Decision Making And Problem Solving: A Practical Guide For Applied Research 3030668681, 9783030668686

Table of contents :
Preface
Acknowledgements
Contents
Contributors
Chapter 1: Decision Making Under Uncertainty and Problem Solving
1.1 Introduction
1.1.1 Continuum of Pure Uncertainty and Certainty
1.1.2 Risk and Uncertainty
1.1.3 Decision Making and Uncertainty
1.1.4 Types of Uncertainty in Decision Making
1.1.4.1 First- and Second-Order Uncertainty
1.1.4.2 Uncertainty from the Ethical Perspective
1.1.4.3 Uncertainty Based on S-R-O Rules
1.1.5 Neurological Correlates of Uncertainty and Decision Making
1.1.6 Limitations of Decision Making Under Pure Uncertainty
1.1.7 Handling Uncertainty in Decision Making
1.1.8 Implications for Decision Making Under Uncertainty
References
Chapter 2: Are Positive People More Flexible in Cognitive Processing? Addressing the Conceptual and Empirical Inconclusiveness
2.1 Introduction
2.2 Perspective I: Positive Emotions and Global/Local Bias in Cognitive Processing
2.3 Perspective II: Positive Emotions and Cognitive Flexibility
2.4 Present Study
2.5 Method
2.5.1 Participants
2.5.2 Measures
2.6 Procedure
2.7 Methodological Considerations
2.8 Results
2.9 Discussion
2.9.1 Implications for Theory and Research
2.10 Implications for Practice
2.11 Limitations and Future Scope
2.12 Conclusion
References
Chapter 3: A Review of Decision Making Using Multiple Criteria
3.1 Introduction
3.2 Concepts Used
3.2.1 Uncertainty/Imprecision
3.2.2 Entities
3.3 Decision-Making Types
3.3.1 Multiple Objective Decision Making (MODM)
3.3.2 Multiple Criteria Decision Making (MCDM)
3.3.2.1 Weighted Product Model (WPM)
3.3.2.2 Weighted Sum Model (WSM)
3.3.2.3 The TOPSIS Method
3.3.2.4 The AHP Method
3.3.2.5 The Fuzzy AHP Method
3.3.2.6 ELECTRE
3.3.2.7 Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)
3.3.2.8 Multiple Attribute Decision Making (MADM)
3.4 Steps in MCDM Methodology
3.4.1 Methods of Estimation of Weights
3.5 Emotion-Based Decision Making
3.5.1 Emotion-Based MCDM
3.6 Conclusion
References
Chapter 4: Mid-Brain Connective for Human Information Processing: A New Strategy for the Science of Optimal Decision Making
4.1 Introduction
4.2 Background
4.2.1 Human Brain, Information Processing, and Decision Making
4.3 Main Body
4.3.1 Emotions, Cognition, and Mid-Brain Connectome
4.3.2 Brain Integration: Emotions, Cognition, and Mid-Brain Connect
4.4 Conclusions
References
Chapter 5: Need of Improving the Emotional Intelligence of Employees in an Organization for Better Outcomes
5.1 Introduction
5.2 Need of Improvements in Emotional Intelligence
5.3 Factors to Be Considered for Identifying Emotional Intelligence
5.4 Factors of Emotional Intelligence
5.4.1 Self-Awareness
5.4.2 Awareness of Others
5.4.3 Authenticity
5.4.4 Emotional Reasoning
5.5 The Value of Emotions
5.5.1 Data
5.5.2 Absolution
5.5.3 Communication
5.5.4 Self-Management
5.5.5 Creating Self-Management
5.6 Inspiring Performance
5.7 Persuasive Actions of Leadership
5.8 Three Steps Toward Improved Emotional Intelligence
5.9 Conclusion
References
Chapter 6: Slow and Fast Thinking for Problem Solving Under Uncertainty
6.1 Introduction
6.2 What Thinking Is and Why It Helps in Problem Solving
6.3 Slow Thinking and Why Do We Not Think Slowly?
6.3.1 Subjective Illusion Breaker
6.3.2 Surprises and How Such Triggers Slow Thinking
6.4 Fast Thinking and Why Do We Think Fast?
6.4.1 Perturbation Theory Supports Fast Thinking
6.4.2 You Got Skills: You Can Think Fast
6.4.3 Intuition Accelerates Fast Thinking
6.5 How Thinking Speed Affects Problem Solving
6.6 Conclusion
References
Chapter 7: Decision Making in Positive and Negative Prospects: Influence of Certainty and Affectivity
7.1 Introduction
7.2 Method
7.2.1 Participants
7.2.2 Measures
7.2.3 Choice Under Uncertainty
7.2.4 Certainty in Choice
7.2.5 Positive Affectivity and Negative Affectivity Schedule
7.3 Results
7.3.1 Choice under Uncertainty
7.3.2 Certainty in Choices
7.3.3 Certainty and Affectivity Determining the Choice
7.4 Discussion
References
Index

Citation preview

Sachi Nandan Mohanty  Editor

Decision Making And Problem Solving

A Practical Guide For Applied Research

Decision Making And Problem Solving

Sachi Nandan Mohanty Editor

Decision Making And Problem Solving A Practical Guide For Applied Research

Editor Sachi Nandan Mohanty Department of Computer Engineering College of Engineering Pune Pune, Maharashtra, India

ISBN 978-3-030-66868-6    ISBN 978-3-030-66869-3 (eBook) https://doi.org/10.1007/978-3-030-66869-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Late Professor (Dr.) Biranchi Narayan Puhan Former Vice-Chancellor of North Odisha University (NOU), Odisha, India

Preface

It was always my dream, during my Ph.D. work at IIT Kharagpur, India, to write a book about cognition and decision making under an environment of uncertainty. Day-to-day human life and activities are governed by two major factors, feelings and thoughts, which we more formally name emotions and cognition, respectively. Both these governing factors are considered to be internal happenings and cannot be monitored by anyone else. Decision making under uncertainty is quite a difficult task. Emotion and cognition are the most important functions being carried out in the frontal lobes of the brain, hence predominantly assigned to the problem-solving and decision-making process. This book consists of seven chapters covering many areas, such as decision making under uncertainty and problem solving, decision making under the environment of multiple criteria, positive people more flexible in cognitive processing, mid-brain connective for human information processing, improving emotional intelligence of employees in an organization for better outcomes, slow and fast thinking for problem solving under uncertainty, and decision making in positive and lastly negative prospects: the influence of certainty and affectivity. A new development in neuroscience is cognitive neuroscience, which combines the dual approach of understanding human behaviour as symbolic and physiological, according to Simon (1992). Both these approaches are now accepted as normal and natural ways of understanding the underlying cognitive processes in the fields of education and administrative behavioural approaches. Emotion and information processing have expanded interest in cognition as well as in neurological functions in the field of education for facilitating learning and in the field of management for smooth decision making. The impression of all the chapters in this book covers the diversified domains of cognitive psychology, decision making under uncertainty, in the field of education as well as the management sector. The principal audience for this book is students and researchers who want to consider emotion and cognition as topics for future studies and research. As both topics are widespread and evolving areas of research nowadays, a diversified audience whose curiosity embraces emotion and its practical corollaries is expected. Pune, Maharashtra, India

Sachi Nandan Mohanty vii

Acknowledgements

I owe my heartfelt thanks to all the people who have enabled me to edit this book and all the authors for contributing their chapters. The completion of this undertaking could not have been possible without the participation of the expert reviewers. So, I wish thank the subject matter experts who could find time to review the chapters and deliver those on schedule. My special thanks to Dr. Suneeta Satpathy, Associate Professor, Department of Computer Science & Engineering, College of Engineering Bhubaneswar, Odihsa, India, for her help with proofreading. I also express my special thanks and gratitude to Ms. Olivia Ramya Chitranjan and Ms. Lilith Dorko for their dedicated support and help in publishing the book. Finally, a ton of thanks to Prof. J.P Das, University of Alberta, and Prof. Damodar Suar, Dept. of Humanities and Social Science, Indian Institute of Technology Kharagpur, West Bengal, India for their constant motivation, encouragement, and valuable suggestions as and when needed. An attempt at any level cannot be satisfactorily completed without the support and guidance of my parents and friends. So here I thank my parents as well as friends for constantly encouraging me to make this book possible. To end, I am thankful to Lord Krishna who blesses me with sound health and abilities and gives me courage to perform and complete my work in a successful manner.

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Contents

1 Decision Making Under Uncertainty and Problem Solving������������������   1 Smriti Pathak, Roshan Lal Dewangan, and Sachi Nandan Mohanty 2 Are Positive People More Flexible in Cognitive Processing? Addressing the Conceptual and Empirical Inconclusiveness����������������  13 Papri Nath, Rabindra Kumar Pradhan, and Sachi Nandan Mohanty 3 A Review of Decision Making Using Multiple Criteria��������������������������  27 Mahendra Prasad Nath, Sachi Nandan Mohanty, and Sushree Bibhuprada B. Priyadarshini 4 Mid-Brain Connective for Human Information Processing: A New Strategy for the Science of Optimal Decision Making ��������������  47 Rashmi M. Shetkar and Sachi Nandan Mohanty 5 Need of Improving the Emotional Intelligence of Employees in an Organization for Better Outcomes��������������������������������������������������  63 R. S. M. Lakshmi Patibandla, V. Lakshman Narayana, and Sachi Nandan Mohanty 6 Slow and Fast Thinking for Problem Solving Under Uncertainty��������  77 Nilay Awasthi and Sachi Nandan Mohanty 7 Decision Making in Positive and Negative Prospects: Influence of Certainty and Affectivity����������������������������������������������������������������������  91 Sachi Nandan Mohanty and Suneeta Satpathy Index������������������������������������������������������������������������������������������������������������������  107

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Contributors

Nilay Awasthi  Amity University Mumbai, Mumbai, India Roshan  Lal  Dewangan  Department of Applied Psychology, Kazi Nazrul University Asansol, Asansol, WB, India Sachi  Nandan  Mohanty  Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India V.  Lakshman  Narayana  Department of IT, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, AP, India Mahendra  Prasad  Nath  Computer Science & Information Technology, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India Papri Nath  Indian Institute of Management Tiruchirappalli, Tamil Nadu, India Smriti  Pathak  Department of Humanity and Social Science, Indian Institute of Technology Kharagpur, Kharagpur,, WB, India R. S. M. Lakshmi Patibandla  Department of IT, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, AP, India Rabindra Kumar Pradhan  Department of Humanity and Social Science, Indian Institute of Technology Kharagpur, Kharagpur,, WB, India Sushree  Bibhuprada  B.  Priyadarshini  Computer Science & Information Technology, Siksha ‘O‘ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India Suneeta  Satpathy  Department of Computer Science & Engineering, College of Engineering Bhubaneswar, Bhubaneswar, Odisha, India Rashmi M. Shetkar  IISc Banglore, Banglore, India

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Chapter 1

Decision Making Under Uncertainty and Problem Solving Smriti Pathak, Roshan Lal Dewangan, and Sachi Nandan Mohanty

1.1  Introduction You cannot be certain about uncertainty. — Frank Knight (1921)

The study of subjective uncertainty is crucial to understand the process of judgement and choices. Uncertainty arises from incomplete information, the random nature of a situation, ignorance, laziness, and exertion. Uncertainty occurs when the probabilities and magnitude of events are uncertain. In other words, a lack of knowledge regarding the situational outcome or the involved risk may lead to uncertainty. Even the specific knowledge of these elements cannot eliminate uncertainty caused by the probabilistically determined outcomes. Sometimes the situation itself cannot provide all the information: ‘uncertainty’ reflects the degree of incompleteness from lack of richness and amount of information required to understand a situation. Individuals confront an uncertain situation following their motivation. Three traditional paths have been recognised in understanding how people handle uncertainty: knowledge seeker, certainty maximizer, and intuitive statistician-­ economist (Smithson, 2008). The knowledge seeker view focuses mainly on the persuasion of novel information and experience and tends to tolerate uncertainty and ignorance to attain this goal. The certainty maximizer approach is more concerned S. Pathak Department of HSS, IIT Kharagpur, Kharagpur, West Bengal, India e-mail: [email protected] R. L. Dewangan Department of Applied Psychology, Kazi Nazrul University Asansol, Asansol, West Bengal, India e-mail: [email protected] S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Nandan Mohanty (ed.), Decision Making And Problem Solving, https://doi.org/10.1007/978-3-030-66869-3_1

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about the devastating consequences of uncertainty and unpredictability on the holistic capabilities of an individual. This approach states that an inspiration to eliminate the anxiety resulting from uncertainty regulates this process (Berger & Calabrese, 1975). The relationship between anxiety and uncertainty has been recognised as twofold (Behar, 2001; Izard, 1991; Mandler, 1984), and anxiety is also considered as the emotional equivalent of uncertainty (Gudykunst & Nishida, 2001). The third tradition, intuitive statistician-economist, is mainly concerned with rationality in decision making and focuses on probability theory. This approach considers humans as hedonic beings who seek pleasure and avoid pain. This point of view emerges from psychophysics, perception, cognition, and information processing models. Smithson (2008) further explained uncertainty in three constructs, named probability or randomness, delay in consequences, and the absence or lack of clarity. Probability and uncertainty had always been correlated with each other. Uncertainty can also be understood as a degree of incompleteness whereas absence is defined as a type of this incompleteness. Uncertainty includes probability, vagueness, and ambiguity, a degree of incompleteness that is a form of ignorance. Ignorance is further divided into distortion and incompleteness (Smithson, 2012). Some researchers linked these concepts to behavioural precedence and suggested that people do follow and manage these types of uncertainty. Ellsberg’s (1961) experimental work on gambling demonstrated the behavioural effects of ambiguity or unknown probabilities, and also stated that a rational individual can reduce uncertainty to probability. Uncertainty is frequently incited by unanticipated changes in learned stimulus-response-outcome (S-R-O) association. This S-R-O rule simply states that persons learn certainty or uncertainty with the help of association between stimulus and response, and the positive and negative outcomes of this association guide our future decision making. So, in future, we prefer to choose an outcome that provides a certainty of rewarding outcome. For example; if a response (enter) to a stimulus (shopping centre) provides a good outcome (good products), then the S-R-O association would be strong. Now this strong association would provide a degree of certainty or uncertainty about the situation in future decision making whether to go to that centre again.

1.1.1  Continuum of Pure Uncertainty and Certainty This continuum serves two poles: deterministic model and pure uncertainty. Problems under risk are represented in the area between these two poles (Taghavifard, Damghani, & Moghaddam, 2009) (Refer Fig. 1.1). This model explains that the degree of certainty fluctuates depending upon the amount of available knowledge a person has regarding the problem situation, suggesting a variation in the solution is provided by each individual. Probability measures the likelihood of occurrence for an event and is always regulated by the knowledge possessed by the decision maker, but includes a risky situation. The deterministic side indicates the probability of one (or zero) and contains complete

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Pure Uncertainty Model

Probabilistic Model

Ignorance

Risky Situation

Deterministic Model Complete Knowledge

Fig. 1.1  Schematic diagram of uncertainty

knowledge of the situation and pure uncertainty reflect pure ignorance, has flat (or all equally probable) probability. This flat uncertainty involves the highest risk in a decision situation.

1.1.2  Risk and Uncertainty Risk is an expression of the probability or likelihood of occurrence of an outcome. Risk assessment of a situation typically involves uncertainty analysis of beneficial outcomes. Uncertainty explains the quality of knowledge we possess regarding the risk involved in the situation. We try to reduce uncertainties, but a risk-based approach to decision making strives to recognise and meticulously treat the uncertainty in the decision-making process. As argued in decision theories (Camerer & Weber, 1992; Tversky & Kahneman, 1992), some decisions are classified as “a decision taken under precise uncertainty” or “decisions taken under risk.” Here the probabilities of occurrence of an outcome in future are known. Another type of “decisions taken under uncertainty” does not involve any knowledge and estimation of such probabilities. A risky situation defines both such situations (Knight, 1921; Morgan, Henrion, & Small, 1990). Risk as also a type of uncertainty is explained in a further section.

1.1.3  Decision Making and Uncertainty We encounter the process of selection and choosing among several options daily. Decision making requires thoughtful activity, critical analysis, and action towards the problem situation. As correctly stated by Franklin G. Moore (1964), “decision making is the blend of thinking, deciding and acting.” Decision making is also defined as a capacity to select between contending courses of action dependent on their general estimation of results (Balleine, 2007). These actions could be related to social, economic, and moral decisions. The degree of uncertainty related to the expected consequence generally changes with circumstances and represents a conceptual difference in the decision-making process (Weber & Johnson, 2009). We are often confronted with a situation that does not include all the necessary information, and we need to decide although our knowledge of the consequences is uncertain.

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These situations can be understood in a continuum from complete ignorance to certainty. Research has been carried out on decisions under ambiguity or risk (Schiebener & Brand, 2015). The decision-making principles included the study of uncertainty in terms of risk, ambiguity, and probability. Edward (1984) considered the normative principle as the only important principle in decision making and emphasised maximising subjective expected utility. It has been found that outcome regulates our behaviour to a large extent. Immediate results and outcomes provide certainties whereas delay creates uncertainty. Delay in consequences exerts same influences as uncertainty, and reflect that our response uncertainty may have evolved from our response to delay. People take a greater risk if losses are delayed, whereas they tend to avoid risk if they face immediate loss. Theorists have developed the normative and prescriptive framework for decision making in unknown probability. Cognitive and personality factors have an important role in such a situation where an individual decides in the absence of information. People exhibit risk aversion tendency with the accessibility of a certain option and a risk-seeking tendency in the presence of two uncertain options of almost equivalent worth (Mohanty & Suar, 2013). Kahneman and Tversky (1979) proposed the prospect theory and pointed out the limitation of the descriptive model of risky decision making such as expected utility theory (Friedman & Savage, 1948) and subjective utility theory (Leonard, 1954). “Cumulative prospect theory” is an evolved form of this model (Tversky & Kahneman, 1992). The prospect theory stated that people are both risk seeking as well as risk aversive. Risk seeking and risk aversion both depend upon the way a person perceives gain or loss related to the option. People hesitate to take a risk for ‘slightly’ better outcome. Research suggests that certain and uncertain emotions create appraisal-congruent judgement (Lerner & Keltner, 2000; Tiedens & Linton, 2001). These emotions are associated with specific appraisal, and trigger cognitive and physiological changes that guide subsequent behaviour. Appraisal of certainty and control moderate and mediate the impact of emotion such as fear and anger influencing decision making in an uncertain and ambiguous situation (Lerner & Keltner, 2001). People react to the risky situation on two levels: emotional and cognitive. Risk as a feeling hypothesis (Loewenstein & Lerner, 2003) states that emotions respond to cognitive evaluation; however, the cognitive process is less active in emotional reaction. People identify probabilities and outcome valence in process of cognitive evaluation, whereas in emotional reaction they are sensitive towards associated imagery and time proximity.

