The Wiley Blackwell Handbook of Judgment and Decision Making

By Gideon Keren

• A comprehensive, up-to-date examination of the most important theory, concepts, methodological approaches, and applications in the burgeoning field of judgment and decision making (JDM)
• Emphasizes the growth of JDM applications with chapters devoted to medical decision making, decision making and the law, consumer behavior, and more
• Addresses controversial topics from multiple perspectives – such as choice from description versus choice from experience – and contrasts between empirical methodologies employed in behavioral
  economics and psychology
• Brings together a multi-disciplinary group of contributors from across the social sciences, including psychology, economics, marketing, finance, public policy, sociology, and philosophy

2 Volumes

• Language: English
• Publisher: Wiley
• Release date: Nov 30, 2015
• ISBN: 9781118912799

Book preview

Table of Contents

Cover

Volume 1

Title Page

Contributors

1 A Bird’s-Eye View of the History of Judgment and Decision Making

Some Early Historical Milestones

The Initial Period, 1954–1972 (Handbook of Judgment and Decision Making, 1974)

The Second Period (1972–1986) (Handbook of Judgment and Decision Making, 1988)

The Third Period (1986–2002) (Handbook of Judgment and Decision Making, 2004)

The Fourth Period (2002–2014) (Handbook of Judgment and Decision Making, 2015)

A Bird’s-Eye View

References

Part I: The Multiple Facets of Judgment and Decision Making: Traditional Themes

2 Decision Under Risk: From the Field to the Laboratory and Back

Introduction: From the Field to the Laboratory

Modeling Risky Choice

Alternative Behavioral Models of Risky Choice

From the Laboratory to the Field

Conclusion

Acknowledgments

References

3 Ambiguity Attitudes

Introduction

Ellsberg Urns and Other Operationalizations of Ambiguity

Stylized Facts From Laboratory Experiments

Evidence on External Validity of Laboratory Measures

Conclusion and Outlook

Acknowledgments

References

4 Multialternative Choice Models

Introduction

Facets of the Choice Situation

Process Distinctions

Choice Models

Summary

References

5 The Psychology of Intertemporal Preferences

Introduction

Discounting Behavior

Psychological Determinants

Applications of Discounting to Decision Making

Conclusions

Acknowledgments

References

6 Overprecision in Judgment

Introduction

Research Paradigms

The Balance of the Evidence

Ecological Evidence of Overprecision

Moderators of Overprecision

Explanations

Underprecision

Debiasing Overprecision

Incentive-Compatible Scoring Rules for Eliciting Precision in Judgments

(Mis)Perceiving Expressions of Confidence

Future Research

Coda

References

Part II: Relatively New Themes in Judgment and Decision Making

7 Joint versus Separate Modes of Evaluation: Theory and Practice

Introduction

Clarifying Several Issues Concerning Evaluation Mode

General Evaluability Theory

Implications of Evaluation Mode and Evaluability

References

8 Decisions from Experience

Introduction

The Description–Experience Gap in Risky Choice

Sampling Error and the Description–Experience Gap

Search Policies and the Description–Experience Gap

The Anatomy of Search in the Sampling Paradigm

Models of Decisions From Experience

Probability Weighting and the Description–Experience Gap

Beyond Monetary Gambles and Beyond a Simple Dichotomy

The Description–Experience Gap and Risk Communication

Let Us Not Give Descriptions Short Shrift

Conclusions

Acknowledgments

References

9 Neurosciences Contribution to Judgment and Decision Making: Opportunities and Limitations

Introduction

Methods

Individual Decision Making

Social Decision Making

Limitations

The Future

References

10 Utility: Anticipated, Experienced, and Remembered

Historical Background

Components and Judgments of Experienced Utility

Measuring Instant and Total Utility

Context Dependence

Maximization Failures

Summary

Acknowledgments

References

Part III: New Psychological Takes on Judgment and Decision Making

11 Under the Influence and Unaware: Unconscious Processing During Encoding, Retrieval, and Weighting in Judgment

Introduction

Defining Unconscious Influences

Unconscious Influences on Three Aspects of Judgment

A Controversy: Questioning Unconscious Effects on Judgment

Scaling Up Models of Unconscious Decision Making

Conclusion

Acknowledgments

References

12 Metacognition: Decision making Processes in Self-monitoring and Self-regulation

Introduction

Metacognitive Processes During Learning

Metacognitive Processes During Remembering

Retrospective Confidence in One's Answers and Judgments

Conclusions

Acknowledgments

References

13 Information Sampling and Reasoning Biases: Implications for Research in Judgment and Decision Making

Introduction

Manifold Reasons for Biased Sampling

Sampling Errors and Biases in Judgment and Decision Making

The Ideal of Unbiased Sampling in a Representative Design

Unequal Sample Size as a Source of Illusions

Impact of Hedonic Sampling

Unbiased Sampling as a Source of Bias: The World We Live In

Concluding Remark

Acknowledgment

References

14 On the Psychology of Near and Far: A Construal Level Theoretic Approach

Introduction

Mentally Traveling Across Psychological Distance

On High-Level and Low-Level Features

Overview of Empirical Evidence

Impact of Distance-Dependent Construal on Prediction

Impact of Distance-Dependent Construal on Preferences

Distinguishing CLT from Other Theoretical Approaches

Concluding Thoughts

References

15 Optimism Biases: Types and Causes

Introduction

Terms and Effects

Optimism in Studies Involving Self–Other Comparisons

The Desirability Bias

Conclusion

Acknowledgment

References

16 Culture and Judgment and Decision Making

Introduction

Risky Decision Making

Risk Perception

Intertemporal Choice

Consistency Between Preferences and Choices

Causal Attributions

Conflict Decisions

Confidence Judgments

Optimism

What Counts as a Decision?

Insights from the Constructivist Approach to Culture and JDM

Future Research Directions

Conclusion

References

17 Moral Judgment and Decision Making

Introduction

Attempts to Understand Moral Judgment and Decision Making: Major Research Themes and Their Explananda

Methodological Desiderata

Understudied Areas

Concluding Remarks

Acknowledgments

References

Volume 2

Title Page

Contributors

Part IV: Old Issues Revisited

18 Time-pressure Perception and Decision Making

Introduction

Impact of Time Constraints on Decision Making

Time-Pressure Perceptions

Time-Pressure Applications

Conclusion

References

19 Cognitive Hierarchy Process Models of Strategic Thinking in Games

Introduction

Background: What are Games, and What is Game Theory?

Conclusions

Acknowledgments

References

20 Framing of Numerical Quantities

Introduction

Origins and definitions

Chapter themes

Risky-Choice Framing

Attribute Framing

Frames as Part of the Communication Process

Who is Susceptible to Framing?

Framing on Unipolar Scales

Suggestions for Future Research

Conclusions

References

21 Causal Thinking in Judgments

Introduction

Basic Causal Concepts and Distinctions

Fundamental Properties of Causal Reasoning

Elementary Causal Inferences

Probabilities From Causes

Causal Schemas

Predictions and Diagnoses in Multiple Cause–Effect Schemas

Judgments of Causes and Effects in a Common-Effect Schema

Scenarios and Multi-Causal Situation Models

Causal Cognitions Play a Major Role in Many Researched Judgments

Causal Reasoning in Choices and Decisions

So what?

