around. However, the reciprocal relationship exists, even if it is in the minority, when scientists try to make the history of their science and historians and philosophers can show them that they are projecting problems into the past that were not those of their predecessors. However, science historians themselves cannot help but project more recent concepts onto ancient texts. Thus, in a text of the 12th Century, the Arab mathematician Al Karaji distinguishes between different types of mathematical proof, and it would be tempting to recognize in his distinctions those that we could make between proof by geometric construction, proof by algebraic calculation, and proof that provides in addition to the calculation of formal and conceptual justifications. Understanding what sort of thing a proof is, is a philosophical problem: to what extent can a conclusion be proved, what are the requirements of proof, what is its conceptual scope? The historian must then try not only to identify the differences in Al Karaji’s text, but also to show how these differences, while allowing these contemporary problems to be addressed, differ from the ways in which they are formulated today. The history and

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philosophy of science therefore consists of developing interactions between science and these disciplines, while proposing a reflection on the evolution of scientific concepts and methods, which also implies a reflection on the evolution of the relationships between such scientific works and their consideration in a history of the discipline, and finally a reflection on the history of scientific and philosophical concepts that are themselves linked to the interpretation of these works. The recursive loops between science, history, and philosophy are very complex in this field. We can talk about reflexive interdisciplinarity. It is easier when the temporal distance makes it possible to better identify the actual posterity of past problems. We can classify these four types of interdisciplinarity (by interdisciplinary models, by interpenetration of methods, by combination and competition of hypotheses, by reflective interdisciplinarity) into a more encompassing class, that of integrative interdisciplinarity: the different disciplines are intimately integrated with each other. 1.2.5. Interactions by combining disciplines Let us now consider interactions that do not require researchers from different disciplines to master all or part of the methods and models of other disciplines. In these situations, one could also speak of multidisciplinarity, but this would mean forgetting that if each discipline will examine one aspect of a system, each perspective is likely to change the conclusions of the other perspectives. A typical case is that of interdisciplinarity in archaeology and paleontology. We can speak of interdisciplinarity by convergence on intertwined phenomena. These disciplines use dating methods involving physical and biochemical instrumentation, pollen specialists, zoologists, geologists, 3D imaging specialists, etc. In these fields, experimental

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scientific disciplines provide markers of distinction between strata, between periods, between types of occupation, for example pastoral, or agricultural, or hunting/gathering, and provide benchmarks for the temporal ordering of the data collected. Thus, by combining geology and zoology, it can be discovered that the layers of sediment in a cave have a temporal inversion in some areas, because burrowing animals have dug and dumped debris that were at depth on the surface – which may change the dating of the discovered bones. Paleontologists or archaeologists provide on the one hand other markers (debris from lithic industries, type of bones) and on the other hand, a coherent anthropological interpretative arrangement of these markers. But the relationship between the two groups of disciplines could be reduced to a command of markers, and instructions for use – which indicate the limits of use of such a marker – and their combination. While the archaeologist or paleontologist must be familiar with the principles and conditions of use of the methods, they do not need to have the researcher’s skills in this field. Nor do they make research hypotheses about new procedures for geologists or chemists, or biologists, who do not need to produce new interpretative archaeological or paleontological hypotheses themselves. It is even necessary that these researchers not be allowed to “act like an archaeologist” – as some American geologists unfortunately do at the end of their careers, who tackle the problem of which pass Hannibal passed through by stripping part of the ground of high mountain ponds, and think only of the type of data they need for their discipline and for the examination of such a deep stratum, forgetting that it is necessary to leave as much data as possible intact for other types of methods, including those that have not yet been invented. Archaeologists today have this precaution in mind, because they may regret that their predecessors destroyed quantities of data of which they did not perceive the interest (they were

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not interested in excreta, for example, and they did not attempt to determine which pollens were caught in a soil layer). It is also the archaeologist who can draw conclusions about the human meanings of the relationships between the data provided by the different methods. For example, we have found this kind of multidisciplinary relationship in projects that involve biochemists and historians, all studying the history (since Antiquity and the Middle Ages) of “ointments” or tars extracted from juniper wood and used for medication. The isolation of the active elements of these tars is a matter of chemistry, and in vitro evaluations of their medical effects are a matter of biomedicine. Historians provide documents that indicate the evolution of uses. The phenomena studied by each discipline remain intertwined, but the methods are less so; it is mainly needed to ensure their cohabitation in the same field. In addition, the different types of phenomena themselves each have their own mode of entanglement with the other phenomena, and this defines the conditions for the conservation of bones, pollens, etc. under different geological, climatic, or even accidental conditions (earthquakes, fires). 1.2.6. Interdisciplinarity of reciprocity between contexts Let us come to a type of interdisciplinary interaction that requires bridging two disciplines that do not share their methods, but only part of their field and objects. One may wonder why such disciplines should collaborate. This is because the work and results of each have a significant impact on the research objects and contexts or on the research conditions of the other. Here again there is multidisciplinarity, but problems in one discipline pose problems for the other and vice versa. The methods are

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disciplinary; it is the impact of problems from one discipline to another that is interdisciplinary. We can mention the relationships between the researchers working at the CEA on the ITER project, and the sociologists who analyze the working conditions of nuclear technicians and researchers, on the one hand, and the social repercussions of ITER’s work on both the interactions of technicians, administrative staff, researchers, and management, and on the interactions of ITER employees with the region’s population, on the other. Scientific and engineering researchers are studying the problems of diffusion of tungsten dust torn from the walls by plasma (ITER), which has therefore become radioactive and poses a risk of contamination. On the sociological side, it is a question of observing how staff manage to follow very strict rules of non-contamination, how their members talk about it or do not talk about it to their entourage (for engineers as well as technicians and secretaries) and how they protect themselves cognitively and emotionally from contamination problems and make these tensions compatible with maintaining the social position they have in ITER vis-à-vis their entourage. The work routines that are put in place in this way have biases, and some combinations of these biases can produce harmful effects in a dangerous environment. Knowledge of such bias entanglement effects is useful for engineers who design safety measures, and conversely, accurate knowledge of radioactive effects can change representations and uses. Management is concerned about the impact on society of nuclear problems, and may want to convince the public that there are fewer concerns in the field of fission, or have more information internally on the human factor in programs that attempt to address safety issues. Indeed, history has shown that it is accumulations of small human errors or negligence that can lead to serious problems in this area (not to mention the design errors of ancillary

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elements, for example, in Fukushima, where emergency power generators were placed in basements at seawater pumps and were the first to drown). For his part, the sociologist – or psychologist – seeks to analyze the ways in which humans manage the pressure imposed by these safety protocols and the concern to avoid any negligence. Finally, he is interested in the ways in which these concerns, worries or trust are resocialized in the relationships between employees, between them and their families, and between these families and the environment of small towns or cities surrounded by rural activities specific to this region – a socio-political environment that the company must also take into consideration. In this type of interaction, ITER researchers do not claim to have sociological skills, nor of course do sociologists claim to have fusion skills. But the former are concerned with better understanding the effects of their activities on their social context, or the social skills of technicians in their relations with each other and with the organization, the functioning of such a system requiring very well regulated coordination. Do the latter have the hope of making decisive sociological discoveries? At the very least, they have the advantage of studying a field of research that has been closed to them until now, and which has two very different social contexts, that of internal relationships within a sophisticated technological organization, and that of those for whom ITER is not accessible, but who are socially linked to its workers, and who may be concerned about consequences on their environment, including on the change in social structure – or quite simply on the rise in property prices, which is involved in the multiplication of residences for international researchers. The sociologists’ method is close to ethnomethodology, and requires a significant immersion in both contexts, to fully understand the evolution of their links. In short, each discipline defines its field in its own way, but what interests them both are the

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contextual effects that research data and problems provide to each other’s research and problems. The latter two types can be placed in the broader box of multidisciplinarity, but without forgetting that they raise problems that are more than the sum of the problems of each discipline. We can talk about multidisciplinarity more than additive. 1.2.7. Transdisciplinarity reception of science

between

science

and

the

While interdisciplinarity is necessary to explore how a technico-scientific context relates to social contexts, it is also necessary to better understand the divides between science and society, the misunderstandings that are the causes of these divides and the social processes of belief and opinion building that are the sources of these misunderstandings. One might think that this is the purpose of sociology or communication sciences, but these mutual divides and misunderstandings also require a better understanding of some scientific methods on the one hand, and of the processes of cognition and the formation of collective beliefs on the other, which requires not only interdisciplinarity, but also the development of mediations between different modes of knowledge, and therefore transdisciplinary relationships. Some scientists still believe that their undisputed social role is only to provide results validated by formal reasoning and controlled experiments to ordinary citizens who, lacking the technical skills to evaluate these results, only have to trust the authority of scientists, whose work has proven its efficiency through these technological applications from which all citizens benefit. The problem is that these applications have also had unintended, but sometimes very harmful, effects, which has led to a certain mistrust in public opinion – but it can be said that this is not a new problem:

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the same is true of past technological revolutions, when they render the skills of an entire corporation obsolete. However, a new problem has emerged, that of the scientific management of complexity and uncertainties. Examples include work on different models of climate change, on the possible harmful effects of drugs or even products that protect crops from insects that are harmful to them, or research that assesses the consequences of various types of pollution on human life expectancy. Since the modes of harm involved are based on very complex and therefore difficult to identify causal chains, scientific data will not be able to directly determine simple causalities. Experiments will show statistical correlations well before they can identify causal mechanisms. The conditions of the experiments themselves are simplified and reduced compared to those of the environmental contexts. Scientists are therefore obliged to indicate the uncertainties that moderate the scope of their results. This creates a strong contrast with the previous type of public validation by the scientific authority, and this contrast can be interpreted as a lack of reliability – whereas from a scientific point of view, taking uncertainties into account ensures, on the contrary, that these results weighted by uncertainties have greater reliability. In the field of health, instructions on lifestyles that go against consumption habits linked to types of sociality (tobacco, alcohol, speed, smoke-producing wood fires, etc.) will be all the less accepted as they must indicate the margins of uncertainty in their scientific justifications. Conversely, if scientists claim to have no evidence of such harmfulness of an insecticide product, their caution regarding results whose interpretation is uncertain lead people to suspect them of colluding with the companies that manufacture these products – and it has been shown that this is indeed the case for some of them.

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Unfortunately, these cases are used to condemn those who persist in taking uncertainties into account. Disciplines, including cognitive psychology, social psychology and cognitive sociology, etc., are obviously very useful in studying the conditions of these mutual misunderstandings and the ways to overcome them. The human and social sciences are divided on these issues between those who study social contexts on the assumption that social actors give meaning to their interactions, a meaning that must first be recognized, and those who attempt to assess the harmful consequences of certain beliefbuilding processes. It must be recognized that on these problems, an interdisciplinarity between these two types of approaches has yet to be built, similar to how methods for presenting data in situations of uncertainty, which could minimize mutual misunderstandings, have yet to be built and evaluated. This is an issue raised at Iméra by several seminars, in particular in the fields of social and scientific identification of pollution, and in those of diagnostic announcements weighted by uncertainties in the health fields, or finally in the problems posed by announcements concerning earthquakes. An interdisciplinary field of research should soon develop on what is not only a misunderstanding, but an opacity orchestrated by GAFAM techniques. Google’s algorithms are not known to its users, who nevertheless provide through their use of Google all the data that Google needs to track their click sequences, draw a profile of them, and propose an order of pages to consult that results from the similarities calculated with other click sequences of other users. What works here is a two-way transdisciplinarity, algorithms towards users and vice versa, but without communication, for users, between the two ways. We could say that this is a hidden transdisciplinarity, which we hope will quickly become a field for research and societal action. In this kind of

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transdisciplinarity, it is the problem posed by the prefix “trans”, the lack of bridges between different modes of inference and conceptualization, and in their absence, the development of perverse mediations, that is the scientific and social problem. 1.2.8. Transdisciplinarity between arts and sciences The relationship between the arts and science is also a transdisciplinary relationship. Let us not forget, moreover, that a link is possible between our two examples of transdisciplinarity. Thus, artistic achievements can be used to make the public more aware of the problems raised by scientists during their research – for example, to raise awareness of certain environmental degradation, but also, by breaking the codes of what is supposed to be rural nature with its small flowers, to make us sensitive to the “urban” plants that grow on the corners of sidewalks – which implies a transdisciplinarity between urban botanists and videographers or designers. Art can reveal to us the aesthetic interest of what was not judged artistic, but which is identified and categorized by a science. Closer interactions between the arts and sciences are possible. Achievements by residents of Iméra have brought together dancers and computer scientists and optical researchers to work on light interferences activated by the laser capture of dancers’ movements, or even simply of passers-by in an exhibition. This type of transdisciplinarity may have an educational or public mobilization objective, or may seek to offset agreed expectations, whether those of scientists or those of the public. We could speak of aesthetic interdisciplinarity when, as in the case of waves, the phenomena highlighted are both those that intrigue the scientist (the fluid physicist, Le Gal) and those that capture the attention of the artist Tejerina Risso

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(and the public) because they combine a certain perceptive importance while offering an intriguing variation from usual perceptive expectations. Classical art assumed that science and art combined to discover regular and harmonic proportions (according to a thesis by Palladio and before him by Piero della Francesca). Contemporary art would rather focus on the intricacies between two modes of sensitivity to what intrigues us, sensitivity to a complexity to be solved for the scientist, and sensitivity to a complexity to be played with for the artist. In such cases, the problem of “trans” becomes the driving force behind creation. We can speak of trans-sensitivity developed in a transcreation. 1.3. Conclusion Let us summarize the distinctions that the various activities of researchers residing at Iméra, and researchers at our university, have enabled us to highlight. We have three main classes: integrative interdisciplinarity, multidisciplinary more than additive interdisciplinarity, and transdisciplinarity. The first class includes interdisciplinarity by interdisciplinary models, the interpenetration of methods, the combination and competition of hypotheses, and reflective and intertemporal interdisciplinarity. These subclasses are not necessarily separate: so, the first three types can often be combined. The second major class includes the subclass of interdisciplinarity by convergence on interrelated phenomena, and interdisciplinarity by the repercussion of problems from one discipline to another. It may be tempting to reduce the first subclass to a sum of disciplines that seem to study the same object, but since the object presents

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entangled phenomena, this is rarely justified. In the second subclass, each discipline is a separate chamber, as it were, while being in the field with the other, but interdisciplinarity is based on the fact that the problems of one of the disciplines are a source of enigmas to be solved for the other. Finally, the third major class is that of transdisciplinarity, which includes research on the “trans” between areas of different conceptualizations and inferences, and essays and productions of transcreation, which become aesthetic when they succeed in awakening trans-sensitivities. Perhaps this typology of types of interdisciplinarity will make you want to discover even more modes of interdisciplinarity, and to make your entourage sensitive to them. It must be remembered, however, that, even in aesthetic contemplation, interdisciplinarity is demanding, since it requires us to grasp at least in part the differences in the perspectives that intersect and fertilize each other. Openness is not enough. Cross-learning is required to appreciate an achievement with respect to its interdisciplinarity. Institutions such as Iméra are basically intended to facilitate openings to this learning. You can find not only the references of the works mentioned in this text, but also the videos of the seminars that present them, and for some, the reports of the discussions of these seminars, on the Iméra2 website.

2 www.imera.univ-amu.fr/ and https://imera.hypotheses.org/.

2 Geography and Computer Science: Reasons for a Marriage, a Marriage of Reason?

2.1. Introduction This chapter attempts to provide answers, as generic as possible in terms of the relationship between geography and IT, to the two questions asked by the organizers of the Rochebrune symposium on complex systems and interdisciplinarity in 2018: “How can knowledge from one discipline be transferred to other disciplines?” and “How can a method from one discipline inform another disciplinary field?” It should be noted from the outset that the two disciplines are a priori very distant in terms of subject matter and cognitive reference frames, with computer science being close to mathematics and engineering, and geography belonging to the field of the human and social sciences. It follows that the expected relationships are much less a matter of conceptual borrowing and exchange, as is common practice between disciplines in fields closer in Chapter written by Denise PUMAIN. This presentation develops a number of ideas already posted in the Le Monde “Binaire” Blog: www.binaire.blog.lemonde.fr/les-entretiens-de-lasif/.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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purpose, than of methodological instrumentalization of one discipline by another. But we will see that this history is not only about geography borrowing “tools” from computer science, that effects on concepts are also observed, and that exchanges are not necessarily one-way. Mainly, it is a question of analyzing why and how informatics has transformed the representations and practices of geographers who have been confronted with it, and secondarily of examining how informatics in the broad sense has been able to take advantage of geographical knowledge. I introduce a strong personal bias based on my experience as a Frenchwoman specialized in human geography; so, this article only partially addresses the question asked. I believe it is of testimonial value, but it cannot be taken as the result of a thorough investigation or a representative experience. I do not want my analysis to be interpreted as an exercise in disciplinary affirmation, quite the contrary. However, the context (Müller 2015) of the status of disciplines, in terms of the social representations that define them and the institutions resulting from these representations that consolidate them, introduces asymmetry into relationships. Paradoxically, computer science, a younger discipline, is in a dominant position compared to geography, probably because of the training in mathematics and/or engineering of those who practice it, and also because of the increasingly considerable resources allocated to them in recent decades. Different forms of bias are thus introduced into scientific exchanges between disciplines and we will note their effects several times. But the purpose here is not to reiterate the lamento of the social sciences, which would be misunderstood, disregarded, and underfunded in favor of the “inhuman and asocial sciences” (according to Vincent Courtillot), nor to claim a highly questionable specific treatment for what would distinguish digital humanities

Geography and Computer Science

from other sciences objective is to trace requests it addresses the technical and moment.

dealing with computer science. a path in a discipline based on to another according to its needs epistemological possibilities of

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My the and the

2.2. Computers and numbers: quantifying geography The relationship between geography and computer science is obviously quite recent; it is part of the “digital revolution” that has affected all sciences and has been growing since the middle of the 20th Century. Even if concerns about quantification, mathematical formalizations, and numerical modeling have emerged in geographical research, sometimes as early as the 19th Century, even if the concern for measurement and numbers can be identified in a multitude of forms in all fields of the discipline, it is the use of computer methods that has been decisive in establishing the generality, reproducibility, and legitimacy of these uses in geography in a sustainable way. This innovation dates back to the 1950s in Sweden, 1960 in Great Britain and the United States, and 1970 in France (Cuyala 2014). As with many other disciplines in the humanities and social sciences, geography is part of what may have been called the “turning point” of quantification (Pumain and Robic 2002). However, the need for calculation is not the primary driving force behind this “revolution”, which very quickly proclaimed itself as “theoretical and quantitative”, with an emphasis on the first term strongly supported by Sylvie Rimbert (1972) and confirmed, in 1978, by the foundation of a European conference still active under this name (Cuyala 2013; ECTQG 2019). Indeed, among French academic geographers, the use of computers was generally preceded, since the early 1970s, by training in statistical and mathematical tools, undertaken collectively in summer schools organized by research institutions (ORSTOM and

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CNRS). These trainings fulfilled the need to rebuild the theoretical foundations of the discipline, and to legitimize its knowledge, whose validation was beginning to be based on proven methods in disciplines that are supposed to be “harder” (Cauvin 2007). The primary epistemological necessity was therefore of a methodological nature, and the use of computer technology was a simple technical adjunct for most geographers of the time. But since much of the work in geography is carried out on medium-scale objects, making direct observations and surveys conducted by the researcher insufficient (although still necessary!) and thus implying the need to process information collected by organizations, the use of calculation is relatively indispensable and much more frequent and systematic than in sociology, for example. 2.2.1. Diversity of practices However, from the very beginning of this “theoretical and quantitative revolution”, geographers’ practices with regard to information technology have been relatively heterogeneous. Some simply used statistical software packages (SPSS, then SAS, ADDAD, etc.), which only required learning the language of punched cards that initiated the computer program and writing formats to adapt the software to their data. Others have learned the basics of computer languages, including the concepts of program, subroutine, and iteration, and then acquired the ability to write some programs not yet integrated in traditional software (for example, calculating distances and selecting objects according to their relative location). Still others pushed the concern for mathematical integrity by going so far as to reprogram functions that existed in standard statistical software packages. Finally, others were innovating, according to the needs of cartography in particular, contributing to the development of the first cartographic representation software such as Symap, and were even already considering the possibilities of simulation

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(Torsten Hägerstrand in Sweden, Richard Morrill and especially Waldo Tobler in the United States, Sylvie Rimbert in France, for example). 2.2.2. Epistemological changes driven by computer science rather than conceptual borrowings Several essential changes were induced in the epistemology and concepts of human geography at that time, although they could not be fully linked to these pioneering uses of computing skills, and even if the use of punched cards and listings seemed to be the most obvious symptom of this at the time! I will not dwell on the first of these changes, which concerns the epistemological position of the discipline, on bases where computer science has not intervened. The adoption of the nomothetic paradigm was a commonly adopted prerequisite, as previously reported by American (Bunge 1962; Gould 1969) and British (Haggett 1965) geographers, although it was contested from the beginning (George 1972), and although it was still denounced as “neopositivist” in several subsequent publications (Stazack 2001; Levy and Lussault 2003). The second set of results led to conceptual innovations, which result from the shift from statistical analyses, hitherto mainly single or bivariate, to multivariate studies, much better suited to the internal representation of a highly integrative discipline in terms of explanatory factors (explanation in geography is most often based on multi-scale processes, in space and time, and which combine effects of “natural” and social origin). The contribution of computer science here is mainly instrumental, although the calculation and synthetic representations tool has been essential to process, analyze, and communicate interpreted summaries of these data in many fields. The effects of computer science on the evolution of geographical concepts are real but indirect here: they result from the interpretations that may have

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been given to the results of the processing of these first kinds of “massive data”. I will give only three examples, taken from the fields of landscape geography and urban geography. 2.2.2.1. An enrichment of the landscape concept The automatic procedures for classifying signals recorded by satellites sending back images of the earth explain the massive and early participation of geographers in remote sensing research. From the early 1970s, the first theses on this path appeared and were often written, admittedly, by people with basic or additional training in computer science. As a result of the quantification of the observations collected and the automation of their classification, interest was focused on the more or less homogeneous sets of land use forms and the ecological or productive systems that are at the origin of their differentiation. Very gradually, the landscape concept, which was first defined at the local level by what could be seen, and which had changed little between the two wars despite the use of aerial photographs, was enriched by new connotations and was also defined at other scales, characterizing entire regions (Brossard and Wieber 1984). 2.2.2.2. A consolidation in urban ecology The power of factor analysis methods, having the ability to summarize a multitude of more or less partially correlated attributes into a few axes, has led to the transformation of some of the results of this methodology into progressively enriched constructs that are assimilated into real concepts. A successful example of this adventure led to the emergence of a sub-discipline called “urban factorial ecology” in geography in the 1960s and 1970s. The result of the work of the Chicago School of Sociology at the beginning of the century, supplemented by the intuitions and then the correlation measurements carried out by urban planners Shevky and Bell in Los Angeles, in 1955, the method became more

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systematic with the work of the geographer Brian Berry (1961). Based on an exhaustive spatial description of the social composition of urban blocks (63 census variables for hundreds of localized units), which he calls “geographical information table”, submitted to a principal component analysis, he identifies and maps three “dimensions” of social differentiation: the economic factor, the family factor, and the ethnic factor. Each of these “factors” corresponds to different spatial modalities of social segregation processes in urban space, resulting in a distribution into circle sectors according to income level and social status, concentric haloes according to life ages and household size, and mosaic clusters for discriminations based on cultural belonging. This new representation of the urban field has toured the world and is one of the pillars of urban geography training found in all textbooks. 2.2.2.3. A new definition of urban functions When, in the mid-1970s, with Daniel Noin and then Thérèse Saint-Julien, we began to compare the socioeconomic profiles of French cities, based on employment data published (on paper) by INSEE, using factor analyses, we were part of a general trend in the use of multivariate analyses by geographers in many fields of study (Racine and Reymond 1974; Brocard et al. 1977; Wieber and Massonie 1983; Pumain et al. 1983). Most of these works produce typologies. When factor scores are mapped, some wonder about the independence of the socio-spatial processes that are likely to generate these uncorrelated spatial distributions. In the field of urban systems, we replace previous statistical analyses, based on a priori categorizations (industrial cities, tertiary cities, workers’ cities, bourgeois cities), with an identification of urban socioeconomic structures based on activities that are most of the time grouped or separated in the same cities, as revealed by correspondence analysis factors and synthesized by a hierarchical bottom-up classification (Pumain 1976).