1.1.4  Types of Uncertainty in Decision Making 1.1.4.1  First- and Second-Order Uncertainty First-order uncertainty portrays arbitrary variety in the results whereas second-order uncertainty represents the imprecision of information concerning the boundaries themselves (Halpern, Weinstein, Hunink, & Gazelle, 2000). The branch of statistics

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and economics has described second-order uncertainty as probability density functions over (first-order) probabilities (Sundgren & Karlsson, 2013). Another state of uncertainty, risk, includes the possibility of an undesired outcome. Frank Knight (1921) differentiated uncertainty from risk and suggested uncertainty was inestimable and the state of lack of knowledge impossible to calculate: it is generally known as Knightian uncertainty. “Uncertainty must be taken in a sense radically distinct from the familiar notion of risk, from which it has never been properly separated.... The essential fact is that ‘risk’ means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating.... It will appear that a measurable uncertainty, or ‘risk’ proper, as we shall use the term, is so far different from an un measurable one that it is not in effect an uncertainty at all.”

1.1.4.2  Uncertainty from the Ethical Perspective Tannert, Elvers, and Jandrig (2007) presented a border classification of uncertainty from an ethical perspective. Objective uncertainty classifies epistemological uncertainty and ontological uncertainty (Van Asselt & Rotmans, 2002). Epistemological uncertainty is produced by holes in the information zone and the researcher tries to fill this gap. The decision maker needs to rely on existing knowledge to reach a solution and work on remaining uncertainties. Ontological uncertainty is brought about by the convoluted specialized, organic, or potentially social frameworks that lead to a muddled circumstance. It is impossible to eliminate such uncertainties by deterministic reasoning and a purely rational decision because of the complex nature of the situation. Therefore, the decisions taken are called quasi-rational. The second type of uncertainty is subjective uncertainty, an inability to operate on moral rules. This concept is directly related to ‘anomie’ (Durkheim, 1893), a condition of social conflict and anxiety where moral values and standards are diminished. Two sub-forms of subjective uncertainty consist of moral and rule uncertainty. Moral uncertainty arises in the absence of applicable moral rules. General moral rules are used in such a situation for guidance, which is called rule-guided decisions. The Hippocratic Oath taken by doctors is an example of such general rules, but it leads to lesser satisfaction. However, some situations requires our intuition more than our knowledge or moral rules. Rule uncertainty needs internalized experience and conviction working on the subconscious level guiding us towards the intuition-guided decision. Bradley and Drechsler (2014) classify three qualitatively different types of uncertainty—ethical, option, and state-space uncertainty. Ethical uncertainty emerges in absence of allocation of precise values to the consequences. In such a situation, a subjective evaluation of a decision maker becomes more crucial. This type of uncertainty is usually overlooked by decision theorists. Option uncertainty involves an uncertain state about the exact consequence associated with an act. The ‘conscious unawareness’ (Walker & Dietz, 2011) of a decision maker, that he may

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not be aware of all relevant possibilities involved in a decision situation, leads to state-space uncertainty. 1.1.4.3  Uncertainty Based on S-R-O Rules The relationship between a stimulus (S) and a response (R) is allied with a positive or negative outcome (O). Earlier experience facilitates the structuring of a stable illustration of S-R-O rule and helps in decision making (Ridderinkhof, Van Den Wildenberg, Segalowitz, & Carter, 2004; Seymour, Daw, Dayan, Singer, & Dolan, 2007). An unexpected and fundamental change in these rules invalidates the previous experienced-based learning and induce uncertainty. Bland and Schafer (2012) identified three types of uncertainty. In cases of expected uncertainty (Angela & Dayan, 2005) or feedback validity (Bland & Schaefer, 2011), S-R-O rules gained from previous occasions are powerless indicators of the results of forthcoming activities, and this intricacy is well known and constant. In the case of unexpected uncertainty, the rare essential change occurring in the situation overthrows the existing S-R-O rules because of their inability to precisely foresee the results, and in volatility, the consistent variations in the environment require a steady refreshing of S-R-O rules.

1.1.5  N  eurological Correlates of Uncertainty and Decision Making Decision making under uncertain conditions has additionally received consideration in the field of cognitive neuroscience. Preuschoff, Mohr, and Ming (2013) summarised the findings of different papers in the field of computational neurosciences and decision making under risk or uncertainty and argued that uncertainty is embedded in our social context and influenced by the affective process. Hansen, Hillenbrand, and Ungereider (2012), in their functional magnetic resonance imaging (fMRI) study on perceptual and categorical uncertainty, reported increased visual activity in the difficult perception of sensory evidence in the presence of prior knowledge. Decreased visual cortical activity was reported for sensory evidence that was easy to perceive but difficult to interpret. Researchers have investigated brain regions involved in uncertainty embedded in any context (Brazil, Mathys, Popma, Hoppenbrouwers, & Cohn, 2017; Geng et al., 2018; Harris, Sheth, & Cohen, 2008; Rigoli, Michely, Friston, & Dolan, 2019). The basal forebrain guides the process of learning, memory, and attention by functioning as a broadcaster of information about uncertainty and surprise embedded in the situation. The corticobasal ganglia loop originated in the anterior cingulate cortex controls information seeking about uncertain rewards (Monosov, 2020). The insula, parietal cortex, temporal cortex, ventromedial prefrontal cortex, and bi- to frontal cortex were discovered to be actuated in the certain condition these territories are commonly for the most part connected with rule certainty, reward, and behavioural

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flexibility (Hutchinson, Uncapher, & Wagner, 2015; Tei et al., 2017; White, Engen, Sørensen, Overgaard, & Shergill, 2014). The uncertain condition triggers activity in the prefrontal cortex, striatum, thalamus, midbrain, amygdala, hippocampus, and the parietal cortex and occipital cortex (Farrar, Mian, Budson, Moss, & Killiany, 2018). The neurological investigation in the context of threat and reward, decision making, and associative learning under uncertainty highlighted the role of the bilateral anterior insula in uncertainty. The anterior insular is crucial for emotional awareness (Gu, Hof, Friston, & Fan, 2013), anticipation (Grupe, Oathes, & Nitschke, 2013), and interception and bodily feedback (Craig, 2011; Seth, Suzuki, & Critchley, 2012). In the same way, the amygdala and dorsal anterior cingulate cortex are concerned with the associative learning of threat (Fullana et al., 2016). These areas prepare the processing of a potentially different outcome in the background of associative learning and uncertainty (Shackman & Fox, 2016). The brain was reportedly more active under conditions of uncertainty compared to certainty. The absence of intersecting brain areas activated during the uncertain and certain condition of decision making recommends a differential activity pattern for both conditions (Morriss, Gell, & van Reekum, 2019). Our brain is wired in a way to minimalise the uncertainty in any context, which thus subsequently enhances our ability to predict the potential gain and loss related to choice and to regulate the course of action (Mirabella, 2014; Peters, McEwen, & Friston, 2017).

1.1.6  Limitations of Decision Making Under Pure Uncertainty Conflict in decision making arises in the presence of various options, and a decision maker tries to resolve this by deciding under uncertainty as a zero-sum, two-person game such as poker, chess, or gambling where there are one winner and one loser. In decision making under pure uncertainty, the decision maker is unaware of the nature and probabilities of any outcome. This situation lacks any optimistic hope or sense of security. Pure uncertainty in the context of personal life-related decision making may utilise some appropriate technique towards the solution, but another problem situation requires some knowledge regarding the problem and available solutions to predict the outcome probabilities. Any situation that involves pure uncertainty interferes with a decision maker’s ability in making a reasonable and defensible decision (Chacko, 1991; Klein, 1994). Such a situation creates hesitancy, indecisiveness, and procrastination in decision makers (Marold, Wagner, Manzey, & Schoebel, 2012).

1.1.7  Handling Uncertainty in Decision Making Lipshitz and Strauss (1997) suggested three strategies to handle uncertainty: suppression, reduction, and acknowledgement. The strategies of suppression include

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denial and ignoring uncertainty. This strategy is mostly based on traditional decision-­making approaches and past experience but lacks applicability in the context of future research and uncertainty. Strategies of reduction aim to manage uncertainty by escalating the available information and predictability of the situation concerned. A decision maker could do this by seeking advice, waiting to gather more information to reach a decision. However, pre-contemplation of the worth of information collection and analysis of specific uncertainties is desirable. For example, in some situations waiting for more information is not worthwhile as costs from postponing choices are probably going to be higher than gain. “Strategies of acknowledgement” involve the selection of a course of action or preparation to avoid possible risks. A person may avoid irretrievable action; analyse the pros and cons; and take prior action to prevent possible negative outcome and improve promptness in confronting unanticipated negative outcomes. Robust decision-­ making, prevention, precaution, no-regret, and flexible strategies are enlisted as approaches comprising tactics of acknowledgement in the context of climate change risk management (Hallegatte, 2009). These tactics help in reducing the time horizon for decisions. Adaptive risk management favours an explicit account of uncertainty involved in the situation, social learning, flexibility, and a continuous reevaluation of the decision taken (Dietz, 2013). To sum up, thorough knowledge about the available options, precautions in unnecessary risk taking, handling risks one by one, and insight about the worst outcome with potential gain and loss and awareness of one’s need and aspirations moderate uncertainty in decision making. Flexibility, adaptability, and reappraisal of decisions are crucial in this process.

1.1.8  Implications for Decision Making Under Uncertainty Uncertainty affects the perception of different issues involved in problem situations at individual and societal levels. Uncertain situations variously affect the decision maker. To understand the different sources of uncertainty and estimate its magnitude is also a tiresome task and exposed to subjective judgement. But one cannot avoid the uncertainty, and one must make a choice in the presence of uncertainty. Regardless of limitations involved in such situations, uncertainty also prompts depth processing and future aspiration for such a situation. The uncertainty involved in our social, economic, and environmental scenarios call for in-depth appraisal of available options. The processing of such situation reflects the aptitude, attitude, and perception of the decision maker. Researchers have argued about the implications of uncertainty for decision making if an uncertain condition is presented in numerical representation with verbal labels (Dieckmann, Peters, Gregory, & Tusler, 2012). Common populations are found to be sensitive towards such labels that simplify their comprehension and use of uncertainty information. It has been reported that uncertainty appraisals lead to systematic processing and structured thinking (Baas, de Dreu, & Nijstad, 2012; Mohanty & Suar, 2014; Tiedens & Linton, 2001; Weary

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& Jacobson, 1997). Uncertainty arguably evokes the need to process a situation more systematically, unlike the certainty-associated situation. The lack of confidence in one’s own judgement in an uncertain situation is considered the main reason behind this systematic processing (Edwards & Weary, 1993; Gleicher & Weary, 1991; Weary, 1990). The positive and negative valence of emotion is influenced by certainty appraisal of the situation such as negative emotions: fear and anger may have the same valence, but fear evokes uncertainty whereas anger evokes a certainty-related response. This appraisal significantly determined the selection of information processing in the decision-making process. As stated by Smith and Ellsworth (1985), a situation with certainty builds the way for clear understanding and the ability to predict the next step whereas uncertainty lacks such understanding. The emotions of anger, happiness, disgust, and contentment were linked with certainty dimension whereas fear, sadness, hope, surprise, and worry were linked with uncertainty. In line with this finding, uncertainty appraisal was identified as related to problem-focused coping (Cheng, Kuan, Li, & Ken, 2010). Problemfocused coping directly aims to overcome the cause of concern, to make an accurate choice, thus leading to good decisions (Folkman & Lazarus, 1984; Luce, 1998). Thus, certainty appraisal initiates the reliance on already available information or avoidance or minimising the distressing emotion, features of heuristics processing, and emotion-focused coping, but an uncertain situation motivates a person to strive for a problem-solving approach: systematic thinking subsequently leads to better decisions.

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Chapter 2

Are Positive People More Flexible in Cognitive Processing? Addressing the Conceptual and Empirical Inconclusiveness Papri Nath, Rabindra Kumar Pradhan, and Sachi Nandan Mohanty

2.1  Introduction Emotions such as love, anger, fear, and excitement have significant roles in our lives. Although emotions are categorized as positive and negative based on their nature and functions, these belong under the same umbrella but form two related but distinct dimensions (Cacioppo, Gardner, & Berntson, 1999; Watson & Tellegen, 1985), thus contributing differently to human survival and growth. Negative emotion favors immediate adaptation whereas positive emotions predict growth in the long run (Fredrickson, 2001). This concept of the benefits of positive emotion was realized much later with the emergence of positive psychology. Since the beginning, researchers on emotion have focused mainly on exploring the functions of such negative emotions as fear, anger, and anxiety as compared to the positive emotions of hope, joy, love, and gratitude (Fredrickson, 2001). The cause for this overemphasis on understanding negative emotions stems from the ability to generate “specific action tendencies” such as flight in fear, ensuring survival. In contrast, in positive emotions any specific action tendencies such as leading to fight or flight are absent, making their adaptive significance unclear. A major turning point in emotion research came with proposal of the broaden-and-build theory (Fredrickson, 2001). The theory proposed that the adaptive value of positive emotion could be understood by its immediate function at the cognitive level, P. Nath Indian Institute of Management Tiruchirappalli, Sooriyur, Tamil Nadu, India e-mail: [email protected] R. K. Pradhan Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Nandan Mohanty (ed.), Decision Making And Problem Solving, https://doi.org/10.1007/978-3-030-66869-3_2

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leading to long-term positive life outcomes. A number of studies support the broaden-and-build theory, demonstrating the significant impact of positive emotion on such cognitive functions as creativity (Hirt, Devers, & McCrea, 2008), decision making (Isen, 1993), and information processing (Clore & Huntsinger, 2007). A review of the existing literature suggested that emotion research results are inconclusive when attempting to explore the association between positive emotions and cognitive processing. Based on such findings, the literature can be divided in terms of two perspectives.

2.2  P  erspective I: Positive Emotions and Global/Local Bias in Cognitive Processing According to the first perspective, positive emotion is biased toward global processing, whereas negative emotion leads to local processing (Fredrickson, 2001; Gasper & Clore, 2002; Gray, 2004). Global processing can be defined as a process of processing a present stimulus based on its overall configuration, whereas local processing involves processing the components while perceiving a stimulus (Tan, Jones, & Watson, 2009). The most popular paradigm used to study the global/local bias in cognitive processing is the global-local visual paradigm (Kimchi & Palmer, 1982). The participants are presented with two comparison figures where the task is to judge which figure matches more closely with a given standard figure (Fig. 2.1). One comparison figure matches with the global and the other matches with the local configuration of the standard figure. The assumption is that participants in a positive emotional state exhibit global bias and are more likely to choose comparison Fig. 2.1 (global), whereas those in a negative emotional state tend to choose comparison Fig. 2.1, exhibiting local bias in information processing.

Fig. 2.1  Example of an item from the global-local task (Kimchi & Palmer, 1982)

Standard Figure

Comparison

Comparison

Figure 1

Figure 2

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According to the broaden-and-build theory (Fredrickson, 2001), negative emotions narrow (local focus) our attention by restricting the focus on target stimuli, whereas positive emotions result in broadened attention (global focus). A number of researchers have demonstrated the association of positive emotion with expanded attentional breath (Clore & Huntsinger, 2007; Fredrickson & Branigan, 2005), or global processing (Olivers & Nieuwenhuis, 2006; Srivastava & Srinivasan, 2010; Vermeulen, 2010).

2.3  P  erspective II: Positive Emotions and Cognitive Flexibility The second perspective advocates the ‘flexibility hypothesis’ (Baumann & Kuhl, 2005; Fortin et al., 2018; Tan et al., 2009). The hypothesis states that positive emotional state improves cognitive flexibility in information processing as per demand of the situation. Cognitive flexibility could be defined as a mental state characterized by flexible responding by being receptive to a variety of incoming information from the environment (Olivers & Nieuwenhuis, 2006). The flexibility hypothesis rejects the association of positive and negative emotions with global-local bias in cognitive processing. The argument emerges from the ‘whole’ concepts from the Gestalt school and the global precedence hypothesis (Navon, 1977), suggesting that global processing precedes local processing. For example, when a stimulus appears, the entire stimulus is processed first and then the details are taken into account. The primary reason for this is that it requires fewer attentional resources to perceive the entire stimulus than perceiving the details. Therefore, global focus appears to be the dominant mode of cognitive processing. Flexibility hypothesis is assessed using a ‘shape detection task’ (Fig. 2.2). This task has only one correct response, creating the demand to switch between desired cognitive processing modes (global/local) to produce the correct response. Results show that participants experiencing a positive emotional state exhibited a better switch between global and local information processing modes as per the task requirement as compared to participants in a negative emotional state, an indicator

Fig. 2.2  Example of items from the ‘shape detection task’ (Baumann & Kuhl, 2005)

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of greater cognitive flexibility displayed by participants in the positive emotion group. The findings of the present study could be explained through personality systems interaction theory (Kuhl, 2000). The theory states that positive emotions increase the activation level of extension memory, a central executive system of the brain, which in turn increases the flexibility to switch from dominant mode of processing (e.g., global processing) to an alternative processing mode (e.g., local processing) (Baumann & Kuhl, 2005). Hence, when the task or situation demands, positive emotions can overcome the dominant processing mode (global) and flexibly switch to the less dominant (local) one. In summary, Perspective I (Gasper & Clore, 2002) examines preferences in cognitive processing, whereas Perspective II (Baumann & Kuhl, 2005) investigates the flexibility to switch between modes of cognitive processing. Apart from the mentioned conceptual inconclusiveness, there also exists empirical inconclusiveness. A critical review of the empirical findings suggests that researchers have used different task paradigms to understand how positive emotions impact either global or local bias (Gasper & Clore, 2002) or cognitive flexibility (Baumann & Kuhl, 2005), causing empirical inconclusiveness. The literature does not show a single study in which both cognitive aspects have been assessed on a common platform. This conceptual and empirical inconclusiveness indicates the need to assess both aspects of cognitive processing (global/local bias and cognitive flexibility) on a comparable platform. The present study attempts to measure global/local bias and cognitive flexibility using a single task paradigm, the global-local flexibility task (GLFT), an adapted version of the global-local visual paradigm (Kimchi & Palmer, 1982). GLFT provides a common platform to examine the assumptions of perspectives I and II and to address the inconclusiveness concerning the exact influence of positive emotions on cognitive processing.