Acknowledgments

References

22 Learning Models in Decision Making

Introduction

Learning and Risk Taking

Strategy Selection

Summary and Conclusion

References

23 Variability, Noise, and Error in Decision Making Under Risk

Introduction

Basic Framework

Extraneous Noise/Error

Intrinsic Variability

Interactions Between Different Approaches and Challenges for the Future

Concluding Remarks

Acknowledgments

References

24 Expertise in Decision Making

Introduction

Defining Expertise

Research on Expertise: Expertise is Schematic

The Role of the Environment in the Development of Expertise

Is General Decision Making Expertise Possible?

Shortcomings of Expertise

Using Expertise

Future Directions

References

Part V: Applications

25 Changing Behavior Beyond the Here and Now

Introduction

Intervention–Behavior Lag

Marginal Benefit to Continued Treatment

Persistence

Conclusion

Acknowledgments

References

26 Decision Making and the Law: Truth Barriers

Introduction

The Law Hinders Accurate Decision Making

Intellectual Deficits of Legal Participants: Harmful Effects of Innumeracy

Cognitive Biases

Conclusion

References

27 Medical Decision Making

Introduction

Biases and Heuristics and the Effect of Debiasing

The Role of Uncertainty

The Role of Affect

Affective Forecasting

Decisions for Oneself Versus Decisions for Others

Nudging

Literacy and Numeracy

Support for Complex Decisions: Patient Decision Aids

Areas for Future Research

References

28 Behavioral Economics: Economics as a Psychological Discipline

Introduction

Public and Health Economics

Industrial Organization and Consumer Decision Making

Labor and Education Economics

Development Economics

Urban and Environmental Economics

Macroeconomics

Conclusion

References

29 Negotiation and Conflict Resolution: A Behavioral Decision Research Perspective

Introduction

The Behavioral Decision Research Approach

Beyond Cognition: Affect and Motivation in Negotiation

Beyond Profit Maximization: Negotiators’ Relational Outcomes

Incorporating the BDR Approach to the Study of Relational Outcomes

Conclusion

References

30 Decision Making in Groups and Organizations

Introduction

Simple Aggregation

Aggregation with Limited Information Exchange

Judge–Advisor Systems

Fully Interacting Groups

Technology and the Future of Group Decision Making

References

31 Consumer Decision Making

Introduction

Consumer Decision Making as an Interdisciplinary Application Area

Development of the Field of Consumer Decision Making

Recent Themes in Consumer Decision Making

Conclusion

Acknowledgment

References

Part VI: Improving Decision Making

32 Decision Technologies

Introduction

Decision Trees

Assessing Probabilities for Continuous and Discrete Cases

Valuing Outcomes

Multiple Objective Decisions Under Certainty with Swing Weights

Conclusion

References

33 A User’s Guide to Debiasing

Introduction

Sources of Bias

Decision Readiness

Modify the Person

Modify the Environment

Organizational Cognitive Repairs

Choosing a Debiasing Strategy

An Example

Final Remarks

References

34 What’s a Good Decision? Issues in Assessing Procedural and Ecological Quality

Introduction

The Simplicity of Savage

Acknowledgments

References

Part VII: Summary

35 A Final Glance Backwards and a Suggestive Glimpse Forwards

Introduction

The Gambling Paradigm

Illustration of Alternative Paradigms

The Current State of the Field

References

Author Index

Subject Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Fourfold Pattern of Risk Attitudes.

Table 2.2 Risk Aversion for Mixed (Gain–Loss) Gambles.

Chapter 03

Table 3.1 Ellsberg two-color problem.

Table 3.2 Ellsberg three-color problem.

Table 3.3 10-number urns and (un)likely events.

Table 3.4 Ambiguity premia in Ellsberg tasks for gains.

Chapter 11

Table 11.1 Examples of unconscious influences on encoding, retrieval, and weighting aspects of information processing.

Chapter 19

Table 19.1 A stag hunt or assurance game with multiple Nash equilibria.

Table 19.2 Payoffs in betting-on-rationality game, predictions (Nash and CH), and results from classroom demonstrations in 2006–2008.

Table 19.3a Payoffs and random states for players P1, P2 in a betting game.

Table 19.3b Payoff lookups for P2 in information set {A}.

Table 19.3c Payoff lookups for P2 in information set {A,B}.

Table 19.3d Payoff lookups for P2 in information set {B,C}.

Chapter 22

Table 22.1 Parameters of the expectancy valence model.

Table 22.2 Parameters of the Bayesian sequential risk-taking model.

Chapter 23

Table 23.1 Probabilities of choice at different levels of sure payoff.

Table 23.2 Probabilities of choice at different probabilities of higher payoff.

Table 23.3 Three choices with money payoffs.

Table 23.4 Common ratio effect pairs.

Table 23.5 Six transitive orderings for {X, Y, Z}.

Chapter 25

Table 25.1 Features likely to bridge time.

Table 25.2 Features likely to produce marginal benefits to continued treatment.

Table 25.3 Pathways to persistence.

Chapter 34

Table 34.1 Stages of decision processes and questions relevant to procedural and ecological quality.

List of Illustrations

Chapter 01

Figure 1.1 Contents of a hypothetical JDM handbook for the period 1954–1972.

Figure 1.2 Contents of a hypothetical JDM handbook for the period 1972–1986.

Figure 1.3 Contents of JDM handbook for the period 1986–2002 (Koehler & Harvey, 2004).

Chapter 02

Figure 2.1 A concave utility function over states of wealth that is characterized by diminishing marginal utility.

Figure 2.2 A visual depiction of how a concave utility function predicts risk aversion in the case of the choice of {gain $50 for sure} over {a 50% chance to gain $100 or else gain nothing}.

Figure 2.3 A representative value function from prospect theory depicting the subjective value of money gained or lost relative to a reference point.

Figure 2.4 A representative probability weighting function from prospect theory depicting the impact of various probabilities on the valuation of a prospect.

Figure 2.5 Framework for building a model of behavior, consisting of three steps of model building (baseline model, model variables, model parameters) and three levels of analysis (typical behavior, individual differences, state differences).

Chapter 04

Figure 4.1 Two-attribute choice space with equal-weighting vector and equipreference contour on which both target (T) and competitor (C) alternatives are located. The D alternatives reflect different contextual (decoy) alternatives designed to favor T. Shaded areas indicate values dominated by T, C, or both.

Figure 4.2 Connectionist depiction of MDFT applied to three alternatives, target (T), competitor (C), and decoy alternative (D).

Chapter 07

Figure 7.1 A graphic illustration of factors influencing evaluability and hence value sensitivity.

Figure 7.2 Hypothesized value function, temporal discounting function, and probability weighting function under joint evaluation (JE) and single evaluation (SE).

Chapter 08

Figure 8.1 How to study decisions from description and experience? The choice task in decisions from description (upper panel) often consists of two lotteries with explicitly stated outcomes and probabilities. In research on decisions from experience (lower panel), three paradigms (and hybrids thereof) have been employed: The sampling paradigm includes an initial sampling stage (represented by seven fictitious draws) during which the participant explores two payoff distributions by clicking on one of two buttons on a computer screen (light gray screen). After terminating sampling, the participant sees a choice screen (here shown in dark gray) and is asked to draw once for real. The buttons chosen during sampling (exploration) and choice (exploitation) are hatched diagonally. The partial-feedback paradigm merges sampling and choice, and each draw simultaneously represents exploration and exploitation. The participant receives feedback on the obtained payoff after each draw (hatched box). The full-feedback paradigm additionally reveals the forgone payoff (i.e., the payoff that the participant would have received had he or she chosen the other option; white box).

