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The Wiley Blackwell Handbook of Judgment and Decision Making, First Edition. Edited by Gideon Keren and George Wu. © 2015 John Wiley & Sons, Ltd. Published 2015 by John Wiley & Sons, Ltd.

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

Decision MakingGideon Keren

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

1

George WuUniversity of Chicago, Booth School of Business, USA

Department of Psychology, Tilburg University, the Netherlands

2 Gideon Keren and George Wu

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 eco-nomics, 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 under-lying 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 vol-umes. 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

A Bird’s-Eye View of the History of Judgment and Decision Making 3

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 phi-losopher 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 hand-book, 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 intro-duced the notion of diminishing marginal utility, and Bentham’s (1879) book An Introduction to the Principles of Morals and Legislation, which proposed some dimen-sions 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 per-formed” (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 incep-tion: 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 recommenda-tions 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 norma-tive 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 lot-teries” 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 descrip-tive 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 math-ematician 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

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.

Handbook of Judgment and Decision Making (1974): 1954–1972

I. Perspectives on Decision Making

1. Descriptive and Normative Concerns of Decision Making

II. Uncertainty

2. Probability Theory: Objective vs. Subjective Perspectives

3. Man as an Intuitive Bayesian in Belief Revision

4. Statistical vs. Clinical: Objective vs. Subjective perspectives.

5. Probability Learning and Matching

6. Estimation Methods of Subjective Probability

III. Choice Behavior

7. Utility Theory

8. Violations of Utility Theory: The Allais and Ellsberg Paradoxes

9. Preference Reversals

IV. Game Theory and its Applications

13. Cooperative vs. Competitive Behavior: Theory and Experiments

14. The Prisoner’s Dilemma

V. Other Topics

15. Signal Detection Theory

16. Information Theory and its Applications

17. Decision Analysis

18. Logic, Thinking, and the Psychology of Reasoning

10. Measurement theory

11. Psychophysics Underlying Choice Behavior

12. Social Choice Theory and Group Decision Making

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


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 intui-tions 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 ten-dency 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 ana-lytical 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 proba-bility 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 inconsis-tent 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 elici-tation 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 gam-bles 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 probabil-ities 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 norma-tive 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 out-comes. 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 nor-mative 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 the-orizing 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 prin-ciple, 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 sci-ences. 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 exper-imental 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 ana-lyze 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 fol-lowing quotation from Brown (1989): “In the sixties, decision aiding was dominated by normative developments. … It was widely assumed that a sound normative struc-ture 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 inter-play 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 investiga-tion. 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 of the field today. The maturation of the field is also captured by the initial spread of the field to areas such as economics, marketing, and social psychology. Figure 1.2 contains a table of contents for this hypothetical handbook.

Chapter 1 (1988) introduces a third category to the normative versus descriptive dichotomy: prescriptive. Keeney and Raiffa (1976) and Bell, Raiffa, and Tversky (1988) suggested that while normative is equated with “ought” and descriptive is equated with “is,” the prescriptive addresses the following question: “How can real people – as opposed to imaginary super‐rational people without psyches – make better choices in a way that does not do violence to their deep cognitive concerns?” (p. 9). This approach has several implications, in particular that violations of EU as in the Allais Paradox might actually reflect some hidden “carrier of value,” such as regret, disappointment, or anxiety (e.g., Bell, 1982, 1985; Wu, 1999) and thus might not necessarily constitute unreasonable behavior. It also anticipates later attempts to use psychological insights to change people’s decisions (Chapter 25, 2015).

Chapter 2 (1988) addresses the debate about the rationality of human decision mak-ing. In a provocative article, L. J. Cohen (1981) questioned whether human irrationality can be experimentally demonstrated. That article appeared in Behavioral and Brain Sciences and spawned a vigorous and heated interchange between Cohen and many scholars, including a number of prominent JDM researchers. This chapter discusses the distinction between being “rational” and being “reasonable,” where rationality for JDM researchers often constitutes coherence with logical laws or the axioms underlying utility theory and reasonable is a looser term that reflects intuition and common sense. The conversation on rationality continues in Chapter 1 (2004) (see also Lopes, 1991).

The first major innovation during this period was the heuristics and biases research program (Kahneman & Tversky, 1974). This program was inspired by the cognitive revolution and summarized in a collection of papers edited by Kahneman, Slovic, and Tversky (1982). Because of the importance of this program, the work on heuris-tics and biases warrants a special part in our second handbook. The first chapter of this part (Chapter 3, 1988) provides a high-level summary of this research, with


emphasis on representativeness (Kahneman & Tversky, 1972), availability (Tversky & Kahneman, 1972), and anchoring and adjustment (Kahneman & Tversky, 1974). A more recent overview on how heuristics and biases developed since then is provided by Chapter 5 (2004), as well as by several articles in Gilovich, Griffin, and Kahneman’s (2002) edited collection.