2.4  Present Study An experimental study was designed to achieve the objective. The study observed the influence of experimentally induced emotions.

2.5  Method 2.5.1  Participants One hundred and two students participated in the study. The age range was 17–23 years and mean age of the participant’s pool was 19.61 years (SD = 1.14 years).

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2.5.2  Measures Emotional State  The Modified Differential Affect Scale (Fredrickson, Tugade, Waugh, & Larkin, 2003) was used to assess the participants’ emotional state. The test consists of five positive (amusement, contentment, happiness) and five negative (anger, disgust, fear) descriptors of emotions as items. Participants rated their emotion on a 7-point scale (1 = “not at all”; 7 = “very frequently”). The reliability of the scale on the study sample was 0.70. Emotion Induction  A performance feedback system associated with reward and punishment was used to induce desired emotional states (Spering, Wagener, & Funke, 2005). For high scorers (above 60%) an additional five marks and for low scorers (below 40%) a deduction of five marks were announced. Average scorers (approximately 60%) formed the neutral feedback group and were not given any feedback. Cognitive processing was measured on the global-local flexibility task (adapted version of Kimchi & Palmer, 1982) as mentioned earlier. The GLFT consists of 100 items (global, local, and neutral). Each item consists of a stimulus triad, with one target figure on the top and two comparison figures (response alternatives) below (Fig. 2.3). Each test figure is a geometrical shape (global form) such as a circle, square, or triangle composed of smaller geometrical shapes (local form). The task of the participant is to identify and indicate which of the two alternatives is more similar to the target figure. For a neutral item, both response alternatives are correct, with one matching with the global and other with the local configuration of the target figure. For global and local items, there is only one correct response, which creates a demand to process the test stimuli either globally or locally for the detection of correct response. The GLFT is composed of two sets (I and II). Set I (items 1–50) follows the globallocal-global sequence and Set II (trial 51–100) follows the local-global-local sequence to control order effects (Bruyneel et al., 2013). The sequence of the presentation of neutral/global/local blocks is shown in Table 2.1. Both set I and II of GLFT begin

Comparison 1

Target

Target

Target

Comparison 2

Local Item

Comparison 1

Comparison 2

Global Item

Comparison 1

Comparison 2

Neutral Item

Fig. 2.3  Example of the items used in the global-local flexibility task (GLFT)

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Table 2.1  Sequence of the presentation of items in the ‘global-local flexibility task’ Neutral Global SET I Block Block G-L-G (G11– Sequence (N1– G15) N10) Neutral Local SET II Block Block L-G-L (L11– Sequence (N1– L15) N10)

Local Block (L16– L20) Global Block (G16– G20)

Global Block (G21– G25) Local Block (L21– L25)

Local Block (L26– L30) Global Block (G26– G30)

Global Block (G31– G35) Local Block (L31– L35)

Local Block (L36– L40) Global Block (G36– G40)

Global Block (G41– G45) Local Block (L41– L45)

Local Block (L45– L50) Global Block (G45– G50)

Note: G global item, L local item, N neutral item

with a neutral block (10 neutral items) in which selection of response alternatives based on individual components of the target figure indicates local processing and selection based on overall configuration indicates global processing. Presentation of the neutral block is followed by the presentation of global/local blocks. Each global/ local block consists of five consecutive global/local items. These global and local blocks appear alternatively in the test and thus require flexibility to process both global and local features of the task stimuli as per the task demand. Therefore, the alternate presentation of global and local blocks captures flexibility in cognitive processing. On average, 3–4 min are required to complete the GLFT.

2.6  Procedure All the participants completed the study as a part of their curriculum. The participants were told that they would complete a lab exercise to determine their attention level, which acted as the cover story. Participants were seated in a room with adequate light and temperature. The desired emotional state for the experiment was induced by delivering performance feedback on actual performance. For this purpose, a curriculum-based test was taken a week before the actual study. The test was given 30% weightage of the total course credit to increase the strength of the feedback for effective emotion induction. On the basis of actual performance, the participants were divided into positive (above 60%), neutral (approximately 60%), and negative (below 40%) feedback groups. The positive group was rewarded with an additional five marks to their actual score whereas the negative group was punished with a deduction of five marks. The neutral group did not receive any feedback. A professor teaching the same curriculum acted as a confederate in delivering the feedback to the participants to enhance the credibility of the feedback procedure. For the experiment, the participants arrived as per their pre-allotted group timings. The groups were divided by the researcher based on the participant’s scores on the given curriculum test. Once seated, the participants received following instructions to complete the GLFT: Here is a matching task. Please look at the ‘target’ object of each block. Which of the two objects shown below each target look similar? Put a tick mark to answer. After certain num-

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ber of targets, you will be asked to mention the exact time. You have to note the time carefully from the provided digital clock and proceed further without any break. Follow the number sequence (column wise) while completing the task. Though there is no time limit but try to finish as early as possible.

As the instructions were about to be completed, the confederate (professor from the course) entered the laboratory to announce the results of the test taken last week. Thereafter, the results were distributed and feedback was announced to the respective groups. Once the feedback was delivered, the confederate left the laboratory for completion of the lab exercise. The experimenter then instructed the participants to quickly respond to an appraisal form, which contained basic information and a state measure of current emotional state. They were asked to rate the emotional state measure in response to how they feel about the reward/punishment associated with the feedback. After completion of the appraisal form, participants completed the GLFT (lab exercise). Finally, the underlying agenda of the research was debriefed to the participants and they departed with a small token of appreciation.

2.7  Methodological Considerations Previous findings show that even the complex nature of that cognitive task (Cohn, 2008) can make participants frustrated when they are unable to perform well. This result might modify the initially reported emotional state itself and hinder observing the concerned causal link. Thus, the nature of GLFT was kept simple and structured. Presentation of a task stimulus for a short time duration favors global processing whereas a longer duration favors local processing (Ninose & Gyoba, 2003). Thus, the time limit was removed from the GLFT. To avoid too much processing from unlimited time, participants were instructed to take note of the time at certain intervals during the task. Instructions were also given to complete the task as soon as possible. A time limit may also cause stress and change the original emotional state completely. Thus, removal of the time limit fulfilled a dual purpose. Past studies suggest multiple measures of emotional state during the experiment may lead to unwanted reduction in the experimental outcome (Spering et al., 2005). Taking this into account, emotional state was measured only twice during the entire experiment. Post-study interview was conducted to check the credibility of the feedback method. The participants reported that they took their performance feedback very seriously and did not have any idea about its association with the lab exercise.

2.8  Results Manipulation Check  The results show a statistically significant difference (p  0.10, CI/RI  0

(3.15)

where y indicates for a certain criterion the numerical difference in the evaluation of two alternatives. The preference function for Type V is

Fig. 3.4  PROMETHREE methodology

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0, for xy£s  y−s  , for s ≤ y ≤ s + r P ( y) =   r 1, for y ≥ s + r 

(3.16)

when s and (s + r) signify, for each test, the indifference and choice y. The multi-criteria preference degree is calculated from k

π ( a,b ) = ∑wh P ( a,b )



h =1

(3.17)



where w stands for each criterion’s weight. Outgoing flow is represented as

ϕ + ( β ) = ∑π ( a,y ) y∈K



(3.18)

Incoming flow is defined as

ϕ − ( β ) = ∑π ( y,a ) y∈K



(3.19)

Net flow is derived from

ϕ (β ) = ϕ+ (β ) −ϕ− (β )



(3.20)

3.3.2.8  Multiple Attribute Decision Making (MADM) MADM addresses the problem of picking an alternative from the attribute list. MADM typically consists of one function, but it may be of two forms. The goal is first of all to pick a solution based on the values and consistency of the characteristics of each option from a set of scores. Second, substitutes for a role model or similar circumstances should be defined. A recent field of analysis has been called case-based motivations. Regardless of the subjectivity criterion, MADM is a relational approach. All goals include knowledge about preferences within an attribute instance and preferences within current attributes (Ren, Wang, & Xue, 2014). Evaluation of these priorities is made either explicitly by the decision maker or based on prior decisions. The simple terminology follows: Let B1, B2, ...., Bm be a set of alternatives to be assessed by criteria X1, X2,....., Xn. Let Pij be the numerical rating of alternative Bi for criteria Xj. Then the general decision function is D(Bi) = (Pi1 ∪ Ri2 ∪ .........∪ Rin) where ∪ represents the aggregation.

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In addition, the decision maker may express or identify a significant/weight rating for the parameters. Of the many ways to do this, the most popular are (a) utility preference functions, (b) the analytical hierarchy process, and (c) a fuzzy version of the classical linear weighted average. Moreover, the criterion may be flexible or clever but for a flexible judgment. The MADM is seeking to find the best solution, one that is more comfortable with all the functions or aims. To obtain the best option, a ranking method is required. If there is no problem with the alternate ML rating, the better choice is the one with the highest value. If the classification itself is a flippant package, it involves a more complex ranking process. Two steps of the fuzzy judgment process typically require several issues with attributes: • Step 1: Added the standard of fulfillment per alternative judgments (rating) on all parameters. • Step 2: The rating of the alternatives in terms of global total satisfaction. Such two primary steps of MADM are solved in a number of ways. The most common weighting method and serial removal method, according to MacCrimmon, may be split into two groups. Key aspects of each method are summed here. (a) Weighting methods: Those are the most widely used for various problems with attributes. A collection of choices with defined attributes and respective instances; a method of attaining numerical or linguistic weights through attributes (referred to as inter-attribute preferences); a method of evaluating attribute values by obtaining numerical or lingue values (weights). The decision maker often has three choices to receive the expectations (importance). Second, the expectations from previous decisions (used in mathematical analysis and case analysis) should be inferred. Furthermore, to interactively receive and combine expectations through attributes. Second, the aggregate methods may be a straightforward system for weighing additives or a complex system of weighing additives. A so-called weighting medium is the most commonly used algorithm:

∑ W ∗L D(A ) = ∑ W t

q

q =1

p



pq

(3.21)

t

q =1

q



where Ap represents alternative p; Wq represents the importance of criteria q; Lpq represents the relative merit of criteria q for alternative i. (b) Sequential Elimination Methods: The decision maker is less intrusive because such approaches do not require biases between attributes. The basic characteristics of the system include collection of alternatives with specified attributes and values, attribute value order, and alternative methods to delete value-based

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attribute alternatives. Principal divisions are (a) comparison of a given alternative attributes with the standard or role model attributes, (b) the pairwise attribute distinction of attributes for two alternatives, and (c) comparison between alternatives with all alternatives with a single value attribute. Albeit the decision maker’s preferences are less stringent, they depend far more on the arbitrary mechanism of evaluation established by the decision maker.

3.4  Steps in MCDM Methodology It is possible to summarize these steps as follows: (i) The problem is identified and the parameters are set. (ii) Suitable data are collected. (iii) Viable/efficient alternatives are set up. (iv) Payoff matrix structure (alternative or list of criteria). (v) Selection of a suitable problem-solving method. (vi) Integration of the preferential system of decision makers. (vii) Select one or more of the best/appropriate alternative(s) for further study. (viii) Four separate classes of MCMD strategies are feasible: size, outranking, priority/utility, and mixed categories.

3.4.1  Methods of Estimation of Weights The relative value or weight of a criterion shows the importance given in the MCDM setting for the criteria by the decision makers. Within the literature various approaches are available; the classification method and entropy method especially are commonly used. • Rating Method • Both parameter weights are expressed on a graphic basis by the decision maker. For a given condition, a higher rating is compared to the other parameters. When the parameters are low, but incorrect outcomes are likely if the number of parameters is large, the procedure is easy and helpful. The definitions of linguistic factors in a fuzzy environment are used by various scholars to determine parameters weight by using triangular fuzzy numbers (TFNs). • Entropy Method • Entropy is a concept that calculates the uncertainty of the natural anomalies in a message’s predicted data output, and the insecurities are represented by a distinct distribution in probabilities. The process entropy calculates and is independent of the decision makers’ opinions and determines the weight of the different parameters within the payment equation. This approach is particularly useful in

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examining parallels between data sets. These data sets can then be translated into a payoff matrix as a number of alternatives where the results of alternative solution are evaluated. The theory of this approach is focused on the knowledge available and its relationship to the value of the criteria. • If the entropy value is small, there is small ambiguity in the metric, the diversification of information is low, and the metric is thus less important. It is an effective approach as it reduces the decision maker’s pressure on large issues. It can also be used as a comparative method for finding consensus in a group and for estimation of the weight of the criteria. On the other hand, the decision maker’s position is decreased when deciding the weights of the parameter (Sakawa, Katagiri, & Matsui, 2012).

3.5  Emotion-Based Decision Making The words emotion and feeling are often used interchangeably. The term emotion denotes the physical phenomena of the body after Damasio (1994), whereas feeling is reserved to such emotions. At the point when individuals are, for example, amazed, pleased, or sickened, their body will go through unique changes that may influence the pulse, skin shade, facial muscles, skeletal muscles, gut, heartbeat, breathing, etc.: these are feelings. Most feelings will incorporate perspiring that expands the power conductivity in the skin, and this can promptly be observed with something like a falsehood identifier. Emotions, then again, are mental wonders. The juxtaposition of feelings and the musings that are related with them causes sentiments. Fundamental emotions are joy, misery, outrage, dread, and nausea. At the point when the body’s feelings for reasons unknown compare to one of these types, we know the explanation and experience the feeling, and thus feel cheerful, dismal, furious, apprehensive, or appalled (b). The start of a cognitive theory of feelings might be ascribed to Simon (1967). Simon expected that a sequential processor that needs to manage various issue circumstances needs a component for interfering with its work on one issue to guide its focus toward another. In this manner, it needs a chain of command of objectives to set needs. Feelings intrude on systems guiding the processor’s focus toward recently apparent dangers or openings influencing pressing needs. Their capacity is to adjust needs and set another chain of command of objectives (Zhang & Lu, 2010). Simon has expressed further (1997) that there is no inborn clash among judiciousness and feelings. Elster (1996) goes above and beyond in a paper explicitly routed to the network of researchers in economics, where he asserts that feelings do in certainty add to levelheadedness and should receive attention, although he does not perceive how they ensure the best choice when it makes a difference. Wilson (1998) in his momentous book on the solidarity of information states (p.  113): “Without the improvement and direction of feeling, reasonable idea eases back and

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crumbles.” He takes note that cognizance fulfills feeling by choosing the activity that upgrades prosperity. Proof for this is given by Damasio’s (1994) fascinating perceptions of the impedance of dynamic capacities in patients with prefrontal flap harm, which demonstrate that in addition to the fact that emotions contribute to levelheadedness, reasonability actually requires feelings (Amin, Razmi, & Zhang, 2011).

3.5.1  Emotion-Based MCDM As indicated by Wilson (1998), consistency is “the hopping together of information by the connecting of realities and actuality based hypothesis across orders to make a typical foundation of clarification.” On the off chance that we also apply this term to the regularizing study of dynamics, this paper has contended for the consistence of the fields of theory, morals, neurophysiology, and choice sciences. The primary picture is basic: decision making includes two unique fields that are isolated by Hume’s inlet, convictions and qualities. Convictions about realities are acquired through thinking, whereas values must be felt. Discernment necessitates that the two convictions and qualities be very much established, and qualities cannot be established all around without feelings. A significant challenge for MCDM is in this manner to work with the feelings of the leader to improve well-founded qualities so as to move toward what we may suitably call passionate reason-ability. Damasio’s work has given us that to settle on significant choices in esteem-loaded setting without feelings is risky. The key function of emotions is to join sentiments to situations so we pick the situation with which we feel generally comfortable. Without feelings, our choices become self-­ assertive. We depend on rule-based choice conduct and the rationale of propriety (March 1994) without considering the main thing that is important, to be specific what it truly might be want to live with the result, accordingly placing us at risk for helpless outcomes. It is fascinating to note, at that point, that despite the fact that feelings are significant in esteem-loaded choice settings, the idea is basically missing from the MCDM writing. Zhang, Lu, and Dillon (2007)) in any case is an exemption. They discuss the related issue of instinct, which likely assumes a significant part in dynamics. Although Wierzbicki’s meaning of instinct does not unequivocally incorporate feeling, the wonder of instinct can be clarified by feelings that make sentiments star or contra certain activities in a clandestine, subcognizant path without thinking (Damasio 1994). In a choice setting, in this way, feeling can make emotions that can work both in an open, cognizant route and in an undercover subcognizant way that we call instinct (Nath, Pandey, Somu & Amalraj, 2018). Wierzbicki worried that choice guide is helpful for imagination and instinct and suggests that choice emotionally supportive networks present data to the chief in rich multidimensional realistic terms, yet he cautions against emphasis on

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consistency and methodology such as pairwise compromises. From our perspective, in any case, interestingly, choice guide inspires feeling, regardless of whether instinct or cognizant sentiments are delivered. A great design to make the situations distinctive is consequently absolutely recommendable, yet to forego consistency would intend to consider silliness, along these lines abusing the primary standard presented in this chapter. The thought behind pairwise compromises is to improve the psychological weight on the chief by lessening the quantity of measurements in esteem space during the weight elicitation measure. This strategy is critical in numerous choice help projects, and it is an issue that the expense of lessening the multifaceted nature is to have the chief think about counterfeit situations. The test, in this manner, is to create measures that can even now present such compromise issues in a manner that is sufficiently striking to inspire feelings when the chief contemplates the other options (Katagiri, Kato, & Uno, 2013). There are reports of usages that target introducing the situations in an organized and striking manner and which utilize weighting, yet without unequivocal reference to elicitation of feeling. One model is that of Belton, Ackermann, and Shepherd (1997), who utilized issue-organizing models in a mix with multi-property assessment with a program called VISA to build up an activity plan for an emergency clinic trust. The two convictions and sentiments are expressly referenced in the chapter. Another model is that of Wenstop and Carlsen (1998), who used choice boards to assess hydropower ventures. To help evoke emotions in the board members, they were given video movies of the influenced territories that indicated “previously” and “after” circumstances, the last being delivered by a craftsman who altered the first film to diminish waterway releases and include repositories, and so forth. The ensuing compromise measure was very reliably performed by the neighborhood board, although less so for the public boards which had a less compelling passionate connection to the region.