Figure 8.2 The description–experience gap. Proportion of choices of the risky option as a function of the probability of the more desirable outcome in 6 of 120 problems studied in Erev et al.’s (2010) choice-prediction competition. Each presents a choice between a risky option and a safe option. The decision problems and the expected values of the risky options are displayed below the graph. Each problem was studied using the four paradigms displayed in Figure 1.

Figure 8.3 The coupling of sampling and decision strategies. Two idealized sampling strategies (a) and correlated decision strategies (b). A piecewise sampling strategy alternates back and forth between payoff distributions, whereas a comprehensive sampling strategy takes one large sample from each distribution in turn. Following sampling, participants make a decision about which distribution they prefer. A roundwise decision strategy compares outcomes (gains and losses) over repeated rounds and chooses the distribution that yields higher rewards in most of the rounds. A summary decision strategy calculates the mean reward per distribution and chooses the option with the higher value.

Figure 8.4 Exploration policy and the description–experience gap. Observed proportions of choices consistent with rare events receiving less impact than they deserve (relative to their objective probability) among infrequent switchers (comprehensive sampling), frequent switchers (piecewise sampling), and in the corresponding decisions from description (for details, see Hills & Hertwig, 2010). Error bars represent standard errors of the mean.

Figure 8.5 How small samples foster discriminability (amplification effect). Experienced differences across 1,000 pairs of gambles as a function of sample size (per payoff distribution). The curves represent (a) the mean of the expected absolute difference, (b) the median of the experienced absolute difference, and (c) the first and third quartiles of the experienced absolute difference. The straight horizontal line represents the average description difference (the objective difference) based on the expected value (15.2) in the simulated ecology.

Chapter 09

Figure 9.1 Overview of brain areas involved in decision making. Anterior cingulate cortex (ACC); Dorsal medial prefrontal cortex (dmPFC); Ventromedial prefrontal cortex (vmPFC); Orbitofrontal cortex (OFC); Nucleus accumbens (NACC); Dorsolateral prefrontal cortex (dlPFC); Superior temporal sulcus (STS); Temporal parietal junction (TPJ).

Chapter 10

Figure 10.1 Utility of an experience across time.

Figure 10.2 Mean (believed) contribution of anticipation, experience, and memory to the total utility of experiences.

Figure 10.3 Predicted and remembered utility evaluated at t±1 rely on mental simulations of past or future experiences had at t0, corrected for differences between the context in which the experience is simulated (t±1) and the context in which it was or will be had (t0).

Chapter 11

Figure 11.1 Example of binocular rivalry stimulus; the original would present the B in cyan font color and the 4 in red font color while participants wear goggles fitted with one cyan lens and one red lens thereby presenting only the B to one eye and only the 4 to the other eye.

Chapter 13

Figure 13.1 Two stages of information transmission according to the cognitive-ecological approach (Fiedler & Wänke, 2009) to understanding biases in judgment and decision making.

Figure 13.2 Graphical illustration of conditional sampling in medical diagnosis. Because false negatives have to be minimized for liability reasons, the set of cases captured by a diagnostic tests is typically more inclusive than the set of cases that actually have the disease.

Figure 13.3 Jointly skewed frequency distributions in three dimensions (i.e., unequal frequency of positive versus negative feedback, more frequent feedback about computers than about telecommunication devices and about Provider 1 than about Provider 2 and Provider 3).

Chapter 15

Figure 15.1 A Taxonomy of Terms.Note: The terms in the gray boxes are specific types of empirical effects. The lines (with associated labels) represent the categories in which specific effects could be interpreted as belonging.

Chapter 18

Figure 18.1 Predicted time-pressure ratings from the three models for the current study. Dashed lines indicate specific predictions discussed in the text in which there is one more day required to complete a task than available.

Figure 18.2 Mean time-pressure ratings from the current. Solid lines display the actual means and dashed lines show the predicted means from the relative ratio model.

Chapter 19

Figure 19.1 Choices in 2/3 of the average game, data from newspaper and magazines.

Figure 19.2 Probability distributions of level types under different Poisson distribution averages τ.

Figure 19.3 Predicted and observed behavior in entry games.

Figure 19.4 Percentage frequencies of looking up different payoff cells, classified by overall lookup (MIN) and choice (Nash).

Figure 19.5 Estimated strategic level types for each individual in two sets of 11 different games (Chong et al., 2005). Estimated types are correlated in two sets (r = .61).

Figure 19.6 Numbers chosen in the first week of of Swedish LUPI lottery (N = approximately 350,000). Dotted line indicates mixed Nash equilibrium. Solid line indicate stochastic CH model with two free parameters. Best-fitting average steps of thinking is τ = 1.80 and λ = .0043 (logit response).

Figure 19.7 Describing level 0–2 steps of CH thinking by response times, information lookups, and brain activity.

Chapter 21

Figure 21.1 A graphical causal model relating binary events relevant to ingestion and digestion in a physiological system. The graph would be a fully specified probabilistic causal model if base-rate probabilities of occurrence were supplied for the two parent events (Eating spicy food and Drinking coffee) and conditional probabilities were supplied for all the relevant links in the graph (e.g., p(Diarrhea|Indigestion)).

Figure 21.2 Three subgraphs illustrating a causal-chain schema, a common-cause schema, and a common-effect schema.

Chapter 22

Figure 22.1 Comparing risk preference for gains and losses.

Figure 22.2 Distribution the observer’s impression estimates after 10 rounds of potential interactions. The dashed line is the predicted distribution for an observer using an adaptive sampling rule and the solid line is the predicted distribution of the impressions for an observer who interacts on every round with the same person. Both observers have learning rates of φ = .5. The adaptive sampler observer has a response consistency parameter set at β = 3. The valence of the person’s behavior is modeled with a standard normal distribution.

Figure 22.3 Mapping of the 10 studied populations according to their performance on the IGT adapted from Yechiam et al. (2005). The figure plots the difference between the given clinical population and its control group in terms of the EV model parameters measuring the attention to loss versus gain (λ) and in attention to recent outcomes (ϕ). The error bars for each difference are the standard errors of the differences. The diameter of each circle is proportional to the difference from the control group in the choice-consistency parameter; the black ring denotes the zero-difference boundary (circles smaller than the ring indicate low sensitivity).

Figure 22.4 Beta distributions capturing different beliefs in the probability that the balloon will not explode.

Chapter 23

Figure 23.1 Frequency of choosing option B as option A is progressively improved.

Figure 23.2 Superimposing a deterministic function.

Figure 23.3 An individual’s frequency of choosing different options versus the same set of Ajs.

Figure 23.4 One option worse in SI terms but with flatter curve.

Figure 23.5 Three utility functions.

Figure 23.6 Three utility functions applied to the SU choice.

Figure 23.7 The same three utility functions applied to the SD choice.

Figure 23.8 A binary choice. .

Figure 23.9 A binary choice involving (something very close to) dominance. .