I. Perspectives on Decision Making

1. Descriptive, Prescriptive, and Normative Perspectives on Decision Making

2. Rationality and Bounded Rationality

II. Probabilistic Judgments: Heuristics and Biases

3. Heuristics and Biases: An Overview

4. Overconfidence

5. Hindsight Bias

6. Debiasing and Training

7. Learning from experience

8. Linear Models

9. Heuristics and Biases in Social Judgments

III. Decisions

11. Prospect Theory and Descriptive Alternatives to Expected Utility Theory

12. Framing and Mental Accounting

13. Emotional Carriers of Value

14. Measures of Risk

15. Multiattribute Decision Making

16. Intertemporal Choice

IV. Approaches

17. Hypothesis Testing

18. Algebraic Models

19. Brunswikian Approaches

20. The Adaptive Decision Maker

21. Process Tracing Methods

V. Applications

22. Medical Decision Making

23. Negotiation

24. Behavioral Economics

24. Risk Perception

10. Expertise

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


It is important to note that the heuristics and biases approach has been criticized by some researchers (e.g., L. J. Cohen, 1981). Gigerenzer and his colleagues (Gigerenzer, 1991; Gigerenzer, Todd and The ABC Research Group, 1999) proposed that heuris-tics may be adaptive tools adjusted to the structure of the relevant environment. This view refrains from employing the strict logical–mathematical rules as a benchmark and centers more on descriptive and prescriptive (rather than normative) facets. A more detailed exposition to this approach can be found in Chapters 4 and 5 of the 2004 handbook.

Chapter 4 (1988) addresses calibration and overconfidence of probability judg-ments (e.g., Lichtenstein & Fischhoff, 1977; May, 1986). Probability judgments are well calibrated if, for example, 70% of the propositions assigned a probability of 0.7 actually occur. Individuals are usually not well calibrated, with typical studies finding that events assigned a probability of 0.7 occur about 60% of the time (see, e.g., Lichtenstein, Fischhoff, & Phillips, 1982, Figure 2). Overconfidence of this sort is a robust phenomenon, documented in a wide variety of domains, using different methods and a variety of events and with both novices and experts. This topic con-tinues to be of major interest to JDM researchers (e.g., Brenner, Koehler, Liberman, & Tversky, 1996; Keren, 1991; Klayman, Soll, Gonzalez‐Vallejo, & Barlas, 1999) and is examined in Chapter 11 of Koehler and Harvey (2004) and Chapter 6 (2015). Overconfidence also features prominently in managerial texts of decision making (e.g., Bazerman & Moore, 2012).

Chapter 5 (1988) is devoted to the hindsight bias (Fischhoff, 1975; Fischhoff & Beyth, 1975). An event that has actually occurred seems inevitable, even though in foresight it might have been difficult to anticipate. Hindsight bias has broad and important implications in different domains of daily life, in particular for learning from experience (Chapter 7, 1988) and for evaluating the judgments and decisions of others (Hogarth, 1987). Indeed, in retrospect or in hindsight, the topic has attracted wide interest and has been discussed in more than 800 scholarly papers (Roese & Vohs, 2012) and is a topic of the 2004 handbook (Chapter 13, 2004).

Chapter 6 (1988) addresses the important question of whether the cognitive biases documented in the heuristics and biases program can be mitigated or even eliminated. The question of debiasing has been addressed by several researchers (e.g., Fischhoff, 1982; see also Keren, 1990), with many concluding that the ability to overcome cognitive biases is limited. However, some researchers have argued otherwise. For example, Nisbett, Krantz, Jepson, and Kunda (1983) conducted some studies on training of statistical reasoning and concluded that “training increases both the likelihood that people will take a statistical approach to a given problem and the quality of the statistical solutions” (p. 339). This topic continues to be of interest to the JDM community, as represented by its appearance in the two subsequent hand-books: Chapter 16 (2004) and Chapter 33 (2015).

The topic of Chapter 7 (1988) is learning from experience. A common assump-tion is that the judgmental biases, surveyed in the previous chapters, will disappear if decision makers gain experience and hence learn from their experience. Contrary to this belief, Goldberg (1959) found that experienced clinical psychologists were no better at diagnosing brain damage than hospital secretaries. Since that paper, many studies have identified reasons that learning from experience is difficult, including faulty hypothesis testing (Chapter 17, 1988), hindsight bias (Chapter 5, 1988),


memory biases, and the nature and quality of feedback (see Brehmer, 1980; Einhorn & Hogarth, 1978). In spite of its clear relevance, learning from experience has never been a completely mainstream JDM topic. Indeed, the learning discussed in Chapter 22 (2015) has a different focus than the learning from experience research reviewed in the 1988 handbook.

Chapter 8 (1988) is devoted to the general linear model (e.g., Dawes, 1979; Dawes & Corrigan, 1974). Chapter 4 (1974) reviewed a large body of research demonstrating the advantage of statistical prediction models over clinical or intuitive judgments. These statistical models generally use linear regression to predict a target variable from a set of predictors. Although the best improvement over clinical judg-ments is obtained by using the optimal weights obtained through regression, Dawes and his collaborators found that it is important to identify the two or three most essential variables, and the weights do not matter much once this is done, that is, unit or even random weights still generally outperform human judgment. Importantly, these researchers also showed that people fail to appreciate the benefits of statistical models over more intuitive approaches.

Chapter 9 (1988) is devoted to the implications of the heuristics and biases program for social judgments. In 1980, Nisbett and Ross wrote an influential book entitled Human Inference: Strategies and Shortcomings of Social Judgment. Much as Edwards (1954) made microeconomic theory accessible to psychologists, Nisbett and Ross introduced the findings of the heuristics and biases research program to social psychologists. In doing so, Nisbett and Ross spelled out the implications of these biases for a number of social psychological phenomena, including stereotyping, attri-bution, and the correspondence bias. Judgment and decision making plays an enor-mous role in social psychological research today. In their history of social psychology chapter in the Handbook of Social Psychology, Ross, Lepper, and Ward (2010) write

the work of two Israeli psychologists, Daniel Kahneman and Amos Tversky, on ‘heu-ristics of judgments’ … began to make its influence felt. Within a decade, their papers in the judgment and decision making tradition were among the most frequently cited by social psychologists, and their indirect influence on the content and direction of our field was ever greater than could be discerned from any citation index. (p. 16)

Gilovich and Griffin (2010) document more systematically the role both JDM and social psychology have played in shaping the research of the other field.