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2.2.2.4. A discovery for the dynamics of city systems But it is because computer scientists associated with our university not only help us to use multivariate analysis software (most of which is developed by members of the Benzecri team: Fénelon, Vielajus, Jambu), but also meet our repeated requests for time-based analysis methods, that we will make progress in conceptual construction. After testing Tucker’s method of analyzing three-dimensional tables, which is difficult to interpret, we successfully apply a classical factorial analysis to the table, superimposing the profiles on three dates for each city, which allows us to draw, manually on the listings, the “trajectory” of each in the space of socio-economic differentiation defined by a bi-factorial plot. The general parallelism of these trajectories observed over a period of about thirty years leads us to distinguish a general trend towards changing employment structures, common to all cities. Observation of this “dynamic” of the city system leads us to infer a new interpretation of the factors previously identified at each date, as revealing the trace of waves of diffusion of innovations, namely for the first factor the first industrial revolution and the inversion of associated urban attractions translated by the “brand image” factor of cities, and for the second factor the economic transformations of the Glorious Thirties that brought to cities a “technical modernity” (Pumain and Saint-Julien 1978, 1979). This “discovery” (or rather this formalized construction of an explanatory intuition that has remained largely implicit until now, as is often the case in HSS) would hardly have been possible without the contributions of IT. But an anecdote may illustrate the difficulties in communication associated with inequalities in the status of disciplines in the representations of the time. Thus, when we presented our results in an interdisciplinary seminar, the great statistician Benzecri himself quite strongly criticized our undertaking, asking a sociologist present to attest the fact, that it was

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incorrect, inconsistent or unthinkable to consider that job categories observed 10, 20 or 30 years apart could retain the same meaning for cities, so much had the social and economic content of these categories been transformed. The method of analyzing trajectories, or stories, that he himself had proposed, was therefore not applicable in the humanities and social sciences! Fortunately, this verdict did not discourage us, and many other similar disappointments (notably from mathematicians opposed to any digitization of the human or social affairs) did not prevent us from building on these foundations what was to become the evolutionary theory of urban systems (Pumain 1997). At the same time, geographers’ computational practices have changed from descriptive uses of statistics to more controlled inference uses. It has also triggered the search for dynamic models and paved the way for new representations in terms of systems. It was by seeking to reproduce the functioning and evolution of these systems by simulation, since the experimental testing of hypotheses is not possible in the social domain, that geographers and computer scientists would together invent what was finally called not a “computational geography”, but a movement to liberate modeling (“de-complexed”) in geography (Banos 2013, 2016). 2.3. Simulation in geography and algorithmic thinking This revolution, initiated by the Swedish geographer Torsten Hägerstrand in the 1950s, promoted by a few scattered attempts by American geographers such as Richard Morrill and Waldo Tobler in the 1960s and 1970s, now heralds some very promising breakthroughs for a real breakthrough in algorithmic thinking in geography. It was especially since the 1980s and in the field of urban and regional models that the multiplication of models integrating system dynamics and concepts of complex systems gradually modified the representations of geography researchers.

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2.3.1. A difficult path In our experience, the process has not been easy (Pumain et al. 1989; Pumain and Sanders 2013). Initially, we were dependent on the mathematical and computer skills of teams of chemists in Brussels (around Ilya Prigogine) or physicists in Stuttgart (around Herman Haken) who had tried to transpose the forms of modeling developed by the inventors of the theories of self-organization and synergy into geographical questions. These models, made up of non-linear equation systems that allow trajectory bifurcations and which produce an “order by fluctuation”, were suitable for the observations we were able to make analytically on the transformation processes of the economy and society of cities. It was a chance to meet these teams, more concerned with empirical confrontations than many mathematicians, and with less constraining and limited model specifications and programming modes in terms of dynamics than the previous models of systems dynamics according to Jay Forrester with difference equations and the associated Dynamo language. With Lena Sanders and Thérèse Saint-Julien, we have therefore successively tested urban dynamics models written in Fortran to simulate evolutions described by differential equations. But in both cases, we are not testing the type of model we would have dreamed of to formalize the interpretations constructed from our statistical analytical observations on systems of cities. Peter Allen’s model was not an interurban model, although it used many elements of urban geographic theories to simulate the evolution of jobs and residences, on a medium spatial scale (a metropolis or a region) and for durations of the order of a few decades, compatible with our time scales. The model of Günter Haag and Wolfgang Weidlich worked with migration matrices and could therefore adapt to a system of cities (Sanders 1992), but it only allowed the evolution of one certainly synthetic (the

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population) variable to be monitored, while proposing interesting results, such as measures of propensity to mobility and distance from the equilibrium state of the observed spatial distribution, in relation to transformations related to current migration processes. In the use of these models, the knowledge of Fortran by simulator-geographers has proved very useful in producing results in cartographic form (by simply writing the format of the model’s simulated numbers output), or to correct an interpretation error by physicists who were reading the migration matrices from column to row while they were arranged from row to column (a Mij flow does not read the same way from one discipline to another!). Until the 1990s, the computing power of computers severely limited the dimensions of simulation models, and, moreover, programming mathematical equations prevented a sufficiently diversified representation of the forms of interaction between cities. The possibility of progress in modeling has come from computer science for geography: it is the flexibility of object and process representations in multiagent models that has finally allowed us to design models adapted to the challenges we could assign to simulation. This is how we can boast of having developed, with the help of a doctoral student in computer science headed by Jacques Ferber, the first multi-agent simulation model produced internationally for geography (Bura et al. 1996). This first model simulated, over a period of 2,000 years, the transition from a system of settlements made up of small villages to a hierarchical and functionally differentiated urban system, by accumulating wealth linked to the exchange of production and allowing the acquisition of new urban functions, introduced exogenously at different time steps. The capacity of the computers still severely limited the dimensions of this theoretical model, which included only 400 interacting populated places. Several years passed before we could recruit another doctoral student, led this time by Alexis

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Drogoul, to produce, with new software, models reproducing the trajectories of a few thousand cities in Europe and the United States (Pumain et al. 2006; Sanders et al. 2007; Bretagnolle et al. 2010). Although practiced between competent and willing people, these collaborations have been few and relatively difficult to implement, mainly due to institutional obstacles. The doctoral dissertations produced in computer science were intended to contribute to the advancement of this discipline, while the problems we submitted to the wisdom of computer scientists were not necessarily part of the core of their research program. The meeting between these two interests was therefore quite unlikely, with courageous candidates for interdisciplinarity taking the risk that their work would not be sufficiently well recognized by their home discipline. Conversely, it was difficult to obtain, from a student specializing in programming software, a strict equivalence between his sometimes hesitant lines of code and specifications of territorial dynamics whose parameters we did not control or even sometimes could not identify the statistical or mathematical form most representative of real social processes. The difficulty of the humanities and social sciences in explaining the gap between their scientifically constructed representations and everyone’s “common sense”, to which, in case of doubt, the computer science student could subscribe, is also a well-known obstacle to fruitful interdisciplinary exchanges. Finally, since these collaborations were rare, the time elapsed between them did not allow any reuse of previous computer work for a later model: the Simpop1 model “family” consisted each time in a complete rewriting of models with new languages on platforms considered more efficient, from Smalltalk to C++ and then to Swarm, so the reproducibility

1 www.simpop.parisgeo.cnrs.fr/.

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of the experiments conducted with these models could not be ensured. 2.3.2. Towards a win-win collaboration Even when the interdisciplinarity of the exercise is accepted between the partners, close negotiation for ontological compromises is required anyway (Louail and Pumain 2014). Although standards are not yet well established, it must be recognized that compromises, on objects as well as on geographical scales, are made when creating simulation models (Banos 2013; Bonhomme et al. 2017). Two epistemological operations can be identified in this interdisciplinary exercise, which requires on the one hand a detailed explanation of geographical knowledge for an algorithmic shaping of problems and on the other hand, a rigorous selection of relationships or objects that can be physically represented in a model – both operations are of course guided by expectations in terms of results produced by the model, but also in terms of innovation and computer sophistication, such as the “factorization” of procedures or the reduction of calculation times. In our experience, these trade-offs are successful when computer scientists and geographers share programming operations together, in a close exchange of knowledge and skills. This small miracle was able to take place in an exceptionally favorable context of multi-year funding for research by the ERC (ERC advanced grant GeoDiverCity), allowing the recruitment of several people, and in the context of close collaboration between two laboratories, structured around several doctoral geographers and geomaticians familiar with computer work (Pumain and Reuillon 2017). Compromises and negotiations between disciplinary requirements have gradually formatted the design of models

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in geography, including advances that can be considered by both sides as methodological and conceptual innovations. It should be noted that the extraordinary increase in computer power during the 2000s is also one of the explanatory parameters. Thus, progress in validation methods, through the use of evolutionary algorithms and intensive computing, allowing conclusions in terms of the sufficiency and necessity of the assumptions introduced into the model (Schmitt 2014; Schmitt et al. 2015) as well as the invention of a method for incremental construction of model families addressing the same problem (Cottineau et al. 2015) are to be credited to this partnership and benefit both geographer-modelers and the creators and developers of the OpenMole simulation platform (n.d.). These advances are illustrative of the successful integration of algorithmic thinking in geography (ReyCoyrehourcq 2016). They are now also being increased by the individualization of programming possibilities, which in recent years has led more and more geographers to propose very effective “applications” in many fields of the discipline, many of which are now aimed at the general public2. 2.3.3. Geography in all digital objects Collaborations between geography and informatics are now multiple and multifaceted, in many fields that go well beyond research, but for whose development research contributions are essential. The continuity of these developments and their social surface probably have no equivalent in the field of HSS. Geographers and computer scientists have a long-standing relationship of mutual utility and necessity that has been perhaps most enduring and remarkable in interdisciplinary institutions. A research 2 Géographie-cités 2017, https://cybergeo.hypotheses.org/; Vallée et al. 2017.

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group supported by the CNRS since at least 1994 has thus enjoyed remarkable longevity, under the successive names of Cassini network, SIGMA, and then MAGIS (2017–2021) (the name change was only there to circumvent the administrative principle of a maximum lifespan of eight years usually granted to these research networks; it proves the need to maintain this form the relationship between the two disciplines is all the more the urgent). The highly-decentralized network organizes very attractive and busy symposiums every year. Since the first CASSINI days (in Lyon, October 13–14, 1994; 2nd in Marseille, November 15–17, 1995; 3rd in Marne-La-Vallée, November 25–27, 1998; 4th in La Rochelle, September 7–8, 2000; 5th in Montpellier, September 26–28, 2001, etc.), the appellation has been transformed into SAGEO conferences, the 14th of which was held in 2018. Here, it is impossible to detail all the innovations resulting from these recurrent interactions and monitored in many working groups. The network has contributed to the development of specialized software such as geographic information systems (with a journal such as SIG La Lettre, renamed as Decryptageo), and methodologies gradually gathered under the label of “geomatics” (including a discussion list leading to the proposal of services such as GeoRezo). What I would like to stress in conclusion is that, in parallel with this impregnation of geographical thinking with concepts derived from information technology, of which the Rochebrune meetings have already given many examples, there were apparently purely technical upheavals, but which resulted in a reintegration of geographical issues into the concerns of computer scientists and more generally into the objects and representations of society. Satellite images, then geographical information systems (GIS), and finally the proliferation of geolocation in all connected objects have profoundly modified the representations and uses of

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geography (Goodchild 2007; Joliveau 2011; Fen-Chong 2012; Lucchini 2015; Lucchini et al. 2016). The “first law” of geography according to Waldo Tobler (FUN-MOOC 2016), according to which proximity is the first condition for social interactions, is at the heart of many of the applications that manage social networks, and that are, therefore, constantly used and enriched by computer scientists. In today’s world, the avatars of the map are permeating the cognitive representations of all smartphone owners, waiting to change, perhaps in the long term, their vision of the world. 2.4. Conclusion If we have to try to go beyond this particular experience, we may wonder what the keys to success would be for an interdisciplinary hybridization operation. It seems to me that three main types of conditions are necessary, which concern the sociology of science, their epistemological compatibility, and the practices of researchers. Sociology of science, because, obviously, the institutional context is essential, both in epistemological terms, to define what can be valued as advanced in a science, and in financial terms, to authorize a significant and sufficient investment to complete the operation. The two aspects are linked, because it is the current paradigm, or more generally the culture of a scientific community, that leads to positive assessments and evaluations that make it possible to finance these innovative and, therefore, “risky” operations when they tend to broaden a disciplinary field by hybridizing it, rather than to deepen existing concepts and theories. Epistemological compatibility, because at some point the two disciplines must be able to take advantage of the intense exchange they require, which is very costly in terms of mutual clarification of fundamental concepts, vocabularies, and methods, highly uncertain about the expected results, and requires long-term and constant collaboration in the construction of a common object or product. Researchers’ practices, because it often

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takes a lot of tenacity to carry out this type of undertaking to its conclusion, with a rather rare combination of just what it takes of disciplinary arrogance not to lose sight of the objective of advancing knowledge, and humility to accept the necessary ontological and methodological compromises useful for convergence with the other. Finally, the chance of discovering partners who fulfill their role brilliantly is a factor that is difficult to weigh a priori, but certainly essential for successful interdisciplinarity! 2.5. References Banos, A. (2013). Pour des pratiques de modélisation et de simulation libérées en géographie et SHS. PhD thesis, Université Paris 1, Paris. Banos, A. (2016). Modéliser, c’est apprendre. Itinéraire d’un géographe. Matériologique, Paris. Bonhomme, C., Commenges, H., Deroubaix, J.-F. (eds) (2017). Dictionnaire passionnel de la modélisation urbaine. L’œil d’or, Paris. Brocard, M., Pumain, D., Rey, V. (1977). Analyse des données: traitements visuels et mathématiques. L’espace géographique, 4, 247–260. Brossard, T., Wieber, J.-C. (1984). Le paysage: trois définitions, un mode d’analyse et de cartographie. L’espace géographique, 13(1), 5–12. Bunge, W. (1932). Theoretical geography. Royal University of Lund, Lund. Bura, S., Guérin-Pace, F., Mathian, H., Pumain, D., Sanders, L. (1996). Multi-agent systems and the dynamics of a settlement system. Geographical Analysis, 2, 161–178. Cauvin, C. (2007). Géographie et mathématique statistique, une rencontre d’un nouveau genre. Trente ans de stages de mathématique et statistique appliquées à la géographie. La revue pour l’histoire du CNRS, 18, 4131.

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Cottineau, C., Chapron, P., Reuillon, R. (2015). Growing models from the bottom up. An evaluation-based incremental modelling method (EBIMM) applied to the simulation of systems of cities. JASSS, 18(4), 9. Cuyala, S. (2013). La diffusion de la géographie théorique et quantitative européenne francophone d’après les réseaux de communications aux colloques européens (1978–2011). Cybergeo, revue européenne de géographie, April. Cuyala, S. (2014). Analyse spatio-temporelle d’un mouvement scientifique. L’exemple de la géographie théorique et quantitative européenne francophone. PhD thesis, Université Paris 1, Paris. ECTQG (2019) The European Colloquium on Theoretical and Quantitative Geography. Luxembourg. [Online]. Available at: http://www.ectqg.eu/ectqg-2019/ [Accessed 01/08/19]. ERC Geodivercity (n.d.). [Online]. Available at: geodivercity. parisgeo.cnrs.fr/blog/. Fen-Chong, J. (2012). Organisation spatiotemporelle des mobilités révélées par la téléphonie mobile en Île­de-France. PhD thesis, Université Paris 1, Paris. FUN-MOOC (2016). Échanges et proximité: la première loi de la géographie. [Online]. Available at: https://www.fun-mooc.fr/ courses/Paris1/16004S02/session02/about [Accessed 01/08/19]. Géographie-cités (2017). Mobiliscope. [Online]. Available at: mobiliscope.parisgeo.cnrs.fr/. GeoRezo (n.d.). [Online]. Available at: georezo.net/. George, P. (1972). L’illusion quantitative en géographie. In La pensée géographique française contemporaine, Collectif. Presses universitaires de France, Saint-Brieuc, 121–131. Goodchild, M. (2007). Citizens as sensors: the world of volunteer geography. Geojournal, 69(4), 211–221. Gould, P. (1969). Methodological developments since the fifties. Progress in Geography, 1, 1–49. Haggett, P. (1965). Locational Analysis in Human Geography. Arnold, London.

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Joliveau, T. (2011). Le géoweb, un nouveau défi pour les bases de données géographiques. L’espace géographique, 40(2), 154–163. Lévy, J., Lussault, M. (eds) (2003). Dictionnaire de la géographie et des espaces en société. Belin, Paris. Louail T., Pumain D. (2014). Interaction d’ontologies informatique et géographique pour simuler les dynamiques multiscalaires. In Ontologie et modélisations par SMA en SHS, Phan D. (ed.). Hermès Sciences-Lavoisier, Paris, 193–207. Lucchini, F. (2015). Temporalités et rythmes urbains. Les interprétations géographiques du temps et les espaces urbains. L’information géographique, 2, 28–40. Lucchini, F., Elissalde, B., Grassot, L., Baudry, J. (2016). Paris tweets, données numériques géolocalisées et évènements urbains. Netcom, 30(3/4), 207–230. Müller, J.-P. (ed.) (2015). Le contexte – Rencontres interdisciplinaires sur les systèmes complexes naturels et artificiels. Chemins de traverse, Paris. OpenMole (n.d.). [Online]. Available at: www.openmole.org/. Pumain, D. (1976). La composition socioprofessionnelle des villes françaises. Essai de typologie par analyse des correspondances et classification automatique. L’espace géographique, 4, 227– 238. Pumain, D. (1997). Vers une théorie évolutive des villes. L’espace géographique, 2, 119–134. Pumain, D., Reuillon, R. (2017). Urban Dynamics and Simulation Models. Springer, Berlin. Pumain, D., Robic, M.-C. (2002). Le rôle des mathématiques dans une “révolution” théorique et quantitative: la géographie française depuis les années 1970. Revue d’histoire des sciences humaines, 6, 123–144. Pumain, D., Saint-Julien, T. (1978). Les dimensions du changement urbain. CNRS, Paris. Pumain, D., Saint-Julien, T. (1979). Les transformations récentes du système urbain français. L’espace géographique, 3, 203–211.

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Pumain, D., Sanders, L. (2013). Theoretical principles in interurban simulation models: a comparison. Environment and Planning A, 45, 2243–2260. Pumain, D., Saint-Julien, T., Vigouroux, M. (1983). Jouer de l’ordinateur sur un air urbain. Annales de géographie, 511, 331– 346. Pumain, D., Saint-Julien, T., Sanders, L. (1989). Villes et autoorganisation. Economica, Paris. Pumain, D., Bretagnolle, A., Glisse, B. (2006). Modelling the future of cities. ECCS’06, Proceedings of the European Conference of Complex systems. University of Oxford, Oxford. [Online]. Available at: http://halshs.archives-ouvertes.fr/halshs-00145925/ en/. Racine, J.-B., Reymond, H. (1974). L’analyse quantitative en géographie. Presses universitaires de France, Paris. Rey-Coyrehourcq, S. (2016). Une plateforme intégrée pour la construction et l’évaluation de modèles de simulation en géographie. PhD thesis, Université Paris 1, Paris. Rimbert, S. (1972). Aperçu sur la géographie théorique, Une philosophie, des méthodes, des techniques. L’espace géographique, 1(1/2), 101–106. Sanders, L. (1992). Système de villes et synergétique. Anthropos, Paris. Sanders, L. (2011). Géographie quantitative et analyse spatiale: quelles formes de scientificité? In Les sciences sociales sont-elles des sciences ?, Martin, T. (ed.). Vuibert, Paris, 71–91. Sanders, L., Favaro, J.-M., Glisse, B., Mathian, H., Pumain, D. (2007). Artificial intelligence and collective agents: the EUROSIM model. Cybergeo, 392, 15. Schmitt, C. (2014). Modélisation de la dynamique des systèmes de peuplement: de SimpopLocal à SimpopNet. PhD thesis, Université Paris 1, Paris.

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Schmitt, C., Rey-Coyrehourcq, S., Reuillon, R., Pumain, D. (2015). Half a billion simulations, Evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model. Environment and Planning B, 42(2), 300– 315. Simpop (n.d.). The Simpop Project. [Online]. Available at: www. simpop.parisgeo.cnrs.fr/. Staszak, J.-F. (2001). La géographie. In Épistémologie des sciences sociales, Berthelot, J.-M. (ed.). Presses universitaires de France, Paris, 593. Vallée, J., Lecomte, C., Le Roux, G., Commenges, H. (2017). La ville à toute heure: le mobiliscope. [Online]. Cybergeo conversation. Available at: https://cybergeo.hypotheses.org/. Wieber, J.-C., Massonie, J.-P., Condé, C. (1983). Dix ans de pratique en géographie quantitative à travers les colloques de Besançon. Annales de géographie, 92(511), 257–267.