Chapter 24

Figure 24.1 The generation of collective knowledge and transmission to individuals as a critical process in expertise development.

Chapter 27

Figure 27.1 Example of a pictograph, also called icon array, to convey the magnitude of the benefit to be obtained from a form of adjuvant chemotherapy in breast cancer.

Chapter 32

Figure 32.1 Decision Tree.

Figure 32.2 Expected monetary value calculation with product B branch pruned off of tree.

Figure 32.3 Objectives hierarchy.

Figure 32.4 Completed spreadsheet with overall values calculated.

Figure 32.5 Raw weight of maximizing the quality of schools nearby reduced to 0.

Figure 32.6 Swing weight for objective A1.3 maximize the quality of schools nearby.

Chapter 33

Figure 33.1 A continuum of debiasing strategies. By itself, new information is not debiasing, as shown on the far left. The other strategies depicted all contain elements of debiasing.

Volume 1

The Wiley Blackwell Handbook of Judgment and Decision Making

Volume I

Edited by

Gideon Keren and George Wu

This edition first published 2015

© 2015 John Wiley & Sons, Ltd

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Library of Congress Cataloging-in-Publication Data

The Wiley Blackwell handbook of judgment and decision making / edited by Gideon Keren, George Wu.

volumes cm

Includes bibliographical references and index.

ISBN 978-1-118-46839-5 (hardback)

1. Decision making. 2. Judgment. I. Keren, Gideon. II. Wu, George.

BF448.W55 2015

153.4′6–dc23

2015002776

A catalogue record for this book is available from the British Library.

Contributors

Sooyun Baik Organisational Behaviour Area, London Business School, UK

Emily Balcetis Department of Psychology, New York University, USA

Daniel M. Bartels University of Chicago, Booth School of Business, USA

Christopher W. Bauman University of California-Irvine, Paul Merage School of Business, USA

Lehman Benson III Department of Management and Organizations, University of Arizona, USA

Colin F. Camerer Division of the Humanities and Social Sciences, Caltech, USA

Jaee Cho Graduate School of Business, Columbia University, USA

Fiery A. Cushman Harvard University, Department of Psychology, USA

Marieke de Vries Tilburg University, the Netherlands

Carsten Erner Anderson School of Management, University of California–Los Angeles, USA

Daniel C. Feiler Tuck School of Business, Dartmouth College, USA

Klaus Fiedler Department of Psychology, University of Heidelberg, Germany

Craig R. Fox Anderson School of Management, University of California–Los Angeles, USA

Erin Frey Harvard Business School, USA

Kentaro Fujita Department of Psychology, The Ohio State University, USA

Yael Granot Department of Psychology, New York University, USA

Uriel Haran Guilford Glazer Faculty of Business and Management, Ben-Gurion University of the Negev, Israel

Reid Hastie University of Chicago Booth Graduate School of Business, USA

Ralph Hertwig Center for Adaptive Rationality (ARC), Max Planck Institute for Human Development, Germany

Robin M. Hogarth Department of Economics and Business, Universitat Pompeu Fabra, Spain

Candice H. Huynh College of Business Administration, California State Polytechnic University, Pomona, USA

L. Robin Keller Paul Merage School of Business, University of California–Irvine, USA

Gideon Keren Department of Psychology, Tilburg University, the Netherlands

Katharina Kluwe Department of Psychology, Loyola University Chicago, USA

Jonathan J. Koehler Northwestern University School of Law, USA

Asher Koriat Department of Psychology, University of Haifa, Israel

Laura J. Kray Haas School of Business, University of California–Berkeley, USA

Florian Kutzner Warwick Business School, University of Warwick, UK

Richard P. Larrick Fuqua School of Business, Duke University, USA

Nira Liberman Department of Psychology, Tel-Aviv University, Israel

Graham Loomes Warwick Business School, University of Warwick, UK

Mary Frances Luce Fuqua School of Business, Duke University, USA

A. Peter McGraw University of Colorado Boulder, Leeds School of Business, USA

John Meixner Northwestern University School of Law, USA

Katherine L. Milkman The Wharton School, University of Pennsylvania, USA

Don A. Moore Haas School of Business, University of California–Berkeley, USA

Carey K. Morewedge Questrom School of Business, Boston University, USA

Michael W. Morris Graduate School of Business, Columbia University, USA

Lisa D. Ordóñez Department of Management and Organizations, University of Arizona, USA

Jillian O’Rourke Stuart Department of Psychology, University of Iowa, USA

John W. Payne Fuqua School of Business, Duke University, USA

Andrea Pittarello Department of Psychology, Ben-Gurion University of the Negev, Israel

David A. Pizarro Cornell University, Department of Psychology, USA

Timothy J. Pleskac Center for Adaptive Rationality, Max Planck Institute for Human Development, Germany

Devin G. Pope University of Chicago, Booth School of Business, USA

Todd Rogers Harvard Kennedy School, USA

Alan G. Sanfey Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands

Krishna Savani Division of Strategy, Management, and Organisation, Nanyang Business School, Singapore

Laura Scherer Psychological Sciences, University of Missouri, USA

Jay Simon Defense Resources Management Institute, Naval Postgraduate School, USA

Jack B. Soll Fuqua School of Business, Duke University, USA

Mirre Stallen Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands

Anne M. Stiggelbout Leiden University Medical Center, the Netherlands

Justin R. Sydnor School of Business, University of Wisconsin, USA

Karl Halvor Teigen Department of Psychology, University of Oslo, Norway

Elizabeth R. Tenney David Eccles School of Business, University of Utah, USA

R. Scott Tindale Department of Psychology, Loyola University Chicago, USA

Stefan T. Trautmann Alfred-Weber-Institute for Economics, Heidelberg University, Germany

Yaacov Trope Department of Psychology, New York University, USA

Oleg Urminsky University of Chicago, Booth School of Business, USA

Gijs van de Kuilen Tilburg University, the Netherlands

Alex B. Van Zant Haas School of Business, University of California–Berkeley, USA

Daniel J. Walters Anderson School of Management, University of California–Los Angeles, USA

Douglas H. Wedell Department of Psychology, University of South Carolina, USA

Paul D. Windschitl Department of Psychology, University of Iowa, USA

George Wu University of Chicago, Booth School of Business, USA

Gal Zauberman Yale University, Yale School of Management, USA

Jiao Zhang Lundquist College of Business, University of Oregon, USA

1

A Bird’s-Eye View of the History of Judgment and Decision Making

Gideon Keren

Department of Psychology, Tilburg University, the Netherlands

George Wu

University of Chicago, Booth School of Business, USA

Any historical account has a subjective element in it and is thus vulnerable to the benefit of hindsight (Fischhoff, 1975; Roese & Vohs, 2012). This historical review of 60 years of judgment and decision making (JDM) research is of course no exception. Our attempt to sketch the major developments of the field since its inception is further colored by the interests and knowledge of the two authors and thus surely reflects any number of egocentric biases (Dunning & Hayes, 1996; Ross, Greene, & House, 1977). Notwithstanding, we feel that there is a high level of agreement among JDM researchers as to the main developments that have shaped the field. This chapter is an attempt to document this consensus and trace the impact of these developments on the field.