Expertise is the topic of Chapter 10 (1988). Although it is typically presumed that experts are more accurate than novices, Goldberg (1959), as described in Chapter 7 (1988), found no difference in performance between experienced clinical psycholo-gists and novices. A substantial literature has found that Goldberg’s findings are not unique. Experts show little or no increase in judgmental accuracy (e.g., Kundell & LaFollette, 1972). In terms of calibration (Chapter 4, 1988), experts are sometimes better calibrated (Keren, 1991) but sometimes not (Wagenaar & Keren, 1985). See Shanteau and Stewart (1992) and Camerer and Johnson (1991) for thorough reviews on the effects of expertise on human judgment. Expertise is also covered in the subsequent two handbooks, Chapter 15 (2004) and Chapter 24 (2015).

The next part is devoted to choice. The topic of Chapter 11 (1988) is prospect theory (Kahneman & Tversky, 1979), one of the most cited papers in both


economics and psychology and a paper that has had a remarkable impact on many areas of social science (Coupe, 2003; E. R. Goldstein, 2011). Prospect theory was put forth as a descriptive alternative to EU theory, built around a series of new vio-lations of EU, including the common‐consequence, common‐ratio, and reflection effects, as well as framing demonstrations, in which some normatively irrelevant aspect of presentation had a major impact on the choices. Kahneman and Tversky organized these violations by proposing two functions: a value function that cap-tures how outcomes relative to the reference point are evaluated and exhibits loss aversion; and a probability weighting function, which reflects how individuals dis-tort probabilities in making their choices. This chapter also discusses a number of other alternative models that were proposed (see Machina, 1987 for an overview of some of these models), but prospect theory remains the most descriptively viable account of how individuals make risky choices (but see Birnbaum, 2008), as dis-cussed in chapters in the two subsequent handbooks: Chapter 20 (2004) and Chapter 2 (2015).

Chapter 12 (1988) covers the themes of framing and mental accounting. One of the most important contributions of prospect theory is the idea that decisions might depend on how particular options are framed. In Tversky and Kahneman’s (1981) famous Asian Disease Problem, the majority of subjects are risk averse when the out-comes are framed as gains (lives saved). The pattern reverses when outcomes are framed as losses (lives lost). These two patterns are reflected in the classic S‐shape of prospect theory’s value function. Thaler (1985) extended the value function to risk-less situations in proposing a theory of mental accounting, defined as “the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities” (Thaler, 1999). These cognitive operations define how individuals categorize an activity as well as the relevant reference point, with pre-dictions derived from using properties of the prospect theory value function. Mental accounting continues to influence applications in economics and marketing (e.g., Chapter 19, 2004; Benartzi & Thaler, 1995; Hastings & Shapiro, 2013) and is most likely to remain a topic of interest for JDM researchers in the coming years. The main difficulty with framing is that the research on the topic is fragmented and there is cur-rently no unifying theory that can conjoin the different types of framing effects (Keren, 2011).

Chapter 13 (1988) discusses models that incorporate a decision maker’s potential affective reactions. The affective reaction in regret theory (Bell, 1982; Loomes & Sugden, 1982) is a between‐gamble comparison resulting from comparing a realized outcome with what outcome would have been if another option were chosen. In contrast, disappointment theory (Bell, 1985; Loomes & Sugden, 1986) invokes a within‐gamble comparison, in which a realized outcome is compared with other out-comes that were also possible for that option. Although the two theories, in particular regret theory, initiated extensive research on the psychological underpinnings of these emotions (e.g., Connolly & Zeelenberg, 2002; Mellers, Schwartz, Ho, & Ritov, 1997), these models are generally not considered serious candidates as a descriptive model for risky decision making (see Kahneman, 2011, p. 288, for an explanation). A broader discussion of the role of affective reactions in decision making is found in Chapter 22 (2004).


Risk measures are the topic of Chapter 14 (1988). Coombs proposed that the value of the gamble reflects its expected value and its perceived risk (Coombs & Huang, 1970). Many risk measures, including variance (Pollatsek & Tversky, 1970), have been proposed and studied (e.g., Luce, 1980). However, despite the intuitive appeal of Coombs’s proposition, attempts to relate measures of perceived risk empir-ically with descriptive or normative utility have at best yielded mixed results (e.g., E. U. Weber, 1988; E. U. Weber & Milliman, 1997).

Multiattribute decision making is the topic of Chapter 15 (1988). Many important decisions have multiple dimensions and thus the choice requires balancing and priori-tizing a number of conflicting objectives. Multiattribute utility models have been used in important real‐world decision analytic applications, such as the siting of Mexico City Airport (Keeney & Raiffa, 1976). Early empirical research on multiattribute decision making is summarized in Hüber (1974), von Winterfeldt and Fischer (1975), and von Winterfeldt and Edwards (1986, Chapter 10). Some of these studies found correla-tions between intuitive valuations of multiattributed options and valuations resulting from an elicited utility model in the 0.7 to 0.9 range (von Winterfeldt & Fischer, 1975). Other studies examined whether subjects obey the axioms underlying these models. These studies documented a number of biases, including violations of some independence conditions (von Winterfeldt, 1980), as well as response-mode effects in which the elicited weights depend on the mode of elicitation (see a summary of some of these results in M. Weber & Borcherding, 1993, as well as in Chapter 17, 2004).