3.6  Conclusion While evaluating any options, various conflicting criteria stand in our way. In modern decision-making phenomena, multi-criteria decision making is gaining extraordinary popularity in real-life problems incorporating multiple data sets and uncertain subjectives. Hence, people assess implicitly different criteria while keeping in mind the consequences of such decisions. Such approaches not only help in proper structuring of the problem under consideration but also assist in better decision making in various planning predicaments while conjointly considering finite or infinite numbers of alternatives at a time. Moreover, such problems emphasize an approximation of effective solutions while satisfying the desired objective.

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Chapter 4

Mid-Brain Connective for Human Information Processing: A New Strategy for the Science of Optimal Decision Making Rashmi M. Shetkar and Sachi Nandan Mohanty

4.1  Introduction The new world of today demands that every individual employee should be an asset to its organization and not a liability. When he or she is empowered to make sound decisions, to make a choice on their own at the right time, this leads to wise, well-­ informed solutions independently. To cater to the needs of the company demands, to prevail in the new era of information and vibrant economy, where human minds and brains are the tools for bringing techniques for adding a competitive edge to the organization, calls for growth of the unique and abstract innate faculties of the brain and mind (Shetkar, Hankey, & Nagendra, 2017) and their competencies (Shetkar et  al., 2017), with focus on the domains of intelligence, emotional, cognitive (Shetkar, Hankey, & Nagendra, 2018), and spiritual (Shetkar 2019a). Above all, there is a need for enhancing one’s consciousness (Shetkar 2020a) at both levels, individual as well as collective, for leading from dynamics in a group into team management, because ‘TEAM means together-­ everyone-­ achieves-more’ (Adishankara Adishankaracharya, 2013). For the new world order in management systems and the prevailing developmental trends, time demands that we focus on a specific approach to decision making from conventional to the holistic, that is, the total Mind-Body-Brain Connectome through the faculties of Vedic wisdom (Nagarkar, 2017) and harnessing of the knowledge base present within (Nagarkar, 2000), and through the development of one’s states of consciousness (Radhakrishnan 1994) within, referred to here as ‘the consciousness-­based approach to management for human capital and connectome” (Shetkar 2020a). This process is mainly for the R. M. Shetkar IISc Banglore, Bengaluru, Karnataka, India S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Nandan Mohanty (ed.), Decision Making And Problem Solving, https://doi.org/10.1007/978-3-030-66869-3_4

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awakening of one’s inner potential by training the mind-body and brain for optimal decision making and its management to thrive and survive in this new age of competition. To lead and survive the competition on a global basis, we need to move from the conventional pre-dominated L-directed, that is, left brain, dominance thinking, or R-directed, that is, right brain, dominance thinking about information, to the holistic and integrated approach to one’s thinking by the optimal potential of the human connectome (Shetkar 2020a). This need calls for the holistic development of the right and the left sides, for both the whole or total development of the human mind for information processing, thereby enhancing decision making. To survive the loads of information in this new information age, the call comes for a holistic mindbody-­brain connectome (Mesulam, 1995), by harnessing the faculties and resources that are said to be innate, that is, already present. These trainings of the utmost important innate functions are also termed as ‘mid-brain’ development of emotion and cognition in modern neuroscience (Mesulam, 2004; Moruzzi & Magoun, 1949). This pivot is also termed as one of the most crucial networks, known as the “executive functioning” (Moruzzi & Magoun, 1949), which can also be understood as the CEO, the central executive functioning (Chan, Shum, Toulopoulou, & Chen, 2008) in terms of modern management and neuroscience (Heilman, Nadeau, & Beversdorf, 2003). This important brain network (Trimble, 2007 has an important role in thinking (Squire et al., 2003), cognition (Jung, Mead, Carrasco, & Flores, 2013), emotion (Qin & Northoff, 2011), and self-management (Kak, 1996) that can be enhanced and trained for its optimal functioning by the tasks such as a one-­ pointed focus, concentration, and an event of a goal-directed activity (Llinas, 2001; Posner & Rothbart, 2009). The frontal regions in the human brain are important in the management of these inherent resources for the functions such as (i) focus, (ii) attention, and (iii) concentration, which makes the human brain different, unique, and abstract from the rest of the species on the planet on which we live (Buzsaki, 2006; Posner & Rothbart, 2009). We, the human beings, are the only species endowed with a vertical spine, in comparison to the rest of the species in the animal kingdom, with a horizontal spine. Considering human life as the experience of nature itself, the human vertical spine, nature’s evolutionary reflection in the human central nervous system (CNS) can also be seen as nature’s reflection of its own intelligence and creativity or cognition. This unique and important system is potentially identified to function on its 90° angle, when the connectome with the mind-body is at its best: this means a straight spine is a healthy and happy spine, which is preferable for becoming managers and leaders, thereby transmitting in making an effective information and decision-making process to optimal. As just said, finding our own GPS-­grounded positioning system (the axis of our self on this planet) and staying connected with our mind–body complex to this global energy system (Scoot Kelso, 1995) in its most endowed and natural way, is a new approach and a science of holistic brain, mind, and self, for effective thinking and enhanced cognition and sound decision making. This development is

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of the utmost essence, from IQ (intelligence quotient) to EQ (emotional quotient) (Immordino-Yang, 2015), and from EQ to CQ (cognitive quotient) (Frederick, 2005), and from CQ to SQ (spiritual quotient) (Phipps, 2012), which is the call of the entire world today. This concept needs to be incorporated in the modern educational system in learning and training (Hatch & Dyer, 2004).

4.2  Background 4.2.1  H  uman Brain, Information Processing, and Decision Making Human brains are extraordinary, endowed with infinite intelligence and cognitive abilities. The typical human brain contains about 100 billion cells. Each cell, also called a neuron, connects and communicates with as many as 10,000 others of its team members or colleagues. These connections and integrations forge an elaborate network of approximately 1 quadrillion (1,000,000,000,000,000) connections, guiding how we talk, walk, eat, breathe, and move. The Nobel Prize winner James Watson, who helped discover DNA, says ‘the human brain is the most complex thing we have discovered in our living Universe (Watson & Crick, 1953). [Woody Allen also mentioned that the human brain is ‘my second favorite organ’ (David, 2017).] Despite our brain’s complexity, its broad topography is simple to understand and symmetrical if we try to approach our own best organ through the lenses of neuroscience. Somewhere the beginning has to happen, in a search for understanding the beginning with a basic concept of brain, mind, and self. This effort becomes simpler when we approach this topic of challenge on the foundation of self (Damasio, 2010), a process of self (Northoff et al., 2006), and self itself in its formation and making (Damasio, 1996). Scientists long ago discovered that a neurological ‘Mason-Dixon line’ (Hutsler & Galuske, 2003) divides our brain into two regions, two hemispheres: the left and the right. The left hemisphere is sequential, logical, and analytical, whereas the right hemisphere is nonlinear, intuitive, and holistic. The key difference in the human brain in comparison to that in other species is that man is endowed with emotion, cognition, and thinking (Fair et al., 2008; Krishnamurti, 1982; Scoot Kelso, 1995; Wolford, Mille, & Gazzaniga, 2000). However, until surprisingly recently, the scientific establishment considered these two brain regions to be separate and unequal. They thought the left side only was important for human growth, and these regions made us human. The right, in comparison to the left, was considered subsidiary, the remnant of an earlier stage of human development, thinking that it was mute, nonlinear, and instinctive, a vestige that nature might have designed for a purpose that humans may have outgrown.

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In the age of Hippocrates, physicians believed that the left side of our brain is the same side that ‘Housed the Heart’ (Cheng, 2001) and was the only essential half. Scientists have accumulated evidence to support this particular view since the 1860s. Soon, the renowned neurologist Paul Broca discovered that a portion of the left hemisphere controlled the ability to speak language (Flinker, Anna, Shestvuk, et al., 2015). The German neurologist Carl Wernicke made a similar discovery about the brain’s ability to understand and comprehend language just a decade later. These studies and the subsequent research findings enabled us to produce a convenient and compelling syllogism. It is this human brain capacity for ‘language’ that allows humans to communicate and comprehend; this understanding makes humans gifted, different from the rest of the beasts. Language resides on the left side of the human brain; the famous regions Broca’s and Wernicke’s areas are devoted for the capacity of language in the left hemisphere of the brain (Hagemann, Waldstein, & Thayer, 2003; Sporns, Chialvo, Kaiser, & Hilgetag, 2004). Therefore, perhaps it was thus understood that the left side of the brain is what makes us human; however, this is not only sufficient, as we will come to the regions that make us human and humane a little later, before we arrive at the main point of emotion, cognition, and the decision-making processes. In connection to the human ability for language, the next gift is the ability to communicate and express.

4.3  Main Body 4.3.1  Emotions, Cognition, and Mid-Brain Connectome As per Darwin, human beings, irrespective of race, cast, creed, or culture, all express their feelings, their emotions, using their face and body in a similar way as part of our heritage and the gift of our evolution (Haught, 2018). Emotions are often defined as a complex set of feelings resulting from physical and physiological changes that affect thought and behavior (Hagemann et  al., 2003). Emotions include feeling, thought, nervous system activation, physiological changes, and behavior, such as facial expressions. Man is the creature who carries a deep thought of the presence of the self within, accompanied by thinking and reasoning about his own presence, identity, and the feeling of the infinite inside and outside. Man is the only creature who carries a ‘quest for the meaning’ and a ‘search for the essence’ of life (Eliade, 2013; Johnson, 2006). I recollect a fable from a bestselling business book of the past couple of decades titled Who Moved My Cheese? (Johnson, 2015), which sold some millions of copies around the world. It is a beautiful tale of two micelings, named Hem and Haw. These two micelings, Hem and Haw, are critters who live in a maze and love cheese. One fine day, following a couple of years of finding their loved cheese in the same

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place, Hem and Haw suddenly recognize that their precious cheddar has gone. ‘Somebody, oh yea, has moved their cheese.’ Discovering this episode, Hem and Haw react very differently to this scenario. Hem, the whiny mouse, wants to wait until somebody puts the cheese back for them, but Haw convinces Hem that they should not wait but should decide on an action to solve their problem by their own act to discover new cheese. Here, in this tale, the micelings make a decision, instead of waiting for the solutions to appear magically. ‘They make a choice(s) to act, to decide, is the morale of the story.’ Finally, the two micelings, Haw and Hem, live happily ever after or at least until their loved cheese is moved again. The deep moral of the story is that changes are inevitable in evolution and when they take place, the wise decision one can make is not to wait for circumstances to change by themselves, but to act, to decide, to respond wisely, to not wait for opportunities or whine, but to search, make it up, and manage the change and situation. Even in human life, considering an issue with the metaphor, in the ongoing conceptual age of fix perceptions from the past several decades, we all constantly observe, moving our own cheese, so to speak. However, in this age filled with information and a wide range of choices, we are no longer in a maze or labyrinth (Mayer, 2006). Although mazes and labyrinths are often lumped together, in most situations they differ in important ways. A maze is a series of compartmentalized and confusing paths, most of which lead to dead ends. When you enter, your objective is to escape, as quickly as you can. A labyrinth is a spiral walking course. When you enter, your goal is to follow the path to the center, stop, turn around, and walk back out, all at whatever pace you choose. In a way these formations depict the abilities of the left and right regions of our brain and the choices we make by our inner default. Mazes are analytical puzzles to be solved; labyrinths are a form of moving meditation. Mazes can be disorienting; labyrinths can be centering. One can get lost in a maze; one can lose oneself in a labyrinth. Mazes engage the left brain, and labyrinths free the right brain, this being the very important point, in making choices, in making a decision. As said earlier, the current era is recognized as the era of cognitive and emotional intelligence (Mayer, 2006), so the prime competency of an organization is the creative cognitive intelligence for management and decision making (Salovey & Sluyter, 1997). This emphasis demands a totally different kind of mind, not merely the right or left brain of an employee, but the integrated and holistic mind endowed with the quality of pattern recognition (Carpenter & Grossberg, 1991), creativity, empathy, and meaning, making (Kauffman, 1996; Taylor, 2001) a move from the conventional style of management of a basic mind. These minds may be of an employee as a computer programmer, or a lawyer who drafts a contract, or MBAs who crunch the numbers, to creative heads, or employees with minds and brains such as artists, inventors, thinkers, designers, storytellers, caregivers, consolers, bigpicture thinkers, and so on. To reap society’s richest rewards and share its essence as the greatest joy come from the field of pure intelligence, our cognition and pure consciousness (Dunne & Richard, 2006; Joseph, 2016; Kabat-Zinn & Davidson,

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2012), by the code and ethics prescribed for business and management in the scriptures (Adishankara in D. Saraswati, Adishankaracharya, 2013). This journey in the new world, the new era, demands a shift in our focus and thinking, from the simple to singular to natural complexity of our own self, society, and our organization (Nisargadatta, 1973). This shift transmit a better thought process, wakeful cognition (Harung, Dennis, William, & Alexander., 1996), and a practical approach in handling one’s emotion to arrive at the best decisions and choices for the self and for the rest of the society and the people we belong to and encounter. As specified earlier, left brain orientation is limited, mainly because its mode is narrowly reductive, deeply analytical, and limited to logic only. The current age of ‘knowledge’ triggers the deeper aspects of inner wisdom and thought management, where an array of forces lies beyond materialism, deep within our mind and brains. This aspect needs to be given thought in a more precise manner for bringing in mainstream business management. The call is combining and integrating the mind, brain, and body complex (Shetkar, 2020a), calling for the refined levels of self (Damasio, 1999) and its essence in making decisions more strategic and fruitful. With the high self-concept (Damasio, 2003), high self-esteem (Nagarkar, 2016), high aspirations, and higher dreams begin from the smaller self (individual or personal self) to the bigger Self (higher pure consciousness) (Radhakrishnan 1994). This stage can be achieved by harnessing and enhancing the brain’s innate frontal capacities (Deppe & Schwindt, 2005), which normally lie dormant, or unconscious (Singh, 1999) into further subconsciousness (Singh, 2009), in terms of human psychology. Here, the high self-concept means training and enhancing one’s innate, inbuilt capacity to recognize and detect patterns and opportunities, further creating artistic, emotional beauty, combining both sides, left as well as right. This step demands the development and nurturing of the mid-brain. The Higher Self, higher aspirations, means the ability to empathize with others, to understand the subtleties of human interaction, to find joy in one’s self and to elicit its effect in our surroundings and every being we encounter  – the ultimate process of right and effective decision making. This effort will take us beyond the horizon for ‘I, Me, and Myself’ to ‘We.’ Because that aspect of the limitations of the concept of ‘I,’ the ego, brings illness, when we replace this ‘I’ with ‘we,’ illness can be replaced with ‘wellness.’ This wider perspective from ego to the expansions of mind (Jung, 1996) lets us reach beyond our boundary to meaningful outcomes of our own decision making for achieving purpose and meaning. Thereby, we share and spread the infinite freedom, infinite wisdom, and infinite ocean of bliss and peace coming from the Whole to each and every individual Soul one encounters. This realization has great potential to brighten and enlighten the spark to ignite creative genius for a new world, new market, and new trends of life. This model is based on the extended work of the author’s experience in the science of meditation, taught to her by her Guru Swami Madhavananda, Dr. Madhav Nagarkar, an authority in Vedic Sciences, ‘Natha Sampradaya’s Soham Meditation Tradition,’ further on from post-doctoral research

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and the Ph.D. thesis (Shetkar 2020a). The pathbreaking findings of the creative cognitive intelligence and human connectome project on brain wave coherence and synchronicity (Qin & Northoff, 2011) achieved by the training of yoga, meditation (C.  M. Ramdev, 2005), relaxation, and stimulations (Nagendra & Nagarathna, 2004) are used in various organizations for mid-brain activation and training at national and international levels. This is a cutting-edge research for learning and training the mid-brain for human excellence, which states the inner dynamics of the optimal functioning of brain and mind-body connect to the highest (Shetkar 2020a).