The present handbook is the successor to the Blackwell Handbook of Judgment and Decision Making that appeared in 2004. That handbook, edited by Derek Koehler and Nigel Harvey, was the first handbook of judgment and decision making. Our overview of the field is prompted by the following plausible counterfactual: What if one or more JDM handbooks had appeared prior to 2004?¹ Handbooks might (and should) alter the course of a field by making useful content accessible, providing organizing frameworks, and posing important questions (Farr, 1991). Although we recognize these important roles, our chapter is motivated by one other function of a handbook: a handbook’s editors serve as curators of that field’s ideas and thus identify which research streams are important and energetic (and presumably most worth pursuing) and which ones are not. This chapter thus provides an overview of the field by considering what we would include in two hypothetical JDM handbooks, one published in 1974 and one published in 1988. We attempt to identify which topics were viewed as the major questions and main developments at the time of those handbooks. In so doing, we reveal how the field has evolved, identifying research areas that have more or less always been central to the field as well as those that have declined in importance. For the latter topics, we speculate about reasons for their decreased prominence.

Our chapter’s organization complements more traditional historical accounts of the field. Many reviews of this sort have appeared over the years in Annual Review of Psychology (e.g., Becker & McClintock, 1967; Edwards, 1961; Einhorn & Hogarth, 1981; Gigerenzer & Gaissmaier, 2011; Hastie, 2001; Lerner, Li, Valdesolo, & Kassam, 2015; Lopes, 1994; Mellers Schwartz, & Cooke, 1998; Oppenheimer & Kelso, 2015; Payne, Bettman, & Johnson, 1992; Pitz & Sachs, 1984; Rapoport & Wallsten, 1972; Shafir & LeBoeuf, 2002; Slovic, Fischhoff, & Lichtenstein, 1977; E. U. Weber & Johnson, 2009). In addition, excellent reviews appear as chapters in various non-JDM handbooks (Abelson & Levi, 1985; Ajzen, 1996; Dawes, 1998; Fischhoff, 1988; Gilovich & Griffin, 2010; Markman & Medin, 2002; Payne, Bettman, & Luce, 1998; Russo & Carlson, 2002; Slovic, Lichtenstein, & Fischhoff, 1988; Stevenson, Busemeyer, & Naylor, 1990); in W. M. Goldstein and Hogarth’s (1997) excellent historical introduction to their collection of research papers; and in textbooks, such as Bazerman and Moore (2012), Hastie and Dawes (2010), Hogarth (1987), Plous (1993), von Winterfeldt and Edwards (1986, pp. 560–574), and Yates (1990).

We have divided 60 years of JDM research into four Handbook periods: 1954–1972, 1972–1986, 1986–2002, and 2002–2014. The first period (1954–1972) marks the initiation of several systematic research lines of JDM, many of which are still central to this day. Most notably, Edwards introduced microeconomic theory to psychologists and thus set up a dichotomy between the normative and descriptive perspectives on decision making. This dichotomy remains at the heart of much of JDM research. The second period (1972–1986) is characterized by several new developments, the most significant ones being the launching of the heuristics and biases research program (Kahneman, Slovic, & Tversky 1982) and the introduction of prospect theory (Kahneman & Tversky, 1979). In the third period (1986–2002), we see the infusion of influences such as emotion, motivation, and culture from other areas of psychology into JDM research, as well as the rapid spread of JDM ideas into areas such as economics, marketing, and social psychology. This period was covered by Koehler and Harvey’s (2004) handbook. In the last period (2002–2014), JDM has continued to develop as a multidisciplinary field in ways that are at least partially reflected by the increased application of JDM research to domains such as business, medicine, law, and public policy.

The present introductory chapter is organized as follows. We first discuss some important early milestones in the field. This discussion attempts to identify the underlying scholarly threads that broadly define the field and thus situates the selection of topics for our four periods. In the next two sections, we outline the contents of two editions of the hypothetical Handbook of Judgment and Decision Making one published roughly in 1974 (to cover 1954–1972) and one published roughly in 1988 (to cover 1972–1986).² As noted, the period from 1986–2002 is covered in Koehler and Harvey’s 2004 handbook and the last period is roughly covered in the present two volumes. We also discuss these two periods and comment on how the contents of these two handbooks reflect the field in 2004 and 2015, respectively. In the final section, we conclude with some broader thoughts about how the field has changed over the last 60 years. Speculations about what future directions the field might take are briefly presented in the final chapter.

Some Early Historical Milestones

Several points in time could be considered as marking the inception of judgment and decision making. One possible starting point may be Pascal’s wager: the French philosopher Blaise Pascal’s formulation of the decision problem in which humans bet on whether to believe in God’s existence (Pascal, 1670). This proposal can be thought of as the first attempt to perform an expected utility (hereafter, throughout the handbook, EU) analysis on an existential problem and to employ probabilistic reasoning in an uncertain context. Two other natural candidates are Bernoulli’s (1738/1954) famous paper Exposition of a New Theory of Measurement of Risk, which introduced the notion of diminishing marginal utility, and Bentham’s (1879) book An Introduction to the Principles of Morals and Legislation, which proposed some dimensions of pleasure and pain, two major sources of utility (see Stigler, 1950). Because neither of these works had much explicit psychological discussion (but see Kahneman, Wakker, & Sarin, 1997 which discusses some of Bentham’s psychological insights), a more natural starting point is the publication of Ward Edwards’s (1954) seminal article The Theory of Decision Making, in Psychological Bulletin, which can be viewed as an introduction to microeconomic theory written for psychologists. The topics of that influential paper included riskless choice (i.e., consumer theory), risky choice, subjective probability, and the theory of games, with the discussion of these topics interspersed with a series of psychological comments. The article’s most essential exhortation is encapsulated in the paper’s final sentence: all these topics represent a new and rich field for psychologists, in which a theoretical structure has already been elaborately worked out and in which many experiments need to be performed (p. 411). Edwards followed up this article in 1961 with the publication of Behavioral Decision Theory in the Annual Review of Psychology. That paper should be seen as a successor to the 1954 article as well as evidence for the earlier paper’s enormous influence: This review covers the same subject matter for the period 1954 through April, 1960 (p. 473). The tremendous volume of empirical and theoretical research on decision making in those six years speaks to the remarkable growth of the emerging field of judgment and decision making.

Two other important publications also marked the introduction of JDM: Savage’s (1954) The Foundations of Statistics and Luce and Raiffa’s (1957) Games and Decisions. These two books cover the three major theories that dominated the field at its inception: utility theory, probability theory, and game theory. A major query regarding each of the three theories concerned the extent to which they had a normative (what should people do) or a descriptive (what do people actually do) orientation. All three theories were originally conceived as normative in that they contained recommendations for the best possible decisions, a view that reflected a tacit endorsement that human decision making is undertaken by homo economicus, an individual who strictly follows the rational rules dictated by logic and mathematics (Mill, 1836).³ Deviations were thought to be incidental (i.e., errors of performance) rather than systematic (e.g., errors of comprehension).

Edwards (1954) made clear that actual behavior might depart from the normative standard and inspired a generation of scholars to question the descriptive validity of these theories. Indeed, one of the hallmarks of the newborn discipline of judgment and decision making was the conceptual and empirical interplay between the normative and the descriptive facets of various judgment and decision making theories. This interplay played an essential role in the development of the field and remains central to the field to this day.