Chapter 16 (1988) addresses intertemporal choice. In a standard intertemporal-choice problem, an individual must choose among outcomes of different sizes that can be received at different periods of time (see also Mischel & Grusec, 1967). A typ-ical example is a choice between $10 today and $11 tomorrow. The utility of a delayed $11 is some fraction of the utility of an immediate $11, with the discount because that outcome is received tomorrow. The classical model in economics, discounted utility (Koopmans, 1960), imposes constant discounting (the discount for delaying one day is the same for today or tomorrow as it is for one year and one year plus a day). Contrary to that model, impatience tends to decline over time, a pattern that is often summarized as hyperbolic discounting (Ainslie, 1975; Thaler, 1981). While the field has to a large extent accepted that discounting is best represented by a hyperbolic function, this tenet has been challenged recently in a stimulating paper by Read, Frederick, and Airoldi (2012). Loewenstein (1992) offers an excellent account of the history of intertemporal choice. Intertemporal choice remains a central topic in JDM research and is covered by Chapter 22 (2004) and Chapter 5 (2015).

The next part covers different approaches to judgment and decision making. The topic of Chapter 17 (1988) is hypothesis testing. Hypothesis testing has implications for a number of areas of judgment and decision making, including learning from experience (Chapter 7, 1988), tests of Bayesian reasoning (Chapter 3, 1974), and option generation. Fischhoff and Beyth‐Marom (1983) proposed that hypothesis testing could be compared to a Bayesian normative standard. This framework has implications for a number of stages of hypothesis testing: generation, testing (i.e., information collection), and evaluation. JDM researchers have documented biases in each of the stages, including showing that individuals generate an insufficient number of hypotheses (Fischhoff, Slovic, & Lichtenstein, 1978) and use confirmatory test


strategies (see Chapter 18, 1974; Klayman & Ha, 1987; Mynatt, Doherty, & Tweney, 1978). Other conceptual and empirical issues related to hypothesis testing are discussed in Chapter 10 (2004).

Chapter 18 (1988) covers algebraic decision models, such as information integration theory (Anderson, 1981). Algebraic models of these sorts use a linear combination rule to integrate cues to form a judgment and were put forth as theoretical frame-works for understanding human judgment. The promise of Anderson’s cognitive algebra was that it could help unpack some aspects of cognitive process, such as the role of different sources of information or the impact of various context effects (e.g., Birnbaum & Stegner, 1979).

Chapter 19 (1988) covers the Brunswikian approach. Hammond (1955) adapted Brunswik’s (1952) theory of perception to judgmental processes. For Brunswik, understanding perception required examining the interaction between an organism and its environment and understanding how the organism made sense of ambiguous sensory information. Hammond (1955) extended Brunswik’s ideas to clinical judg-ment, in his case a clinician estimating a patient’s IQ from the results of a Rorschach test. Hammond’s version of the Brunswik’s lens model related a criterion, say IQ, with some proximal cues, say the results of the Rorschach test, and the clinician’s judgment (see also Brehmer, 1976; Hammond et al., 1975). The Brunswikian view, as spelled out in Hammond et al.’s (1975) social judgment theory, has played a role in JDM research in emphasizing representative design, ecological validity, and more generally, the adaptive nature of human judgment (see Chapter 3, 2004).

The topic of Chapter 20 (1988) is the adaptive decision maker. Payne (1982) pro-posed that the decision process an individual uses is contingent on aspects of the decision task. Though pieces of this idea were found in Beach and Mitchell (1978), Russo and Dosher (1983), and Einhorn and Hogarth (1981), Payne’s proposal is fundamentally built on a proposition put forth by Simon (1955): “the task is to replace the global rationality of economic man with a kind of rational behavior that is compatible with the access to information and the computational capacities that are actually possessed by organisms, including man” (p. 99). Payne surveyed a number of task dimensions that influence which decision process is adopted, including task com-plexity, response modes, information display, and aspects of the choice set. Johnson and Payne (1985) further developed this idea by suggesting that decision makers trade off effort and accuracy and proposed an accounting system for testing this idea. Research on the adaptive decision maker is summarized in Payne, Bettman, and Johnson (1993) and Chapter 10 (2004).

Chapter 21 (1988) reviews process‐tracing methods designed for understanding the processes underlying judgment and decision making. Many mathematical models of judgment and decision making are paramorphic in the sense that they cannot distinguish between different underlying psychological mechanisms (Hoffman, 1960). Cognitive psychologists have introduced a set of process‐tracing methods that provide insight into how individuals process information (Newell & Simon, 1972). Ericsson and Simon (1984) developed methods for studying verbal protocols. The value of these methods was questioned in an influential study by Nisbett and Wilson (1977), who argued that subjects do not always have access to the reasons underlying their judgments and decisions. JDM researchers have employed other


methods that do not rely on self‐reports, such as eye tracking, information boards, or Mouselab (Johnson, Payne, & Bettman, 1988; Payne, 1976; Russo & Rosen, 1975). These methods continue to be used today, albeit not extensively despite the considerably lower costs of employing them (for some recent examples, see Costa‐Gomes & Crawford, 2006; Glöckner & Herbold, 2011; Johnson, Schulte‐Mecklenbeck, & Willemsen, 2008).

In the last part, we turn to applications of judgment and decision making. In their review on decision making, Slovic, Fischhoff, and Lichtenstein (1977) noted that “decision making is being studied by researchers from increasingly diverse set of dis-ciplines, including medicine, economics, education, political science, geography, engineering, marketing and management science as well as psychology” (p. 1). Arkes and Hammond’s (1986) collection of articles also captures this breadth with sections on social policy, economics, law, interpersonal conflict, medicine, social prediction, development, and expertise. Admittedly, the choice of applications for our hypothet-ical second handbook is somewhat arbitrary.