4.3.2  B  rain Integration: Emotions, Cognition, and Mid-Brain Connect The brain has four divisions with unique and specific functions within its four lobes or regions. The four lobes are (i) frontal, (ii) parietal, (iii) occipital, and (iv) temporal (Leslie, 1975; Vilayanur, 2002). The frontal regions mainly function for concentration, focus, and thinking, the parietal with sensory-motor area functioning, occipital for vision, eyesight, and temporal (as the name itself suggests, the temple region of the mind–body complex, with the functions of emotions, etc.) When these four lobes are connected by neuronal network activity, it gives us abilities such as thinking, emoting, cognizing, and decision making, as appropriate in the prevailing circumstances. In the process of cognition, emotion, and sound decision making, these form the key areas, the frontal and mid-brain temporal regions connecting the mind-body-­ brain complex. If neuronal firing occurs in a synchronized manner, it enhances the cognitive and the emotional faculties, giving us the effective decision from within (Daniel et al., 2007). As discussed about the special gift of nature’s evolution to express feelings and emotions, we react and respond with importance in the important events of our lives; this is the basic human instinct (Ron & Wilson, 2014). We either express ourselves by silence or with words, or sometimes, even without a spoken word but with silent internal talk. This ability comes from the senses to feel and flow. Our feelings, in particular our emotions, serve various functions (Mihaly, Abuhamdeh, & Nakamura, 2014). They seem to dominate major aspects of our lives as we learn to live in our particular environments, personal and social. The three important categories of our mental functioning are (i) consciousness, (ii) cognition, and (iii) emotion. Emotions are recognized as the fundamental and important mental functioning without which human life has no meaning to human life. Emotions, as per the research findings in advanced neuroscience, carry out significant roles in human information processing, reasoning, and decision making (Richard, Kanner, & Folkman, 1980). Decision making of expert systems recently introduced emotion to artificial intelligence (Marvin, 2007) as one of the important factors in this process.

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Humans, as well as other species of mammals, have emotional behaviors important for their survival. They help keep us safe in uncertain and dangerous situations. A specific region or a part of the brain in mammals known as the limbic system handles and controls the emotional process. The two important parts of the limbic system are the amygdala and orbitofrontal cortex (Katja et al., 2006). This brain system is modeled mathematically. The amygdala is a subcortical structure within the limbic system that has been implicated, for quite a long time, in neuroscience research, to have a major role in processing and conditioning of fear and other emotions. It integrates sensory inputs from other multiple areas of our brain that are involved in generation of emotions directly or indirectly. It interconnects other cortical as well as subcortical areas, and resides in the temporal lobes, the innermost region of our brain: this forms the major areas of our mid-brain and are largely responsible for the human connectome and mind-brain-body connect. This network in our brain is critical in day-to-day information processing, cognition, and decision making (Hae-Jeong, 2013). It also influences other important functions of our brain such as (i) attention and (ii) learning. Hence, integrity, connections, and close associations are important for sound informational processing of emotional stimuli for the right process of decision making and management, development, and appropriate functioning of the mid-brain. Integration of our emotions and motivation are done by the amygdala, by conditioning emotional reactions. To arrive at the right decision-making point, this process is important: it is done by the close associations between amygdala and orbitofrontal cortex. Emotional reactions coming from the stimuli are learned and processed by the amygdala and passed to the orbitofrontal cortex. Further, inhibition of these reactions is carried forward by the orbitofrontal-­cortex, connected by the amygdala in a close and sensitive manner. Encoding and contextual representations are done by the hippocampus. These brain regions work in close associations to arrive at the right decision-making process (Antonio, Damasio, Damasio, & Lee, 1999). Emotions and intelligence coming from these brain regions are coined as a popular science known as ‘emotional intelligence’ (Erwin, Hess, & Arnold, 2011): this goes beyond the sixth sense and is also understood as the special abilities of our mind. The emotions produced by the aforementioned brain regions, when the neurons are connected correctly, gives us refined quality emotions, and their energy endows us with emotional intelligence, securing a very important place in right input to the information process, leading to effective decision making. Emotional intelligence is the ability to identify, understand, and use emotions positively to manage anger, anxiety, and its perceptions to aid information processing. It helps us communicate and connect well with others, overcome issues, solve problems, manage conflicts, and empathize. Emotional intelligence gives us the ability to refine our perceptions, understanding, and self-regulation, to understand our own self and to understand others. This is the science that integrates and connects emotions and cognitions, important in decision making.

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Varied kinds of deeper categories of emotions of an individual are important as well, also referred as ‘mental states’ that may or may not be endowed with characteristics that drive the behavior and thinking of a person directly. Following their subsequent cognitions and abilities in information management thinking is important for decision making. Such emotional or mental states as anger, disgust, fear, happiness, sadness, and perhaps others trigger our thinking and its management before the process of final decision making takes place. This model, presented by Barret et al., portrays how emotions drive human thinking, cognition, and, finally, the decision itself (Duncan & Barrett, 2007). First-order emotions, also technically known as emotional schemas, form the core of the human mind in driving these decision-making subsidiary processes. They develop with age and learning, consisting of complex combinations of feelings, emotions, cognitions, and human behaviors, which we call maturity. One has a stable amount of groundedness, after traveling and visiting the countryside or some other parts of the world; when one travels for the first time, one becomes grounded. Learning how to detect sincere, innocent, and insincere smiles anywhere in the world makes one mature and ready for life. However, what is that which makes ourself unique, abstract, and a rational decision maker? I think we should start our own personal journey by reflections with our own brain, mind, and self. I would like to quote my own neuroimaging research (Shetkar 2020a) and the changes in the brain’s electrical activity, a beautiful transitional shift in the brain waves from the process of focusing, concentrating to thinking, to cognition, etc. The activity of the stimulation and relaxation (Telles, Reddy, & Nagendra, 2000) of the muscles given to the subjects brought out pulsating electromagnetic brain waves. This study shows the essence of stimulation and relaxation of the muscles, to manage stress and the burnout of the mind, brain (Kalyani et  al., 2011) and body (Hankey & Shetkar, 2016). This relaxation was reflected in the distinct and very specific signatures of the individual’s brain waves, showing that mind, brain, and body being stress free is important for overall information processing and decision making. The roots of such things as human experiences, reactions, and responses are deeply stored in the form of memories and experiences, in distinct brain regions, through special mechanisms of the brain and of the body as well (Brewer et al., 2011): they can be mapped into the gross anatomical structures within the brain in compartments. However, few other emotions are totally based on the specific pattern of the autonomic nervous system, and some others are actually not anatomically or network specific to any brain regions. This conclusion is based on the inherited mechanism, for example, the affect and its process, which may be unique to an individual’s mind (Paul, 2003). Psychological events or mental states emerging from more psychological operations may not be specific to emotions as such in some cases, hence they may not have any input for information processing in decision making. Therefore, some people may just get stuck, and do not make any choice, or make any decision at all. However, others who cross this boundary and reach the zone of

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cognition and beyond become masters of their mind and thoughts, and thus good leaders. Leaders are those who make the best decisions in the world. The major theories for some time explaining and studying how and why we feel emotion state feelings are important; for example, Craig’s salience model, ‘How do you feel now?’ Among these are the evolutionary theories, the James–Lange theory, the Cannon bar theory, and Schacter and Singer’s two-factor theory of emotion (Cannon, 1931; Schacter & Singer, 1962). All say that the cognitive processes are important and a crucial source of the feelings and emotions itself. They comprise powerful motivations, influencing perception, confrontation, creativity, and cognition, which are the intermediaries in the process of information processing and decision making. Integrations of neurons in the different brain regions give us beautiful patterns. Recognition of these patterns, understanding their relationships, is the faculty of cognitive areas or cognitive networks (Bechtel & Abrahamsen, 1991). Of utmost importance, this comes by practice, knowledge, education, common sense, and wisdom. In an important study, Daniel Goleman writes a comparative analysis from his observations and experiences in the form of a story. He mentions that among the group of executives across 15 large companies just one cognitive ability distinguishes a star performer from an average worker and that pattern recognition, the big picture thinking, allows leaders to pick out the meaningful trends from a wealth of information present around them and to think strategically more deeply and far into the future (Daniel, 1995). Such employees rely less on deductive, ‘if– then’ reasoning and more on the intuitive, contextual reasoning that is a characteristic of harmony. Among Fortune 500 and the multinational companies, their CEOs demand poets as managers and decision makers, for placement and hiring in their organizations. They say original thinking comes from these kinds of cognitive brains and creative minds (Heilman, 2005). They are the ones who contemplate the world we live in and feel obliged to interpret, giving expression in a way that makes the reader understand how the world we live in functions and changes from time to time.

4.4  Conclusions The trends have been to move from an ordinary economy to a society in which we currently live that has progressed on logical, linear, and digital, computer-like capabilities, derived from the information age to a society that is potentially driven by a cognitive brain and creative mind for human development, peace, and happiness, rather than only making and sharing profits. This vision can be achieved mainly by the decisions and the choices we make today for our own sake and for forthcoming generations that we may lead, of the inner cognition and wisdom present within. This cause is potentially challenging and worthy, for a new rising, a new wake-up

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call, a new joining in the global awakening, for sound management, aware organization, and conscious living. Making transitions in terms of training mind and brain are the techniques on which we need to focus, to move from a landscape of left– right dominance to integrated mid-brain science (in my PhD thesis), adding the capacity of art as well as heart to our logic and analysis in the making of decisions. Focus and attention in the goal-directed events from a wakeful individual mind to the collective conscious mind enables our innate potential set-up tempo for further human evolution for a new age, in the modern era in this century. The emphases on the science of unifying the mind-body-brain connectome, along with the cognitive basis for emotions, a new ontology of neuroscientific approaches in the understanding mind, will enable appreciation of the secrets of individual decision making. Therefore, the need of the time is training the three important components, the three ‘A’s: attention, awareness, and arousal (Alain, Woods, & Ogawa, 1994; Qin & Northoff, 2011), in the brain’s deeper regions, to enable, train, and activate our midbrain development (Qin & Northoff, 2011) to enliven and help provide the maximum potential of mind and brain for effective information processing and enhanced decision making. Acknowledgments  I would like to acknowledge valuable conversations with Dr. B. N. Gangadhar, Director NIMHANS, Bangalore, India, for expert opinion, his understanding for my experiences in meditation, and helping me develop this science as mainstream for mind, heart, and brain research for a global mental wellness vision. I thank Dr. Alexander Hankey, Scientist, MIT Cambridge, London, for helping me in scientific research, and Dr. H. R. Nagendra, Chancellor, Vyasa University, Bangalore, for passing infinite blessings felt and experienced at the turning points of my life and this work. Overall, credit for the scientific research goes to my Guru, Dr. Madhav Nagarkar (Swami Madhavananda), and our ‘Natha-Sampradaya Lineage.’ Without the experience in meditation, such work would not have been possible.

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Nagarkar, M. (2017). Patanjal Yoga Sutra  – The Science of Meditation. Pada 1, verse 3. Pune, India: Swaroopyoga Pratishthan. Nagendra, H. R., & Nagarathna, R. N. (2004). New perspectives in stress management. Bangalore, India: Swami Vivekananda Yoga Prakasana. Nisargadatta, S. (1973). I am that: Conversations with Sri Nisargadatta Maharaj, 2 vols. (M. Friedman, Trans.). ChetanaBombay, India Norman, G. (1974). Carl Wernicke, the Breslau School and the history of aphasia. Selected Papers on Language and the Brain (pp. 42–61). Berlin, Germany: Springer. Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., & Panksepp, J. (2006). Self-referential processing in our brain: a meta-analysis of imaging studies on the self. NeuroImage, 31, 440–457. https://doi.org/10.1016/j.neuroimage.2005.12.002. Paul, E. (2003). Emotions revealed: recognizing faces and feelings to improve communication and emotional life. Times Books, 220. Phipps. (2012). Spirituality and strategic leadership: the influence of spiritual beliefs on decision making. Journal of Business Ethics, Springer. Posner, M. I., & Rothbart, M. K. (2009). Toward a physical basis of attention and self-regulation. Physics of Life Reviews, 6(2), 103–120. Qin, P., & Northoff, G. (2011). How is our self related to midline regions and the default-mode network? Neuroimage, 57(3), 1221–1223. Radhakrishnan. (1994). Mandukya Upanishad, Ch.1, Verse 1– 7. In: Radhakrishnan S. Principal Upanishads. Harper Collins, New Delhi, India. Richard, S. L., Kanner, A. D., & Folkman, S. (1980). Emotions: a cognitive-phenomenological analysis. In Theories of emotion (pp. 189–217). New York: Basic Books. Ron, S., & Wilson, N. (2014). Roles of implicit processes: instinct, intuition, and personality. Mind and Society, 13(1), 109–134. Salovey, P. E., & Sluyter, D. J. (1997). Emotional development and emotional intelligence: educational implications. New York: Basic Books. Schacter, S., & Singer, J. (1962). Cognitive, social and physiological determinant of an emotional state. Psychological Review, 69, 379–399. Available at: http://www.ncbi.nlm.nih.gov/ pubmed/14497895. Scoot Kelso, J.  A. (1995). Dynamic patterns: the self-organization of brain and behavior. Cambridge, MA: MIT Press. Shetkar, R. (2019a). ‘Shivani effect’ in presented ‘talks on hypothetical of model’  – hypothesis presented as post-doctoral work. Shetkar, R. (2020a) ‘Shivani effect paper series 1’, in review. Shetkar, R., Hankey, A., Nagendra, H. R. (2017). First person accounts of Yoga meditation yield clues to the Nature of Information in Experience. Cosmos and History. Shetkar, R., Hankey, A., & Nagendra, H.  R. (2018). Association between Cyclic Meditation and Creative Cognition: facilitating connectivity between the frontal and parietal lobes. International Journal of Yoga (IJOY). Simon, B.-C. (2003). The essentials difference: the truth about the male and female brain. New York: Basic Books. Singh, J. (1999). Vijnanabhairava or divine consciousness. Motilal Banarasidass Publishers, Delhi, India (1979, reprint). Singh, J. (2009). Spanda-Karikas: The Divine Creative Pulsation. Motilal Banarasidass Publishers, Delhi, India (1980, reprint). Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418–425. Squire, L. R., Bloom, F. E., McConnell, S. K., Roberts, J. L., Spitzer, N. C., & Zigmond, M. J. (2003). Fundamental neuroscience (2nd ed.pp. 1353–1394). San Diego, CA: Academic Press. Taylor, M. C. (2001). The moment of complexity: emerging network culture. University of Chicago Press, Chicago.

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Telles, S., Reddy, S. K., & Nagendra, H. R. (2000). Oxygen consumption and respiration following two yoga relaxation techniques. Applied Psychophysiology and Biofeedback, 25(4), 221–227. Trimble, M. R. (2007). The soul in the brain. Baltimore: The Johns Hopkins University. Vilayanur, S. R. (2002). Encyclopedia of the human brain, 4 vols. San Diego, CA: Academic Press. Watson, J. D., & Crick, F. H. C. (1953). A structure for deoxyribose nucleic acid. Nature, 171, 737–738. Wolford, G., Mille, M. B., & Gazzaniga, M. (2000). The left hemisphere’s role in hypothesis formation. Journal of Neuroscience, 20(6), RC64.

Chapter 5

Need of Improving the Emotional Intelligence of Employees in an Organization for Better Outcomes R. S. M. Lakshmi Patibandla, V. Lakshman Narayana, and Sachi Nandan Mohanty

5.1  Introduction Among the most significant exploration themes, emotional intelligence or emotional quotient promoted incredible expectations among industry pioneers and also expert chiefs. On April 26, 2019, Marvel Studio released one of the most anticipated sci-fi motion pictures of the current year: “Vindicators: Expiration Spirited.” At this time, the chief changed his mainframe into a robot contrivance that could do bot complex calculations and furthermore sense the emotional scene between the legend and the courageous woman of the film. It will not be hard for anybody to envision that before the end of the twenty-first century arrives it will be encompassed by these genuinely keen robots (Salovey & Mayer, 1990) (Fig. 5.1). All in all, insight can be ordered into two stages: the first is coherent acumen and the other is passionate insight, which are estimated by intellectual measure and sensitive proportion, separately. We hominids are honored by the ownership together, yet legitimate intelligence has been our preferred youngster for ages. In the late 1990s, the term man-made brainpower gained great fame among analysts, and numerous effective endeavors were made to orchestrate intelligent knowledge (Balakrishnan & Shunmuganathan, 2012) (Fig. 5.2). Numerous specialists have upheld that feelings assume a significant function in basic reasoning, learning, memory capacities, dynamics, and numerous other aspects. The term emotional intelligence was first utilized in an article by Keith Beasley in 1987  in the British magazine Mensa. Despite that, in sci-fi motion R. S. M. Lakshmi Patibandla · V. L. Narayana Department of Information Technology, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Guntur, AP, India S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Nandan Mohanty (ed.), Decision Making And Problem Solving, https://doi.org/10.1007/978-3-030-66869-3_5

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Fig. 5.1  Use of robots in emotional intelligence

Fig. 5.2  Understanding emotional intelligence (EI) concepts

pictures, for example, “The Terminator” (1984), “Transformers” (2007), and many others, enthusiastic insight into the machine has regularly been depicted as a risky thing for humanity. Throughout the years, scientists understand the reality that showing machines “how to feel” is similarly significant as showing them “how to think.” Passionate insight causes the machine to develop in another dimension that was not investigated previously (Mckenna & Webb, 2013). Developing a sincerely smart machine appears to be weird and new from the outset, yet these kinds of machines have noteworthy favorable circumstances. With the capacity to process a huge amount of information and to perform huge complex calculations in practically no time, machines however cannot see or comprehend the sentiment of the client and adjust their capacities correspondingly. A client now and again feels exceptionally baffled and on edge when an unforeseen blunder message is experienced and wants to vent the displeasure on the machine, yet the machine has no clue what the person is experiencing and have limited capacity to react in a helpful manner (O’Boyle, Humphrey, Pollack, Hawver, & Story, 2011). In any case, a machine with some

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Fig. 5.3  Need of EI

degree of enthusiastic insight can react back to the client with some gainful arrangements and at last diminish the client dissatisfaction by some degree (Farh, Chien, Seo, & Tesluk, 2012). Counterfeit emotional knowledge is developing rapidly. Presently we have android applications on a cell phone that could monitor small changes in your outward appearances by utilizing the camera of your telephone and can tell whether the feeling you are demonstrating is genuine or counterfeit (Relojo, Pilao, & Dela Rosa, 2015). Notwithstanding that, the machine can tell about your mindset essentially by tuning in to your voice, the normal language preparing (NLP) (Fig. 5.3). For about 50 years, computer scientists and specialists have been putting forth a valiant effort to fabricate machines that can communicate with us intelligently (Ahiauzu & Nwokah, 2010). The greatest achievement accomplished on this way is “SOPHIA THE ROBOT.” Sophia is the principal social humanoid intuitive shrewd robot, truly one of its sort, created by the Hong Kong-based organization Hanson Robotics. Sophia ‘came into this world’ on February 14, 2016. For integration of emotional intelligence with artificial intelligence (AI), emotionally canny AI operators occupy the space and are very proactive now for the spots where people are attempting to see one another. Today about 52% of clients around the globe use AI-controlled innovation. Gartner ventures that by 2020 you are more likely to have a discussion with a chatbot than your life partner (Farh et al., 2012). Man-made intelligence has just upset a great many enterprises, for example, producing, gaming, media, and amusement. Incorporating passionate insight with AI is an entangled undertaking, yet a great many large organizations have just achieved some progress in it. From the aspect of AI, enthusiastic knowledge is identified with AI insofar as ordering of large amounts of information, self- and outside

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Fig. 5.4  Integration of emotional intelligence (EI) models with AI

mindfulness, profound learning, well-being, and morals. The joining of enthusiastic knowledge with aspects of AI is shown in Fig. 5.4. Google and Apple photograph clergymen in “incredible recollections” recordings and realize that a video of a child making first strides will bring more happiness than the photograph of an oat box taken for a shopping list (Joseph & Newman, 2010). YouTube knows how we will grin and snicker at a feline video and backlash with sickening dread at the misuse of a youngster. Facebook is a monster social and passionate learning motor. It contains many of our enthusiastic recollections, for example, photographs and recordings (Moon & Hur, 2011). It likewise knows our companions and family members with whom we associate, and our taste, what we like or dislike. It unites these three things at a staggering scale to choose what goes into our feeds to draw in us both socially and inwardly (Dabke, 2016). A customized film of 2018 recollections you got a week ago is only one such model. Microsoft manufactured Xiaolce, the most mainstream social chatbot on the planet. Xiaolce thinks about both IQ (intelligence quotient) and EQ (emotional quotient) while deciding. It has the character of a multiyear-old Chinese young lady who is solid, thoughtful, warm, and has an incredible comical inclination. Few individuals would contend that an individual could beat a trend setting innovation framework at ingesting and examining information thus in territories; for example, this AI is demonstrating an important instrument that does not compromise human representatives the slightest bit: rather, it upgrades their work.