Both probability and utility theory (and to some extent game theory; see, e.g., Nash, 1950) are founded on axiomatic systems. An axiomatic system is a set of conditions (i.e., axioms) that are necessary and sufficient for a particular theory. As such, they are useful for normative purposes (individuals can reflect on whether an axiom is a reasonable principle; see Raiffa, 1968; Slovic & Tversky, 1974) as well as descriptive purposes (an axiom often provides a clear recipe for testing a theory; see the discussion of the Allais Paradox later in this chapter). Luce and Raiffa (1957) identified some gaps between the normative and descriptive facets of EU theory. For each of von Neumann and Morgenstern’s (1947) axioms, they provided some critical comments questioning the validity of that axiom and examining its behavioral applicability to real-life situations. For instance, the discussion of the reduction of compound lotteries axiom foreshadowed later experimental research that established systematic violations of that axiom (Bar-Hillel, 1973; Ronen, 1971). Similarly, doubts about the transitivity axiom anticipated research that demonstrated that preferences can cycle (e.g., Tversky, 1969). These reservations were small in force relative to the more fundamental critique levied by Maurice Allais’ famous counterexample to the descriptive validity of EU theory (Allais, 1953). The Allais Paradox, along with the Ellsberg (1961) Paradox, continues to spawn research in the JDM literature (see Chapters 2 and 3 of the present handbook).

Somewhat later, a stream of research with a similar spirit explored whether subjective probability assessments differed from the probabilities dictated by the axioms of probability theory. The research in the early 1960s, much of it conducted by Edwards and his colleagues, was devoted to probability judgments and their assessments. Edwards, Lindman, and Savage (1963) introduced the field of psychology to Bayesian reasoning, and indeed a great deal of that research examined whether humans were Bayesian in assessing probabilities. A number of early papers suggested that the answer was generally no (Peterson & Miller, 1965; Phillips & Edwards, 1966; Phillips, Hays, & Edwards, 1966). Descendants of this work are still at the center of JDM (see Chapter 6 in this handbook).

The study of discrepancies between formal normative models and actual human behavior marked the beginning of the field and has served as a tempting target for empirical work. Indeed, according to Phillips and von Winterfeldt (2007), 139 papers testing the empirical validity of EU theory appeared between 1954 and 1961. Although the contrast between normative and descriptive remains a major theme underlying JDM research today, most JDM researchers strive to go beyond documenting a discrepancy to providing a psychological explanation for that phenomenon. Simon (1956) provided one early and influential set of ideas that have shaped the field’s theorizing about psychological mechanisms. He proposed that humans satisfice or adapt to their environment by seeking a satisfactory rather than optimal decision. This adaptive notion anticipated several research programs, including Kahneman and Tversky’s influential heuristics and biases program (Kahneman & Tversky, 1974).

It is also worth noting that the field was an interdisciplinary one from the beginning. Edwards had a visible role in this development by bringing economic theory and models to psychology, a favor that psychologists would return years later in the development of the field of behavioral economics. The interdisciplinary nature of the field was also reflected in monographs such as Decision Making: An Experimental Approach (1957), a collaboration between the philosopher Donald Davidson, the philosopher and mathematician Patrick Suppes, and the psychologist Sidney Siegel. The clear ubiquity and importance of decision making also meant that the application of JDM ideas included fields ranging from business and law to medicine and meteorology.

We next turn to the contents of our four handbooks, two hypothetical and two actual. Although these handbooks illustrate the growth and development of the field over the last 60 years, we also see throughout the interplay between the normative standard and descriptive reality, as well as the interdisciplinary nature of the field.

The Initial Period, 1954–1972 (Handbook of Judgment and Decision Making, 1974)

The period from 1954 to 1972 can be viewed as the one in which the discipline of behavioral decision making went through its initial development. As we will see, many of the questions posed during that period continue to shape research today. By 1972, the field had an identity, with many scholars describing themselves as judgment and decision making researchers. In 1969, a Research Conference on Subjective Probability and Related Fields took place in Hamburg, Germany. In 1971, that conference, in its third iteration, had changed its name to the Research Conference on Subjective Probability, Utility, and Decision Making (or SPUDM for short), hence broadening the scope of that organization and reflecting in some respects the maturation of the field. SPUDM has taken place every second year since that date (see Vlek, 1999, for a history of SPUDM).⁴

Suppose, in retrospect, that we were transported back in time to 1972 or so and tasked with preparing a handbook of judgment and decision making. How would such a volume be structured and how does the current volume differ from such a hypothetical volume? Figure 1.1 contains a list of contents of such a volume, retrospectively assembled by the two of us. In preparing this list, we have assumed the role of hypothetical curators, with the caveat that other researchers would likely have constructed a different list.⁵

c1-fig-0001

Figure 1.1 Contents of a hypothetical JDM handbook for the period 1954–1972.

As the previous section indicated, three major themes have attracted the attention of JDM researchers since the inception of the field and continue to serve as the backbones of the field to varying extents even today: uncertainty and probability theory; decision under risk and utility theory; and strategic decision making and game theory. Accordingly, three sections in Figure 1.1 correspond to these three major pillars of the field.

Our first hypothetical volume contains an introductory chapter (Chapter 1, 1974) that presents an overview of the normative versus descriptive distinction, a distinction that had been central to the field since its inception. (We denote the chapters with the publication date of that hypothetical or actual handbook because we at times will refer to earlier or later handbooks; references to the hypothetical works are given in bold.) The Handbook then consists of four parts:

Uncertainty;

Choice behavior;

Game theory and its applications;

Other topics.

Hundreds of volumes have been written on the topic of uncertainty. For physicists and philosophers, the major question is whether uncertainty is inherent in nature. The development of the normative treatment of uncertainty as in modern probability theory is described in Hacking’s (1975) stimulating book. Researchers in JDM, however, assume that uncertainty is a reflection of the human mind and hence subjective. Accordingly, the second part of our imaginary volume is devoted to the assessment of uncertainty.

Chapter 2 (1974) serves as an introduction to this part and contrasts objective or frequentist notions of probability with subjective or personalistic probabilities. In a series of studies, John Cohen and his colleagues (J. Cohen, 1964, 1972; J. Cohen & Hansel, 1956) studied the relationship between subjective probability and gambling behavior. They found violations of the basic principles of probability such as evidence of the gambler’s fallacy. Indeed, Cohen’s work anticipated Kahneman and Tversky’s heuristics and biases research program (see Chapter 3, 1988).

Bayesian reasoning, a major research program initiated by Edwards (1962) (see also Edwards, Lindman, & Savage, 1963) is the topic of Chapter 3 (1974). This program was motivated by understanding whether people’s estimates and intuitions are compatible with the Bayesian model, as well as whether the Bayesian model can serve as a satisfactory descriptive model for human probabilistic reasoning (Edwards, 1968). Using what has become known as the bookbag and poker chip paradigm, Edwards and his colleagues (e.g., Peterson, Schneider, & Miller, 1965; Phillips & Edwards, 1966) ran dozens of studies on how humans revise their opinions in light of new information. This research inspired Peterson and Beach (1967) to describe man as an intuitive statistician and argue that by and large statistics can be used as the basis for psychological models that integrate and account for human performance in a wide range of inferential tasks (p. 29). However, Edwards (1968) also pointed out that subjects were conservative in their updating: opinion change is very orderly … but it is insufficient in amount … [and] takes anywhere from two to five observations to do one observation’s worth of work (p. 18). The notion of man as an intuitive statistician was soon taken on by Kahneman and Tversky’s work on heuristics and biases, and the tendency toward conservatism was later challenged by Griffin and Tversky (1992) (see also Massey & Wu, 2005).