3 Conceptual Modeling and Multidisciplinary Dialogue

3.1. Introduction The modeling of complex systems increasingly requires crossing the views of several disciplines. But should we immediately end up with a synthesis of these different points of view, and therefore the construction of a common representation of the system, or should we formalize each point of view separately and then articulate them in a shared representation? We distinguish a common representation that would be unique, from a shared representation where everyone understands the point of view of others and has identified possible articulations. It is this last option, which is to keep each point of view distinct, that we have been investigating for several years. First, we distinguish for each point of view the theory, which is the understanding we have of the system expressed in general in natural language, from the model, which is the formalization (differential equations, etc.) of this discourse (Meurisse 2004; Livet et al. 2010). The theoretical discourse is formalized using ontologies (Phan et al. 2014) in order to have a graphic, synthetic representation and, we hope, shareable with other Chapter written by Jean-Pierre MÜLLER.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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disciplines because it is independent of the specific formal models manipulated by the disciplines. This multiperspective methodology has been explained in various publications (Müller et al. 2011; Müller et al. 2012; Duboz et al. 2013). We will show its application to a problem of wild pepper domestication in Madagascar and show how it can instrument both debates on key concepts and articulations between points of view. Wild pepper in Madagascar (or voa tsiperifery, literally the “wound-healing leaf”) is harvested destructively from vines that grow in the primary rainforests of northeastern Madagascar. Its attractiveness as a luxury product leads to overexploitation and eventually to its disappearance. This is why a domestication program has been launched bringing together a group of researchers focusing on: – its characterization by botanists and geneticists to know which species (s) to market; – its reproducibility by agronomists and ecologists; – its qualification by biochemists to know its qualities; – its profitability for the agents of the sector by socioeconomists; – the mastery of its handling by quality engineers; – its social feasibility by sociologists. A representative of each of these disciplines was surveyed and his or her speech formalized in the form of a specific ontology. We will show how the confrontation of these different ontologies allows: – on the one hand, to exhibit the debates on concepts shared by different disciplines, illustrated here by the debate on the notion of species; – on the other hand, to articulate complementary points of view to facilitate dialogue, illustrated here by the

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articulation of the notion of supply chain with the notion of quality chain. A possible articulation with the biochemical point of view will be outlined. The advantages and disadvantages of this approach will finally be discussed, but before that, we will present some points of methods. 3.2. Representation of theoretical discourses We will represent the discourses by formalizing the concepts present in them. A concept is the idea one has of something. This concept can be linked to an individual object: my concept of my car, my concept of the ecosystem that interests me. It can be linked to an intangible object of thought: my concept of beauty, equity or what a concept is. These are the individual concepts (or individuals). Finally, it can be a category, like a way of grouping objects: because they have the same size or color, because they are of the same species, etc. These concepts are called categorical concepts (or categories). These are the boxes used to classify the world. Of course, an object (or at least its idea) can be in several categories at once (large, blue, and of a given species). These concepts construct how reality appears to us. One of the important processes of knowledge in general, and science in particular, is first of all to create a world of distinctions: for example, the minerals from the animals from the plants. And this world of distinctions must be operational to make reality understandable, if not predictable. These concepts appear through the words we use. Even if we don’t know what it means for someone to be blue, at least we know that they have a category of blue things called “blue things”. There are very often labels on the boxes. Terminology is therefore the (only?) entry point to concepts. But how can we be sure of the nature of the objects that are named? Above all, how can we ensure that two observers

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talk about the same object or category of objects when they use the same word? The only entry points we have are definitions. A definition links a word to a set of other related words. If the same word is not related to the same set of related concepts (not just related words because they could also use different words for the same concepts), it is related to different concepts. Conversely, if two different words are related to the same set of related concepts, then they are related to the same concept. As a result, we obtain a network of related concepts (and associated words) where, in addition, the relationships themselves are named (and can therefore also be concepts). It is this network that we call a conceptual model or ontology. One of its properties is to be closed, that is all the concepts necessary to define the other concepts of the same network are found in the network or the concept is considered atomic, that is it is not necessary to explain it further for our usage. For example, it may not be necessary to explain that an animal or plant is composed of cells and these cells of atoms, nor how we distinguish between plants and animals to describe an ecosystem. Therefore, any conceptual network defines its level of granularity (atomic concepts) and its extent (the domain covered), thus defining a conceptual scale. When concepts are directly related to spatial or temporal concepts, the conceptual scale strictly corresponds to the usual concept of scale with a range and granularity. To represent these terminological and/or conceptual graphs, a number of formalisms have been proposed, such as formal logics or frame languages, and more recently description logics (DL), ontologies (OWL), and thesaurus languages (SKOS). We have chosen to use a graphical representation to highlight the graph structure of the conceptual model, using UML (Unified Modeling Language), although UML does not offer the expressiveness that one would expect from such formalisms, as it is mainly used by computer scientists.

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UML introduces a number of diagrams among which the class diagram is the most appropriate to graphically represent concepts. Concepts are represented by a class composed of a box with two parts: an upper part with the name of the concept (with a capital letter and in the singular) and a lower part with the list of attributes. In Figure 3.1a, the concept of a farmer is represented with three attributes: name, age, and size. The concepts are linked to each other with a number of possible arrows: – The line with a white triangle to represent a generalization/specialization relationship to construct concept taxonomies (Figure 3.1b), the concept of farmer is a specialization of the concept of person: every farmer is a person (or the set of farmers is included in the set of people, but not the opposite); – The line with a white or black diamond to represent the part-of relationship (Figure 3.1c), a tree is part of a forest, or a forest is made of trees; – The single line (although sometimes with arrows to indicate no one or) with a name to represent any relationship. It is annotated with cardinalities, that is how many objects of one category can be linked to the objects of the other (Figure 3.1d), there is a property relationship (or association) between the concept of farmer and the concept of plot, and a plot belongs to no one or a single farmer (0...1) and he can have as many parcels as he wishes (*).

a) class, attributes

b) generalization

c) part-of

d) association

Figure 3.1. Some basic UML representations. For a color version of the figures in this chapter see www.iste.co.uk/waldeck/methods.zip

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Using UML, a definition can easily be translated into a UML graph. For example, Collins defines livestock as animals kept on a farm. The resulting graph is shown in Figure 3.2. The livestock is composed of animals (or the animals are part of the livestock) and the livestock has a location relationship with the farm concept (a livestock is located on a single farm that has only one livestock). Note that livestock does not cover backyard animals and does not necessarily place them on a farm. This allows us to easily display conceptual nuances. This UML formalism will be used throughout this chapter.

Figure 3.2. UML class diagram for a livestock definition

3.3. Disciplinary views on species In order to characterize the species to be domesticated, plants (or stems) meeting the local term voa tsiperifery were collected according to the diversity of their morphology as well as of the eco-climatic contexts in which they grow. To explore the characterization modalities, we interviewed a botanist, a biochemist, and a geneticist. The aim was not to model the discourse of these three fields in general, but only the concepts that these particular people, trained in these fields, used in this project to characterize the plants collected, that is a point of view. Already, only the botanist talks about plant while the other two talk about stem. In conceptual models, we respect this distinction by knowing that they are talking about the same object. On the other hand, in the text, we will use the term “stem”.

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From a botanical point of view (Figure 3.3), the objective is explicitly to identify the species to which each of these plants belongs. To do this, a botanist will select a number of botanical variables. A botanical variable is a part of a plant and a set of possible values. For example, a botanical variable may be the leaf and a list of its possible shapes: oval, spatulate, hastate, etc. Each stem will therefore be described using this selection of variables and their value, thus providing a botanical signature of the plant (the signature term was not used by the botanist). It is then possible to group the stems by similarity of these signatures obtaining different groups called morphotypes (the boxes). In 1923, de Candolle used five botanical variables on Madagascar peppers and obtained eight morphotypes. The Missouri Botanical Garden did this very recently on 9 variables and obtained 12 morphotypes. This purely statistical aggregation process is called categorization. But the work of a botanist, following the naturalist Linné (1707– 1778), is to arrange the plants in taxa, which are named morphotypes (boxes with a label). This is the classification process. Thus, the plant world is classified into families, genera, species, and varieties (for agronomists only) which are therefore taxa, knowing that a family groups several genera, and a genus several species. In this case, the plants we are interested in are in the piper family and piper genus. A taxon is associated with a description by certain botanical variables, this association being published by a botanical journal referring to it. Thus, the eight morphotypes of de Candolle are at the base of eight different species, three of which would correspond to the voa tsiperifery: piper borbonense, piper pachyphylum, and piper hemiei (at the bottom of Figure 3.3). It is the botanist’s responsibility to find out which species correspond to the morphotypes he has identified on the basis of the stem sampled. In this case, the botanist found four morphotypes, which would suggest that one of the Candolle species would actually be two distinct species, but this is an open question (Razafimandimby 2011).

Figure 3.3. Botanical conceptual model

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Figure 3.4. Conceptual genetic model

The geneticist interviewed started from the same stems, but, for reasons of treatment cost, only took a sample of stems on the basis of a number of criteria discussed with

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both farmers (based on marketing practices) and botanists (based on morphotypes) (Figure 3.4). DNA was first extracted, followed by specific genes called markers. A repository shared by specialists in the field defines the markers to be used to ensure a good quality analysis. A gene consists of a coding part that cannot change otherwise the function of that gene would be lost, and a non-coding part whose sequence will vary by genetic mutation. The measurement of the difference between the sequences of noncoding parts of the same gene between two samples is proportional to the age of their common ancestor. Two by two comparisons of the samples allowed them to establish a phylogenetic tree representing the relative ages of the common ancestors. Finally, a phylogenetic reference system associating species with already sequenced genes allows for identifying the species by proximity. In the absence of this repository, it is always possible to perform statistical clustering to obtain categories or genotypes. The biochemist’s task is to perform a biochemical characterization of the different stems (Figure 3.5). Since it is a spice and more particularly a pepper, the two elements that were to be analyzed were the level of piperine that gives strength to the pepper, and the composition of essential oils (called organoleptic) that defines its aroma. To do this, different parts of the plant were to be crushed after undergoing certain pre-treatments (drying, etc.) and subjected mainly to two analytical processes: – spectrophotometry to evaluate its piperine level; – distillation to obtain the essential oils, followed by chromatography. The latter would produce a profile composed of peaks whose height indicates the proportional quantity, position (or Kovats index), and shape constitute a signature of the molecule present. A reference frame made it possible to associate a Kovats index and a spectrum with a given molecule, thus obtaining a relative composition of the main molecules, the smallest peaks being generally neglected.

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Figure 3.5. Conceptual biochemical model

Without going into detail, it appears that wild Madagascar pepper has less piperine than black pepper (so it is less strong), but contains around 14% essential oils instead of 4 to 5% for black pepper (so it is much more aromatic) (Rambolarimanana 2014). It should be noted that these analyses do not only cover the consumable parts (berries), but also the other parts, paving the way for considering purely essential oils, or even pharmaceutical channels with the entire plant. In addition, the biochemist carried out statistical clustering on the molecular profiles of the different stems, which had not been requested, obtaining four categories or

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chemotypes. Without a reference frame associating molecular profiles with species, it is not possible to go any further and determine species through this channel, but obtaining these chemotypes is another way to identify the species present. To summarize, the different stems constituting the initial sample passed in whole or in part through three processing chains allowing them to be analyzed from different points of view: botanical (form), phylogenetic (filiation), and organoleptic (aromatic composition). According to each of these points of view, we obtain for each stem a particular profile on which each one has carried out a categorization by statistical clustering, thus obtaining the identification of morphotypes, genotypes, and chemotypes respectively. Already at this level, we do not obtain the same number of types for each point of view and the fact that two stems belong to the same type from a certain point of view, does not imply that they belong to the same type from another point of view. There is no consistency between phylogenetic proximity and morphological or organoleptic expression of the genetic code. Perhaps it would be necessary to add the characterization of the ecological context of the samples to obtain better matches. The association of these types with named species is done through reference frames that associate profiles with species. These standards only exist for botanical and genetic profiles and here again, there is no consistency. To conclude, since taxonomic classifications are historically the work of botanists, all researchers agreed that it is the botanical classification that prevails. Nevertheless, the juxtaposition of points of view highlights that the notion of species is a social construct highly organized around the collective construction of reference frames and that the mapping of species to observations, especially if they are duplicated in different points of view, is more than problematic, going so far as to question the very notion of species. The conceptual modeling that we carried out made it possible to clarify a debate that was anticipated by the

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participants, but never formalized. To instrument this debate, conceptual models should be revised to clarify the terms of the debate, including the categorization and classification processes. 3.4. Sectors and qualities For this part, we interviewed a socio-economist and a quality engineer. We will therefore present their respective discourse before describing the possible articulations. In this project, the socio-economist’s objective is to support the actors from the sector to establish specifications that would enable the sector to be organized in the best possible conditions. A specification is a set of requirements describing the production methods used, the necessary coordination between actors (sequencing), the qualities required for products throughout the supply chain, and the controls implemented to ensure that the products have the required qualities. The object studied by the socio-economist is the supply chain itself. A supply chain is a set of interacting and spatially located actors, each carrying out one or more actions transforming products into new products (or the same in a different state: dried, packaged, etc.). That is the social dimension. At one end of the chain are the so-called primary products (pepper on vines called voa tsiperifery) collected by the picker and at the other end the commercial product purchased by the consumer (Madagascar wild pepper: a possible brand not yet registered). It is the flow of products that constitutes the possible link with quality analysis, but not at the same granularity as we will see. Finally, the economic dimension consists of investigating the production costs of each actor in the chain from harvest to marketing, including transport costs since the actors are spatially located, as well as the prices charged in trade throughout the chain. For each actor,

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the purchase price of the input products (there are none in primary production), the cost of production, and the selling prices of the product(s) obtained, as well as the volumes traded, make it possible to calculate, for each actor, what is the added value, thus how the profits are distributed throughout the chain, and secondarily to qualify the viability of the sector. Since the specifications have, in part, an impact on costs, it is therefore necessary to go back and forth between the analysis of the sector and the construction of the specifications in order to ensure the best possible compromise between product quality (and/or compliance with imposed standards) and economic viability. The quality engineer will make a much more detailed analysis of the chain of transformations (storage, washing, drying, transport, etc.) that will transform the state of a product or the product itself into another product. In this context, the state of the products is defined essentially by their qualities. There are three types of qualities: – the sanitary quality, which covers both the presence of toxins in the product and the presence of bacteria; – the nutritional quality, which covers its composition in lipids, proteins, and carbohydrates as well as the composition in vitamins and antioxidants; – the organoleptic quality that covers all five senses: - tactile: with characteristics such as texture and hardness; - visual: such as color and shape; - gustatory: what can be analyzed by the tongue: sweet, salty, spicy, etc.; - aromatic: what can be analyzed through the nose, so all the aromas; - auditory: the noise produced when it is moved or chewed.

Figure 3.6. Conceptual model of supply chains

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Figure 3.7. Conceptual model of quality analysis

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It was therefore necessary to characterize how the different transformations would affect all these qualities in order to improve the processes. This analysis only concerns the processes and does not take into account the actors who implement them. There is an obvious complementarity between the supply chain point of view and the quality point of view around two concepts. The concept of transformation from a quality point of view is clearly a detailed analysis of the actions from a supply chain point of view. The supply chain analysis adds the costs and could refine them from the detailed description from the quality point of view. The other point of articulation is the notion of quality itself. It was extremely detailed in the quality approach, but allowed us to build a dialogue with the establishment of the specifications and the definition and consideration of standards. In addition, the quality approach uses quality analysis procedures that could be used as a control procedure throughout the supply chain. Finally, organoleptic quality could be associated with the biochemical point of view, particularly with regard to flavors, and opened the door to a mapping between the biochemical composition of essential oils and flavors. 3.5. Validation and communicability These schemas were validated by simply sending a schema and its textual description to the interested parties. Other experiences have shown that the scheme alone is not easily readable by people who are not trained in this type of representation. For this reason, and to the extent that each class and the links surrounding it can be reformulated in the form of a definition, or that each link in the schema can be restored in the form of a subject-verb-object sentence, the schema has been transcribed entirely in natural language. However, we believe that a face-to-face validation is

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essential to ensure that we have faithfully reproduced the specialist’s thinking. To ensure better communicability of conceptual models, another approach is to go through a more intuitive representation for specialists in the field and convert it a posteriori to UML. This is what we did in another project in which we developed an ontology to describe a supply chain by asking participants to describe the supply chain (Figure 3.8) by organizing: – a sequence of actors (in yellow) for the case where there were the most intermediaries, by characterizing their product exchange modes (in orange); – a sequence of transformations (in red) of the products (in blue) from one end of the chain to the other; – the association of actors to the transformations. From there, it was easy to extract a taxonomy of the actors, transformations, and products chosen by the specialists to describe their supply chain and thus obtain a conceptual model while keeping the drawing of the field as an object of mediation in a co-construction. The next step is a restitution where, with the prior agreement of the interested parties, all the diagrams must be presented to everyone. The way in which the coconstruction of the possible articulations between the points of view is to be conducted has yet to be developed. Our analysis of possible articulations could serve as an agenda for such a meeting, followed by the proposal to explore other articulations that we would not have seen. In any case, the objective is not to merge these schemas, but to identify and specify the interfaces between points of view: boundary concepts or links to be co-defined (such as the notion of species) or invented to communicate among points of view (such as bridges between actions and transformations).

), trade (orange), processing (red), and products (blue)

Figure 3.8. A channel with stakeholders (yellow), trade (orange), processing (red), and products (blue)

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Figure 3.9. Result of the extraction of the conceptual model for the fish sector

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3.6. Conclusion We proposed using the conceptual models presented in the form of UML diagrams as tools to elicit and therefore display the concepts convened by different points of view around the same issue: the domestication of Madagascar wild pepper. We presented five points of view and showed how they help to clarify complementarities, but above all the points for which their articulation requires joint work to adjust or develop the boundary concepts and links between the points of view. The problem of validation remains an open problem, both in terms of the communicability of the conceptual models obtained and the meaning of validation at the conceptual level. Is it a conformity to the discourse and which one or ones? Or a conformity to reality, but which one? Should we be satisfied with an endogenous criterion such as conceptual closure: everything is defined on the basis of atomic concepts? How do we determine, in a point of view, atomic concepts seen as a kind of conceptual granularity? These are all questions that still need to be explored. Finally, these different points of view are intended to be articulated between them, which also raises a question of method, which is briefly outlined in this chapter. Very generally, engineering in technical fields has succeeded in communicating the different specialities through a standardized representation of the object they contribute to build in the form of drawings, and more recently their digitized version using CAD (computer-aided design). This representation is essentially geometric, although dynamics are beginning to be integrated into it in order to simulate the mechanisms. With regard to socioecological systems, there is still a need to find this standardized representation, whether to build a shared understanding or to target a form of socio-ecological engineering. Cartography plays a major role in this and is clearly the territorial avatar of the CAD. We believe that

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conceptual models, including of the territories, could play this role as a support for multidisciplinary and multistakeholder communication and aggregation. 3.7. References Duboz, R., Müller, J.-P. (2013). Modélisation des socio-écosystèmes. Instrumenter le dialogue multidisciplinaire. In Modéliser et simuler: épistémologies et pratiques de la modélisation et de la simulation (vol. 1, vol. 2), Varenne, F., Silberstein, M. (eds). Matériologiques, Paris, 865–896. Livet, P., Müller, J.-P., Phan, D., Sanders, L. (2010). Ontology, a Mediator for Agent-Based Modelling in Social Sciences. JASSS, 13(1), 3. Meurisse, T. (2004). Simulation multi-agent: du modèle à l’opérationnalisation. Thesis, Paris VI. Müller, J.-P. (2014). Les points de vue et leur modélisation. In Ontologies et modélisation par SMA en SHS, Phan, D. (ed.). Hermes-Lavoisier, Paris, 111–129. Müller, J.-P., Diallo, A. (2012). Vers une méthode multipoint de vue de modélisation multi-agent. Systèmes multi-agents: ouverture, autonomie et co-évolution. Actes des JFSMA’12, Honfleur, France, 17–19 October. Müller, J.-P., Diallo, A., François, J.-C., Mathian, H., Sanders, L., Phan, D., Waldeck, R. (2011). Building ontologies from a variety of points of view. 7th Conference of the European Social Simulation Association (ESSA 2011), Montpellier, France, 19– 23 September. Phan, D., Müller, J.-P., Sibertin-Blanc, C., Ferber, J., Livet, P. (2014). Introduction à la modélisation par SMA en SHS: comment fait-on une ontologie? In Ontologies et modélisation par SMA en SHS, Phan, D. (ed.). Hermes-Lavoisier, Paris, 21– 51.

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Rambolarimanana (2014). Variabilités chimiques des poivres sauvages de Madgascar ou Tsiperifery: cas du district d’Anosibe An’ala et de la commune de Beforana. M.Phil thesis, École supérieure des sciences agronomiques, University of Antananarivo, Antananarivo. Razafimandimby (2011). Études écologiques et ethnobotanique de Tsiperifery (Pepier sp) de la forêt de Tsiazompaniry pour une gestion durable. End of study thesis, École supérieure des sciences agronomiques, University of Antananarivo, Antananarivo.

4 Network Analysis: Linking Social and Ecological Dynamics

4.1. Introduction This chapter is intended for researchers and students involved in interdisciplinary research on interactions between societies and the environment. Network analysis methods have been used for several decades by some disciplines (sociology and physics), and are undergoing rapid developments in others (geography, anthropology, and ecology). They are also raising a growing interest in interdisciplinary research, such as the study of socialecological systems. Indeed, these methods open new perspectives for understanding the processes involved in the structure and dynamics of these systems, as well as their emerging properties. They also constitute integrative tools that facilitate collaboration between the humanities and social and life sciences, since the same formalisms and theoretical frameworks apply to the analysis of interaction networks, whether to describe social, ecological or socioecological processes.

Chapter written by Vanesse LABEYRIE, Sophie CAILLON, Matthieu SALPETEUR and Mathieu THOMAS.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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However, network analysis methods remain relatively complex to implement, both for field data collection and analysis, and results interpretation can be difficult. Thus the use of these quantitative methods, that allow the analysis of the structural characteristics of networks, implies reflecting beforehand on their articulation with qualitative approaches. Such reflection will ease the elaboration of clear hypotheses that the network analysis would test, as it will help to explain the results of quantitative analyses, in particular by specifying the nature of the interactions and by contextualizing them in the more global systems of interactions, between individuals within social groups, between individuals and their environment, or between components of the environment. The objective of this chapter is to introduce the basic principles of network analysis and to provide an overview of the main types of questions that can be answered in the field of ecological and social systems studies. It aims at enabling the readers to identify whether these analyses are relevant for their research questions, and to think about their complementarity with methods and tools from both the biological sciences and the human and social sciences. Case studies are mostly drawn from the work of the MIRES scientific network (Méthodes interdisciplinaires sur les réseaux d’échange de semences – Interdisciplinary methods on seed exchange networks). 4.1.1. Societies-environment interactions, what complex systems? Given the complexity of environmental problems, the development of interdisciplinary approaches has become necessary, giving rise to a field of study focusing on socialecological systems (SES Berkes and Folke 1998). These approaches conceptualize the interactions between societies and their environment by defining SES as a set of actors

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whose livelihood depend on the functioning of ecosystems, which they influence through their practices. SES are therefore coupled systems of social and ecological interactions that can be represented as networks (Figure 4.1): (1) actors are embedded in networks of social, economic, or political relationships that influence their practices; (2) they interact with the different entities that make up ecosystems via these practices; and (3) the different entities that make up ecosystems interact with each other (Bodin and Tengö 2012). All interactions between actors, between actors and components of ecosystems, and between components of ecosystems, constitute a social-ecological system. Social entity Ecological entity Social interactions Ecological interactions Social-ecological interactions Figure 4.1. Network of coupled social and ecological interactions (according to Bodin and Tengö, 2012). For a color version of the figures in this chapter see www.iste.co.uk/waldeck/methods.zip

These coupled networks of social and ecological interactions have the characteristics of complex systems. Indeed, the emergence and the dynamics of links within these networks result from self-organization processes, themselves resulting from the interactions at work between the components of the system. These social-ecological networks have emerging properties related to the structure of interactions, which cannot be deduced from the individual properties of the entities because of the large number of interactions between them (“the whole is more than the sum of the parts”). As a result, the behavior of these systems is non-linear and their empirical study, by observation and

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experimentation or simulation, requires modeling tools that explicitly take into account the interactions between entities. It is in this perspective that analytical methods based on network models, derived from graph theory, have been applied to the study of social-ecological systems (Janssen et al. 2006). These methods explicitly take into account the interactions between entities and are therefore suitable tools for analyzing complex systems. They reduce complexity by highlighting regularities, repeated patterns of connectivity between entities, which reflect the processes involved in the functioning of the SES and determine its properties (Bodin et al. 2006; Bodin and Crona 2009; Bodin and Tengö 2012). These approaches make it possible to address two main categories of questions related to the study of socio-ecological systems: – what are the processes involved in the emergence and dynamics of the structure of coupled networks of social and ecological interactions? – how, in turn, do the structure of these networks determine the functioning of socio-ecological systems? 4.1.2. Introduction to network formalism Network formalism represents entities as nodes and interactions as links. A set of attributes (or descriptors) can be associated with nodes and links. In particular, links can be directed according to the direction of interaction between the two entities. The structure of the network constitutes a kind of footprint of the social and ecological processes involved in the system under study. By analyzing this structure, it is thus possible to highlight the processes involved and to quantify their effects. Various methods exist for this; we will only describe some of them in the examples below. Before that, we will review the main categories of approaches according to the scale of analysis of the network structure.