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5.2  Need of Improvements in Emotional Intelligence Passionate knowledge, or EQ, continues to be an inexorably well-known aptitude to have in the expert world. Many might be asking why passionate knowledge keeps on expanding in significance among peers in a developing working environment. Basically, passionate insight is not a pattern. Significant organizations have aggregated measurable verification that workers with passionate knowledge without a doubt influence the primary concerns. Truth be told, organizations with representatives that have elevated levels of passionate insight see significant increments in all deals and profitability. In a serious work environment, building your EQ aptitudes is essential to your expert achievement. The following are ten different ways to build your EQ. 1. Use an emphatic style of conveying. Emphatic correspondence goes far toward acquiring regard without seeming to be excessively forceful or excessively uninvolved. Sincerely smart individuals realize how to impart their assessments and needs in an immediate manner while yet regarding those of others. 2. React as opposed to responding to struggle. During examples of contention, enthusiastic upheavals and sentiments of outrage are normal. The sincerely keen individual realizes how to remain quiet during upsetting circumstances. They do not settle on incautious choices that can prompt much more serious issues. They comprehend that in the midst of contention the objective is a goal, and they settle on a cognizant decision to concentrate on guaranteeing that their activities and words are in agreement. 3. Use undivided attention abilities. In discussions, sincerely wise individuals tune in for lucidity rather than simply trusting in their turn to talk. They ensure they comprehend what is being said before reacting. They additionally focus on the nonverbal subtleties of a discussion, which forestalls mistaken assumptions, permits the audience to react appropriately, and shows regard for the individual they are addressing. 4. Be inspired. Sincerely clever individuals are self-spurred and their demeanor propels others. They set objectives and are versatile, notwithstanding challenges. 5. Practice approaches to keep up an uplifting disposition. Try not to think little of the intensity of your disposition. A contrary mentality effectively contaminates others if an individual allows this. Sincerely canny individuals have a familiarity with the states of mind of everyone around them and monitor their mentality appropriately. They comprehend what they have to do so as to have a decent day and an idealistic standpoint: this might ­incorporate having an incredible breakfast or lunch, taking part in supplication or reflection during the day, or keeping positive statements at their work area or PC.

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6. Practice mindfulness. Genuinely clever individuals are mindful and natural. They know their own feelings and how they can influence everyone around them. They additionally understand the feelings and nonverbal communication of others and utilize those data to improve their relational abilities. 7. Take scrutiny well. A significant piece of expanding your passionate insight is to have the option to take time to scrutinize. Rather than becoming insulted or cautious, high-EQ individuals take a couple of seconds to comprehend where the scrutiny is coming from, how it is influencing others or their own presentation, and how they can productively resolve any issues. 8. Feel for others. Truly shrewd people acknowledge how to identify. They fathom that compassion is a trademark that shows eager quality, not a deficiency. Sympathy makes them relate to others on a basically human level. It opens the door for regular respect and understanding between people with fluctuating evaluations and conditions. 9. Use authority aptitudes. Genuinely keen people have extraordinary power capacities. They have raised prerequisites for themselves and set a model for others to follow. They get down to business and have remarkable dynamic and basic reasoning aptitudes. This attitude considers a higher and logically beneficial level of execution for the duration of regular daily existence and pounding endlessly. 10. Be open and pleasant. Really sharp people put on an act of being responsive. They smile and emanate a positive proximity. They utilize fitting social capacities reliant on their relationship with whomever they are close. They have exceptional social capacities and abilities to pass on indisputably, whether the correspondence is verbal or nonverbal. A considerable number of these aptitudes may appear to be most appropriate for the individuals who comprehend fundamental human brain science. Although high EQ abilities may come all the more effectively to normally compassionate individuals, anybody can create them. Less sympathetic individuals simply need to work on being progressively mindful and aware of how they communicate with others. By using these means, you will be well headed to an expansion in your passionate insight level.

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5.3  F  actors to Be Considered for Identifying Emotional Intelligence Emotional intelligence includes many abilities and practices that can be learned and created. Here are some indications of individuals with low EQ and those with high EQ (Table 5.1).

5.4  Factors of Emotional Intelligence 5.4.1  Self-Awareness Self-awareness is the place passionate insight starts. Until the individual gets mindful of his/her environment, how those encounters influence them, and individual qualities, convictions and standards are set up the individual will battle to identify with others. Self-awareness is perceiving how our own feelings impact our own conduct, influence the associations with others, and in the event that we can possibly impact someone else’s passionate state. Think about that: • Our feelings are in a steady condition of progress starting with 1 minute then onto the next. • You may encounter various feelings at any one time. • Stress makes specific trouble of enthusiastic mindfulness, and we are most drastically averse to be aware of and effectively handling our feelings. • Stress causes inability to think straight in cognizance, hindering the capacity to isolate feelings from conditions, constraining the capacity to oversee oneself, the circumstance, and to impact others. Self-awareness is having a view apart from yourself of your character, your qualities, shortcomings, contemplations, convictions, inspiration, and feelings. Lucidity of these discernments can change depending on conditions, for example, at work versus at home. Mindfulness is the entryway to seeing how others see you, your disposition, and your conduct during social connections.

Table 5.1  Indications of people with low and high emotional intelligence (EQ) People with low EQ Often feel misunderstood Get upset without reason Become overpowered by feelings Have issues being assertive

People with high EQ Understand the connections between their feelings and how they act Remain totally relaxed during upsetting circumstances Are ready to encourage others toward a shared objective Handle troublesome individuals with thoughtfulness and strategy

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5.4.2  Awareness of Others Awareness of others is vital to the creating and lifespan of seeing someone. Enthusiastic insight is established in social knowledge with a large number of our feelings that are activated during the association with others. Familiarity with others is where social mindfulness and conduct meet. Social mindfulness is the capacity to comprehend and react to the necessities of others. As a proportion of passionate insight, the competency of monitoring others is described by sympathetic conduct. Sentiments of sympathy become demonstrations of empathy when we choose to take part in helping someone else. Sympathy is monitoring, being in touch with, and understanding, the emotions, considerations, and encounters of someone else. If you fail to understand the situation, you may be seen as relentless and unfeeling. Taking care of business and trust can be a result, one that is basic to the improvement of a relationship. Within the six variables of self-awareness, attention to others expands the person’s qualities and capabilities as they identify with these feelings: • Empathy: capacity to take the point of view of and feel the emotions of someone else and to understand that individual’s feelings, needs, and concerns. • Compassion: following up on your sentiments (sympathy) toward someone else. • Organizational awareness: the capacity to comprehend the legislative issues inside an association and how they influence the atmosphere of a work environment. • Service: the capacity to comprehend and address the issues of representatives, customers, and different partners.

5.4.3  Authenticity Authenticity is a social capacity, characterized as not bogus or duplicated, however certified and genuine. It has been depicted as a consistent procedure of speaking to one’s actual nature or convictions; being consistent with oneself and to others. An individual is viewed as true to the degree that his/her lead toward another copies what he/she really accepts.

5.4.4  Emotional Reasoning Emotional reasoning has been portrayed as a conviction, that on the off chance that you feel something, it must be valid. As significant as feelings seem to be, they should be approved before following up on them. Not all feelings are valid; however, by one means or another, they do identify with the current circumstance, which legitimizes the need to perceive our feelings, assess the proof introduced, and decipher it precisely before acting.

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5.5  The Value of Emotions 5.5.1  Data Feelings give us data, with quite a bit of that information originating from nonverbal substance. The persistent progression of data implies that quite a bit of it is deciphered sub-intentionally, influencing our feelings without cognizant mindfulness.

5.5.2  Absolution Feelings become an absolution through crying, chuckling, communicating outrage — an arrival of pressure because of what is being experienced. These activities are characterized as methods for handling the stressors of life.

5.5.3  Communication Feelings are methods for communication. Think about the motions, outward appearances, or stances we use to impart sentiments during discussion. These nonverbal connections have enthusiastic substance, but it is hard to communicate nonpassionate significance through nonverbal communication.

5.5.4  Self-Management Self-management is the capacity to direct feelings and, eventually, your conduct. As capabilities develop one can control the driving forces activated by individuals and circumstances. Authority over motivation is the capacity to recognize and stifle the impulse to do or say something that may not be proper, to react, not respond.

5.5.5  Creating Self-Management 1. Figuring out how to channel what is driving a negative feeling – fault, outrage, dread, allegation, anxiety, absence of information 2. Treating the control components of overseeing and building up the powerful components of driving. 3. Moderating the disorderly and flighty nature of feeling. 4. Learning how to utilize feelings to sort them out.

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5. Creating flexibility during times of emergency Self-Management = Recovery Time = Resiliency The noticeable requirement for self-management is the capacity to recuperate from upsetting or emergency circumstances: self-administration is a proportion of strength. Five elements constitute self-management: 1 . Self-control: acknowledgment of feelings and directing reactions. 2. Trustworthiness: respectability and keeping up qualities and convictions. 3. Conscientiousness: proprietorship and responsibility. 4. Adaptability: changing the board. 5. Versatility: emergency recuperation from courses of events.

5.6  Inspiring Performance For a pioneer, motivating execution is a summit of the other five components of enthusiastic insight, starting with mindfulness. To rouse execution is to make a persuasive situation that drives execution at a level higher than the person’s ordinary yield. In an influential position, motivation is the capacity to impact individuals to accomplish more than they have ever suspected conceivable.

5.7  Persuasive Actions of Leadership 1. Reach and influence Predictable and viable endeavors to see how to successfully interface with workers, peers, and different partners. Persuasive pioneers come out from behind employment titles and their work areas to guarantee their crowd heard what was planned. 2. Tune in with deliberateness as opposed to hearing with channels. Listening is a mindful, drawing-in, capacity to connect points of view with the truth of what others are experiencing. Time and again we use channels while collaborating with others or search for openings as opposed to accumulating more data. Authority includes figuring out how to tune in with the expectation of expanding basic aptitudes considering enthusiastic thinking. 3. Encourage learning Authority is tied in with encouraging the learning procedure with others, driving people to revelation for themselves rather furnishing individuals with answers. Help is the way toward posing the inquiries that support cooperation and advance development. Help urges others to take part in the procedure of being a donor.

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To encounter motivation, individuals need to feel included. Consideration goes past tuning in, to the greeting of posing inquiries and giving input. Consideration causes individuals to feel associated with the activities and procedures that lead to dynamics and the accomplishment of objectives. The result of this mutual commitment in the improvement of arrangement carries proprietorship to every donor. Figuring out how to encourage conversation and commitment, shows the initiative characteristics of vision and impact. Point by point information on the assignments from the individuals who are answerable for executing the procedures permits the pioneer to satisfy the job of being an influencer and the supporter’s job of execution. Groups perform substantially more productively when these jobs are characterized, acknowledged, and executed. 4. Impact versus inspiration Authority is frequently communicated as the way toward spurring individuals. The pioneer’s job is really one of making a dream and impacting individuals toward hierarchical objectives. A director’s job is one of control — process, cutoff times, administration conveyance, and recuperation — controlling the mayhem inside the association. Together, the pioneer and the administrator make an inspirational situation that engages the workforce’s commitment. The degree of capability with which a pioneer shows these credits corresponds legitimately to the trust built up by the supporter. Capability is conveyed not through a solitary demonstration but through a mix of numerous abilities that can be educated and essentially applied during everyday connections among pioneers and adherents. The association between where every one of us is at the present time and our best can be found in the abilities of passionate insight (Table 5.2). Emotional intelligence (EI) is a number of scholarly capacities that can be estimated through an individual or aggregate appraisal. Feelings impact every choice we make and each circumstance in which we find ourselves. A few investigations have analyzed the job that feelings play in our lives, with expanding backing of EI as a fundamental arrangement of capabilities for self-administration and in creating and supporting associations with others. Since EI is a subjective arrangement of aptitudes, the information given in the variables that characterize emotional intelligence can be scholarly, and valuable in adjusting our conduct for everyday living. Learning the six elements of EI, how everyone affects conduct, and the social connections among these six elements are important to pioneers and devotees alike. The skills of EI can be learned as they are subjective capacities created through investigation and functional application. The best performing associations will guarantee that both pioneer and nonpioneer are surveyed and taught in the abilities of passionate insight, as they identify with the person. EI is an ideal crossing point for the pioneer’s impact over technique, the supervisor’s authority over procedure, and the adherent’s execution of assignments.

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Table 5.2  The effects of low and high levels on factors of EI Low level Self-awareness Inability to focus Poor listener Excitable Indecisive Nonanalytical Awareness of Inhibits others contribution Us versus them Cannot relate Partial Authenticity Insincerity Untrustworthy Plagiarist Unpredictable Unreliable Emotional reasoning

Self-­ management

Inspiring performance

Outcomes of low-level EI Disconnected Untrustworthy Failure to lead Lack of results

Apathetic Oblivious Closed Uninviting Socially inept Disengaged Lack of continuity Noncollaborative Hoards ideas

Marginal performance Unknown outcomes Group think Turnover/attrition Volatility Explosive Unaccountable Reactionary Tension Ineffective Unapproachable Inefficient Negativity Quits easily Feeling trapped Repelling Falling short Dictatorial Self-preservation Unprepared Turnover Group think Disenfranchised Breeding pessimism Illogical Inconsistent Indecisive Nonanalytical Unimaginative

High level Controlled Attentive Calm Measured Logical

Outcomes of high-level EI Source of inspiration Dependable Emotional commitment Goal achievement

Empathetic Conscious Open Receptive/affective Competent Collaborative Genuine Stable Mutual trust Participatory Original What you see is Shares vision what you get Dependable Exceeds expectation Rational Predictability Constant Independence Resolute Process-oriented Retention innovator Adaptive Accepting Assimilates Inclusive

Calm Reassuring Productive Systematic Committed Feeling empowered Democratic Strategist Blue sky culture of coaching

Stability Ownership Relief Social intelligence Curious/supportive Engaging Exceeds goals Respect for others Retention Organizational citizenship behavior (OCB)

5.8  Three Steps Toward Improved Emotional Intelligence Creating emotional intelligence is a progressive procedure. The excursion varies from individual to individual. Regardless, as indicated by Andrews, the accompanying activities may lead you to better mindfulness, compassion, and social aptitudes. 1. Perceive your feelings and name them What feelings would you say you are noticing at this moment? Would you be able to name them? When in a distressing circumstance, what feelings normally emerge?

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How might you want to react in these circumstances? Would you be able to stop to delay and reexamine your reaction? Pausing for a minute to name your sentiments and temper your reactivity is a vital advance toward EI. 2. Request criticism Review your self-discernment by asking chiefs, associates, companions, or family how they would rate your enthusiastic insight. For instance, obtain some information about how you react to troublesome circumstances, how versatile or sympathetic you are, or potentially how well you handle strife. It may not generally be what you need to hear, yet it will usually be what you have to hear. 3. Understand writing Studies show that perusing writing with complex characters can improve sympathy. Perusing stories from others’ points of view allows us to gain knowledge into their contemplations, inspirations, and activities and may help improve your social mindfulness. How to Establish a Culture of Emotional Intelligence? Building EI in yourself is a certain element, yet affecting others to receive a progressively compassionate outlook can be a test. To make a culture of high EQ, administrators and chiefs must model sincerely wise conduct. “In the event that you need to change how your association does in EI, you can set standards for how individuals convey and how they deviate,” says Andrews.

What’s more, you have to perceive and praise those that show enthusiastic knowledge. “Begin making legends of individuals who help others,” says Andrews. “It’s not simply the individual who got to the highest point of the mountain first – it’s all the individuals who helped them. On the off chance that you need to support great group conduct, remember it, and get it out for what it is.”