Chapter 4 (1974) covers the distinction between clinical and statistical modes of probabilistic reasoning. In this terminology, clinical refers to case studies that are used to generate subjective estimates, while statistical reflects some actuarial analytical model. In a seminal book, which influences the field to this day, Meehl (1954; see also Dawes, Faust, & Meehl, 1989) found that clinical predictions were typically much less accurate than actuarial or statistical predictions. As noted by Einhorn (1986), the statistical models were more advantageous because they accepted error to make less error. Dawes, Faust, and Meehl (1993) reviewed 10 diverse areas of application that demonstrated the superiority of the statistical models relative to human judgment.

Chapter 5 (1974) is devoted to the issue of probability learning (e.g., Estes, 1976). A typical probability-learning study involves a long series of trials in which subjects choose one of two actions on each trial. Each action has a different unknown probability of generating a reward. This topic was extensively studied in the 1950s and the 1960s (for an elaborate review, see Lee, 1971, Chapter 6). Researchers discovered that subjects tended toward probability matching (Grant, Hake, & Hornseth, 1951): the frequency with which a particular action is chosen matches the assessed probability that action is the preferred choice. This phenomenon has been repeatedly replicated (e.g., Gaissmaier & Schooler, 2008) and is noteworthy because human behavior is inconsistent with the optimal strategy of choosing the action with the highest probability of generating a reward.

Chapter 6 (1974) covers estimation methods of subjective probability. Although this topic was still in its infancy, the emergence of decision analysis (see Chapter 19, 1974) emphasized the need to develop and test methods for eliciting probabilities. Some of the early work in that area was conducted by Alpert and Raiffa (1982; study conducted in 1968), Murphy and Winkler (1970), Savage (1971), Staël von Holstein (1970, 1971), and Winkler (1967a, 1967b). More comprehensive overviews of elicitation methods are found in later reviews, such as Spetzler and Staël von Holstein (1975) and Wallsten and Budescu (1983).

The subsequent part of our imaginary handbook is devoted to utility theories for decision under risk and uncertainty (Chapter 7, 1974). Already anticipated by Bernoulli (1738/1954) EU theory was formalized in an axiomatic system by von Neumann and Morgenstern (1947). This theory considers decision under risk, or gambles with objective probabilities such as winning $100 if a fair coin comes up heads. A later development by Savage (1954), subjective expected utility (hereafter, thoughout the handbook, SEU) theory, extended EU to more natural gambles such as winning $100 if General Electric’s stock price were to increase by over 1% in a given month. Savage’s framework thus covered decision under uncertainty, using subjective probabilities rather than the objective probabilities provided by the experimenter. Some of the early research in utility theory was an attempt to eliminate the gap between the normative and the descriptive. For example, Friedman and Savage (1948) famously attempted to explain the simultaneous purchase of lottery tickets (a risk-seeking activity) and insurance (a risk-averse activity) by positing a utility function with many inflection points. Many years later, the lottery-ticket-purchasing gambler would be a motivation for Kahneman and Tversky’s (1979) prospect theory, an explicitly descriptive account of how individuals choose among risky gambles (see also Tversky & Kahneman, 1992).

This line of research embraced what has become known as the gambling metaphor or the gambling paradigm. Research participants were posed with a set of (usually two) hypothetical gambles to choose between. The gambles were generally described by well-defined probabilities of receiving well-defined (and generally) monetary outcomes. The gambling metaphor presumed that most real-world risky decisions reflected a balance between likelihood and value, and that hypothetical choices of the sort Would you prefer $100 for sure, or a 50–50 chance at getting $250 or nothing? offered insight into the psychological processes people employed when faced with risky decisions. The strengths and limitations of the gambling paradigm are discussed in the concluding chapter of this handbook.

Savage’s sure-thing principle and EU theory’s independence axiom constitute the cornerstones of SEU and EU, respectively. The most well-known violations of these axioms, and hence counter examples to the descriptive validity of these theories, were formulated by Allais (1953) and Ellsberg (1961) and first demonstrated in careful experiments by MacCrimmon (1968). The Allais and Ellsberg Paradoxes are described in Chapter 8 (1974), as well as other early empirical investigations of EU theory (e.g., Mosteller & Nogee, 1951; Preston & Baratta, 1948). Decision under risk and decision under uncertainty continue to be mainstream JDM topics and appear in this handbook as Chapter 2 (2015) and Chapter 3 (2015).

Chapter 9 (1974) discusses preference reversals. Lichtenstein and Slovic (1971) documented a fascinating pattern in which individuals preferred gamble A to gamble B, but nevertheless priced B higher than A. This demonstration was an affront to normative utility theories, because it demonstrated that preferences might depend on the procedure used to elicit them. More fundamentally, this demonstration was a severe blow to the notion that individuals have well-defined preferences (Grether & Plott, 1969) and anticipated Kahneman and Tversky’s (1979) more systematic attack on procedural invariance (see Chapters 11 and 12, 1988). It also set the stage for theorizing on how context can affect attribute weights (Tversky, Sattath, & Slovic, 1988) as well as an identification of a broader class of preference reversals, such as those involving joint and separate evaluation (e.g., Chapter 18, 2004; Chapter 7, 2015) and conflict and choice (e.g., Chapter 17, 2004).

Chapter 10 (1974) surveys measurement theory (e.g., Krantz, Luce, Suppes, & Tversky, 1971; Suppes, Krantz, Luce, & Tversky, 1989), in particular the measurement of utility. The methodological and conceptual difficulties associated with the assessment of utility were recognized at an early stage and attracted the attention of many researchers (e.g., Coombs & Bezembinder, 1967; Davidson, Suppes, & Siegel, 1957; Mosteller & Nogee, 1951). Different attempts at developing a theory of measurement have taken the form of functional (Anderson, 1970) and conjoint (Krantz & Tversky, 1971) measurement. Although measurement theory received much attention by leading researchers in psychology (e.g., Coombs, Dawes, & Tversky, 1970; Krantz, Luce, Suppes, & Tversky, 1971) the interest in these issues has declined over the years for reasons that remain unclear (e.g., Cliff, 1992). Nevertheless, we believe that measurement is still an essential issue for JDM research and hope that these topics will again receive their due attention.⁶

The topic of Chapter 11 (1974) is psychophysics. The initial developments of psychophysical laws are commonly attributed to Gustav Theodor Fechner and Ernst Heinrich Weber (Luce, 1959). The most fundamental psychophysical principle, diminishing sensitivity, is that increased stimulation is associated with a decreasing impact. The origins of this law can be traced to Bernoulli’s (1738/1954) original exposition of utility theory and is reflected in the familiar economic notion of diminishing marginal utility in which successive additions of money (or any other commodity) yield smaller and smaller increases in value. Psychophysical research has also identified a number of other stimulus and response mode biases that influence sensory judgments (Poulton, 1979), and these biases, as well as the psychophysical principle of diminishing sensitivity, have shaped how JDM researchers have thought about the measurement of numerical quantities, whether the quantities be utility values or probabilities (von Winterfeldt & Edwards, 1986, 351–354).