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Structural analysis makes it possible to infer the processes involved in network formation and their dynamics according to the theory of social network analysis, which distinguishes two main types of processes (Lusher and Robins 2012): (1) endogenous, self-organizing processes, linked to so-called “dependent” factors, that depend on the position of the node within the network; and (2) exogenous processes, determined by so-called independent factors, linked to the intrinsic characteristics or attributes of the nodes and links, and that do not depend on their position within the network. These two types of processes interact in the emergence and evolution of the network structure. For example, a link may be formed between two people because they have a common friend (endogenous process of transitivity), or because they speak the same language (exogenous process of homophily), or because of the combined effect of these two factors. 4.1.2.1. Endogenous processes The analysis of structural patterns at different levels, from the node to the entire network, enables the detection of different types of endogenous processes potentially shaping the network structure. The level at which the structural patterns will be analyzed has to be defined in relation with the questions addressed. A first category of questions concerns the so-called local structural patterns. At the node level (which is the basic component of a network), a common question that arises is to know whether some actors have more relationships than others. The metric to measure this is the degree, i.e. the number of links a node has (Figure 4.2). It is then possible to specify whether nodes with a high degree are more popular than others, that is whether a large number of nodes make connections with them (high incoming degree), or whether they are more active, that is they establish connections with a large number of nodes (high outgoing degree).

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Figure 4.2. Degree

Other types of questions require us to examine the connectivity patterns between several nodes, particularly if we want to test whether the relationships between pairs of nodes (dyads) tend to be reciprocal, or whether indirect reciprocity mechanisms are at play that would then be reflected by the patterns of cyclical triads (Figure 4.3). On the contrary, it is possible to test whether hierarchy structure the networks, which translates into patterns of transitive triads. In this case, a node A linking to a node B will never receive a link directly established by node B, nor indirectly established by a node C to which node B has established a link. To test whether these processes have a significant effect on the network structure, statistical methods can be used to compare the observed frequency distribution for the different types of patterns with a random distribution, to test whether the links were established at random between the nodes.

Cyclical triad

Transitive triad Figure 4.3. Triads

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A second category of questions requires the analysis of macroscopic structures in networks. Connectivity patterns between nodes are then examined globally, seeking groups of nodes with similar connectivity patterns, connecting in a similar way to other nodes or to each other. This is the case, for example, when we wish to identify communities, that is groups of nodes that have more interactions with each other than with other parts of the network. These communities are generally connected by “weak ties” that act as bridges or brokers, resulting in macrostructures of the “structural holes” type (Figure 4.4a). It is also possible to highlight the existence of a central group of highly interconnected nodes to which poorly connected nodes are linked, resulting in coreperiphery structures (Figure 4.4b). a)

b)

Figure 4.4. a) Communities and structural hole. b) Core-periphery structure

Finally, the last category of questions requires the measurement of metrics at the scale of the network as a whole. Different metrics can be used to characterize network cohesion, the most commonly used being density, that is the ratio of the number of links in the studied network to the number of links that a complete graph with the same number of nodes would have. 4.1.2.2. Exogenous processes Exogenous processes are driven by nodes and links attributes, by opposition with the structure of the network in

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itself. In the case of node characteristics, for example, we may wish to highlight the existence of homophily processes by testing whether individuals speaking the same language are more likely to interact with each other than with individuals speaking another language. We then examine the frequency distribution of the pairs of connected individuals (dyads) speaking the same language, compared to the one expected under the random (null) hypothesis. These may also be ties characteristics, for example, testing whether relatives are more likely to interact with each other than unrelated individuals (i.e. kinship ties). The following sections present some illustrations of the methods that can be used to analyze the structure of networks and test the effect of endogenous and exogenous processes. These examples deal with networks of social interactions within socio-ecological systems, that determine the circulation of biological objects and ecological knowledge. They mobilize a range of social networks analysis methods (Wasserman and Faust 1994; Degenne and Forsé 1999). 4.2. Examples of applications to the study of interactions between societies and the environment Social interactions between actors affect ecosystem dynamics via their role in the circulation of biological resources and of the knowledge associated with their management. The application of network analysis methods to the study of these two main types of mechanisms will be illustrated by three case studies whose results have been published and analyzed in the articles by Thomas and Caillon (2016), Labeyrie et al. (2016), and Salpeteur et al. (2016). Our objective is to illustrate the type of research questions that network analysis can address in this area, and to present some methods that can be used in this domain. The first two examples illustrate how social interactions guide the circulation of tangible objects that are associated

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with ecological flows: for example, the circulation of seeds of cultivated plants, domestic animals, and associated pathogens (Thomas and Caillon 2016; Labeyrie et al. 2016). The second example shows that these interactions are also decisive in the circulation of intangible objects, that is the knowledge and know-how associated with the use and management of the environment (Salpeteur et al. 2016). 4.2.1. Crop seed circulation and social networks Seed circulation networks play a key role in the dynamics of crop diversity (Coomes et al. 2015). These networks, shaped by different social processes, are based on various types of pre-existing relationships between individuals. The two case studies presented illustrate this relationship between the organization of societies and the circulation of cultivated plants in two geographical areas, Kenya and Vanuatu. 4.2.1.1. Sorghum seed organization in Kenya

circulation

and

ethnolinguistic

Population genetic studies at various spatial scales, ranging from the African continent to a village, have shown a relationship between the geographical structure of sorghum genetic diversity and that of the ethnolinguistic diversity of farmer populations (Harlan and Stemler 1976; Labeyrie et al. 2014; Westengen et al. 2014). The hypothesis that seed circulation is more intense within ethnolinguistic groups than between groups has been proposed to explain this relationship, which would limit gene flows between the sorghum populations they grow and thus favor their differentiation. In this context, network analysis was mobilized to test whether ethnolinguistic homophily structures seed circulation networks connecting farmers in African rural societies, that is whether seed gifts and exchanges are more frequent between members of the

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same ethnolinguistic group than randomly (Labeyrie et al. 2016). To test this hypothesis, we conducted our study in a village located in a contact zone between three language groups belonging to two ethnolinguistic units in the Mount Kenya region. Surveys were conducted among 197 households to document sorghum seed circulation events, representing about two-thirds of the households in this area. This has allowed us to build the seed circulation network between individuals (Figure 4.5). The chosen network analysis methods require working on a closed network, thus excluding households that had no ties with other households surveyed. The analysis therefore focused on a subsample of 156 nodes and 235 non-oriented ties (“A and B exchanged seeds”), representing only 28% of the total ties reported in the surveys.

Figure 4.5. a) Global open seed circulation network (197 nodes, 1,067 links; nodes kept for the closed network in red). b) Closed sub-network (156 nodes, 235 links)

The statistical analyses were designed to test whether intra-group links – within (1) residential; (2) language; and

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(3) ethnolinguistics groups – were more likely than randomly expected. We also controlled the effect of geographical distance, by testing whether relationships were more likely between geographically close households. The effect of three endogenous processes was also tested: (1) popularity, that is the probability of a link between two nodes increases with the number of links they already have; (2) transitivity, that is the probability of a link between two nodes increases when they are connected to the same node; (3) the group’s activity, that is the propensity of a node to establish links increases with the number of links established by the ethnolinguistic group to which it belongs. The non-parametric statistical method used (Exponential Random Graph Models, Robins et al. 2007) tests the effect of these different factors by comparing the value of the observed statistics, that is the frequency of the patterns, with that expected under the null hypothesis, that is if relationships between individuals were established randomly. The results obtained (Table 4.1) show that the effect of residential homophily is significant, and that linguistic homophily is observed only for one of the groups (the Mbeere), but not for the other two who belong to the same ethnolinguistic unit (the Chuka and the Tharaka). Farmers are therefore more likely to exchange with people belonging to the same residential and ethnolinguistic unit, but exchanges are not limited between language groups belonging to the same ethnolinguistic unit. There is also a significant effect of geographical distance, with the probability of a link being formed between two people being positively correlated with their geographical proximity. In addition, the Mbeere group has a lower activity than the other two language groups, that is a lower probability of making connections. And finally, the results show a significant effect of activity and transitivity, which are endogenous processes frequently observed in social networks (Lusher and Robins 2012).

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Effect

Estimation

Link

– 4.71 (0.53)***

Residential homophily

0.81 (0.15)***

Linguistic homophily (Mbeere)

1.84 (0.43)***

Linguistic homophily (Chuka)

– 0.68 (0.42)

Linguistic homophily (Tharaka)

0.75 (0.42)

Exogenous covariates: Geographical distance (km)

– 0.99 (0.10)***

Language group activity (Chuka)

1.04 (0.35) **

Language group activity (Tharaka)

0.61 (0.25)*

Endogenous covariates: Individual activity

4.77 (1.82)**

Transitivity

0.72 (0.11)***

Multiple connectivity

– 0.01 (0.04)

Table 4.1. Results of the ERGM Model. The parameter estimate is expressed in log odds and the standard deviation is indicated in brackets: *P < 0.05; **P < 0.01; ***P < 0.001

The interpretation of these results required the use of ethnographic literature and further investigation to understand the social norms that regulate seed gifts between individuals and lead to the observed structure. Our surveys showed that the vast majority of seed gift events take place between relatives (72%), particularly between women and their in-laws (45%). The ethnographic literature describing the rules of alliance and residence among these societies throw light on these results. These studies show that marriage alliances are concluded mainly between individuals from the same ethnolinguistic unit (ethnolinguistic endogamy), which was confirmed by our surveys (Table 4.2), and that women generally come from residential units far from their husbands’ (residential exogamy) and settle in the latter’s residential unit (virilocal residence), now becoming full members of their in-laws kin groups. The social

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proximity of women to their in-laws therefore explains residential homophily, while the absence of matrimonial alliances between ethnolinguistic units explains homophily on this scale. Language group Chuka Tharaka Mbeere

Chuka

Tharaka

Mbeere

21 (20)

18 (26) 29 (34)

2 (15) 3 (20) 21 (12)

Table 4.2. Number of observed matrimonial alliances between language groups, and theoretical number expected under the null hypothesis, that is if the alliances were randomly established between individuals (in brackets). χ2 test = 88.20, ddl = 4, P-value < 0.001

This example therefore provides a good illustration of how network analysis can be used as a tool for interdisciplinary dialogue, making it possible, in this case, to decipher the social processes involved in the circulation of biological objects – seeds – and thus to help explain the distribution of sorghum genetic diversity in the studied area. Population genetics, by revealing a correspondence between the distribution of sorghum genetic diversity and the distribution of ethno-linguistic diversity of human populations in the studied area (Labeyrie et al. 2014) and on the African continent (Westengen et al. 2014), highlighted the interest of using network analysis to test the effect of ethnolinguistic organization on seed circulation. At a second stage, the interpretation of these results required a more detailed understanding of the social relationships underlying seed exchanges, and therefore to make use of extensive ethnographic information regarding matrimonial alliance and residency rules. Conversely, information about the effect of social norms on seed circulation has been used in further research to inform network models simulating the dynamics of cultivated diversity under different scenarios (Barbillon et al. 2015).

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4.2.1.2. Circulation of different categories of crops in Vanuatu Ritualized exchanges of high-value objects have been widely studied by anthropologists in Oceania. They show, among other things, that the major ceremonies organized by individuals of high social status, the Big Men, were based on a multitude of exchanges of ritual objects and socially valued food plants. These Big Men won their title by virtue of the generosity with which they redistributed their wealth, especially during these final ceremonies. Although the Big Men no longer exist in Vanuatu today, our study aimed to test whether these hierarchical systems have left their mark on the circulation of the most commonly traded objects, that is food plants (Thomas and Caillon 2016). Network analysis was used in this case to characterize food plant circulation networks, and in particular to test the existence of individuals more active than the average in exchanges. These plants do not have the same socio-cultural value, being classified locally into three categories: (1) subsistence species – mainly starchy tubers; (2) accompanying species consumed with the previous ones at mealtimes; and (3) species that are usually snacked outside meals such as fruits and nuts. These three categories hold different statuses, subsistence species being culturally more valued than others. A secondary objective of this study was therefore to compare the circulation networks of these plant categories, in order to compare their structure. To test these hypotheses, a field study was carried out in a 10-km zone along the coast on the island of Vanua Lava in the Banks region, north of the Vanuatu archipelago. Surveys to identify events in the circulation of plant material of all cultivated plants – 31 species divided into 254 varieties – were conducted among 16 households, on a total of 30 adults who migrated from the neighboring island of Mota Lava. In the case of plants and of people, the investigators opted for an exhaustive approach: the interviews aimed to understand

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how these 30 first-generation migrants found each of the food plant varieties on this coast for the first time: who received the plant in the household and who gave them the plant. The circulation network of all the cultivated plants reconstituted this way included 2,049 oriented links (“A gave plants to B”), but only 577 of these links were retained for the ERGM analysis, which required the removal of links established outside the study area (Figure 4.6).

Figure 4.6. Closed seed circulation network (30 nodes and 577 links). Round knots represent women and squares represent men

The statistical analyses were designed to test: (1) whether some individuals give more or receive more than expected under the random assumption, measured by the frequency distribution of outgoing and incoming degrees, and (2) whether individuals who give more receive less, measured by the correlation between incoming and outgoing degrees. The effect of exogenous and endogenous processes was also monitored.

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Regarding exogenous factors, we tested: (1) whether relationships are more likely between people of the same gender (homophily), and (2) whether relationships are more likely between people with a similar number of connections outside the community (degree of incoming external links). Regarding endogenous factors, we controlled the effect of direct (number of reciprocal dyads) and indirect (frequency of circular triads) reciprocity, and hierarchy (frequency of transitive triads). These analyses were carried out using ERGM (Exponential Random Graph Models) models, applied to the global network (aggregated) and to the three subnetworks corresponding to the three categories of cultivated plants. The results obtained (Table 4.3) show that the Big Men’s hierarchical exchange systems are still reflected in the way food plants circulate, even if the gifts are not ritualized. Indeed, they highlight the existence of individuals giving more than the average (the frequency distribution of outgoing degrees differs significantly from that expected if interactions were random), and receiving few cultivated plants from other inhabitants (no correlation between incoming and outgoing degrees). On the other hand, the effect of direct and indirect reciprocity is not significant, but the study of such processes would require analyzing how crop circulation networks fit into society’s overall exchange system, and thus documenting the circulation networks of other types of material goods, services or knowledge. In addition, the comparison of the circulation networks of the three categories of cultivated plants shows that the circulation of the most culturally valued plants, that is subsistence crop, is governed by stricter exchange rules than the other categories.

Endogenous factors

0.286(0.166)

0.113(0.084)

– 0.195(0.049)*

Cyclic Triads (ATC)

Alternative path with 2 links (A2P)

0.342(0.403)

Incoming degree

Transitive Triads (ATT)

– 0.392(0.377)

Reciprocity

0.774(0.323)*

– 3.519(1.075)*

Arc

Outgoing degree

Aggregated

Effects

– 0.03(0.167)

– 1.841(7.48)

0.156(0.658)

1.076(0.311)*

– 0.537(0.503)

– 3.517(14.928)

– 3.506(0.752)*

Accompaniment

Table 4.3. Results of the ERGM

– 0.132(0.057)*

0.034(0.102)

0.237(0.173)

0.999(0.301)*

0.613(0.343)

– 0.146(0.41)

– 4.522(0.874)*

Starchy foods

Circulation network

– 0.004(0.104)

– 0.253(0.354)

0.057(0.356)

0.507(0.361)

0.624(0.335)

– 0.703(1.084)

– 4.821(0.664)*

Snack bar

Network Analysis: Linking Social and Ecological Dynamics 85

0.006(0.003)*

– 0.008(0.003)*

Degree of incoming external links from recipients

Heterophily based on the incoming degree of external links – 0.013(0.005)*

0.009(0.004)*

0.013(0.003)*

– 0.087(0.049)

– 0.087(0.053)

0.096(0.042)*

Table 4.3(continued). Results of the ERGM

0.01(0.003)*

Degree of incoming external links from senders

* p < 0.05

Exogenous factors

– 0.009(0.024)

0.027(0.022)

0.079(0.026)*

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In this study, while the same statistical methods (ERGM) were applied, network analysis was used according to a different approach than in the example on sorghum circulation: it is an anthropological question that called for the use of network analysis. In addition, the research process followed a different sequence than in the case of sorghum: a first field phase involving surveys and participant observation allowed (1) to identify the different species and varieties of cultivated plants and to highlight their biocultural status; (2) to gather ethnographic information on the functioning of society and the characteristics of individuals. This information was used before implementing the network analysis to define the hypotheses to be tested, and a posteriori to interpret the results. Ethnographic knowledge showed that individuals giving more plants than others were mostly the oldest migrants who managed to secure their land rights better according to the criteria of the island’s original inhabitants. 4.2.2. Circulation of knowledge and structuring of knowhow The analysis of social networks also makes it possible to study the circulation of knowledge and information within populations of natural resource users. It can be used on the one hand to analyze the information and knowledge exchange networks themselves (e.g. by mapping the exchange of advice on a theme), and on the other hand to analyze the structure of the social group(s) under focus, to see to what extent this structure explains the patterns of knowledge distribution within these groups. It has been shown, for example, that in some cases the central individuals in the advice exchange networks were also the most knowledgeable (case of medicinal plants, Tabi region in Mexico, Hopkins 2011). Similarly, it has been shown that local ecological knowledge may vary between groups of

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individuals structured according to their way of using natural resources (Crona and Bodin 2006). The case study proposed here (Salpeteur et al. 2016) aimed to explore the role of informal relationships1 in structuring knowledge and skills within a nomadic herding community in Gujarat, India. Two hypotheses guided the implementation of the research protocol, based on the distinction between theoretical and experience-based (practical) knowledge: (1) informal relationships, since they facilitate the verbal exchange of information or theoretical knowledge between connected individuals, may be associated with a greater homogeneity of knowledge between these individuals; and (2) informal relationships reflect a structuring into social groups that share the same experience, and are therefore also potentially associated with a greater homogeneity of knowledge. Two types of informal relationships were studied: migration relationships (linking individuals who have been on nomadization together), corresponding to a shared experience, and friendly relationships, which involve intense verbal exchanges, but not necessarily a common practice. Since relationships between men and women cannot be expressed in terms of friendship in this socio-cultural context, this type of relationship has been studied separately for each sex. The survey was conducted in the village of Mindiyala (Kachchh district), including 135 individuals, representing 113 households, or about 25% of the village estimated at 450 households. The questionnaires included a section documenting social interactions (links) and a section with a 1 Here, by informal relationship we mean an inter-individual relationship that is not subject to a strict normative framework. For example, an employment relationship will be considered formal, since it involves relatively well-defined standardized behavior from the protagonists.

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set of questions to assess ecological knowledge in four areas: soil classification, ethnoveterinary techniques, local sheep breeds and ethnobotanical knowledge. The data obtained was analyzed in two steps. First, the interaction networks were mapped, and groups of highly connected individuals were extracted from these networks. In two cases (migration network and female friendship network, figures 4.7 and 4.8), independent sub-networks emerged directly. In the case of the male friendship network (Figure 4.9), the Girvan-Newman clustering method was used to extract these groups (Girvan and Newman 2002). These groups were then included in the analysis as individual attributes.

Figure 4.7. Migration groups. The nodes in red are the surveyed individuals (round: male, square: female); the blues are the individuals mentioned, but not surveyed (represented by default as round, when gender is unknown)

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Figure 4.8. Friendship network, women. The marked segmentation of the network can be noticed.

Figure 4.9. Friendship network, men. Each color corresponds to to a cluster extracted using the Girvan-Newman method

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Second, matrices of similarity between individuals were constructed from their responses in the four knowledge domains. A multivariate permutation variance analysis (PERMANOVA) was then performed, with the similarity matrices being the dependent variable and membership in migration and friendship groups being included as independent (explanatory) variables. Other individual attributes were also tested (age, activity, kinship groups, education level). A separate analysis was conducted for migration groups and friendship groups to test their respective influence on variations in the four domains studied. LEK races

LEK soils

LEK tado

Botanical LEK

Value R2 Migration 0.15472 groups Segments 0.09361 Gender 0.02332 Age 0.05835 Schooling Ceasing of activity * p < 0.05; ** p < 0.01

0.16419**

0.19299**

0.19291*

0.10410* 0.01845

0.12342** 0.02212 0.05350

0.09044

0.03479* 0.05861

0.09290**

Table 4.4. Results of the PERMANOVA analysis including migration groups. LEK: local ecological knowledge. Tado: tested veterinary technique. Segments: patrilineal lineage segments (kinship groups). Cessation of activity: time elapsed since the abandonment of the shepherd’s activity

Analyses carried out from migration groups (Table 4.4) show that several variables explain a significant part of the observed variability, in three areas of knowledge (soil, veterinary, and botanical). Migration groups, in particular, account for a significant part of the observed variability in these domains, between 16 and 19%. This means that responses tend to vary between individuals from different migration groups and to be similar within groups.