5.9  Conclusion The activities and responses that individuals show in their everyday life are for the most part dependent on the feelings that we have called EI. Individuals utilize their senses when an individual has more prominent comprehension and relational abilities that prompt appropriate choices and timely management of issues. Now, we have talked briefly about emotional intelligence, the need of improving emotional intelligence, factors that should be considered for distinguishing emotional intelligence, and steps to be actualized for improving the emotional intelligence of all representatives in an association.

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References Ahiauzu, A., & Nwokah, N. G. (2010). Marketing in governance: Emotional intelligence leadership for effective corporate governance. Corporate Governance, 10(2), 150–162. Balakrishnan, S., & Shunmuganathan, K. L. (2012). A JADE implementation of integrated agent system for E-mail coordination. International Journal of Computer Applications, 58(5), 5–9. Dabke, D. (2016). Impact of leader’s emotional intelligence and transformational behavior on perceived leadership effectiveness: A multiple source view. Business Perspectives and Research, 4(1), 27–40. Farh, C., Chien, I.  C., Seo, M.-G., & Tesluk, P.  E. (2012). Emotional intelligence, teamwork effectiveness, and job performance: The moderating role of job context. Journal of Applied Psychology, 97(4), 890–900. Joseph, D. L., & Newman, D. A. (2010). Emotional intelligence: an integrative meta-analysis and cascading model. Journal of Applied Psychology, 95(1), 54–78. Mckenna, J., & Webb, J. (2013). Emotional intelligence. British Journal of Occupational Therapy, 76(12), 560. Moon, T. W., & Hur, W.-M. (2011). Emotional intelligence, emotional exhaustion, and job performance. Social Behavior and Personality, 39(8), 1087–1096. O’Boyle, E.  H., Humphrey, R.  H., Pollack, J.  M., Hawver, T.  H., & Story, P.  A. (2011). The relation between emotional intelligence and job performance: A meta-analysis. Journal of Organizational Behavior, 32(5), 788–818. Relojo, D., Pilao, S. J., & Dela Rosa, R. (2015). From passion to emotion: Emotional quotient as predictor of work attitude behavior among faculty members. Journal of Educational Psychology, 8(4), 1–10. Salovey, P., & Mayer, J.  D. (1990). Emotional intelligence. Imagination, Cognition, and Personality, 9, 185–211.

Chapter 6

Slow and Fast Thinking for Problem Solving Under Uncertainty Nilay Awasthi and Sachi Nandan Mohanty

6.1  Introduction If you expect science to give all the answers to the wonderful questions about what we are, where we are going, what is the meaning of the universe … you could easily become disillusioned and look for a mystic answer … We are exploring, trying to find out as much as we can about the world. People [ask], “Are you looking for the ultimate physics laws?” “No, I’m not. I’m looking to find out more about the world. If it turns out there is an ultimate law which explains everything, so be it; that would be very nice to discover. If it turns out it’s like an onion, with million years … then that’s the way it is. But whatever way it comes out, it’s nature, she’s going to come out the way she is! Therefore we shouldn’t pre-decide what it is we’re going to find, except to try to find out more. If you think that you are going to get an answer to some deep philosophical question, you may be wrong– it may be that you can’t get an answer to that particular problem by finding out more about the character of nature. But I don’t look at it like that; my interests in science is to find out more about the world, and the more I find out, the better.” – Richard Feynman

Thinking ability has been a very thought-provoking topic among the discussions of the human race for ages. The common perception developed is that an individual who can think faster is labelled as smarter and in contradiction an individual who is not able to think faster is labelled as being not as smart. We often relate the power of thinking to the knowledge an individual possesses, and this norm is adjusted to our minds from childhood so that we all believe on that norm, until a threshold day when the truth enlightens our mind. Following, after a certain time the truth surfaces, the truth being that there is no direct correlation between the thinking ability of one individual and the knowledge one possess, but instead the thinking ability N. Awasthi Amity University Mumbai, Mumbai,, India S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India

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depends upon how an individual plays with the thoughts coming into the mind and if we can draw a line connecting the thoughts in chronological order and closing the loop. We often tend to deploy the thinking process mainly when we have encountered a problem and are in need of a solution; then we start the engine of our mind and start debugging the problem. The thinking process is like a feedback control system where we know the trend of the outcome and have certain inputs with the given constraint to streamline our approach. Every time we have some output, we use that output for giving feedback to our thinking system to reach the optimum outcome, which solves our problem. Generally, the thinking process is divided into two tracks: slow thinking and fast thinking. The ability of an individual to fuse both these tracks efficiently while solving a problem determines how effectively we have solved a problem and how much time this process required. For better understanding of these two tracks, let us consider the slow thinking track as a normal road as we have in our cities and the fast thinking track as an F1 race track. When we put the imagery of an F1 race track and the normal road together, we can see the differences. Just imagine yourself when driving a car on the normal road and when driving on a F1 race track; we can easily conclude how comfortable and swift the drive was on the F1 track and on the contradictory image, when driving on normal roads, we know how effortful the drive is. This chapter brings into the picture the differences between both thinking tracks and then paint the methodology of each track, so that we come to know when we should think slowly and when to think fast and how choosing among these tracks helps in developing better guidance for problem solving. Problem-solving sets, up to a point, were amenable to study from an associative-­learning (behavioristic) perspective. That is, sets could be conceived (and experimentally investigated) as the product of a variable number of learning (conditioning) trials, as subject to extinction (from counter-set problems) and “spontaneous recovery” (Roger and Lyle 1994).

6.2  What Thinking Is and Why It Helps in Problem Solving Nobody can think straight who does not work. Idleness wraps the mind. Thinking without constructive action becomes a disease. – Henry Ford

Just ask yourself a question: What is thinking? The very first thing that we do when we try to find the answer to the question, ‘What is thinking,’ is to start decoding the question and start thinking. Well, it is ironical as you are thinking to answer the question, which is asking to define thinking. Do we still need the answer for the question? We have undergone through the process of thinking while answering it. In layman terms we can define thinking a sense of act that we do either to reach a conclusive answer or trying to picture something new. If you would have observed closely when trying to figure out the answer to the question asked earlier, that when we encountered the first word “What,” our mind immediately decoded the meaning that we are being asked something here. Then, when we encountered the word “Thinking” and when we had the entire picture that we are being to define thinking,

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then we did not get our answer immediately, instead there was some delay in the process of arriving at the answer. Throughout our life from birth until now, we have seen many things and heard many things and also learned many things, and all these things have developed biases in our mind. Biases can be defined as the stored information in our mind which we have acquired until now, such as the meaning of the word “What” here. We instantly knew we were being asked a question and even when we encountered the word “Thinking” we knew the meaning, but when you put both the pieces together and figured out that what do you mean by thinking, we started to take a moment and tried to get the answer and this process was certainly not an instantaneous one, as we often encounter the word “what” and “thinking” but we are certainly not asked frequently to define thinking. All these biases have a larger effect in determining the time taken for the thinking process to complete and also the quality of our solution. Looking at it more closely, the biases not only affect the thought process time but also determine which track of thinking we are using, or rather, you tend to follow one of the tracks unknowingly. Consider the same question, we were able to decode the meaning of “what” quickly as we have been exposed to this word many times, so this bias had an effect on decoding time: that is, it enabled fast thinking, as you got your answer swiftly, without any effort. Whereas when we are asked the question “What is thinking,” we do not get an instant answer and we have to give some effort to thinking what the best answer can be. The main reason for taking so much time to figure out the answer is because we do not have the bias for this question. That is, our mind has not stored the answer for this question, as we have not been asked it so frequently. This circumstance enables the slow thinking track in our mind, when we are thinking. We often tend to overlook or mostly do not reason out why we take less time for some questions and more time even if the other question is relatively easy, but it because of the exposure which our mind has had throughout life. A creative activity that requires the interplay of divergent and convergent thinking is creative problem solving—the cognitive process of searching for a novel and inconspicuous solution to a problem (Ritter & Mostert, 2017). In Fig. 6.1, the plan of approach to a solution is illustrated. The plan of action is a significant aspect in the journey of problem solving as the plan of action defines the route to the solution. As happens in our life, if we do not take the right route to our house, then we might land up somewhere else. Similarly, if we miss out on deciding the correct path and do not execute the right plan of action, then we might not reach the correct solution. Albert Einstein once said “If I had an hour to solve a problem, I’d spent 55 minutes thinking about the problem and 5 minutes thinking about solutions.” As rightly said, the initial step to problem solving should be defining your problem statement: looking out for the exact problem, as superficially the problem might be very generic but when deeply assessed we might get to know the real loophole, the missing element, the reason for the problem. Once we have clearly defined the problem, we exactly know what we have to solve. Given a situation, we do not define our problem statement and start proceeding with the solution. As said earlier, superficially a problem looks very generic, but when we look into it, we get to the core of the problem; so, if we do not assess the problem correctly then there

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Fig. 6.1  Problem-solving plan of action

Problems

Thinking

Solution

Fig. 6.2  Significance of thinking

is high probability that you started walking on the wrong track that will lead nowhere but to a dead end and which will certainly will not be the solution. The next step in the flow is devising the course of action and the solution. The key to reach the best solution for a given problem is nothing but the course of action. If we make the best plan of action, we are closer to the best solution. Then, in the final step we check the quality of our solution by implementing it. Thinking provides us various filters through which we can see the problem and crack it. Figure  6.2 shows the importance of thinking while performing problem solving. A problem is always associated with chaos, as when we are struck with a problem we are not really aware what to do with it, which leads our mind to a disturbed state. As the figure shows, a problem is shown as random curved lines, that is, showing the degree of disorder in our thoughts regarding the problem. Thinking helps us in streamlining the thoughts related to the problem and in trying to reduce the complexity of the problem by unwrapping it. The curved lines show a higher

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degree of disorder, but when shifted to the thinking domain, they tend to become more streamlined and the degree of disorder decreases. This illustration indicates that the thoughts related to the problem were all jumbled and highly irregular in fashion, but when we started thinking, a force was generated that caused the thoughts to become streamlined: when we think, we can see a connection between the thoughts coming to our mind and, when we place them in chronological order, a course of action is indicated. When we have an ordered picture in our thoughts, it shows us a plan of action in one frame. Thereby, thinking helps to decrease the chaos in our mind and provides us with a framework for our thoughts that gives a multi-dimensional view to our solution. Then the streamlined thoughts can convert the ideas to a solution and, as shown in the figure, in the solution domain the straight lines indicate that when we have a solution to a problem we are very clear with our thoughts with very little disorder and chaos in our mind related to that problem. It is also seen that the state of your mind widely affects the thinking process and the kind of solution you reach. Individuals in a positive mood state engage in heuristic information processing (Schwarz & Clore, 1996) and avoid risk in decision making (Isen & Patrick, 1983). Individuals in a negative mood state do the opposite (Schwarz, Bless, & Bohner, 1991). Individuals experiencing a positive mood state prefer simple, intuitive solutions to problems, use broad categories in classification tasks (rather than specific categories), and make decisions more quickly on the basis of less information, relative to individuals in a neutral mood state (Isen & Daubman, 1984). In a negative mood state, when a situation is problematic people tend to process information more systematically because they lack confidence in their own judgements (Edwards & Weary, 1993). Thus, our mental state or mood adversely affects our capability in the decision-making process for problem solving (Sachi & Damodar, 2014).

6.3  Slow Thinking and Why Do We Not Think Slowly? Earlier, we drew an analogy between the slow thinking process and driving on the normal roads of our town or cities. Driving on the roads is not easy as we have humps, potholes, pedestrians, sudden alerts, rash drivers, and so on. Similar to driving on roads affected by interrupts, slow thinking also has its own characteristics. A problem in any field can hinder the process of problem solving. Ever wondered why some problems feel very easy to solve when compared to others? The reason is that if we faced similar problems earlier we have developed the biases to those problems, which helps us in developing a solution. For new problems, we have no prior solution knowledge, so when we face these for the first time we start thinking slowly to find an answer. These biases act like cache memory, storing the information that we often require, so for each access to that information, the microprocessor needs not access main memory, which consumes more time. Slow thinking methodology for problem solving provides a very detailed workflow and gives us a multidimensional approach to a solution. In this flow we have to determine the answers to many

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questions to find a solution. Answering all the questions takes time and thus is termed slow thinking. The various characteristics of this path are very important to shape the track and define the importance of this thinking approach.

6.3.1  Subjective Illusion Breaker The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.  – Stephen Hawking

Let us consider painting a picture. We might use many colours while painting, and similarly in slow thinking we answer many questions before we arrive at the solution. So, although we might think we have finished the picture but actually we are developing an illusion. Similarly, we think we have found a solution that is right but when we try it we can see the caveats. Slow thinking helps to break that illusion in our mind that pleases ourselves we have reached the answer but in reality we have not thought well enough. We may feel our picture is complete but someone else can provide better minute details. Similarly, we try to develop an illusion that we have the solution although we have not really questioned all the perspectives. This action of our brain is involuntary. The path of slow thinking has more resistance: you need to question from different angles. So finally, when we have covered all the questions related to the problem and have all the answers, the picture is completely painted. This process requires time because we are not impulsively trying to find the answer but instead are trying to understand the problem better by getting a 360° view. We can see the difference between seeking the answer impulsively and trying to paint the problem by covering all possible questions to be answered. The illusion is defined subjectively as this illusion is only a mere layer we develop in our mind unknowingly. If we try to put it in mathematical terms, then slow thinking can be defined as function of max (number of questions). The better we define and understand our problem, the clearer will be our thinking of the best solution path.

6.3.2  Surprises and How Such Triggers Slow Thinking It is often observed that the inner core of the human body remains stable when the person is not encountering surprises. We often encounter surprises with an impulsive thought such as Hey! What? How come this? And this takes us by a sudden change in our core self, which affects our thinking process. Surprises are usually not expected by anyone. For an instant when we encounter a problem, a few elements of the problem may take us by surprise. As discussed earlier, the effect of biases in the thinking process is that we are surprised we definitely do not have any background related to the surprise elements and so we are stuck. Then the slow thinking process steps in, and as we do not know the answer, we investigate the solution. This

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triggering of slow thinking initiates the deep thinking process, which is very effortful as we try to interpret each and every aspect of the problem.

6.4  Fast Thinking and Why Do We Think Fast? We often think that fast thinking is somehow associated with cleverness, a witty nature, and how intellectual you are. Instead, fast thinking is one of the involuntary actions; whenever we come across any problem, as we humans can believe in procrastination but certainly our mind hates procrastination, so every time we come across any problem and we have no biases related to it, our brain tries to develop a solution layer as fast as possible, impulsively. For this reason, we often tend to answer impulsively every time we are asked something. In Fig.  6.3 is shown the mechanism of fast thinking. Initially when we have a problem to solve, we are bombarded with many questions regarding the problem, covering almost every aspect. The initial state is represented in Fig. 6.3 as an ideal state. When we first see the problem we have many questions in our mind and to solve the problem we need to find answers to all these questions. Fast thinking acts as a minimizing function, in that it looks for answers for the minimum questions frame a solution; if we can pick out such questions and just try to get the answers to these, we are thinking in fast mode. The reason to switch to the fast-thinking mode is when we face a problem we are in a hurry to break through to a solution or at least to a start to the solution path. So, if for example ten questions come to our mind related to the problem, involuntarily we try to choose a minimum number of these ten questions to answer, which will help us in framing a general picture of the solution. The fast-thinking mode is called the minimising mode as it is very impulsive in nature, requiring minimum questions to be answered, thus reducing thinking time. The selection of the minimum questions to be answered depends on the priority of the questions to be answered and also on the individual’s thought process, which

Fig. 6.3  Fast-thinking mechanism

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questions they want to answer to paint the general picture of the solution. This choosing is a very big loophole in the fast-thinking process: we might be able to pick up four (for example) questions of the ten questions that came to our mind related to the problem, and even if we have the answer to those four questions and can paint a general picture of the solution and the path to the solution, we will miss some details. We have picked four of ten questions to get the sense of the solution and could answer those four questions but not the remaining six questions. As the remaining six questions were also related to the problem and covered certain aspects of the problem, which we should have taken into consideration for the solution, because we were in fast-thinking mode we skipped those six questions and are not aware of the information those could have provided regarding the problem and its effect on the solution and problem-solving process. Even if we were able to paint the solution picture, we would have missed out on the details. The solution to avoiding this loophole is to keep reiterating your solution development process. We should try to answer all the questions related to the problem. We can answer the questions in batches, thus triggering fast thinking by answering a small set of questions. As we are solving all the questions, we are also covering all the aspects related to the problem that help us in problem solving. Dividing questions into batches depends upon the interrelationship between the questions, so we divide those questions into batches related to each other.

6.4.1  Perturbation Theory Supports Fast Thinking In simple words, the perturbation theory suggests that, when we have a complex system to be solved, for example, a complex set of equations that we are not able to solve, then we can solve a simpler set of equations, whose characteristics are the same as that of complex system equations, and the solution are approximately the same. On the same line, when we face any problem and we have no idea how to proceed towards a solution, we try to draw a link between our thoughts and the problem and map the problem into those terms that we know. After we have mapped the problem, we try to find a solution. This entire process comes under fast thinking, as we are focussing much more on getting the answer, by mapping the problem onto simple terms instead trying to figuring out the problem in its actual form. Generally, when we get stuck on something, we think of a solution very quickly to get the obstacle out of the way, certainly not focussing on the quality of the solution. It may happen so that if we had thought about the problem much more deeply and given it more time rather than hurrying into the solution, we could have solved the problem more efficiently (Mohanty & Suar, 2014). As we played the perturbation theory card involuntarily to accelerate our thinking process, we might overlook some important elements of the problem, but later when we proceed with the acquired solution, we might realize the caveats in our solution and then reform our solution. This looking back takes time, and restructuring your entire process according to your newly optimised solution is a new task. We humans are macroscopic beings who certainly

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cannot deploy the superposition phenomenon of quantum physics, where we can simulate all the solutions and then proceed with the best solution. Depending upon every individual, we humans tend to deploy superposition up to some degree in our minds, where we think of a problem from a multidimensional view and investigate every solution, but we cannot use superposition to the full extent. This behaviour of our mind, of developing a solution of a complex problem by devising a relatable simple problem and solving the simple problem, might save some time at the initial stage of problem solving (Dominowski & Bourne (1994). We might be able to get the answer very quickly and demonstrate the efficiency of the solution but later we might review that solution when loopholes appear in our solution.