Chapter 22 (1988) is devoted to medical decision making. Although Lusted’s (1968) textbook on medical decision making showed a clear influence of judgment and decision making research with coverage of topics such as Bayesian analysis, hypothesis testing, and decision trees, Elstein (1976) nevertheless noted that “psychological research on human judgment and decision making has had little effect on medical practice.” That changed within a few years. A field journal, Medical Decision Making, started in 1981, with many of the early forays into medical decision making involved replicating JDM findings such as overconfidence (Christensen‐Szalanski & Bushyhead, 1981), hindsight bias (Arkes et al., 1981), or framing (McNeil et al., 1982). The topic of medical decision making appears in Chapter 29 (2004) and Chapter 27 (2015).

Judgment and decision making also had a major influence on negotiation research, the topic of Chapter 23 (1988). Bazerman and Neale (1983) argued that JDM research should be relevant for understanding negotiation because “negotiation is a decision making process in which parties jointly make decisions to resolve conflicting interests.” In a series of papers, Bazerman, Neale, and some of their collaborators demonstrated the role that JDM topics such as overconfidence, anchoring, and framing had on the cognition of negotiators. Many of these results are summarized in Neale and Bazerman (1991). The more recent connection between JDM and negoti-ation research is discussed in Chapter 29 (2015).

We noted earlier that Edwards (1954) translated economics ideas in a way that psy-chologists could understand. Thaler (1980) returned the favor by translating psychological research (mostly Kahneman and Tversky’s work) for economists. Chapter 24 (1988) reviews early research on behavioral economics, or economics in which economic actors are prone to decision errors that JDM researchers understand well (see Camerer & Loewenstein, 2004 for a history of behavioral economics). Although behavioral economics was still in its infancy at the end of this period, some major puzzles for classical economics had been established (e.g., DeBondt & Thaler, 1985; Kahneman, Knetsch, & Thaler, 1986). Behavioral economics and behavioral finance have shown a rapid growth since 1988. Koehler and Harvey contained a chapter on behavioral finance (Chapter 26, 2004), which might be thought of as


behavioral economics applied to understanding financial decision making and markets. Chapter 28 (2015) shows the enormous influence of behavioral economics on just about every subfield of economics.

The topic of Chapter 25 (1988) is risk perception. Risk perception is the study of the subjective perception of hazards and risks such as nuclear accidents or accidents involving motor vehicles. Slovic (1987) published an influential review article on risk perception, putting forth the puzzle of the large gap between lay and expert percep-tions of risk. Part of this gap can be explained by the influence of some of the heuris-tics and biases proposed by Kahneman and Tversky (Chapter 3, 1988), but much of this gap is due to other factors that influence lay perceptions of risk, such as control-lability or the catastrophic potential of a risk (see also Johnson & Tversky, 1983).

The chapters in our second hypothetical handbook reflect the continued maturity of the JDM field. We summarized the field in 1974 by highlighting its mathematical and cognitive orientation. In 1988, many aspects of JDM research reflected the same ped-igree. Indeed, the Society of Judgment and Decision Making’s first meeting in 1980 took place right after the meeting of the Psychonomic Society partly because many of JDM’s leading researchers attended that meeting (see Shanteau, 2003a, 2003b, 2004 for a history of the early years of the Society of Judgment and Decision Making).

However, JDM research had clearly spread into other areas of psychology (notably social psychology) and into economics and marketing, a trend that has continued to this day. Most notably, two of Kahneman and Tversky’s contributions, prospect theory and the heuristics and biases program, were becoming widely known in psy-chology and economics and other areas of the social sciences, partly because of the translational work of Nisbett and Ross (1980) and Thaler (1980).

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

The first actual Handbook of Judgment and Decision Making appeared in 2004. The preface to the volume captures the maturity of the field and reflects trends that have continued to this day. To illustrate, Koehler and Harvey (2004) write

The 1980s and 1990s also saw the field spread from its origins in psychology to other disciplines, a trend that had already begun in the 1970s. At present it is prob-ably the case that most judgment and decision making research is conducted outside psychology departments, reflecting in part the heavy recruiting of researchers in this area by business schools. (p. iv)

Koehler and Harvey’s chapters are listed in Figure 1.3. Many of the chapters found in that volume are traditional JDM topics and thus serve as natural follow‐ups to chapters in our two hypothetical handbooks. Because our first two handbooks were hypothetical, the chapters in Koehler and Harvey are more comprehensive and historical than they would likely have been in our hypothetical world. For example, there are chapters on risky decision making (Chapter 20, 2004), the calibration of probability judgments (Chapter 9, 2004), and the distinction between normative and descriptive (Chapter 1, 2004). Of course, even some of the traditional topics reflect