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On the other hand, the analyses conducted on the basis of the friendship groups, both female and male, show almost no significant effect of the “friendship group” variable (Tables 4.5 and 4.6). In the case of the women’s friendship network, no effect appears. In the case of male friendship networks, friendship groups significantly explain a part of the variability only in the case of soil knowledge (Table 4.6). TEK races

TEK soils

TEK tado

Botanical TEK

Value R2 Friendship groups

0.14698

0.14338

0.13857

0.17434

Segments

0.28307

0.34274

0.39054

0.35205

Age

0.05071

0.05647

0.06449

Schooling Ceasing of activity

0.10358

* p < 0.05; ** p < 0.01 Table 4.5. PERMANOVA results for female friendship groups

TEK races

TEK soils

TEK tado

Botanical TEK

Value R2 Friendship groups

0.15284

0.17089**

0.16344

0.14917

Segments

0.20455

0.23666

0.20721

0.23250

Age

0.02769

0.06010

0.05131

0.05373

0.05093

0.04537

0.07000

0.06028

Schooling Ceasing of activity

* p < 0.05; ** p < 0.01 Table 4.6. PERMANOVA results for male friendship groups

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These results allow us to better understand the transmission of ecological knowledge in this specific case and to test our two hypotheses. It is clear that friendship networks, which involve the oral transmission of knowledge – mostly theoretical – do not seem to participate in the transmission of this knowledge, since there is no specific homogenization of this knowledge within highly connected groups of individuals. On the contrary, migration networks seem to play a decisive role: individuals belonging to the same migration groups tend to have similar responses (and therefore similar knowledge) in three areas. This shows that informal relationships are not systematically accompanied by the transmission of ecological knowledge. Migration groups, which represent groups of individuals sharing the same experience, are a central place for the transmission and homogenization of knowledge within these nomadic herding communities. Here, social network analysis was used to map two types of informal relationships and to study the role of these relationships in the transmission of local ecological knowledge. The approach does not integrate life sciences, but is based on an association between several disciplines in the human and social sciences: ethnoecology, social anthropology, and sociology (for its expertise in the method of analyzing social networks). Network formalism allows here to systematically map and analyze fluid social relations, whose role in knowledge transmission is difficult to grasp by other means. The results of the social network analysis can then be used and combined with additional statistical analyses. 4.3. Discussion: a necessary link between the quantitative and the qualitative The proposed examples illustrate the high interest of network analysis methods as a tool for interdisciplinary

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approaches, creating a space for discussion and establishing links between disciplines. A unique formalism enables ecological interactions to be represented, for example the circulation of plant material and associated gene flows, and social interactions, for example friendship relations between individuals. This formalism sets a common ground for the involved disciplines, and is associated with similar methods of analysis to answer their respective questions. The examples on the circulation of cultivated plants illustrate how one method can be used with different approaches, allowing us to address questions arising in one case from population genetics and in the other from social anthropology. In the first case, the hypothesis to be tested had been well defined beforehand and aimed at understanding the patterns of geographical distribution of cultivated diversity. Network analysis then prompted the collection of more in-depth ethnographic information, to document the social processes at work in shaping seed circulation networks. In the second case, the anthropological approach, involving long field stays, generated a large number of hypotheses about the processes structuring plant circulation networks, which were addressed with network analysis. Several back-and-forth moves between ethnographical accounts and network analysis results were necessary to narrow the hypotheses and interpret the results, finally leading to a better understanding of social interactions. These examples highlight the need to associate network analysis with qualitative approaches, which are essential to understand the social rules and norms regulating interactions between individuals, and are also necessary to understand how these interactions fit into the general system of interactions in the studied social systems. However, disadvantages to network analysis approaches also need to be taken into account. In particular, it is

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necessary to underline the complexity and amount of time that are required for data collection and processing, when implementing network analysis methods. The questions addressed in the examples required the collection of all interactions between individuals, that is the description of the entire network. This required a large number of interviews that could be tedious and repetitive for both respondents and interviewers. A significant difficulty was also to identify unambiguously the individuals cited by the respondents, that required a large amount of information to be collected during the surveys. Another disadvantage to note is the lack of appropriate statistical methods for the analysis of open networks (i.e. including links between respondents and other persons who were not interviewed). In these examples, a large part of the collected data could not be included in the analyses, because the available methods can only be applied to complete, closed networks, and therefore exclude relationships between individuals surveyed and those who were not. In the future, the analysis of social-ecological networks requires the development of approaches adapted to the analysis of open networks and the development of lighter sampling strategies. Ongoing methodological developments for the analysis of the temporal dynamics of networks open up promising perspectives for understanding the functioning of social-ecological systems and the mechanisms involved in their resilience. The development of methods for the analysis of multi-scale networks is also promising in this field of research.

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4.4. References Barbillon, P., Thomas, M., Goldringer, I., Hospital, F., Robin, S. (2015). Network impact on persistence in a finite population dynamic diffusion model: Application to an emergent seed exchange network. Journal of Theoretical Biology, 365, 365– 376. Berkes, F., Folke, C. (1998). Linking social and ecological systems for resilience and sustainability. In Linking social and ecological systems: management practices and social mechanisms for building resilience, Berkes, F., Folke, C., Colding, J. (eds). Cambridge University Press, Cambridge. Bodin, Ö., Crona, B. (2009). The role of social networks in natural resource governance: What relational patterns make a difference?. Global Environmental Change, 19, 366–374. Bodin, Ö., Tengö, M. (2012). Disentangling intangible socialecological systems. Global Environmental Change, 22(2), 430– 439. Bodin, Ö., Crona, B., Ernstson, H. (2006). Social networks in natural resource management: what is there to learn from a structural perspective?. Ecology and Society, 11(2). Coomes, O.T., McGuire, S.J., Garine, E., Caillon, S., McKey, D., Demeulenaere, E., Emperaire, L. (2015). Farmer seed networks make a limited contribution to agriculture? Four common misconceptions. Food Policy, 56, 41–50. Crona, B., Bodin, Ö. (2006). What you know is who you know? Communication patterns among resource users as a prerequisite for co-management. Ecology and society, 11(2), 7. Degenne, A., Forsé, M. (1999). Introducing social networks. Sage Publications, Thousand Oaks. Girvan, M., Newman, M.E.J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826. Harlan, J.R., Stemler, A. (1976). The races of sorghum in Africa. In Origins of African plant domestication, Harlan, J.R. (ed.). Mouton, Paris, 465–478.

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Hopkins, A. (2011). Use of network centrality measures to explain individual levels of herbal remedy cultural competence among the Yucatec Maya in Tabi, Mexico. Field methods, 23(3), 307– 328. Labeyrie, V., Deu, M., Barnaud, A., Calatayud, C., Buiron, M., Wambugu, P., Leclerc, C. (2014). Influence of ethnolinguistic diversity on the sorghum genetic patterns in subsistence farming systems in Eastern Kenya. PLoS One, 9(3). Labeyrie, V., Thomas, M., Muthamia, Z.K., Leclerc, C. (2016). Seed exchange networks, ethnicity, and sorghum diversity. Proceedings of the National Academy of Sciences, 113(1), 98– 103. Lusher, D., Robins, G. (2013). The formation of social network structure. In Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications, Brailly, J., Viallet-Thevenin, S. (eds). Cambridge University Press, Cambridge, 16–28. Robins, G., Pattison, P., Kalish, Y., Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173–191. Salpeteur, M., Patel, H., Molina, J.L., Balbo, A., Rubio-Campillo, X., Reyes-García, V., Madella, M. (2016). Comigrants and friends: informal networks and the transmission of traditional ecological knowledge among seminomadic pasto-ralists of Gujarat, India. Ecology and Society, 21(2). Thomas, M., Caillon, S. (2016). Effects of farmer social status and plant biocultural value on seed circulation networks in Vanuatu. Ecology and Society, 21(2). Wasserman, S., Faust, K. (1994). Social network analysis: Methods and applications, Vol. 8. Cambridge University Press, Cambridge. Westengen, O.T., Okongo, M.A., Onek, L., Berg, T., Upadhyaya, H., Birkeland, S., Brysting, A.K. (2014). Ethnolinguistic structuring of sorghum genetic diversity in Africa and the role of local seed systems. Proceedings of the National Academy of Sciences, 111(39), 14100–14105.

5 Interdisciplinarity and VUCA

5.1. Introduction The acronym VUCA appeared in a military context (Barber 1992) to refer to volatile, uncertain, complex and ambiguous situations. These four terms have had a strong resonance in management as organizations are typically faced with choices that potentially impact their survival in increasingly unstable environments. However, the acronym VUCA generates interpretations that vary according to the disciplines and situations in which it is used, sometimes in a contradictory or ambiguous sense. This is not without consequence, because, depending on the interpretations, the decision-making strategies are not the same. We will therefore try to clarify these terms by examining the point of view adopted by different disciplines involved with the study of decision-making. The first objective is to clarify each of the VUCA terms in a meaning which has some anteriority by referring to a field of analysis from economics and psychology classically called “decision theory”. By comparison, we will see how another discipline,

Chapter written by Roger WALDECK, Sophie GAULTIER LE BRIS and Siegfried ROUVRAIS.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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management science which is also concerned by decisionmaking, has reinterpreted the VUCA terms. The multiplicity of points of view from different disciplines on the same object will highlight the ambiguity of the terms. The next section of this chapter will present what is meant by decision theory in the context of this paper. Then, we will analyze the concept of VUCA from the two points of view “management” and “decision theory” in which it has been used. 5.2. Decision theory Many disciplines in the social sciences, including economics, management, and psychology, are interested in decision-making. The way in which each of these disciplines approaches this subject is strongly influenced by their epistemological and methodological presuppositions as well as the objective pursued. Thus, the psychologist’s main objective is to understand how humans act in different decision-making contexts. The economist’s objective is to have a decision-making model that allows him to develop a comprehensive theory of economic and social phenomena. It is not surprising that psychologists have favored an empirical approach, while economists have tried to establish a decision theory based on axioms of rationality allowing formalization. Nevertheless, with advances in behavioral decision theory based on experimental laboratory observations (Camerer et al., 2003), a greater alignment of the two approaches took place, culminating in the Nobel Memorial Prize in Economic Sciences awarded to psychologist Daniel Kahneman and economist Vernon Smith in 2002. Other scientists have studied decision “in context”, particularly in companies, and Herbert Simon’s (1980) work on procedural rationality is remarkable in this respect. Procedural rationality emphasizes the cognitive decisionmaking processes, that is, the very construction of the

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mental model and the deliberative processes implemented. The aim of these studies is in the comprehension of decisionmaking in environments where the future consequences of a decision are not known in advance. Simon’s approach also shows that in complex environments, individuals use heuristics that simplify decision-making rather than an instrumental approach to decision-making as assumed by proponents of a rational approach. Instrumental rationality implies the ability of the decision-maker to choose the most appropriate means to achieve his objectives. Instrumental rationality is often attacked on the grounds that it does not describe the reality of decision-making. This criticism highlights a difference in objectives between those who support a normative/prescriptive approach to decisionmaking, “how individuals should decide”, and those who have a descriptive approach, “how individuals actually make their decisions in often highly complex situations”. The normative/prescriptive approach has been developed by disciplines whose objective is precisely to support decisionmaking (Bouyssou and Roy 1993; Keeney and Raiffa 1993). However, the two approaches, prescriptive and descriptive, can be mutually enriching: the normative/prescriptive approach sets out a framework for defining rationality concepts and suggesting to the decision-maker a process and tools leading to a “good” decision, that is an analysis of the consequences of each decision in an “informed” way and taking into account his or her preferences. The descriptive approach makes it possible to understand the psychological biases of decision-making and to define good procedures, particularly in situations of rapid decision-making or stress. It also enriches the methodologies of the prescriptive approach. For example, methods for specifying subjective probabilities on a model parameter (Howard 1988) are constructed to avoid certain biases such as anchoring bias, representativeness bias, etc. (Tversky and Kahneman 1974).

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5.3. An interdisciplinary look at VUCA We will compare two approaches on the different VUCA terms. The first is a discourse related to the management sciences and based on the publication by Bennett and Lemoine (2014), who provided precise definitions for the VUCA terms. We compare these definitions with those used in economics and psychology and more specifically those coming from the field of decision theory using a formal approach of decision making. 5.3.1. VUCA definitions in management Bennett and Lemoine proposed the following definitions. Volatility is defined as follows: A volatile situation can be defined as one that is unstable or unpredictable; it does not necessarily involve complex structure, a critical lack of knowledge, or doubt about what outcomes may result from key events. Volatility is therefore an unstable change due to predictable causes, but whose magnitude or occurrence is sometimes unpredictable. For these authors, information is available and they give the example of oil price fluctuations. The authors therefore seem to describe phenomena similar to those observed for financial assets: prices may deviate permanently from the fundamental value of the asset, but one cannot predict how large the bubble will be and when the price reversal will occur. The definition used by these authors would be in this case quite similar to the one found in finance. The authors differentiate the notion of volatility from that of uncertainty. The latter is defined as follows:

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Uncertainty is a term used to describe a situation characterized by a lack of knowledge, not as to cause and effect but rather pertaining to whether a certain event is significant enough to constitute a meaningful cause. Uncertainty is not volatility. A volatile situation is one in which change is likely, but that change may come quickly and at varying magnitudes; an uncertain situation, on the other hand, is not so volatile—in fact, there may be no change inherent in it at all. The authors highlight that it is the lack of adequate information for asserting the importance of an event that creates uncertainty. The occurrence of an event can have an effect, but it is not known if it will be significant. They take the example of terrorism by suggesting that even by understanding the causes, it is not possible to predict the nature and occurrence of an attack. The search for information is at the heart of the strategy to overcome uncertainty. The definition in addition to the given examples would advocate for uncertainty or radical uncertainty as defined in section 5.3.2.1 in Bennett and Lemoine’s definition. According to them, complexity is defined as having: Many interconnected parts forming an elaborate network of information and procedures, often multiform and convoluted, but not necessarily involving change. [...] In complex situations, a great deal of effort is required to collect, digest, and understand the relevant information in its entirety. The authors understand complexity primarily through the cognitive overload it creates: “Moving into foreign markets is frequently complex; doing business in new countries often

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involves navigating a complex regulations, and logistics issues.”

web

of

tariffs,

laws,

It seems that the complexity for these authors is in the complexity of dealing with different and new, but not necessarily connected, information. Finally, the notion of ambiguity is a lack of understanding of cause-and-effect relationships: “Ambiguity characterizes situations where there is doubt about the nature of causeand-effect relationships.” The authors suggest that it is the novelty of the situation that does not allow these relationships to be inferred and give as an example the transition from paper to digital media where new consumption patterns have been difficult to predict. Ambiguity resembles to what will be called “model uncertainty” in the following. 5.3.2. Definitions from decision theory First of all, it should be noted that decision theory has not directly used the acronym VUCA even if its purpose is the study of decision-making in VUCA contexts. Nevertheless, each VUCA term can be defined within decision theory acknowledging however the multiple influences coming from other disciplines, in particular from economics and psychology for the social sciences or computational and statistical sciences. We will start with the notion of uncertainty, because it is at the very heart of any decision-making involving future unknown consequences. 5.3.2.1. Uncertainty The terminology developed by economists following Frank Knight’s work (1921) considers three typical situations for

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decision-making: an uncertain context corresponds to a decision where the possible outcomes of an action are known, but the probability of each outcome is unknown. Each realization corresponds to a state of nature. For example, a government’s decisions on economic policy are based on growth scenarios representing different future states of the economy. The certainty situation therefore corresponds to the borderline case where only one future state exists, that is a perfect prediction of the consequences. Knight considered that risk corresponds to an uncertain situation where the probability of occurrence of each state of nature is given. For each growth assumption that a government would make, there are associated probabilities of occurrence. The notion of uncertainty therefore has a clear interpretation in probability, since it corresponds to the notion of a random variable. A random variable is a function that associates a set of results or gains with a space of possible events in a random experiment (such as a die roll with six elementary events). Moreover, Laplace’s principle of insufficient reason stipulates that the individual evaluates each state of nature (event) with an equal probability if by a lack of knowledge he/she has no reason to give more weight to one state than to another. According to this principle, any uncertain decision problem may be transformed into a risky decision problem and Bayesian inference may be used to revise probabilities in the presence of new information. Finally, radical uncertainty corresponds to an individual who is unable to list the possible events related to a random experiment. For example, it may be difficult to say what the environmental effects of a 4-degree global warming will be. Many decision-making problems are considered to be, sometimes wrongly, a matter of radical uncertainty. These differences of type are important because they impact on the decision criteria that can be defined for each of them. In the following, we will use the term uncertainty in a common

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sense, the context making it clear when the above variations of meaning between risk and uncertainty are relevant. In decision theory, uncertainties of different nature are considered: temporal uncertainty, epistemic uncertainty, idiosyncratic uncertainty in decision-making and strategic uncertainty. Temporal uncertainty refers to a variability of a property over time. Volatility is a temporal uncertainty and is therefore only a special case of uncertainty. Epistemic uncertainty corresponds to the definition of a potentially statistical model linking a dependent variable, the decision-maker’s objective for example, to several independent variables that are all causes that affect the achievement of the objective. Since the independent variables are imperfectly known, i.e. they are themselves random variables, the dependent variable is therefore a random variable. If there is a probability distribution defining the achievements of the independent variables then it is possible to define a probability distribution on the decision-maker’s objective (risk situation). For example, a decision-maker, with the objective of maximizing profit by developing a new market, may have uncertainty about several parameters of the profit function: the number of competitors in the market, the cost function for the new product and the price set by his competitors. Statistical prediction is therefore possible if we can infer a probability distribution on the dependent variable, here profit, from the probability distributions of the independent variables. In a prescriptive approach to decision-making, once a probability distribution over the dependent variable has been defined, there are criteria for classifying or choosing among risky alternatives, such as stochastic dominance or the criterion of expected utility. Finally, the model itself may be uncertain, which would correspond to a decision-maker who does not know how to calculate a profit (model uncertainty).

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Another form of uncertainty is related to the decisionmaker subjectivity. Choosing among several risky alternatives requires the specification of a decision rule. The decision rule can take different forms depending on the nature of uncertainty, that is whether or not a probability distribution is known over the final objective (risk versus uncertainty). In a descriptive approach, psychological biases exist that call into question certain decision rules like the expected utility rule which has initially been considered as approximating correctly human behavior under risk or uncertainty. Moreover, a decision with different objectives requires an aggregation rule specifying the decision-maker’s preferences over the multifactorial consequences of a decision. There may be ambiguity about how to compare alternatives with multifactorial consequences. In a prescriptive approach, the MAVT (multi-attribute value theory) is an aggregation method that defines a value function for each alternative by operating a weighted sum. It therefore requires the definition of a model, that is a specific form of the value function, and is therefore akin to model uncertainty. Nevertheless, the model, here the specification of a value function, does not correspond to an objective function like a profit function. Rather there is subjectivity in defining such a function coming from uncertainty about the decision-maker’s preferences. It involves, for example, defining the relative importance over multiple objectives followed by the decision-maker, that is the weights, the latter being idiosyncratic. Lastly, a final type of uncertainty is strategic uncertainty (in the sense of game theory): this uncertainty arises when the achievement of a decision-maker's objective also depends on the action of other stakeholders: for example, what will the reaction of competitors be to the launch of a new product? Strategic uncertainty, by taking into account the actions of other players, introduces social interactions into the decision. It introduces additional complexity into

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predicting the possible outcomes of the game. It involves the formation of beliefs about the actions of other players, and also beliefs about the beliefs that these players will have about the actions of other players... in an infinite regression. Under rational expectations, beliefs actually correspond to equilibrium strategies, that is strategies for which no player would be willing to deviate unilaterally. Nevertheless, more realistically expectations can be adaptive, as players learn by observing past actions to form a conjecture about future actions. In strategic uncertainty, there may therefore exist “reaction” loops between players’ actions, which is not the case when a decision maker must form beliefs in a “stationary” environment (such as making a weather forecast for a city on a given date). Game theory has developed many tools for the analysis of social interaction and experimental economics has set up experiments to better understand decision-making in such contexts. 5.3.2.2. Volatility In finance, volatility reflects the magnitude of changes in the prices of a financial asset over a given time scale. Financial asset prices are subject to periods of high volatility followed by periods of calm. The presence of past data makes it possible to quantify these changes: volatility is obtained by calculating the standard deviation of profitability, which makes it possible to quantify the probability of loss. More generally, volatility can be defined as a quantification of the changes over time of a certain variable. Volatility as defined by Bennett and Lemoine is not fundamentally different from this definition. 5.3.2.3. Complexity A manager must have a systemic vision of his organization in order to manage it. The supply chain is an example of the complexity of a business structure composed of goods producers, distributors, wholesalers, and retailers. It is difficult to understand how decisions made at one level

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of the supply chain will affect another level. We are dealing with strategic uncertainty. But the difficulty of forecasting lies not only in the complexity of the components, but also in the analysis of the structure and dynamics of their interactions. Inventory management is of this type of complexity: even if the objective is simple to define, that is to minimize costs including inventory costs and out-of-stock costs, the dynamics produced by this type of system can be difficult to predict and very different in nature depending on the system: convergence towards a fixed point, cyclic dynamics, chaos. In the VUCA literature, the notion of complexity appears to be a combination of two concepts that need to be distinguished, however, because their nature is different. In a common sense, this reflects a very high degree of complication that renders the understanding of the situation difficult. In the context of a decision, this refers to situations where too many elements are to be taken into account: too many alternatives, too much information to analyze... and complexity is linked to a cognitive overload of the decisionmaker to clearly analyze the situation. The VUCA literature also attempts to differentiate it from the notion of ambiguity by suggesting that ambiguity results from a doubt about the existence of a cause-and-effect relationship (Bennett and Lemoine). Nevertheless, in the sense of complex systems, complexity refers to systems with many elements (more or less complex themselves) interconnected in non-linear relationships with potentially feedback loops. These systems produce effects that are difficult to predict before they are modelled or simulated using agent-based modelling. The socalled complex systems have precisely this ability to move abruptly from one state to another based on a tiny change in one or more parameters, that is to undergo a phase transition. The passage of water from a solid to a liquid and then vaporous state as a function of temperature involves these threshold effects corresponding to phase transitions.

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Complex systems also have an emergence property, that is the appearance of new phenomena at a higher level. Specifying each of the elements of a system is not sufficient to deduce some properties at the system level: there is no reductionism of the global properties to the properties of the elements. The interactions between the elements, as well as the topological structure through which these interactions take place, are essential for the analysis of global phenomena. In addition, it is difficult to have an accurate estimate of the critical phase transition level. This difficulty comes both from the model uncertainty, the model being a simplification of reality and therefore subject to imprecision, but also from the imprecision of measurement of some parameters. Predictions and decision-making in this type of system are difficult to carry out and the reliability of these models are questioned: making a forecast on the level of climate change and its consequences is an illustration of the challenge decision-makers face in complex systems. 5.3.2.4. Ambiguity In decision theory, ambiguity reflects the situation of a decision-maker who prefers to choose a risky alternative where the probability of a favorable outcome is known versus a risky alternative where it is unknown. Daniel Ellsberg (1961) highlighted these choice biases where ambiguity results from an aversion to a lack of information on the probabilities of occurrence. The proposed experimental game consists of randomly drawing a ball from an urn containing white and black balls. The player wins a prize if the ball is of the color he has previously chosen. There are two types of versions: a “risky” version in which the player knows that the urn contains as many white balls as black balls. An uncertain version where no information is given to players on the composition of the urn. Laboratory experiments very generally show a preference for the risky version. This is a paradox because the risks are the same in both games (one chance in two to win). This aversion to uncertainty about

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risk influences the behavior of individuals who prefer the existence of objective probabilities. The aversion to ambiguity has a strong impact on the valuation of financial assets and has contributed to amplifying the subprime crisis, with investors fleeing from assets for which it was impossible to objectively assess risks (Cramer and Gollier 2008). More generally, ambiguity is the quality of an object to be subject to multiple interpretations, making it difficult to make decisions. The latter is present in new situations where the decision-maker in need of guidance and faced with multiple sources of uncertainty considers that the situation is not conducive to a “rational” analysis. A rational decisionmaking process involves defining the decision-maker’s objectives, giving a set of choices, specifying the decisionmaker's preferences on the consequences associated with alternatives and finally, determining the risks associated with alternatives and the decision-maker’s preferences regarding risk. The aim here is to build a model that allows an analysis of the situation by specifying the cause-andeffect links, in particular between risky inputs and an overall objective representing the preferences of the decision-maker whose risk is specified for each alternative. Ambiguity arises when a decision-maker does not have the ability to make a choice either because of a lack of information on the consequences of these choices (ambiguity related to the treatment of uncertainty for van Mumford et al.) or because it is impossible to clearly establish the objectives of the decision (ambiguity related to the purpose of the decision for van Mumford et al.). Another source of ambiguity is related to the presence of several stakeholders, making the above decision-making process more complicated because it implies: – that stakeholders agree on the elements for building the model for the situation analysis;

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– that the stakeholders agree on the process for reaching a potential agreement or consensus: this is about defining the collective rules for making a choice. These sources of uncertainty are not always clearly expressed, making the situation ambiguous, that is subject to multiple and highly subjective interpretations. In a “complex systems” environment, additional ambiguity arises from the lack of a clear or accepted model explaining the observed phenomena, a potential disagreement on the impact of stakeholders’ actions on the system, and measurement problems on different model parameters. For example in the management of common goods like fish stocks, several actors come into play: regulators responsible for ensuring the preservation of the resource, resource users (fishermen), and also final consumers, because they have an impact on fishermen’s behavior. The ecosystem can be modeled in the simplest way as a prey-predator system. Here, the implementation of a regulatory system requires a compromise between the different actors on the rules of the game: level of fishing, sanction to be implemented for compliance with the rules... Nevertheless, the acceptance of and compliance with these rules will also require the actors’ understanding of the dynamics at stake and their assessment on the level of resources. 5.4. Discussion We will conclude this chapter with a discussion of the disciplinary differences between Bennett and Lemoine’s definitions and those of decision theory (Table 5.1). First of all, some definitions of Bennett and Lemoine’s paper may have several readings and our analysis is necessarily a subjective interpretation of their text.