The closing Chapter 12 (1974) of this part goes beyond individual decision making and examines social choice theory (Arrow, 1954) and group decision making. Arrow’s famous Impossibility Theorem showed that there exists no method to aggregate individual preferences into a collective or group preference that satisfies some basic and appealing criterion. This work, along with others, also motivated some experimental investigation of group decision making processes. One of the first research endeavors in this area, Siegel and Fouraker (1960), involved a collaboration between a psychologist (Sidney Siegel) and an economist (Lawrence Fouraker), again reflecting the interdisciplinary nature of the field. Group decision making is covered in subsequent handbooks: Chapter 23 (2004) and Chapter 30 (2015).

The next part of our first fictional handbook covers game theory (von Neumann & Morgenstern, 1947) and its applications. Luce and Raiffa (1957) introduced the central ideas of game theory to social scientists and made what were previously regarded as abstract mathematical ideas accessible to non mathematicians (Dodge, 2006). The same year also marked the appearance of the Journal of Conflict Resolution, a journal that became a major outlet for applications of game theory to the social sciences. In the 1950s and 1960s, game theory was seen as having enormous potential for modeling and understanding conflict resolution (e.g., Schelling, 1958, 1960).

Schelling (1958) introduced the distinction between (a) pure-conflict (or zero sum) games in which any gain of one party is the loss of the other party; (b) mixed motives (or non-zero-sum) games, which involve conflict though one side’s gain does not necessarily constitute a loss for the other; and (c) cooperation games in which the parties involved share exactly the same goals. Chapter 13 (1974) presents the empirical research for each of these three types of games conducted in the pertinent period. Merrill Flood, a management scientist, conducted some of the earliest experimental studies (Flood, 1954, 1958). Social psychologists studied various versions of these games in the 1960s and 1970s (e.g., Messick & McClintock, 1968). Rapoport and Orwant (1962) provided a review of some of the first generation of experiments (see Rapoport, Guyer, & Gordon, 1976, for a later review).

The prisoner’s dilemma has received more attention than any other game, with the possible recent exception of the ultimatum game, probably because of its transparent applications to many real-life situations. Chapter 14 (1974) surveys experimental research on the prisoner’s dilemma. Flood (1954) conducted perhaps the earliest study of that game, and Rapoport and Chammah (1965) and Gallo and McClintock (1965) presented a comprehensive discussion of the game and some experiments conducted to date. See also Chapter 24 (2004) and Chapter 19 (2015), as well as the large body of work on social dilemmas (e.g., Dawes, 1980).

The final part of the handbook is devoted to several broader topics that are not unique to JDM but were seen as useful tools for understanding judgment and decision making. Chapter 15 (1974) reviews Signal Detection Theory (Swets, 1961; Swets, Tanner, & Birdsall, 1961; Green & Swets, 1966). The theory was originally applied mainly to psychophysics as an attempt to reflect the old concept of sensory thresholds with response thresholds. Swets (1961) was included in one of the earliest collection of decision making articles (Edwards & Tversky, 1967), an indication of the belief that signal detection theory would have many important applications in judgment and decision making research.

Information theory (Shannon, 1948; Shannon & Weaver, 1949) is the topic of Chapter 16 (1974). In the second half of the twentieth century, information theory made invaluable contributions to the technological developments in fields such as engineering and computer science. As Miller (1953) noted, there was considerable fuss over something called ‘information theory,’ in particular because it was presumed to be useful in understanding judgment and decision processes under uncertainty. The great hopes of Miller and others did not materialize, and after 1970 the theory was hardly cited in the social sciences (see, however, Garner, 1974, for a classic psychological application of information theory). Luce (2003) discusses possible reasons for the decline of information theory in psychology.

Chapter 17 (1974) describes decision analysis. Decision analysis, defined as a set of tools and techniques designed to help individuals and corporations structure and analyze their decisions, emerged in the 1960s (Howard, 1964, 1968; Raiffa, 1968; see von Winterfeldt & Edwards, 1986, 566–574, for a brief history of decision analysis). Decision analysis was soon a required course in many business schools (Schlaifer, 1969), and the promise of the field to influence decision making is reflected in the following quotation from Brown (1989): In the sixties, decision aiding was dominated by normative developments. … It was widely assumed that a sound normative structure would lead to prescriptively useful procedures (p. 468). This chapter presents an overview of decision-aiding tools such as decision trees and sensitivity analysis, as well as topics that interface more directly with JDM research, such as probability encoding (Spetzler & Staël von Holstein, 1975; see also Chapter 6, 1974) and multiattribute utility theory (Keeney & Raiffa, 1976; Raiffa, 1969; see also Chapter 14, 1988).

The last chapter (Chapter 18, 1974) of this first handbook covers thinking and reasoning, which is included although the link with JDM had not been fully articulated in the early 1970s when our hypothetical handbook appears. The chapter discusses confirmation bias (Wason, 1960, 1968) and reasoning with negation (Wason, 1959), as well as the question of whether people are invariably logical unless they failed to accept the logical task (Henle, 1962). In some respects, Henle’s paper anticipated the question of rationality (e.g., L. J. Cohen, 1981; see Chapter 2, 1988) as well as research on hypothesis testing (Chapter 17, 1988; Chapter 10, 2004).

Before moving on to the next period, we make several remarks about the field in the early 1970s. Although JDM has always been an interdisciplinary field and was certainly one in this early period, the orientation of the field was demonstrably more mathematical in nature, centered on normative criteria, and closer to cognitive psychology than it is today. This orientation partially reflects the topics that consumed the field at this point and the requisite comparison of empirical results with mathematical models. But another part reflects a sense at that time of the useful interplay between mathematical models and empirical research (e.g., Coombs, Raiffa, & Thrall, 1954). For a number of reasons, many of the more technical of these ideas (e.g., information theory, measurement theory, and signal detection theory) have decreased in popularity since that time. Although these topics were seen as promising in the early 1970s, they do not appear in our subsequent handbooks.

Game theory, along with utility theory and probability theory, was one of the three major theories Edwards (1954) offered up to psychologists for empirical investigation. However, game theory has never been nearly as central to JDM as the study of risky decision making or probabilistic judgment. Chapter 19 (2015) argues that this may be partially because of conventional game theory’s focus on equilibrium concepts. The chapter proposes an alternative framework for studying strategic interactions that might be more palatable to JDM researchers (see also Camerer, 2003, for a more general synthesis of psychological principles and game-theoretic reasoning under the umbrella behavioral game theory).

Finally, there was great hope in the early 1970s that decision-aiding tools such as decision analysis could lead individuals to make better decisions. Decision analysis has probably fallen short of that promise, partly because of the difficulty of defining what constitutes a good decision (see Chapter 34, 2015; Frisch & Clemen, 1994) and partly because of the inherent subjectivity of inputs into decision models (see Chapter 32, 2015; Clemen, 2008). Although the connection between decision analysis and judgment decision making has become more tenuous since the mid-1980s, it nevertheless remains an important topic for the JDM community and is covered in Chapter 32 (2015).⁷,⁸

The Second Period (1972–1986) (Handbook of Judgment and Decision Making, 1988)

Our second imaginary handbook covers approximately the period 1972–1986. This period reflects several new research programs that are still at the heart