Part I: Approaches

1. Rationality and the Normative/Descriptive Distinction

2. Normative Models of Judgment and Decision Making

3. Social Judgment Theory: Applying and Extending Brunswik’s Probabilistic

Functionalism

4. Fast and Frugal Heuristics: The Tools of Bounded Rationality

5. Yet Another Look at the Heuristics and Biases Approach

6. Walking with the Scarecrow: The Information-Processing Approach to

Decision Research

7. Computational Models Of Decision Making

Part II: Judgments

8. Inside and Outside Probability Judgment

9. Perspectives on Probability Judgment Calibration

Part III: Decisions

17. Context and Conflict in Multiattribute Choice

18. Internal and Substantive Inconsistencies in Decision Making

19. Framing, Loss Aversion, and Mental Accounting

20. Decision Under Risk

21. Intertemporal Choice

22. The Connections between Affect and Decision Making: Nine Resulting Phenomena

23. Group Decision and Deliberation: A Distributed Detection Process

24. Behavioral Game Theory

25. Culture and Decisions

Part IV: Applications

26. Behavioral Finance

27. Judgment and Decision making Accounting Research: A Quest to Improve the

Production, Certification, and Use of Accounting Information

28. Heuristics, Biases, and Governance

29. The Psychology of Medical Decision Making

30. Judgment, Decision Making, and Public Policy

10. Hypothesis Testing and Evaluation

11. Judging Covariation and Causation

12. A Tale of Tuned Decks?Anchoring as Accessibility and Anchoring as

Adjustment

13. Twisted Pair: Counterfactual Thinking and the Hindsight Bias

14. Forecasting and Scenario Planning

15. Expertise in Judgment and Decision Making: A Case for Training Intuitive

Decision Skills

16. Debiasing

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


some substantial shifts in how researchers think about these phenomena. For example, Chapter 12 (2004), on anchoring, contrasts Kahneman and Tversky’s (1974) anchoring and adjustment account with Strack and Mussweiler’s (1997) selective accessibility explanation. Chapter 20 (2004) on decision under risk includes a discussion of new work on the probability weighting function and its implications for choice patterns such as the common‐consequence effect (Prelec, 1998; Tversky & Kahneman, 1992; Wu & Gonzalez, 1996). Chapter 17 (2004) on multiattribute choice reflects work on conflict and choice sets (Huber, Payne, & Puto, 1982; Simonson & Tversky, 1993) as well as research on reason‐based choice (Shafir, Simonson, & Tversky, 1992). However, several chapters represent fundamentally new research programs and would not have been part of the previous handbooks. We highlight some of these new lines of research below.

Gigerenzer’s Chapter 4 (2004) reflects a critique of Kahenman and Tversky’s heu-ristics and biases program (Gigerenzer, 1996; Gigerenzer, Hoffrage, & Kleinbölting, 1991). Other work in the Gigerenzer program argues for the evolutionary adaptive advantage of certain heuristics, such as the recognition heuristic (e.g., D. G. Goldstein & Gigerenzer, 2002). This research stream has been quite controversial, with some researchers suggesting that the work mischaracterizes the heuristics and biases program (Kahneman & Tversky, 1996) and others pointing to significant boundary conditions for these heuristics (Hogarth & Karelaia, 2006).

Busemeyer and Johnson’s Chapter 7 (2004), on computational models, discusses how connectionist models have been used to understand decision processes. The most influential work in this line is Busemeyer and Townsend’s (1993) decision field theory. That theory adopts a sequential sampling process to explain decision phe-nomena as disparate as violations of stochastic dominance and preference reversals (Chapter 9, 1974). Computational models have also been used to generate patterns that otherwise would be explained by loss aversion and nonlinear probability weights (e.g., Jessup, Bishara, & Busemeyer, 2008; Roe, Busemeyer, & Townsend, 2001; Chapter 22, 2015). These models have also been used to explain a wide variety of choice set effects (e.g., Bhatia, 2013; Chapter 4, 2015).

Hsee, Zhang, and Chen’s Chapter 18 (2004) expands on the traditional preference reversal (Chapter 9, 1974) to consider the differences between joint and separate evaluation modes of decision making. Hsee (1996) showed that the willingness to pay for a dictionary with 20,000 entries but a torn cover was higher in joint evaluation than that of a dictionary with 10,000 entries and an intact cover, but that the pattern was reversed when the dictionaries were considered in isolation. He argues that this reversal arises because attributes in the joint and separate mode differ in their evaluability.

Rottenstreich and Shu’s Chapter 22 (2004) examines the role of affect on decision making. Although researchers since James (1884) have debated what constitutes an emotion, it is nevertheless clear that emotions and other kinds of affective reactions such as mood influence decision making (see, also Zajonc, 1980). Some affective reactions directly influence how objects are evaluated (e.g., Isen, Nygren, & Ashby, 1988; Rottenstreich & Hsee, 2001), whereas choice settings sometimes produce affective reactions, such as regret, disappointment, anxiety, or anticipation directly (Bell, 1982, 1985; Loewenstein, 1987; Mellers et al., 1997; Wu, 1999). Although affect is clearly an important topic for decision making, research in this area, even


today, is still in its early stages, partly because of the large number of emotions relevant to decision making and their diffuse effects on information processing and evaluation and partly because of disputes among researchers about what constitutes an emotion. See Lerner, Li, Valdeso, and Kassam (2015) for a recent synthesis of the growing literature.

Choi, Choi, and Norenzayan’s Chapter 25 (2004) discusses cultural influences on decision making. The 1990s saw an explosion of research in cultural psychology, with much of this work spelling out the influence of culture on various aspects of psychological processing (e.g., Markus & Kitayama, 1991). Chapter 25 looks at a slice of this large literature, documenting some of the effects of culture on probabilistic judgments, risk preferences, and the search for information. The influence of culture research on JDM reflects a broader trend in which researchers use ideas advanced in other areas of psychology, such as motivation or dual process theories of cognition, to develop new implications for judgment and decision making (e.g., Kahneman & Frederick, 2002; Liberman, Idson, & Higgins, 2005; Chapters 11, 14, and 16, 2015).