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VUCA

Decision theories

Bennett and Lemoine

Volatility

Temporal variability of a parameter: special case of uncertainty.

Identical, but not identified as a type of uncertainty.

Epistemic: model (cause-andeffect relationship), variability of model parameters. Uncertainty

Time including idiosyncratic volatility on objectives, preferences. Strategic: on collective decisionmaking processes, on the behavior of other players.

Complexity

Systems composed of elements (themselves more or less complex) interconnected in nonlinear relationships with potentially feedback loops. The causal relationships between different parameters are known, but the effects are difficult to predict due to non-linearity and feedback loops.

Uncertainty or radical uncertainty, i.e. inability to assess the consequences of an action or to estimate its importance. Epistemic, excluding model uncertainty.

Information complexity.

Characteristic of a situation where the probabilities are unknown. Ambiguity

Lack of clearly identified epistemic and idiosyncratic models and difficulty in understanding collective decision-making processes. Table 5.1. Definition of VUCA terms

Absence of an epistemic model.

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Nevertheless, the exercise is interesting because it highlights the difficulty of setting up an interdisciplinary framework: reinterpreting a text from one discipline against the yardstick of another discipline. In particular, some terms are polysemic and may be used either in a common meaning or have a disciplinary meaning implying ambiguity in their interpretation. The two approaches to defining VUCA concepts clearly start from a different point of view. Bennett and Lemoine define their concepts based on practical management situations and in a sense closer to what is commonly understood for each of the terms. Bennett and Lemoine’s definition of uncertainty emphasizes epistemic uncertainty, but ignores one of its form, which is model uncertainty. They rather point out about the significance of the causes which presupposes the definition of a model. Other types of uncertainty, such as strategic, temporal, idiosyncratic, are not addressed as such. Moreover, Bennett and Lemoine state that volatility is not uncertainty which is not the case in financial decision theory. In addition, the Knightian concepts of radical uncertainty, uncertainty, and risk are not clearly identified by Bennett and Lemoine and it is difficult to deduce to what type of uncertainty they are referring. For example, their notion of unpredictability could be matched by each of radical uncertainty, uncertainty, or risk (volatility in finance is risk). If an event is not certain then forecasting is imperfect and comes to defining the chances of occurrence of an event. In situations of uncertainty, future states are defined and so predictable but the likelihood of each state are difficult to estimate that is the occurrence of each state are unpredictable. But unpredictability could also refer to radical uncertainty. Their notion of complexity is not defined in terms of complex systems, except when they mention interconnection,

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but this latter term referring only to information that needs to be analyzed jointly. For the most part, the discourse is about informational complexity, where complexity is probably to be taken in a common meaning. Finally, ambiguity seems to be linked to the difficulty of establishing a model of the situation, that is defining causeand-effect relationships. VUCA is clearly a transdisciplinary concept whose definitions vary according to the disciplines and situations in which it is used. In the future, it will be necessary to reach a consensus on the definitions to be given to each of these terms. This chapter is an attempt to do so. 5.5. References Barber, H.F. (1992). Developing Strategic Leadership: The US Army War College Experience. Journal of Management Development, 11(6), 4–12. Belisle, C., Linard, M. (1996). Quelles nouvelles compétences des acteurs de la formation dans le contexte des TIC ?. Éducation permanente, 127, 19–47. Bennett, N., Lemoine, J. (2014). What a difference a world makes: Understanding threats to performance in a VUCA world. Business Horizons, 57(3), 311–317. Bouyssou, D., Roy, B. (1993). Aide multicritère à la décision: méthodes et cas. Economica, Paris. Camerer, C.F., Loewenstein, G., Rabin, M. (eds) (2003). Advances in behavioral economics. Russell Sage Foundation Press and Princeton University Press, New York and Princeton. Cremer, J., Gollier, C. (2008). La faute à l’incertitude. Les Échos. [Online]. Available at: www.lesechos.fr/20/03/2008/LesEchos/ 20135-079-ECH_la-faute-a-l-incer-titude.htm#. Ellsberg, D. (1961). Risk, Ambiguity, and the Savage Axioms. Quarterly Journal of Economics, 75(4), 643–669.

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Howard, R.A. (1988). Decision analysis: Practice and promise. Management Science, 34(6), 679–695. Keeney, R.L., Raiffa, H. (1993). Decisions with multiple objectives– preferences and value tradeoffs. Cambridge University Press, Cambridge. Knight, F. (1921). Risk, Uncertainty and Profit. The Riverside Press, New York. Simon, H. (1980). From substantive to procedural rationality. In Method and appraisal in economics, Latsis, S. (ed.). Cambridge University Press, Cambridge. Tversky, A., Kahneman, D. (1974). Judgement under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. Van Mumford, J., Wirén, M., Zettinig, P. (2017). What is rational action in the VUCA world?. 43rd European International Business Academy Conference, Milan, Italy, 14-16 December. Viviani, J.-L. (1994). Incertitude et rationalité. Revue française d’économie, 9(2), 105–146.

6 Learning Methodology for VUCA Situations

6.1. Engineering education & training and highly reliable organizations The organizational and human problems experienced within organizations raise questions. The stakes in individual or collective decision-making can be high: a mistake in choice can have irreversible consequences, which highly reliable organizations, in particular, cannot tolerate. At the same time, the difficulties of decision-making processes in complex or uncertain contexts (Klein 1999; Lipshitz et al. 2001) give rise to contradictions: the complexity and multiplicity of the rules to be considered, combined with the need for rapid decision-making, can further increase the information burden and undermine the ability of decision-makers to discern, especially if they lack expertise in the face of the originality of situations where unforeseen events, both environmental (for example natural, hardware, software) and human, emerge. How can actors in original and unpredictable situations, and more precisely decision-makers, face and be prepared for various complexities? Chapter written by Sophie GAULTIER LE BRIS, Siegfried ROUVRAIS and Roger WALDECK.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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This research activity, conducted since 2015 with a population of engineering students, aims to further develop the concept of reliability and resilience in terms of leadership and decision-making, where the role of the decision-maker is particularly decisive. To study the decision-making process in complex and uncertain situations with a view to increasing reliability and strengthening the discernment capacities of the decision-maker, a phased learning methodology is proposed. It is composed of observation and analysis sequences of behaviors and actions of small teams in higher education. It was conducted in two accredited engineering schools, through a family of sequences on simulator and/or in real experiential situations. This work shows a certain relevance for working on the discernment capacity of the future decision-maker confronted with arbitrations in the face of procedures that contradict each other or cannot be applied. The objective is to show how iterative research work on pedagogical modalities can be the subject of theorizing and validating workshops on the management capabilities of so-called complex situations. As an anchor point, this research is based on the theoretical work of the research stream of highly reliable organizations (known by the acronym HRO1) and the actionist movement. These streams of research are relatively close to each other, because they approach the concept of reliability not from the perspective of organizational failures, but rather from the perspective of increased reliability where the role of the leader is prominent. Indeed, these research streams place the individual at the heart of the reliability process; their work consists of identifying the sources of resilience by improving the behavior of the leader and his team and is part of crisis situation studies (Weick 1993). In addition, to strengthen decision-making capacities in complex and uncertain situations in contexts with high rule prevalence, the 1 High Reliability Organization.

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concept of metarule studied in a dynamic environment (Davis et al. 2009, Le Bris et al. 2019) is also used. Thus, echoing the organizational and human issues experienced within companies and risky activities, this research work is linked to pedagogical work that focuses on preparing students to manage complex and unexpected situations, including emerging, unpredictable and sometimes chaotic factors. Section 6.2 of this chapter recalls the issues surrounding decision-making in the so-called volatile, uncertain, complex, and ambiguous environment (identified by the acronym VUCA). It lists the requirements around decision-making graduate attributes for the accreditation of engineering programs, questions the limits of the weight of rules in relation to decision-making, and positions the study in the light of complex systems. Section 6.3 discusses the theoretical framework used. Its state of the art specifies the different approaches relating to the concept of reliability, in particular with a note on lines of research that approach reliability first from the angle of organizational failure (case of the theory of normal accidents, the stream of research current of crisis management, the stream of research on the human factor) and then from the angle of increased reliability (actionist movement, highly reliable organizations stream). In methodological terms, a specific section, section 6.4, then presents the Design Based Research conducted since 2015 to infer and strengthen transversal decision-making skills in complex situations among engineering students. The experimental means used to answer a research question related to transversal decision-making skills, as well as the tools used for related observations and analyses, are presented. For each adopted approach, the first results are presented. 6.2. Issues at stake The managerial and theoretical issues of reliability and decision-making processes are nowadays studied in

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environments marked by Volatility, Uncertainty, Complexity, Ambiguity, called VUCA environments (see Chapter 5). Indeed, due in particular to the increasing complexity of technologies (Weick 1995), professional and personal environments marked by volatile, uncertain, complex, and ambiguous parameters are now raising both theoretical and practical questions for higher education institutions, in particular the way in which training program designers can prepare their learners, and future professionals, for an unpredictable and uncertain future in order to manage situations with VUCA characteristics. The preparation of teams of students to manage complex and unexpected situations, subject to the requirements of the competence reference framework for engineering education in France, has thus led to the definition of VUCA decisionmaking training objectives and learning outcomes formulated in terms of skills, and implemented in two higher education institutions: – the École Navale, which trains future career naval officers. The role of naval officers is to provide command functions within operational units (warships, submarines, naval aeronautics fleets, marine commandos). During their training, they develop the skills that will lead them to strategic and operational functions in the Navy; – the IMT Atlantique Bretagne-Pays de la Loire, a leading general engineering school that combines digital technology and energy in its training and research. Under the aegis of the Ministry of Industry and Digital Affairs, the graduate school offers a curriculum with an integrated competence approach with diversified pathways in response to the increased needs of organizations facing digital, energy, and environmental change. In the case of the École Navale, these learning outcomes are important, because the future officers, as decision-

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makers, will have to drive vessels linked to an imperative of reliability (e.g. aircraft carriers, submarines, aircraft), an irreversibility of error in decision-making and at the same time a strong compliance to rules. In the case of IMT Atlantique, these objectives are also very sensitive as transversal skills are required for its future engineers, who most often become managerial executives in organizations undergoing major changes and strategic positioning, marked by regulations, standards and global competition. The development of skills around instant and reflective decision-making raises several conceptual and methodological questions that remain to be resolved in the engineering education community. It is also important to consider how to develop the discernment capacity of a future decision-maker (executive or officer) and thus train him/her to make decisions in VUCA environments during his/her training. The question is on the learning methods and tools engineering schools can set up to address and overcome these challenges in order to prepare graduates for their future responsibilities and engineering titles. 6.2.1. VUCA phenomenon classes These issues are therefore associated with questions about the actors themselves and their ability to manage VUCA situations, individually and collectively in particular, the ability to decide, in the event of a dilemma, on the procedures to be followed and integrating the results of their decision on the level of reliability of the system they manage. In other words, how can the VUCAbilities of a non-expert confronted with this type of situation be characterized? The objective of this chapter is to explain the method used to characterize these VUCAbilities based on the implementation of situations described in section 6.2.

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This work consists of identifying, for an actor in a situation, his level of control in a given environment and observing the evolution of his skills by varying the magnitude of situational variables. Thus, for example, for an individual facing a low level of environmental volatility, low uncertainty, low ambiguity, and low complexity, what is his level of control of the situation on an efficiency scale? The pedagogical context of this work made it possible to position the skills of an individual or a team in a VUCA environment whose four variables, volatility, uncertainty, complexity, ambiguity, can have a different level of magnitude (graduated from low to high), as represented in Table 6.1. Disruption components of a situation Magnitude/ variability

Volatility

Uncertainty

Complexity

Ambiguity

Low

Little variation in factors

Identified parameters

Simple factor organization

Plausible interpretation (of a rule or process)

Average

Predictability of change and factors

Incomplete and limited information, partial knowledge

Several sources and components, simple structure

No obvious interpretation

High

High unpredictability of factors

Unidentified, unknown and non-measurable parameters

Numerous parameters and factors, disorganization of these factors, many cause-andeffect relationships that do not allow for an established structure to be created

Table 6.1. VUCA situation analysis grid

No interpretation possible, undecidability, indemonstrable statements

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Table 6.1 shows the positioning grid of the situational characteristics chosen to accompany the assessment of an individual's VUCAbility in terms of decision-making, on another graduated scale of efficiency. If the graduation of some of the disturbance components increases from low to medium and then high, what are the consequences in terms of learning outcomes (e.g. cognitive, procedural and behavioral levels and thresholds)? In the proposed analysis, the definition of complexity adopted takes into account, in particular, a large number of interdependent elements, creating a cognitive overload linked to the difficulty of analysis. The science of complex systems takes into account a different definition of complexity (see Chapter 5) and we present Thomas-Vaslin’s (2017) characterization: The science of complex systems takes more into account contingency, the absence of linear causality, and also the occurrence of chance and the dynamic co-emergence of non-linear systems in closed, variable and saturated environments with interactions that generate networks with chaotic behaviors, variations in initial and therefore unpredictable conditions. Although we seek to integrate an interactive whole across space and time and understand the evolution of these systems in a global way, we remain with partial knowledge, aggregated knowledge, innumerable, unknown, and non-measurable parameters, undecidability and indemonstrable statements. Therefore, it is not necessarily possible to demonstrate, calculate, reproduce or make forecasts. The science of complex systems seeks to understand the properties, global characteristics and evolution of systems, interactions and organizations that occur or break down over time, taking into account the

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history of systems and their restriction according to the constraints, disruptions and selections that occur. The study of decision-making in VUCA situation management, share some of the characteristics stated by Thomas-Vaslin in terms of complexity. This implies considering “partial knowledge”: VUCA decision-makers are in the same situation as the modeler of complex systems: an inability to understand the entire system. This cognitive overload results in an inability of actors to integrate all information in a limited period of time. They face cognitive saturation and ambiguities; non-expert actors can rely on a repertoire of available responses built in a situation, but this repertoire is smaller for a non-expert audience; the dynamics created by these actors with limited rationality are difficult to predict. In addition, the pedagogical conditions and situations create potential emergent properties, a feature of complex systems. Insofar as there are rules to follow, the nature of which is sometimes contradictory, or whose actions seem impossible to apply in a very short time, this implies improvised or spontaneous actions that the actors in the situation (engineering students not yet experienced) must manage in the heat of the situation, actions that notably involve the group leader taking responsibility. When the initial framework (rules to be followed for each type of situation) is not appropriate, it leads to the emergence, through non-compliant cases, of properties of new situations that can be capitalized on as new good practices or creative innovations, or even potential future rules that can enhance organizational reliability. The interpersonal dimension was added in Table 6.2 as the fifth variability in the pedagogical experiences presented in Rouvrais, Gaultier Le Bris, and Stewart (2018). As the

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interpersonal dimension is not expressed on a single scale, it should be specified that the low level of magnitude on this dimension corresponds to a single individual, the average level is reached with at least two individuals and if they are from different cultures and/or disciplines, it is the high level of magnitude. 6.3. Theoretical framework of organizational reliability The search for reliable responses in the implementation of learning methods was based on work on the concept of organizational reliability studied in management sciences (Bierly and Spender 1995; Schulman 1996; Weick 1993; Weick, Sutcliffe, and Obstfled 1999). It raises questions in current environments, particularly about the place of individuals in the reliability process: for some research fields, the individual is identified as a source of error rather than reliability, including the research stream dealing with human factors (Rasmsussen, Leplat, and de Terssac 1989; Reason 1990). 6.3.1. Running highly reliable and actionist organizations Adopting a different stance, the Highly Reliable Organizations research stream (HRO) and the Actionist movement approach the concept of reliability from a perspective of increased reliability. These two movements promote human behavior in the reliability process. Thus, for the actionist movement, interactions between individuals can be a source of greater reliability. By studying the characteristics of organizations with high reliability, Roberts (1990) shows their exceptional level of performance.

No interpretation possible, undecidability, indemonstrable statements

Numerous parameters and factors, disorganization of these factors, many causeand-effect relationships that do not allow for an established structure to be created Unidentified, unknown, and nonmeasurable parameters

High unpredictability of factors

Mono-discipline and culture

Multidisciplinary and multicultural team

2 or more individuals

2 or more individuals

Average

High

Table 6.2. VUCA situation analysis grid with interpersonal dimension

No obvious interpretation

Several sources and components, simple structure

Incomplete and limited information, partial knowledge

Predictability of change and factors

Low

Plausible interpretation (of a rule or process)

Simple factor organization

Identified parameters

Little variation in factors

Ambiguity

1

Complexity

Uncertainty

Interpersonal

Disruption components of a situation Volatility

Magnitude/ variability

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Weick (1993) analyses the behavior of actors confronted with both contexts: – information overload: Weick (1995) defines information overload as an accumulation of written, rewritten, updated, and readjusted procedures as operations develop; – constant turbulence: consisting of exceptional, accidental, and unexpected situations. Roux-Dufort (2000) points out by quoting Weick (1995) that “reliability is nothing more or less than the ability to manage fluctuations, incidents, unexpected situations produced by the technological systems we operate. High reliability organizations are not organizations of constancy, but organizations of the unexpected. The result is a different behavior of actors in highly reliable organizations that will activate their intuition and “define situations much more heuristically than would organizational procedures that are often unsuitable for immediate handling of the situation” (Weick 1995); – of increasing complexity: the third characteristic described by Weick (1995) concerns the increasing complexity of contexts. This growing complexity of technologies also provides an opportunity to build meaning. 6.3.2. Selected models The work of these two research streams has driven the learning methodology proposed in this chapter. One element caught our attention in particular. Indeed, these lines of research, although very close to each other, show a divergence on the compliance to rules as a source of reliability. This point of divergence gave us the opportunity to focus on the research work concerning the strategy of rules in complex environments and to appreciate the role of the concept of metarules in the class of situations studied. Our work thus aimed to (1) characterize the modes of observation and action that promote organizational

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reliability and (2) integrate the metarule approach into the decision-making process. We considered that the concept of metarule studied in dynamic environments (Davis 2009) can help non-experts in decision-making in VUCA environments. Metarules are N+1 level rules that are based, for dealing with complexity, on an abstract representation of the system to be controlled (Gaultier Le Bris 2014). The advantage of mobilizing the metarules is to preserve the integrity of the controlled system (Le Bris et al. 2019). They consist of making it possible – for a non-expert public (learners) – in the event of cognitive saturation, to refer to an integrating framework of broader procedures and thus to change the register of rules to be followed (less detailed) and to set a level of priorities for the actions to be undertaken. This discernment can have both a positive effect on the targeted level of reliability and address time constraints. Also, the comparison of this concept of metarules with a class of specific situations marked by a high level of complexity, a time constraint, and a requirement of reliability can promote the understanding and treatment of a complex situation for a non-expert supervising a small team (Le Bris et al. 2019). 6.4. Cross-disciplinary decision-making skills: designoriented research Kamp (2016) pointed out that in order to deal with complex and multifactorial situations, it is now necessary to adapt engineering training. So how can this consideration of uncertainty and complexity be addressed in the training? The means implemented to answer a research question related to transversal competencies in complex decisionmaking situations, as well as the methodologies used to verify and validate the proposed and future training models are presented in this section. Among the audiences in training who received leadership and decision-making

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training, groups from two engineering schools in (1) the École Navale and (2) the IMT Atlantique were analyzed. The location of engineering establishments in Brest, France, has oriented the field of study towards the maritime environment in the management of a nautical situation where unexpected events (variation in the meteorological context, ships approaching each other, isolated dangers, man overboard cases, etc.) can occur at any time causing the emergence of coordination movements, interactions between the group’s actors, new responses. 6.4.1. Research methodology for learning In order to develop skills with a non-expert audience – here students or future officers of these two engineering schools – in VUCA contexts, this work took inspiration from the “Design Based Research” (DBR) approach, which makes it possible to analyze, design, evaluate, and refine iteratively the sequences of the process implemented using observation variables relating to the collective behavior of student teams in response to disruptions in complex and unexpected situations. In this context, the choice to initiate the method initially focused on the selection of the theoretical framework. As mentioned in section 6.3, the work on the concept of reliability, and in particular that which approaches the individual as a source of reliability, constitutes the first analytical framework adapted to our class of studies. In a second step, the choice of the methodological approach was oriented towards Design Research in Education, which makes it possible to continuously improve training practices and offers. Its principles: under the supervision of researchers and practitioners, it helps transform learning into a community of analysis and improvement. It includes regular interactions between researchers, teachers, practitioners (domain experts), and learners where learners become actors and responsible for their learning (collaborative learning, no

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learner isolation). In addition, the DBR method is linked to the Experimental research method (Collins et al. 2004) with real learning situations (Anderson and Shattuck 2012), more complex than environments simulated or reproduced in an overly academic setting. In this sense, it is linked with the research stream of J. Dewey, which is rather pragmatic (experiential learning). The DBR provides concepts, methods, processes, and tools. Its step-by-step results can be transferred to other contexts; practitioners and learners (reflective practitioners) are involved in the cycles, mixed and varied evaluation methods are thus usable. Its formalized research process remains flexible and iterative; however, it is of the engineering type with agile methods involving user-oriented project management. This process is iteratively structured as follows: (1) identification of a concrete problem/need in training; (2) analysis of the identification of the sources of the problem, the formulation of variables and learning environments; (3) design and development of a training offer (by researchers, practitioners and learners) using a theoretical anchor (in this study, the theoretical framework of HROs and actionists) with methods, processes and tools; (4) implementation; (5) evaluation of interventions with learners in a collaborative and participatory way; (6) qualitative and quantitative evaluation (taking into account the effects of variables, dissimilarities between theory and practice), new understandings (with the emergence of concepts, artifacts, properties), definition of design principles or rules; (7) revision of concepts and methods based on success and limitations of the practice evaluated with new assumptions for models, a new set of operating variables; (8) reiteration. 6.4.2. From model to reality In order to identify reliable response modes to VUCA environments, it was necessary to work with complex and

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uncertain situations and thus identify the level of reliability of the decisions taken. To do this, Design Based Research is applied to two study populations: (1) a group of students from the École Navale and (2) a group from the IMT Atlantique. The first population corresponds to students from the École Navale, oriented towards a specific profession, with public initial training (bridge watch team). For them, nautical situation exercises are deployed on the École Navale’s navigation simulator (scale 1 of a navigation bridge). Also, in order to identify individual and collective strategies that promote organizational reliability, scenarios are developed in order to propose, on this means, situations with a high level of complexity and with time constraints. The credibility of the scenarios is sought on the basis of the study of real situations (by qualitative and then quantitative approach thanks to the collection of data on the feedback of the National Navy’s surface vessels) and the work of Le Bris et al. (2019). Questionnaires with scales for measuring observed variables are used for data collection. The observation of these team behaviors concerns the level of reliable actions which were undertaken (variable 1), the level of complexity of the situation (variable 2), the use of nautical rules (variable 3), and the use of metarules (variable 4). These variables are measured using a five-point Osgood and Likert scale. These scales have been built ad hoc due to the specificity of the environment as stated in Le Bris et al. (2019). Thus, the level of reliable answers is measured with “the achievement of objectives (1), the state of the system (2), the capacity to pursue the mission (3)”. “The level of complexity is measured by the degree of pressure and uncertainty. We measure the use of metarules by the compliance to general principles (identified as metarules by experts).” The simulator approach has advantages: simulation experimentation is adapted in management sciences (Harrison et al. 2007), but it is still little used (Cartier and Forgues 2006).