6.4.2  You Got Skills: You Can Think Fast Over time, when we do a specific thing all the time, we tend to master that thing. Later we have developed a skill in doing that particular thing and when we face any problem in the domain involving that thing, we instantly come up with the solution as we know in and out of it. Similarly, when we are trying to solve a problem and when the problem involves many elements, we can get over those elements very quickly in which we have developed skills. Take a simple example: suppose the refrigerator in our house stops working. We might not be able to solve the problem as we do not know any of the inner workings of the refrigerator, or even if we do know the inner workings, we would not have repaired refrigerators as frequently as the mechanic would have done. So, the mechanic can see the problem and the solution quickly when compared to a layperson who also knows the inner workings of a refrigerator. The reason that the mechanic was able to think faster and come up with the solution was he has repaired so many refrigerators and faced all sorts of problems and know the solutions. He has developed a skill that triggers his fast thinking so he can see the problem clearly and give the solution. A person who also knows the inner workings of the refrigerator might also be able to solve the problem but needs much more time. So, the skills we develop also develop the related biases so whenever a problem arises involving that element, an individual will be instantly able to relate to the bias and find the correct solution. Examining the influence of expertise can provide some possible explanations for the development of intuition. However, it must be remembered that although expertise and intuition are related constructs, they are not synonymous; intuition is capable of influencing performance beyond the effect exerted by expertise alone (Eubanks, Murphy, & Mumford, 2010).

6.4.3  Intuition Accelerates Fast Thinking The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honours the servant and has forgotten the gift. – Albert Einstein

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Over our lifetime up to now we have developed our armour of intuition through perceiving various things, and this triggers our intuitive behaviour. From childhood, we have learned through various means, and one such way was through feedback, where we did something and then were told either it should be done like this or do not do this; such feedbacks have monitored our thoughts and reactions since. Most of us have done something even if our mind was us telling not to do it, but our strong intuition made us do that. All these feedbacks gained until now indirectly help us in coming up with those strong instincts. Every feedback that we gained and the larger number of times we received that same or similar feedback have helped us define our basic thinking level. When we face a problem, our mind automatically deploys the mechanism to classify the problem and map it to past feedback: if there is a match we apply intuition and do a certain thing a certain way. Intuition may influence problem solving by triggering unconscious associations between contextual cues and salient affective experiences (Simonton, 1980). It is often said that “Intuitions are the best hypothesis,” because intuitions are related to our subconscious minds and we humans might not clearly understand the calculations going on in our subconscious mind. Thus, when we have intuitions we must trust them as intuitions result from calculations going in our subconscious mind: these calculations involve how we perceive things, the effect of understanding a particular topic in our behaviour and other norms seen in our life. All these intuitions and norms helps us in thinking fast, as by having intuitions related to a solution of a problem, we can predict how to go along this solution discovery path. So, such thoughts as “It can be like this?”; “What if it’s this way?” All these thoughts indicate that we are trying to make a prediction. So, all these predictions, intuitions, are very fast and automatic in nature, which thereby make us come to a solution very quickly. The correctness of a prediction can only be seen when employing our solution and seeing if it really solves the problem; even if does not actually solve the complete problem, predictions as the result of intuitions always help us to solving a problem, as it is said a “good start implies that half our work is done.” Daniel Kahneman believes that expert intuition can sometimes be accurate: this occurs when there is “an environment that is sufficiently regular to be predictable” and “an opportunity to learn these regularities through prolonged practice (Kahneman, 2011).

6.5  How Thinking Speed Affects Problem Solving At last we come to the core of the chapter, where we discuss the effect of thinking speed on the problem-solving process. You are quite familiar with both thinking tracks and how they work. We have discussed both tracks and explained their working, but when we work on a problem we have both the tracks available. Initially when we have a problem, the fast-thinking track comes into play, as it is automatic in nature and highly impulsive. Our mind switches to the fast-thinking mode, and if we cannot break through the solution path, the slow-thinking track is used. The call for slow thinking depends on the individual’s choice, as if the person is satisfied

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with the suggested solution from the fast-thinking track, they can overlook the command to switch to slow thinking and eliminate the problem, whereas if they still want to get the complete solution, they will think deeply regarding the problem by activating the slow thinking track. Through this, they will generate the complete solution. The slow-thinking track always has the final say in the problem solving as whenever the fast-thinking mode gets into a problem, it calls the slow-thinking mode into action. Individual ability to solve a problem efficiently depends upon the capability to effectively switch between both tracks to get the best solution. Therefore, for optimised performance and high yielding solutions, we need to tune the tracks accordingly. Figure 6.4 shows the imagery illustrating difference between slow thinking and fast thinking. Initially we have a problem and then we have a composite function of two tracks, slow thinking and fast thinking. The quality of our solution depends up how effectively we have switched between and tuned both the tracks. In Fig. 6.4, the slow-thinking track is drawn through random continuous curved lines: there is only one line that is curved at different places in irregular fashion. You can also see that there is only one starting point and one ending point, and in between these there are many irregular curved paths. We deploy slow thinking to a problem at a given time instant and we get a corresponding output. The irregular curved lines indicate the complexity of the slow-thinking process: while performing slow thinking we examine all the aspects related to the problem. Each curved edge represents every question and thought that comes to our mind, and because all these thoughts and questions are related to a single problem we represent it by a single line. So, when we try to solve one question, it will lead to another question and this goes on until we have tackled all the questions and devised a solution. The irregular fashion denotes the chaos in our mind and how eager we are to solve the problem, as when an individual is addressing a problem, they are really caught up with it. Suppose you try to follow the path from the starting point to the end arrow for slow thinking track, we will take time to reach the final point. This indicates that slow thinking

Fig. 6.4  Slow thinking versus fast thinking

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takes up time, but with a clear and optimised approach we will reach an optimum solution. On the other hand, we see a straight line for the fast-thinking track, indicating that we come to come to the final point, the end arrow, very quickly, but the absence of curve indicates that we really have not touched all aspects related to the problem. So, the solution we have devised is not the solution to the superficial layer of the problem and or for the complete problem. Further, if we follow the track from starting point to endpoint, the end arrow, we will reach it very quickly when compared to slow thinking. We tend to save time here, but when we deploy our devised solution to evaluate the solution, we may discover that our solution is incomplete and must turn back and restart the thinking process and debug our process to find the problem. If we cannot figure out the loophole in our solution, we will have to restart the problem-solving process. Thereby, we might save time initially when figuring out the solution, but later when we realise that our solution is not complete, we will have to spend time again to see through the solution and probably more than the time required had we used the slow-thinking track.

6.6  Conclusion Through the entire chapter we had started with what thinking actually is and how we perceive it. Then we came to the checkpoint where we discussed slow- and fast-­ thinking tracks. We discussed both the tracks in detail, and how our mind fools us through illusions, and various scenarios were discussed. Every time we encountered a loophole in the approach a solution was given. Then we closely examined how we can optimise the problem-solving process by fusing both tracks together and effectively jump between them to get the high-yielding solution to the given problem.

References Dominowski, R. L., & Bourne, L. E. (1994). History of research on thinking and problem solving. In Handbook of perception and cognition, thinking and problem solving (Vol. 2). New York: Academic Press. https://doi.org/10.1016/B978-­0-­08-­057299-­4.50007-­4 Edwards, J.  A., & Weary, G. (1993). Depression and the impression-formation continuum: Piecemeal processing despite the availability of category information. Journal of Personality and Social Psychology, 64(4), 636–645. Eubanks, D. L., Murphy, S. T., & Mumford, M. D. (2010). Intuition as an influence on creative problem-solving: The effects of intuition, positive affect, and training. Creativity Research Journal, 22(2), 170–184. Isen, A.  M., & Daubman, K.  A. (1984). The influence of affect on categorization. Journal of Personality and Social Psychology, 47(6), 1206–1217. Isen, A. M., & Patrick, R. (1983). The effect of positive feelings on risk taking: When the chips are down. Organizational Behavior and Human Performance, 31(2), 194–202. Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus, and Giroux.

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Mohanty, S.  N., & Suar, D. (2014). Influence of mood states on decision making under uncertainty and information processing. Psychological Reports (SAGE), 115(1), 44–64. https://doi. org/10.2466/20.04.PR0.115c16z2 Ritter, S. M., & Mostert, N. (2017). Enhancement of creative thinking skills using a cognitive-based creativity training. Journal of Cognitive Enhancement, 1, 243–253. https://doi.org/10.1007/ s41465-­016-­0002-­3 Schwarz, N., Bless, H., & Bohner, G. (1991). Mood and persuasion: Affective states influence the processing of persuasive communications. In M.  P. Zanna (Ed.), Advances in experimental social psychology (Vol. 24, pp. 161–201). San Diego: Academic Press. Schwarz, N., & Clore, G. L. (1996). Feelings and phenomenal experiences. In E. T. Higgins & A.  W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp.  433–465). New York: Guilford Press. Simonton, D.  K. (1980). Intuition and analysis: A predictive and explanatory model. Genetic Psychology Monographs, 102, 3–60.

Chapter 7

Decision Making in Positive and Negative Prospects: Influence of Certainty and Affectivity Sachi Nandan Mohanty and Suneeta Satpathy

7.1  Introduction This chapter aims to examine the choice of individuals in certain and uncertain options and both uncertain options in gain and loss domains. It also tests whether certainty and affectivity influence the individuals’ decision. The maximization of expected utility as a guide to understanding decision behavior was first suggested by Bernoulli (1986) after observing that individuals do not value risky prospects at their expected values. Von Neumann and Morgenstern 2nd (1947) provided an axiomatization of Bernoullian utility theory. Accordingly, individuals choose an alternative in a decision task with the highest expected utility. The relevant attributes of the utility function are monetary gain and losses. The utility of a choice is the probability multiplied by the monetary value, summated over all options. In the domain of losses, people are averse to risk that shows concave utility function (Friedman & Savage, 1948). Prospects are judgments in terms of their probability distributions over gain and losses (Von Neumann & Morgenstern 2nd, 1947). Kahneman and Tversky (1979) proposed an alternative theory, called ‘prospect theory (PT),’ that includes empirically observed tendencies. People generally do not weigh the utility of outcomes by their respective probabilities as assumed in expected utility theory. Instead, a tendency exists to overweigh certain outcomes, relative to those that are uncertain. This tendency is referred to as the certainty effect. When a choice between two positive prospects (only gains) is compared with its mirror image choice between the two corresponding negative prospects (only S. N. Mohanty (*) Department of Computer Engineering, College of Engineering Pune, Pune, Maharashtra, India S. Satpathy Department of Computer Science & Engineering, College of Engineering Bhubaneswar, Bhubaneswar, Odisha, India

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losses), individuals typically reverse their preferences. This phenomenon is called the ‘reflection effect.’ To simplify, in risky decision making, people typically disregard common components between alternatives, focusing instead on elements that distinguish the alternatives. This tendency is termed as the ‘isolation affect.’ All the foregoing phenomena are inconsistent with what would be predicated by traditional utility theory. To explain these and other tendencies, Tversky and Khaneman (1992) formulated the PT that differs from the expected utility theory. Differing from the utility theory, which is unique up-to positive linear transformations, PT defines a value function on ratio scale (i.e., unique up-to positive ratio transformations). Furthermore, this value function does not measure attitudes toward risk but only the value of outcomes under conditions of certainty. Instead of the objective probabilities used in expected utility theory, PT utilizes decision weights that reflect the outcomes. Low probabilities are overweighted and high ones generally are underweighted. Individuals prefer certain options in positive prospects and uncertain (risky) options in negative prospects. PT treats choice situations involving pure risk differently from those involving speculative risk in which gains and losses are combined. Finally, the value function in PT measures the subjective value of outcomes relative to some references point that may vary as a function of problem presentation. The emphasis is on changes in wealth or assets, not on final asset positions as the utility theory. These findings warrant investigation in a different cultural context, different sample, and a different time period to repose the confidence of researchers in earlier findings. Based on foregoing discussion, the following hypothesis is formulated: H1: Most individuals will prefer risk aversion in positive prospects (certainty, isolation and reflection effects) and risk seeking in negative prospects. The information processing theory mentions that the individual faced with a stimulus situation searches for information to reduce uncertainty about the stimulus situation (Fowler, 1965). Putting it otherwise, increase in certainty of the stimulus situation depends on the availability of information. The extreme choices and the shift in choices on risky and cautious situations following group discussion and social comparison of choices in groups are found to be positively associated with certainty and shift in certainty, respectively (Suar, 1992). This finding suggests that the extent of certainty/confidence in a choice varies directly with the extent of information in favor of that choice. Evidence affirms that people feeling uncertain about choices process information more systematically than do people who consistently feel certain (Weary & Jacobson, 1997). Those individuals tend to process information more systematically because they do not possess adequate information in favor of their choices (Edwards, Weary, von Hippel, & Jacobson, 2000). The extent of certainty and confidence is the introspective conviction regarding the rightness of preferences based on information. Because feeling certain is an internal cue that one is already correct and accurate, it may also suggest that further processing is not necessary. We extend this reasoning and suggest that any choice behavior associated with the feeling of certainty suggests more information possession and processing and that any choice associated with the feeling of uncertainty

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suggests less information possession and processing. Based on the foregoing discussion, we proposed the following hypothesis: H2: The more the possession and processing of information in favor of choices, the more will be the certainty in those choices. Several lines of research suggest that a decision maker’s moods can influence risk taking. It has been found that when the probability of winning is low, individuals in a happy mood bet less and when the probability of winning is high, they bet more (Isen & Patrick, 1983). When the stakes are high, subjects in a happy mood set higher probability levels for winning as the minimum necessary for accepting a bet; and for lower stakes, they set lower probability levels. The influence of moods on risk-taking behavior suggests the direct impact of positive affect on cognitive information processing (Isen & Daubman, 1984). People experiencing positive moods tend to recall positive words, personal experiences, and events. The reverse is true when people are in negative moods (Anderson, 1974; Bower, 1981; Isen & Daubman, 1984). Such literature suggests that people experiencing positive affect try to protect and maintain their positive state (Isen & Simmonds, 1978) and attempt to avoid substantial losses (Arkes, Herren, & Isen, 1988). Positive affect promotes positive evaluations and negative affect causes negative evaluations (Forgas & Bower, 1987; Forgas et  al. 1990). According to this view, the decision maker’s response to risk stimuli depends on the gambler’s stakes: when the stakes are high, subjects in positive mood are more risk averse to avoid a large loss and when faced with low-risk stakes, they seek risk so that they can benefit from the gain without putting too much on the line. Mood captures the day-to-day feelings that people experience and is not focused on any particular object, event, individual, or behavior (Gasper, 2004; Isbell, 2004). Affect can be measured as a trait or a state (Watson, Clark, & Tellegen, 1988). In trait form, affect is more stable and can be positive affectivity (PA) and negative affectivity (NA). The same can be used in state taxonomy, but a person’s state fluctuates regularly, capturing how people feel from moment to moment, hour to hour, and day to day. Transferring the mood states to traits, the following hypothesis is formulated: H3: Higher positive affectivity will promote risk aversion and higher negative affectivity will promote risk seeking in decision making. Researchers have pleaded that certainty and affectivity effect information processing. Research has generally demonstrated that high positive affectivity leads to heuristic processing. On the other hand, those people having negative and neutral affectivity search for quality of arguments and appear to elaborate and process information more systematically than those people having positive affectivity (Bless et al., 1996; Mackie & Worth, 1989; Schwarz & Bless, 1991). High negative affectivity and more certainty will decide the extent of choice. In other words, we intend to examine whether high levels of certainty and negative affectivity jointly associated with information processing determine the choice under uncertainty. On the basis of the foregoing discussion, the following hypothesis is proposed: H4: More negative (positive) affectivity and certainty in the choice will increase (decrease) more information processing to determine the choice.

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7.2  Method 7.2.1  Participants Four hundred (male = 310, female = 90) undergraduate and post-graduate students from engineering and the management streams of the Indian Institute of Technology Kharagpur, West Bengal (India) participated in the study. With the prior permission of the concerned teacher, in each class of about 40 students, the researcher briefed about the purpose of the study and informed content was sought from the participants. Those who signed the informed consent and agreed to participate were given the questionnaire during the class hour to fill out and return to the researcher. The participants took about 40 min to complete the questionnaire. The sociodemographic profiles of male and female students were compared using the F test, and their gender composition and birthplace were compared using a χ2 test. More male students participated in the study compared to female students because of the high enrollment ratio (>0.80) of male students in engineering and management education. Most of the boys were natives from urban areas and the girls were from semi-urban areas. Very few male or female students were from rural areas. The female students were a little older than their male counterparts and had studied more years in formal educational institutions than male students. Of the total, 79.5% of the students had no job experience and the reaming students had a maximum of 4 years of experience. Both male and female students were predominantly from a nuclear family, having from one to five members, and a few were from joint/extended families. The average annual income of parents and family members of male and female students did not differ. On average, the annual income varied from as low as 5000 to as high as 6500 lac Indian rupees (Table 7.1).

Table 7.1  Sample profile Characteristic Gender Birth place Urban Semi-urban Rural Age Years studied Job experience Family size Income (in INR)

Descriptive statistics N (%) N (%)

M (SD)

Male 310 (77.50)

Female 90 (22.50)

χ2 121.00***

168 (54.20) 86 (27.70) 56 (18.10) 20.81 (4.50) 14.99 (3.06) 0.64 (2.81) 4.67 (1.85) 466399 (771448)

38 (42.2) 43 (47.80) 9 (10.00) 23.61 (3.97) 17.73 (3.52) 1.02 (1.93) 4.69 (1.34) 530888 (988621)

13.42***

INR Indian rupees, *p