Chapter 23 (1988) summarized the early influences of JDM research on eco-nomics. Chapter 26 (2004) illustrates the enormous influence of judgment and decision making on finance. Since that volume, financial economists have documented a number of anomalies in asset pricing that exist at the market level (e.g., Benzartzi & Thaler, 1995; Mehra & Prescott, 1985) as well as at the level of individual investors (Barber & Odean, 2000; Benartzi & Thaler, 2001; Odean, 1998). Many of these anomalies mirror standard JDM laboratory findings, with JDM staples such as prospect theory and overconfidence used as psychological explanations for the observed behavior.

A comparison of the 2004 handbook with our hypothetical 1988 handbook, clearly shows the continued growth and broadening of the JDM field. At the same time, the traditional topics of risky choice and probabilistic judgments remain central. To illus-trate, we consider one piece of data, the program for the 2004 Society for Judgment and Decision Making (SJDM) Conference in Minneapolis, the 25th annual conference of the society.9 The majority of the 24 paper sessions constituted topics that were fea-tured in one of the first hypothetical handbooks: risk (two sessions), ambiguity, inter-temporal choice, prospect theory, loss aversion and endowment, framing, mental accounting, calibration and confidence, anchoring, chance and probability, pricing and evaluating outcomes, negotiation and games, and orders and sequences. Of course, the individual papers reflected new influences and ideas such as the role of motivation or the advent of neuroscience tools. Other sessions featured relatively tra-ditional topics that were not quite central to warrant inclusion in one of the first two handbooks: social choice, cooperation and coordination, and fairness. A minority of the sessions captured new directions of the field: affect (three sessions) and happiness. As we discussed above, affect is part of Koehler and Harvey’s handbook, but happi-ness and well-being in hindsight is a clear omission (see, for example, Kahneman et al., 1993; Kahneman & Snell, 1990). In sum, the core topics that have dominated JDM research since the 1950s were still at the heart of the field in 2004, although the specific nature of the discourse clearly reflects the inflow of new ideas and discoveries, the advent of new research paradigms, and the influence of psychological consider-ations such as affect and motivation.


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

The final period is the topic of the current handbook. Although the JDM community is clearly larger than it was in 2004 (membership in SJDM increased by 62% from 884 members in 2004 to 1,435 members in 2013), Koehler and Harvey’s preface to their handbook could easily have been a piece of this introduction. The current handbook includes chapters that emphasize a number of traditional themes found in previous handbooks (Part I The Multiple Facets of Judgment and Decision Making: Traditional Themes), as well as some traditional themes that either have not appeared in previous handbooks or reflect contemporary thinking (see Part IV Old Issues Revisited). Our handbook also includes some contemporary topics that would not have appeared in one of our first fictional two handbooks (see Part II Relatively New Themes in Judgment and Decision Making), as well as some chapters that reflect the eclectic influence of other areas of psychology on JDM research (Part III New Psychological Takes on Judgment and Decision Making). Two of these chapters reflect increased interest in neuroscience and morality, an increase that is not unique to JDM researchers. Part V Applications illustrates the continued use of JDM ideas for understanding medical, legal, business, and public policy decisions, as well as the recent movement to use JDM ideas to “nudge” individuals or engineer better decisions (e.g., Thaler & Sunstein, 2008). Finally the last part is devoted to a topic that has preoccupied the field since its inception, “Improving Decision Making.”

A Bird’s-Eye View

We have divided the history of JDM into four periods – 1954–1972, 1972–1986, 1986–2002, and 2002 to the present – and undertaken the exercise of thrusting our-selves back in time as hypothetical handbook editors. Our history lesson reveals both change and constancy. In terms of change, the last 60 years have seen a remarkable growth of JDM as a field and probably more critically the impact of JDM research in psychology and economics, as well as in many other social science fields. Some particular research areas (e.g., risky decision making and probabilistic judgment) have endured and continue to be generative, while others (e.g., information theory and algebraic models) have either disappeared or become niche areas. And JDM has gone from a field with a solid center in cognitive psychology and a relatively strong reliance on mathematical models to a field that is now shaped by research in social psychology, neuroscience, and economics as well as cognitive psychology.

In terms of constancy, two core topics, risky decision making and judgment under uncertainty, defined the field in 1954 and continue to be central in JDM research today. In addition, a defining feature of the field is its preoccupation with the norma-tive standard, whether it consists of the axioms of probability or utility theory or some moral principle. In the concluding chapter of this handbook, we discuss how JDM research has been helped and hindered by this dichotomy as well as the gambling par-adigm. Finally, JDM continues to be an interdisciplinary field. Judgment and decision making conferences routinely bring together psychologists, economists, management scientists, philosophers, and statisticians, some of whom are also mixed together in


research collaborations. At first glance, this diversity of backgrounds seems unlikely. But we suspect that many of our field’s researchers were attracted to JDM for the same reasons that both of the editors were many years ago. Our judgments and our decisions are central to how our personal and professional lives unfold, and therefore JDM research plays an essential role in understanding and improving the decisions we make.

We end this chapter by speculating briefly on how the field may unfold in the coming decade. As JDM researchers, we probably should have learned of the falli-bility of human predictions. Nevertheless, we speculate briefly on some of the field’s probable directions. The first direction is neuroscience. Although the interest and expectations of linking neuroscience with judgment and decision making has already penetrated the field to the point that this volume includes a chapter on the topic (Chapter 9), neuroscience coverage of JDM topics is still somewhat spotty, with considerable emphasis on some topics (e.g., loss aversion, ambiguity, fairness, and moral judgments) and a lack of emphasis on others (e.g., probabilistic judgments). Moreover, some researchers have already expressed skeptical views that question the limits and sometimes the merit of this approach (e.g., Fodor, 1999; Poldrack, 2006). Second, we anticipate a larger role of JDM in aiding and shaping real‐world decisions. The importance of this topic is reflected in research that uses JDM and social psychological research to change human behavior and decision making (Chapter 25; Thaler & Sunstein, 2008). However, we hope that JDM also plays a role in the development of decision support systems and decision aids. Although many studies have shown the clear advantage of statistical over intuitive judgment (see Chapter 4, 1974), other studies have also demonstrated a clear preference for the latter (see Arkes, Shaffer, & Medow, 2007). Clearly, JDM can and should play a large role in helping to develop decision aids that humans are more likely to embrace (see note 8).