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Moreover, according to Davis et al. (2007), it “makes the nonlinear relationships between interdependent phenomena explicit” and is a useful tool that can be linked to other more traditional research methods (Dubey 2001). However, it has biases (Weick 1987): a team formed, not in conformity with reality, generalization of the validity of the results (Leplat 1997), a reductionist posture (Dubey 2001) that we have tried to reduce by an approach in a real situation. A second field of study is then analyzed with groups of general engineering students from IMT Atlantique, again according to the Design Based Research (DBR) principle. These students are clearly not related to the nautical environment, because of their profile or their future profession. Water sports exercises are organized in a real situation, in the bay of Brest. Thanks to “man overboard” training scenarios conducted in a real nautical environment (Rouvrais and Gaultier Le Bris 2018), for around 20 students, most of whom are new to this environment. Their behavior and decision-making skills have begun to be analyzed quantitatively since 2017 (see appendix). To observe the effect of the use of metarules on a learner’s reliability and ability to manage complexity and maintain a capacity for discernment in complex and original situations, the level of complexity of situations is modified, continuously and iteratively over several sequences. Thus, in phase 1, the first real nautical situation proposed to students new to the environment is called Simple Situation, the observation concerns the definition and then the application of rules2 with a low level of complexity (one incident during a 20-minute nautical period of the “man overboard” type with also a low level of uncertainty and ambiguity).

2 The objective of the simulation is to create the rules to be followed according to the nature of the situation; the students had to elaborate and define rules to be followed according to the types of nautical situations in order to be able to apply them without being experts in the field. In the same way, they were asked to create metarules.

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When the first step is completed and validated for students, the degrees of difficulty increase, step-by-step on the VUCAlity grid. Indeed, the second real nautical situation is called Complex Situation (same situation, but with a higher level of complexity). The level of complexity is increased by the insertion of different events to be dealt with in a limited time ranging from 5 to 10 minutes (“man overboard”, for example, call from the coast guard to manage, proximity to another boat). After each pair of “Simple Situation” and “Complex Situation” situations where the student is in a position to decide, the level of reliability is measured, based on the scales used on the simulator and previously described. After Phase 1, which includes the two sequences of consecutive situations (Simple and Complex), we discuss with the students the possibility of adopting generic metarules, capitalized by the learners via reflexive debriefings (Reinves 2013); then we proceed to Phase 2, by submitting again a Simple Situation and a Complex Situation to the students. The different sequences provide us with initial indicators to analyze the reliability levels of the teams according to the level of complexity they have been confronted with, the observation concerns the use of the rules used (phases 1 and 2), and the use of metarules (phase 2). They provide information on the students’ discernment and decision-making skills. In one day, students can perform more than twenty real exercises, with increasing VUCAlity. 6.4.3. Learning outcomes Through its flexibility and iterations, Design Based Research (DBR) makes it possible to provide solutions to real 2 The objective of the simulation is to create the rules to be followed according to the nature of the situation; the students had to elaborate and define rules to be followed according to the types of nautical situations in order to be able to apply them without being experts in the field. In the same way, they were asked to create metarules.

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and evolving problems through the practice of learning in the field. It also makes it possible to identify (and formalize) conditions that lead to different effects (by manipulating variables). Each iteration brings new content to be (re)evaluated by gradual maturation. The results of the simulator-based metarule approach (1, École Navale group) show that they are relevant to improving reliability in a context of uncertainty, urgency and complexity (Gaultier Le Bris 2014). The results of the metarule approach in real situations with non-experts (2, IMT Atlantique group) show that they are relevant in the construction of meaning, particularly in the context of collective learning; they make it possible to capitalize on good transferable practices, in an unknown, unpredictable context, with limited and disorganized information (Rouvrais and Gaultier Le Bris 2018). The European network for the accreditation of engineering education establishes eight centers of competence (ENAEE 2017). One of them is specific to judgment and highlights the need for the learning process that should allow master’s level graduates to demonstrate: – the ability to integrate knowledge and manage complexity, to make judgments with incomplete or limited information, including reflection on the social and ethical responsibilities associated with the application of their knowledge and judgment; – the ability to manage complex technical or professional activities or projects that may require new strategic approaches, taking responsibility for decision-making. These two key capacities are directly echoed by the VUCA grid presented, used as a supplement when evaluating a single individual on fixed capacities, that is within our

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framework the competences of the decision-maker in the situation. However, the presence of other actors within the group can also have an effect on an individual’s decision. To integrate this factor into the characterization of decisionmaking situations, an “interpersonal” variable corresponding to the number and profiles of individuals confronted with the VUCA situation must be taken into account. To do this, knowing that interpersonal skills are also found as transversal skills in learning outcomes frameworks, two options are available. The first option would be to integrate the “inter-personal” variable into a column, with also three levels of magnitude integrating the number and composition of the group (e.g. discipline, nationality). It would be stated as follows: – a “low magnitude” level corresponds to a single individual (i.e. no conflict); – an “average magnitude” level corresponds to a group of more than 2 individuals of close discipline and culture or a group already recognized as homogeneous by its experience; – a “high magnitude” level corresponds to a group of more than two individuals with at least one individual of different discipline and/or nationality/culture (i.e. with effects on the critical distribution of roles, exchanges and working organizations, possible conflicts). A second option would be to take into account in the four VUCA components the effects of this “interpersonal” variable, as it is so significant in the variation of each of the other four variables as highlighted in Chapter 5, since a large number of actors in a team or collective can increase the level of uncertainty (action, decision), and also the level of ambiguity (e.g. in terms of behavior) and reinforce the degree of complexity, for example through interactions produced between individuals. Volatility can also be influenced by changes in behavior within the group.

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6.5. Conclusion The objective of this methodological approach was therefore to study how research work on simulators and in real situations can be the subject of the development of transversal skills on the management of complex situations. It is also an opportunity to question the study of the treatment of complexity in a VUCA register and to compare the representation of the concept of complexity in an interdisciplinary way. It thus makes it possible to review principles and methods as well as to collaboratively improve research and training practices in this field. The science of complex systems seeks to understand the properties, global characteristics, and evolution of systems, interactions, and organizations that occur or break down over time (Thomas-Vaslin, 2017, and Chapter 5 of this book). If professional environments are increasingly VUCA, then engineers must be able to manage complex situations with discernment, making decisions based on incomplete or limited information; and countless, unknown, and unmeasurable parameters. Engineers must also be able to take responsibility for their decisions. This chapter proposed handling the complexity of human decision-making systems under VUCA constraints, in training. The Design Based Research method used is scalable and interdisciplinary; here, it allows for linking practice and theory in training, to improve practices, in an iterative way, over long time scales, with and for stakeholders. The proposed learning is social and experiential and seeks to evolve and modify coconstructed rules that are transferable to fields other than marine or engineering. Other learning and research methodologies exist to address decision-making in complex environments. Voinow and Bousquet (2010) review the so-called “collaborative” methods. For these authors, these methods have two objectives: the construction of knowledge shared by all

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stakeholders, including understanding dynamics under different conditions, and identifying and clarifying the impact of different solutions to a problem. Individuals facing complex systems learn to co-construct rules for managing these systems. Co-construction involves a phase of building consensus for the different participants on all political, physical, environmental, and institutional aspects, as well as the relationships between these different aspects. Participatory modeling consists of collective brainstorming to forge a shared knowledge, the ultimate goal being the construction of rules allowing the management of the resource and interpersonal relations in a potentially complex dynamic framework. The field of study presented in this chapter may find elements that are transferable to other fields, particularly in the legal field. In France, the Constitution of October 4, 1958, in force in 2018, is being updated (July 2008). With 16 titles and more than 89 articles, the problems of coherence and the number of related rules are noted in the Constitutional Council’s activity reports and can have an impact on the complexity of the legal system. Today, in the French Constitution, a citizen can hardly and reasonably have the capacity to integrate all these intertwined rules without first being trained or being an expert on the subject under study. In addition to the number of rules to be followed, the number of interactions between rules contributes to the complexity of the system. One rule can trigger another rule with additional effects. An accumulation of cases creates one or even several new general rules (case law) without mentioning cases that have not yet arisen. These reflections can also be applied to other disciplines (medicine, etc.) or other audiences also highlight the investigation possibilities of the DBR approach.

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Finally, the magnitude grid we propose (Table 6.1) adapted to the evaluation of capacities used in nautical situations offers possible transfer prospects to other activities. With regard to the questions raised, its graduation will be refined through tests conducted in 2018–2019 on educational activities as part of the European DAhoy project (www.dahoyproject.eu). Thus, in this project, the decisionmaking capacities related to the VUCAlity grid will be tested, in particular on an educational activity of crisis management (model of the Icelandic volcano Eyjafjöll, eruption in 2010), by the teams of Reykjavik University, but also on other activities carried out at the École Navale and the IMT Atlantique that will make it possible to vary the magnitude levels of the VUCA components in order to better identify and calibrate the skill levels and, ultimately, the VUCAbilities of a future framework. 6.6. Appendix: level of experience and feedback from IMTA students Table 6.3 shows that half of the engineering students, before the training week on “Engineers at sea: risks and reliability, strengthening your decision-making skills” offered by IMT Atlantique, had very little experience of nautical situations: “I have never been to sea”, “I just took a cruise on the Seine for a dinner in Paris”. Confirmed 1

Regular practice 1

Amateur

Novice

Complete novice

3

5

6

Table 6.3. Student profiles of maritime experience, 2017 session

The results of the metarule approach in real and original situations with non-experts (group of learners from IMT Atlantique) show that they are relevant in the construction of meaning, particularly in the context of collective learning;

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they make it possible to capitalize on good transferable practices, in an unknown and unpredictable context, with limited and disorganized information. This work made it possible to develop a “decision-making” framework that was formalized as follows: – D1: recognize and qualify a situation (e.g. ability to observe with insight, identify situational criticality, understand factors, remove “noises” to synchronize with the situation, identify strong and weak signals); – D2: analyze a situation and make a conscious judgment (e.g. think globally, define priorities and focus points, analyze points of uncertainty, be critical, identify risk factors, especially those of the situation, identify the keys to success); – D3: use or infer a decision model (e.g. manage priorities and identify focus points, evaluate, seek optimal solutions); – D4: react and decide in action to face dynamics and VUCAlity (for example, have a sense of initiative and make decisions in a VUCA context, interact and synchronize with the stakeholders in the situation, dare with courage, organize teamwork, manage conflicts); – D5: take responsibility for decision-making and learn from experience (e.g. have a sense of urgency, be adaptable and flexible, be a reflective practitioner, learn from mistakes). Students will have been able to position themselves several times on this framework, in a quantitative way on a performance scale with five levels of achievement, before, during, and at the very end of the experiential activities. Many reflexive exchanges have made it possible to support these self-assessments on the basis of criteria and indicators derived from experiences in nautical situations. In your opinion, what are your levels of decision-making capacity?

COMMENTS ON FIGURE 6.1.– (1) No knowledge or experience. (2) Minimum knowledge and experience. (3) Skill already used correctly in simple problems and situations. (4) Skill already used correctly in relatively complex problems and situations. (5) Proven and validated skills based on concrete experiences in several complex situations.

Figure 6.1. Initial self-assessment of nine general engineering students on the 5 capacities of the reference system, 2018 session. For a color version of the figures in this chapter see www.iste.co.uk/waldeck/methods.zip

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COMMENTS ON FIGURE 6.2.– (1) No knowledge or experience. (2) Minimum knowledge and experience. (3) Skill already used correctly in simple problems and situations. (4) Skill already used correctly in relatively complex problems and situations. (5) Proven and validated skills based on concrete experiences in several complex situations.

In your opinion, what are your current capacity levels in terms of decision-making?

Figure 6.2. Final self-assessment of the same nine general engineering students at the end of the training week on the five capacities of the reference system, 2018 session

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The students’ feedback after the IMT Atlantique nautical situations exercise day reveals points of progress in the way the situation is approached on five main axes: situation management, decision-making, behavior, rule learning, functioning (team or equipment). The work on the perception of the complexity of the situation to be managed by actors – who have little experience – seems relevant based on the experimentation of the situations. Learning areas identified by 
 students

Students’ response to the question “Have you made any progress and, if so, in which area?” “Staying calm and analyzing the situation.” “Yes, I am better able to manage the urgency and the communication it involves.” “Clearly yes, I had no serious experience at sea and now I think I can manage in the case of a simple cruise with friends.” “I have especially strengthened my ability to react to a danger: to make and execute decisions.”

Management 
of the situation

“Yes, I have learned to take responsibility for my decision-making, especially in a rather critical situation where no mistakes are ‘allowed’) and to make ‘bad’ experiences a stroke of luck!” “Yes. Before this week, I wasn’t aware of so much danger at sea. I never thought I’d see so much equipment for safety at sea. I have learned a lot, both in terms of precautions to be taken and in terms of decision-making. Preparing for this type of problem will allow me to better probe a situation and better organize a crew to manage rescue operations.”

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“Yes, in the area of decision-making and communication with the team.” “Yes, I think I have strengthened my skills in role distribution, rapid decision-making and taking a step back.”

Decisionmaking process

“I have especially strengthened my ability to react to a danger: to make and execute decisions.” “Absolutely. We found ourselves in ‘critical situations’, which significantly stimulated us. We realized that it is not easy to stay calm and make the right decisions in a hectic environment.” “Yes, I have learned to take responsibility for my decision-making, especially in a rather critical situation where no mistakes are ‘allowed’) and to make ‘bad’ experiences a stroke of luck!” “Staying calm and analyzing the situation.” “Absolutely. We found ourselves in ‘critical situations’, which significantly stimulated us. We realized that it is not easy to keep calm and make the right decisions in a hectic environment.”

Behavior

Learning rules

“Debriefing after each practical exercise helps a lot and influences the way things are approached. The map exercises are very important to check our good understanding and gain confidence in our choices [...] Also shows that a multitude of (good) solutions are possible, but not all optimal and depend on everyone's goal. So, I think I have strengthened myself (at different levels) in all the above categories.” “I think I have strengthened my capacities, especially in the field of protocols at sea. Point D3 was strongly tested during this intersession and I learned some skills from it.”

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Team functioning (communicationroles)

Equipment operation

“Yes, in the area of decision-making and communication with the team.” “Yes, I think I have strengthened my skills in role distribution, rapid decision-making, and taking a step back.” “Yes, I learned some interesting things like using basic survival equipment, and knowing how to react roughly to a risky situation.”

Table 6.4. Qualitative feedback on maritime experience, 2018 session

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Gutierrez Rodriguez, S., Servigne, S. (2007). Métadonnées spatiotemporelles temps-réel. Revue des sciences et technologies de l’information. Série ISI: ingénierie des systèmes d’information, 2, 12, 97–119. Harrison, J.R., Lin, Z., Carroll, G.R., Carley, K.M. (2007). Simulation modelling in organizational and management research. Academy of Management Review, 32(4), 1229–1245. Journé, B. (1999). Les organisations complexes à risques: gérer la sûreté par les ressources. Études de situations de conduite de centrales nucléaires, en français. PhD thesis, École Polytechnique. Journé, B., Raulet-Croset, N. (2008). Le concept de situation: contribution à lʼanalyse de lʼactivité managériale dans un contexte dʼambiguïté et dʼincertitude. M@n@gement, 11(1), 27– 55. Kamp, A. (2016). Engineering Education in a Rapidly Changing World, 2nd edition. Center for Engineering Education, Delft. Klein, G. (1999). Sources of Power: How People Make Decisions. MIT Press, Boston. Le Boterf, G. (2006). Ingénierie et évaluation des compétences, 5th edition. Eyrolles, Paris. Le Bris, S., Madrid-Guijarro A, Martin D.P (2019), Decisionmaking in complex environments under time pressure and risk of critical irreversibility: the role of meta rules. M@n@gement, 1, 1–29. Leplat, J. (1997). Regards sur l’activité en situation de travail, Contribution à la psychologie ergonomique. Presses universitaires de France, Paris. Lestonat, E. (2014). VUCA: former les managers à l’incertitude. Thot Cursus [Online]. Available at: http://cursus.edu/article/22176/ vuca-former-les-managers-incertitude/#.WcE8vNFpw2x. [Accessed December 2017] Lipshitz, R., Klein, G., Orasanu, J., Salas, E. (2001). Focus Article: Taking Stock of Naturalistic Decision-making. Journal of Behavioral Decision-making, 14(5), 331–352.

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Martin, D. (2010). Rationalité limitée et capacité à structurer l’action: principaux enjeux et défis associés à 3 classes de phénomènes. In La rationalité managériale en recherches. Mélanges en l’honneur de Jacques Rojot, Bournois, F., Chanut, V. (eds). 125–135. Mendoça, D., Webb, G., Butts, C. (2010). L’improvisation dans les interventions d’urgence: les relations entre cognitions, comportements et interactions sociales. Tracés, revue de sciences humaines, 18, 69–86. Perrow, C. (1994). The limits of safety: the enhancement of a theory of accident. Journal of contingencies and crisis management, 2, 212–220. Rasmussen, J. (1989). Learning from Experience. In Les facteurs humains de la fiabilité dans les systèmes complexes, Leplat, J., de Terssac, G (eds). Octarès, Toulouse, 359–381. Reason, J.T. (1990). Human Error. Cambridge University Press, Cambridge. Roberts, K.H. (1990). Managing High Reliability Organization. California Management Review, 32, 101–113. Roberts, K.H., Stout, S.K., Halpern, J.J. (1994). Decision Dynamics in Two High Reliability Military Organizations. Management Science, 40(5), 614–624. Rouvrais, S. (2013). Développer la pratique réflexive des étudiants par l’autoévaluation: d’une logique de formation diplômante à une logique d’apprentissage responsable et durable, retour sur trois expériences dans une formation d’ingénieur, en français. Réseau CEFI-Écoles: l’évaluation des acquis en formation d’ingénieur: témoignages, Chimie ParisTech, France. Rouvrais, S., Gaultier Le Bris, S. (2018). Breadth Experiential Courses to Flexibly Meet New Programme Outcomes for Engineer. Advances in Intelligent Systems and Computing Series, 627(1), 326–342. Rouvrais, S., Gaultier Le Bris, S., Stewart, M. (2018). Engineering Students Ready for a VUCA World?. A Design based Research on Decisionship. Proceedings of the 14th International CDIO Conference, Kanazawa, Japan, June–July.

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Roux-Dufort, C. (2000). Le regard de Karl Weick sur la fiabilité organisationnelle: implications pour la gestion des crises, Working document. CNRS, Laboratoires de recherche économiques et sociales. Suchman, L. (1993). Response to Vera and Simon’s Situated Action: A Symbolic Interpretation, Cognitive Science. A Multidisciplinary Journal, 17(1), 71–75. Thomas-Vaslin, V. (2017). Questionnements et connaissance de la complexité. In Qu’est-ce que la science… pour vous ?, Silberstein, M. (ed.). Matério-logiques, 251–256. Thomas-Vaslin, V., Jacquemart, F. (2016). Approche de la résilience et perturbations des systèmes complexes par une évaluation globale. Congrès mondial pour la pensée complexe: les défis d’un monde globalisé, Paris. Weick, K.E. (1987). Organizational Culture as a Source of High Reliability?. California Management Review, 29(2), 112–127. Weick, K.E. (1993). The Collapse of Sensemaking in Organizations: The Mann Gulch Disaster. Administrative Science Quarterly, 38(4), 628–652. Weick, K.E. (1995). Sensemaking Publications, Thousand Oaks.

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7 Approaches to and Applications of Graphemics

We will look at an example of interdisciplinarity by interdisciplinary models, according to Livet’s typology (Chapter 1). The main subject will be writing: the discipline of linguistics builds a structural model of the act of communication, the one of statistics a mathematical model of computerized writing, steganography a mathematical model of the variation of computerized writing to insert hidden information, and biometrics a physical model of the act of writing on the keyboard. By rediscovering common notions, all these disciplines are driven to ask new questions and, in some cases, to develop new formalisms. 7.1. Writing and linguistics Writing is a modality of representation of human language and therefore primarily a linguistic matter. However, linguistics has long denied it, discriminated against it, and cursed it: it is sufficient to quote the founding father of linguistics, Ferdinand de Saussure, who would have said in his lectures:

Chapter written by Yannis HARALAMBOUS.

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Language and writing are two distinct systems of signs; the second exists for the sole purpose of representing the first. The linguistic object is not both the written and the spoken forms of words; the spoken forms alone constitute the object. But the spoken word is so intimately bound to its written image that the latter manages to usurp the main role. People attach even more importance to the written image of a vocal sign than to the sign itself. A similar mistake would be in thinking that more can be learned about someone by looking at his photograph than by viewing him directly. (Saussure 1972, p. 45) This extract is interesting on two levels, since on the one hand it illustrates the blatant discrimination of the language written by Saussure (moreover, he does not even bother to indicate that by “language” he means the oral modality of the language) and on the other hand it is, without Saussure being able to imagine it in his time, the precursor of the current problems of virtuality: in the era of digital and artificial intelligence, is there always a difference between a photograph and a face? In any case, Saussure’s passionate attitude (and that of the Leipzig Neogrammarians who preceded him) is based on a principle that can be called the “original sin” of linguistics: the principle of the prevalence of oral language over written language. We can quote Aristotle: “Ἔστι μὲν οὖν τὰ ἐν τῇ φωνῇ τῶν ἐν τῇ ψυχῇ παθημάτων σύμϐολα, καὶ τὰ γραφόμενα τῶν ἐν τῇ φωνῇ” (Aristotle, 1936 translation), in other words: “the sounds emitted by the voice are the symbols of states of the soul, in the written words are symbols of the words emitted by the voice” (Shank, 2016 translation), which makes “written words” second-class citizens of the linguistic fact, since they are only linked to the “states of the soul” through the intermediary of “sounds emitted by the voice”.

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It is therefore not surprising that writing is neglected by the mainstream linguists, even if from time-to-time some courageous linguists (such as Vachek and Hořejsí from the Prague school) proclaimed its equal importance with that of speech. Another aggravating historical factor has been the impossibility of following the classic naming scheme of linguistic sub-disciplines. Indeed, the latter very judiciously used phonetic terms for the general study of sounds emitted by humans, and phonology for the study of sound classes as distinct elements of a system – called phonemes. The simplest way would have been to follow this scheme and define “graphetics”, “graphology”, and “graphemes”. However, the first term has never been used: it was not until the 1960s that someone began seriously to classify the “general graphemic forms drawn by humans for use in communication” (a definition similar to that of phonetics); the second term was taken up by the pseudoscience of graphology as early as 1900, which “claims to be able to systematically deduce psychological characteristics of an individual’s personality from the observation of his handwritten writing” (Wikipedia); only the third term is commonly used (but with a large number of different definitions, see (Pellat 1988)). There have been attempts to invent new terms: the author uses the term graphemics (“graphématique” in French) as a counterpart to phonology, others have proposed “graphonomy”, “grammatology” (this term, originally introduced by Gelb (Gelb 1963) in the 1960s, became famous through Derrida’s homonymous book (Derrida 1967), which is more philosophical than linguistic), and at a higher level: “grapholinguistics” (according to the German term Schriftlinguistik), etc. It was in the 1980s that the theoretical foundations of graphemics were established. Jacques Anis (Anis 1988) listed three approaches to this discipline:

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– phonocentrism, represented mainly by Saussure, who considered that written language was subordinate to oral language and distorted it; – phonographism, represented by Vladimir Gak (Gak 1976) and Nina Catach (Catach 1986), who consider that oral and written languages are systems of equal importance, but that the latter is determined by the former and cannot exist without it; – autonomism, represented by Jacques Anis himself (Anis 1988), who considers that the written word can be studied in complete independence from the spoken word. Thus, Catach (1978, p. 120) defines the grapheme as the smallest distinctive and/or significant unit of the written chain, composed of a letter or group of letters, with a phonic and/or semic reference in the spoken chain1. The example she gives is the French word “pourchasser” which means to hunt. According to her, it includes the following eight graphemes:

, , , , , , , . She classifies the graphemes into three categories: – phonograms: graphemes used to transcribe phonemes (e.g. in the French word “gare”, meaning “station”); – morphograms: notations of morphemes, especially located, to reinforce them, at the joints of words, maintained graphically identical whether pronounced or not (e.g. feminine/masculine, singular/plural markers, etc.);

1 Note that if we strictly observe this definition, letters that do not have a phonic reference like non-aspired in French cannot be graphemes. The example of was already a problem for Saussure (Saussure 1972, p. 53) who said: “Aspirated h no longer exists unless the label is applied to something which is not a sound but which prevents liaison and elision. Again we are involved in a vicious circle, and h is but a fictitious offspring of writing.”

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– logograms: word notations, in which the “script” becomes one with the word, from which it cannot be separated (e.g. / [to/has], / [quince/corner], / [baril/was], etc.). For Catach, the main function of logograms was distinguishing between homophones (same phonetic representation, different scripts, and meanings): “a logogram is not an ideogram (i.e. the representation of an idea), since sound is written, but there is more to it than sound.” According to her (Catach 1986, p. 268), there are logogrammic letters whose function is to distinguish the meaning of words and to help in their immediate visual knowledge. But rather than analyzing each letter as a grapheme of some type in these cases, Catach proposed, as a more economical alternative, that we consider these words as global graphemes – so coing and coin were logograms for her. Note that Catach does not talk about punctuation or symbols such as &, @, %, or writing systems other than the alphabetic ones. Anis (Anis 1983, pp. 33–34) proposed a radically different but complementary approach to Catach’s. For him: – a grapheme is the smallest unit of the written form of the expression; – the analysis of graphemes is done in a way similar to phonology methods, that is by using distinctive minimum pairs, a method widely used by structuralists; – if the word does exist in writing (Anis limited himself to alphabetic and abjad writings), this is not the case for the syllable: there is no explicit marker for the syllables. Nevertheless, one can define a written vowel, “likely to form the nucleus of a syllable” (for example, and being words, they are by definition also written vowels) and a written consonant that “forms its satellite” (in this definition, Anis was inspired by phonological theories, and in particular

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Malmberg’s approach (Malmberg 1971, p. 57)), he called the first nodes and the second sates. This short introduction to the history of graphematics was intended to show the reader the difficulty of studying written language and the resulting dilemmas: should the link with phonology be taken into account? Otherwise, and if we consider writing without any phonological influence – as if we were dealing with the writing of an extinct language or an extraterrestrial language as in the linguists’ fetish film, Arrival by Denis Villeneuve (2016), inspired by Ted Chiang’s novel Story of your Life (Chiang 2002) – is there any way to recover the linguistic properties of oral language through writing? It appears that the answer to this question is yes, and the following section is a brilliant example. 7.2. Spectral decomposition to the rescue of linguistics Can we define nodes and sates like Anis did, and find two classes of graphemes corresponding to the vowels and consonants defined by traditional grammars? Canadian linguists Patricia Thaine and Gerald Penn of the University of Toronto (Thaine and Penn 2017) do this in an ingenious way. They use the notion of p-frame, defined as follows. Let C be a character string2 C = c1c2 ⋯ cn ; then a pframe is a pair of letters (ci-1,ci+1) (with 1 below, andd the other tw wo letters aree on the x axiis. To have h a more conclusive result r for thee French langguage, we haave treated the corpuss of Frenchh Wikipedia (dated May M 1, 2017), compriising 947,7883,813 wordds (we have kept only the 41 letteers mentionned above). Afteer 2 hours and 30 minutes m of calculation, c the algorithhm provideed us with 1,718 1 p-fram mes and the spectral deccomposition of this maatrix (Figure 7.2).

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Figure 7.2 2. Spectral de ecomposition in nduced by the e 948M words on Frencch Wikipedia (May ( 2017). 3 tha We can see very clearly c at consonannts and vow wels are seeparated by the t abscissa axis (except for the letteer , whichh seems too be on the voowel side), and, a as prediccted by Thainne and Penn (2017), thhe semi-voweel is verry close to th he dividing line l betweenn vowels annd consonantts.

To conclu ude section n 7.2, we note n that the t mathem matical ap pproach too spectral decomposition applieed judiciou usly to Frrench grap phemes hass made it possible p to classify th hem as reepresentatiives of voweels or conso onants, witth surprisin ng ease an nd precisioon. The speectral apprroach is a generaliza ation of 3 To improve the t readabilitty of this ima age, we multiplied the y values by 10 0 to the poweer of 0.75 × loog10(M/|y|), where M iss max(y) or |min(y)|, deepending on the t sign of y.. As this funcction is an in ncreasing onee, it does noot affect the rank of the y values. v

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the minimum pairs method; the positive values on the y axis correspond to Anis nodes and the negative values to sates. We can therefore conclude that by the Thaine and Penn method (2017), we experimentally find Anis’s nodes and sates, and that these correspond quite naturally to vowels and consonants defined by traditional grammars. In section 7.3, we will consider another approach to writing that allows linguistic characteristics (in this case, syllabic and morphemic boundaries) to be obtained through operations that fall under another discipline, namely biometrics. 7.3. Application in biometrics Acts of speech, after having been studied by linguists, have also interested psychologists, anthropologists, and more recently computer scientists specialized in speech recognition. Manual writing, on the other hand, has been mainly handled by a pseudoscience called “graphology” (whose existence continues to discredit graphematics). But recently another type of act of writing has been studied: the one where the writer uses the computer keyboard as a writing tool (Ballier 2019). By monitoring the keyboarding activity of experimental subjects, data of the following type (this is the ritual phrase Hello World!) have been retrieved (Figure 7.3). In Figure 7.3, taken from Villani (2007), we can observe key press and release times, to the nearest millisecond4. Lines 1, 8, and 14 represent the pressure of the shift key, which remains pressed, while the letter on the following line

4 The numbers in the Press and Release columns represent milliseconds since January 1st, 1970, the beginning of the Unix era. Thus, 1114450752445 must be read as 1,114,450,752 seconds and 445 msec, i.e. on April 4, 2005, at 5 h 39 UTC and 445 thousandths of a second.

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is being entered. According to Villani (2007), from a data entry of 650 characters, a person’s identity (out of 36) has been identified with an accuracy of 99.4% (for the same text copied by everyone, or 98.3% for text freely chosen by each person), which makes this a very reasonable biometric identification method. Of course, performance drops when an individual enters text on different keyboards, and 650 characters still require around 1′30″ to be entered, which is much slower than a fingerprint or a retina scan, but this method has the advantage of being applicable with very few means: a simple computer keyboard is enough. Entry

Loc.

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l

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?

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1114450751373

1114450753776

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!

1

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1114450752445

1114450752885

Figure 7.3. Time-stamps of the key pressing and releasing when entering the “Hello World!” sentence

In 2004, a team of German linguists studied the IKI (Interkey Interval = time between two keys) of German texts. They found surprising results (Weingarten et al. 2004, p. 169):

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– subsyllabic segmentation: when a syllable exceeded four characters (which frequently happens in German where 10letter words such as quietschst can be monosyllabic…), there was an additional IKI of 10 to 30ms after the fourth letter, which was interpreted by psychologists as a motorized sequence segmentation effect, that is the brain sending instructions to the fingers in four-key sequences; – syllabic segmentation: between intra-morphemic syllables (i.e. belonging to the same morpheme5), they found additional IKI delays of 40–70ms. This delay is not correlated with the frequency of the word, and is the same for both real and imaginary words; – morphemic segmentation: between morphemes, they found additional delays of 100–150ms, and these are indeed correlated with the frequency and lexicality of the word. This fact is normal since, after all, if morphemes are invented, they cannot be recognized as such, and if morphemes exist, but not the compound word, then the subject will take a “moment’s thought” to grasp the word’s semantics, which, in turn, will appear in the intermorphemic IKI. Thus, a word such as (“bottle opener,” consisting of lexical morpheme (bottle), grammatical morpheme (plural marker), lexical morpheme (opening) and grammatical morpheme 5 A morpheme is a sequence of phonemes or graphemes that constitutes the smallest unit of meaning; for example, in the French word [brown], we have three morphemes: the lexical morpheme , the feminine marker and the plural marker . The German language makes extensive use of lexical (as well as grammatical) morphemes, as in the word (probability theory) with lexical morphemes (true) and (appearance), the adjectivizing grammatical morpheme (equivalent to the French suffix ), the grammatical substantive morpheme (equivalent to the French suffix ), the grammatical morpheme (genitive), the lexical morpheme , and the grammatical morpheme (ending the noun).

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(suffix)) will be entered with IKI delays of the following magnitudes: “Fla[40-70]sche[10-30]n[100-150]öf[40-70]fner.” Through this method, we rediscover the morphological structure of the word through keyboarding characteristics, which is revealing of the way in which the human brain manages linguistic data, whether they are related to graphemes or phonemes. Thus, Weingarten et al. (2004), and also a team of French researchers (Bonnin et al. 2001), seem to favor Anis’s autonomist hypothesis which, in this context, stipulates that graphic representations are independent of phonemic representations. In section 7.4, we will consider another approach to graphemics: the use, for cryptographic purposes, of the variation inherent in a linguistic operation, namely transcription from a writing system to another. This variation has its origins in the ambivalent and multiple relationships of the oral and written word, relationships that are reflected in the different approaches to the written word: phonocentric, phonographic, autonomist, and also etymological/historical, as well as inspired by cultural borrowings. 7.4. Application in steganography Steganography is a branch of cryptography that deals with communication where the presence of hidden content in a message must escape third party observers. A typical example from Roman antiquity is that of “nulls”. Null encryption is a kind of hidden code. This method consists of marking certain letters of a text with a particular sign: only these few letters carry meaning. These are called “identified”. The rest of the letters framing the marked

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letters are called null and void: they are meaningless. Their mission is to deceive any indiscreet eye since the wording of the letter only serves to mask the real text (Collard 2004). 7.4.1. Steganographic approach to Greeklish In section 7.4.1, we will describe a joint work with Caroline Fontaine on a steganographic method based on variations in Latin transcription of Greek text. Indeed, in Greek-language social networks there is a fairly widespread practice of writing Greek text in Latin characters (commonly called greeklish), for ease (this avoids changing virtual keyboard) or style (effect of modernity or adherence to Western culture). However, this practice does not follow any specific standard and at least five approaches are used, often simultaneously: – the approach inspired by Western philology. The latter considers that the letters , , correspond to occlusives aspirated in ancient Greek and, therefore, that their transcription is obtained by the letter representing the non-aspirated occlusive (therefore, ,

, ) followed by representing aspiration, and so we have → , → , → ; – the phonetic approach according to the network of grapheme-phoneme correspondences of English or German: → , → (as in the author’s name), → , → , → , → , etc.; – ad hoc phonetic approaches, for example → , → (as in the case of the Latin transcription of the Russian name Khrushchev), → , etc.; – the graphic approach, which is inspired by the visual similarities of Greek and Latin graphemes: → , → , → , → , → , sometimes even using figures for certain graphemes: → , → , etc.;

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– more rarely, the use of the same key on the keyboard: the fact that Greek letters , , , , , are assigned to the same keys on the Greek keyboard as Latin letters , , , , works like a code and some scripters use Latin letters to represent Greek letters assigned to the same key, even though they have no phonetic or graphic link between them. In this way, would be written . This approach only happens episodically and without any systematic approach. To illustrate our comments, here is what Androutsopoulos (2001) obtained after asking 70 people to write the word (= “address”) in greeklish: (12 times), (7 times), (7 times), , ,

(3 times), , , , (twice), and the hapaxes , , , , , , , , , , , , . The variety of approaches illustrates the creativity of Greek writers, but above all the multitude of approaches to writing: Approaches 2 and 3 reflect the phonocentrism that reigned, as a undisputed linguistic ideology in Greece, since the demotic movement of the early 20th Century, while Approach 4 is more a matter of autonomism, since the grapheme is considered as a sign independent of any phonetic correspondence. Finally, Approach 1 is influenced by the West’s vision of Greece on the country’s language and culture. Technically, these approaches are often incompatible with each other, which prohibits certain combinations of methods in the same syllable or word. Thus, the word would be written as according to Approach 3, according to a mixture of Approaches 1 and 3, according to a mixture of Approaches 2 and 4, but the form

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* that would result from the use of for (Approach 1) and for (Approach 4) is simply illegible. Our method is to use transliteration approach choices as information hidden in the message. In addition to the logical incompatibilities mentioned in the previous paragraph, there are also criteria of plausibility: the use of two different methods in the same word should be avoided, for example, by writing the phrase where the first is transcribed according to Approach 1 and the second according to Approach 4: there is no logical incompatibility, but it is unlikely that someone would use both methods in the same word. 7.4.2. Steganographic method: evaluation Our method consists of choosing different transliteration methods for the words of a text, depending on the information that will be hidden in the message. Let’s imagine, for example, that for the graphemes of a word there are two possible transliteration methods (which is an underestimation, since some graphemes can be transcribed in no less than four different ways, such as : , , , ), so this word would carry one bit of information. To try to evaluate, even if only roughly, the performance of this method, we propose the following calculations: – assume that only the following 13 graphemes can have multiple transliterations: , , , , , , , , , , , , ; – the Greek Wikipedia corpus (dated September 1, 2018) contains 90,542,689 words, of which 73,616,745 contain at least one of the graphemes mentioned; – Moby Dick’s English text has 1,192,635 characters; if we ignore case differences and numbers and restrict ourselves to

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the 26 letters of the Latin alphabet and 6 punctuation marks, these characters can be coded on 4 bytes; – so, by dividing the number of words from the Greek Wikipedia that are candidates for a transliteration variation by four times the number of characters from Moby Dick, we find that we can hide, in the first one, a quantity of information equal to 15 times the text of the second, which is enormous in itself; – another example: the text of the translation of Homer’s Iliad into modern Greek which can be retrieved from the Project Gutenberg site, contains 111,583 words, 78,622 of which are candidates for a variation in transcription, so we can hide a text of 19,655 Latin characters, the equivalent of the first nine pages of the text that the reader has before his eyes, including spaces. Of course, this evaluation only takes into account the amount of information that can be hidden through this method, to which must be added the degree of plausibility (or “naturalness”) of a text that constantly alternates between different transliteration methods, and it may well be that our method is sub-optimal in this respect. Nevertheless, it illustrates well the multitude of theoretical approaches to writing: phonocentrism, autonomism, and historicism. When a writer chooses the transliteration approach 2 from Greek, e6 is placed in the continuity of the phonocentrism movement that results from the diglossia that reigned in the Greek linguistic space between the 18th Century and 1976, when the demotic was introduced as an official state language. When choosing Approach 4, e adopts an autonomist approach, since e removes the phonetic reference of Latin letters and uses it graphically. When e chose Approach 1, e places eirself in a 6 We are using Spivak’s gender-neutral pronouns (https://en.wikipedia. org/wiki/Spivak_pronoun).

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globalist context, where the long tradition of German, French, and British Hellenists reflected in the Latin transcription of Greek words used in these languages affects eir vision of the relationship between the scriptures. To summarize in one example: by writing the Greek medical term (which became aphthous in French and Aphthous in German) as , e chooses to favor the visual aspect; by writing , e chooses to give the pronunciation (for theta e has no choice but to make the only English grapheme corresponding to the voiceless dental fricative /θ/ is , and neither French nor German has this phoneme); and finally by writing , e aligns himself with the Hellenistic tradition and the scriptural evolution of this word in the West. 7.5. Conclusion The brief series of heterogeneous examples we have just given in this chapter have in common the interaction between disciplines in response to a given phenomenon – in this case, the written modality of language. It is clear from the examples that the theoretical approach (inspired by ambient structuralism) that Anis introduced in 1983 could be verified mathematically in 2017 by spectral decomposition and that the notions of syllable and morpheme were traced biometrically in 2006. Finally, in the last example, we have seen how different approaches to writing can have unexpected and unimagined applications in a discipline as distant as cryptography. 7.6. References Androutsopoulos, J. (2001). Ἀπὸ dieuthinsi σὲ diey8ynsh, ὀρθογραφικὴ ποικιλότητα στὴ λατινικὴ μεταγραφὴ τῶν ἑλληνικῶν. In Proceedings of the 4th International Conference on Greek Linguistics, Agouraki, Y., et al. (eds). University Studio Press, Nicosia.

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Anis, J. (1983). Pour une graphématique autonome. Langue française, 59, 31–44. Anis, J. (1988). L’écriture, théories et descriptions, Vol. 10. De Boeck Université, Brussels. Aristotle (1936). De l’interprétation. Traduction et notes par J. Tricot. Librairie Philosophique J. Vrin, Paris. Ballier, N., Pacquetet, E., Arnoldt, T. (2019). Investigating key-logs as time-stamped graphemics. Proceedings of the /gʁafematik/ 2018 Conference. Fluxus, Brest (forthcoming). Bonin, P., Peereman, R., Fayol, M. (2001). Do phonological codes constrain the selection of orthographic codes in written picture naming? Journal of Memory and Language, 45, 688–720. Catach, N. (1978). L’orthographe. Que sais-je?, Vol. 685. Presses universitaires de France, Paris. Catach, N. (1986). L’orthographe française, traité théorique et pratique. Nathan, Paris. Chiang, T. (2002). “Story of your Live”. In Stories of your Live and Others. Tor Books, New York. Collard, B. (2004). Les langages secrets dans l’Antiquité grécoromaine. Folia Electronica Classica, 8. [Online]. Available at: http://bcs.fltr.ucl.ac.be/FE/08/stegano.htm. Derrida, J. (1967). De la grammatologie. Les Éditions de Minuit, Paris. de Saussure, F. (1972). Cours de linguistique générale. Payot, Paris. Gak, V.G. (1976). L’orthographe du français. Selaf, Paris. Gelb, I. (1963). A Study of Writing. The University of Chicago Press, Chicago. Haralambous, Y. (2007). Fonts and Encodings. From Advanced Typography to Unicode and Everything in Between. O’Reilly, Sebastopol. Malmberg, B. (1971). Les domaines de la phonétique. Presses universitaires de France, Paris.

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List of Authors

Sophie CAILLON

Pierre LIVET

CEFE Université de Montpellier France

Centre Gilles Gaston Granger Université d’Aix-Marseille France

Sophie GAULTIER LE BRIS

Jean-Pierre MÜLLER

LEGO École Navale UBL Brest France

CIRAD Montpellier France

Yannis HARALAMBOUS IMT Atlantique Lab-Sticc Brest France

Vanesse LABEYRIE CIRAD Montpellier France

Denise PUMAIN Géographie-cités Université Paris 1 France

Siegfried ROUVRAIS IMT Atlantique Lab-Sticc Brest France

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

172

Methods and Interdisciplinarity

Matthieu SALPETEUR

Roger WALDECK

IRD PALOC Paris France

IMT Atlantique LEGO Technopôle Brest-Iroise Brest France

Mathieu THOMAS CIRAD AGAP Montpellier France

Index

A, B

I, K

algorithmic thinking, 36 ambiguity, 109, 110, 113, 122, 126 analysis network, 70, 76, 87, 93, 95 arts and sciences, 1, 3, 19 autonomism, 152 biological resources, 76 biometrics, 159

Iméra, 1 interaction, 6, 7, 13, 15, 33, 57, 69, 72, 89 interdisciplinarity, 1, 8 integrative, 11 reflexive, 11 interrelated phenomena, 20 knowledge, 10, 23, 24, 26, 35, 39, 76, 84, 87, 89, 122–124, 126, 134, 136 representation, 47

C, D, G circulation, 76 classification, 51, 56, 89 communication, 16, 30, 66 complexity, 108, 114, 122 computational geography, 31 concept, 11, 27, 28, 47, 49, 100 contexts, 13 decision theory, 100 decomposition spectral, 154 geolocation, 37 graphemics, 151

L, M, N linguistics, 149 model, 5, 31, 46, 48, 100, 106, 113, 128 modeling, 45 multi perspective, 46, 100 -scale, 27, 95 multidisciplinarity, 4, 11, 13, 16, 66 networks, 3, 4, 6, 37, 38, 40, 69–76, 79, 81, 82, 84, 87, 89, 92–95, 123

Methods and Interdisciplinarity, First Edition. Edited by Roger Waldeck. © ISTE Ltd 2019. Published by ISTE Ltd and John Wiley & Sons, Inc.

174

Methods and Interdisciplinarity

O, P, R ontologie, 48 phonocentrism, 152 phonographism, 152 remote sensing, 28 risk, 105, 110 S, T sector, 57 self-organization, 32 simulation, 33 species, 50 steganography, 162 structural patterns, 73 synergy, 32 system(s) city, 32 complex, 31, 45, 70, 71, 72, 109, 110, 114, 123, 124, 136, 137

geographic information, 37 socio-ecological, 69, 70, 72, 76, 95 writing, 156 transdisciplinarity U, V, W UML, 48 uncertainty, 17, 102, 104, 113, 122, 126 urban ecology, 28 validation, 61 volatility, 108 VUCA, 99, 122 decision theory, 104 in management, 102 wild pepper, 46

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