We end by turning to what topics JDM should but may not necessarily embrace. Our review of 60 years of JDM research raises two inescapable questions for the field: Have we really tackled the most important issues associated with judgment and decision making? Or has the field fallen victim to the streetlight effect, in which we have mainly addressed problems for which we have a ready‐made methodology (much as a drunkard would look for keys under the street light because it is easy to do so, not because the keys are likely to be there)? We hope that we have provided readers with the background and the inspiration to tackle both of these questions on their own, but in our minds, the answer to both questions is equivocal: sort of yes, and sort of no. It is clear to us that we have learned an enormous amount over the last 60 years about how judgments and decisions are made, but it is equally clear to us that the field has been relatively silent on many vital questions. Important issues such as how decisions are represented, how options or hypotheses are generated, the role of crea-tivity in inventing potential solutions and making decisions, how reference points are formed or adapted, and how decisions involving intangible outcomes are made, to mention just a few, have received little if any attention. In our concluding chapter, we suggest some principles for organizing the next 60 years of judgment and decision making research that we hope will shape the selection of research questions as well as the quality of the impact of research in these areas. In doing so, we hope that the field continues to attract researchers for the same reasons both of the editors fell


in love with the area many years ago. Our judgments and our decisions are central to how our personal and professional lives unfold, and therefore JDM research plays an essential role in understanding and improving the decisions we make.

Notes

1. Although psychological handbooks have become increasingly popular in recent years, this initial appearance was nevertheless long overdue. Handbooks of social psychology and experimental psychology appeared much earlier (e.g., Lindzey, 1954; Murchison, 1935; Stevens, 1951). Even relatively specialized handbooks in subfields such as mathematical psychology, and learning and cognitive processes predate Koehler and Harvey (2004) by decades (e.g., Estes, 1978; Luce, Bush, & Galanter, 1963).

2. Two years reflects the time from when authors first start drafting a handbook chapter to the publication of the eventual handbook. The planning fallacy (Buehler, Griffin, & Ross, 1994) and our recent experience suggests that even two years may be optimistic.

3. This perspective is reminiscent of George Boole who believed that his Boolean algebra was not just a mathematical branch but also served as a descriptive model of human thought. Accordingly, his book was entitled An Investigation of the Laws of Thought (1854).

4. The program for these conferences and all others can be found here: http://eadm.eu/spudm_history/. The 1969 program, in particular, reflects many of the topics discussed in this section. This conference is now run by the European Association for Decision Making (EADM). The Society for Judgment and Decision Making (SJDM) conference is compar-atively younger, running from 1980 to the present. For a complete list of SJDM programs, see http://www.sjdm.org/history.html. The collection of newsletters also provides an impressionist perspective on how the field has changed: http://www.sjdm.org/newsletters/.

5. We list the ideal set of chapters. The actual set of chapters, of course, reflects the avail-ability of the right authors and the ability of these authors to produce the requisite chapter over a reasonable time frame.

6. One criticism of JDM research that we address in the concluding chapter is that current JDM research produces piecemeal knowledge and as a result does not always contribute to a well‐defined unitary picture. In the early stages of the field’s development, leading JDM researchers struggled with measurement issues, as reflected in the three volumes on the foundations of measurement (Krantz, Luce, Suppes, & Tversky, 1971; Luce, Krantz, Suppes, & Tversky, 1990; Suppes, Krantz, Luce, & Tversky, 1989). Although Cliff (1992) noted that the developers of measurement theory were “among the most creative and pro-ductive minds in scientific psychology” (p. 186) and proposed that this work should be considered as one of the intellectual achievements of that time, it is also clear that measurement theory had little impact on JDM research, perhaps because these ideas are viewed too abstract or complex.

7. Part of the connection is due, once again, to Ward Edwards, who became a major contrib-utor to many aspects of decision analysis (see von Winterfeldt & Edwards, 1986).

8. One topic not addressed in any of our handbook chapters is the design of decision expert systems. Experts systems have been employed to aid decisions in a wide range of areas, including engineering, medicine, oil drilling, traffic control, and weather forecasting. Although research in this area has primarily been the province of information systems scholars, the topic is clearly relevant for JDM researchers. For instance, should a physician make a diagnosis based on her intuition or consult a medical expert system? JDM research

http://eadm.eu/spudm_history/

http://eadm.eu/spudm_history/

http://www.sjdm.org/history.html

http://www.sjdm.org/newsletters/

http://www.sjdm.org/newsletters/


strongly supports the superiority of experts systems in many domains (e.g., Chapter 4, 1974). However, in many cases, decision makers strongly prefer to use a human expert. For instance, Arkes, Shaffer, and Medow (2007) found that participants have an unequiv-ocal preference for physicians who do not use computer‐assisted diagnostic support sys-tems over those who use them: “participants always deemed the physician who used no decision aid to have the highest diagnostic ability” (p. 189). These results and many others strongly suggest that JDM researchers can (and should) play a role in designing expert systems that are more likely to be endorsed by human users over human judgment.

9. The program is available here: http://www.sjdm.org/programs/2004-program.pdf.

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