Introduction, Mandeep K. Dhami, Anne Schlottmann, and Michael R. Waldmann

In conclusion, rather than present a summary of the preceding chapters, we invited nine eminent past presidents of the Society for Judgment and Decision Making (SJDM) to provide personal perspectives on the concept of JDM as a dynamic skill. These scholars were not asked to comment on the chapters in this book, but rather to highlight their personal points of contact with the notion of JDM as a dynamic skill. The following perspectives offer historical accounts, and also point to future lines of research.

Shanteau describes how over the years he has highlighted the importance of training and skill acquisition in JDM, but feels “blue” that this view has not been more popular. Wallsten remembers the benefits of learning for JDM performance found in a study that he conducted 30 years ago, and confesses that he has only recently begun to revisit this important finding. Fischhoff points out that a sound understanding of the normative implications of tasks has laid a better foundation for the study of dynamically changing skills, especially in development. Levin and colleagues provide useful examples of their research on the developmental and neurological bases of JDM skills. Reyna highlights how her fuzzy trace theory taps into JDM processes that develop over time and experience, has neurological correlates, and may be evolutionarily adaptive. Baron reveals how he now finds himself in search of the developmental origins of the types of moral heuristics and biases that he has studied during his career. Hogarth shares three steps he has developed during decades of teaching decision making that can help people make better decisions. Klayman reveals that despite decades of studying learning and development of JDM, he still seeks a greater understanding of how decision makers “get that way.” Finally, Birnbaum points to the methodological factors that have limited our understanding of JDM as a skill, and presents a challenge for future researchers: to explain how and why JDM skills change. Overall, the following perspectives provide a rare glimpse of the personalized views of those who have made significant contributions to the field of human JDM.

A central theme of this book is how people learn from samples. In this chapter, we look at this issue from two viewpoints. First, we question the manner in which we – as scientists – sample the environments of the experimental participants who engage in the judgment and decision-making tasks that we study. To what extent are these samples representative of the natural decision-making ecology that our participants face in their everyday lives? Second, by actually sampling people's activities in their natural ecologies, we seek to characterize how they experience these environments. In particular, we investigate how one feature of environments (the presence or absence of feedback) affects inferences (the confidence people express in their decisions).

The first question is, of course, not new. For many years, psychologists have been concerned about how to generalize behavior from experimental evidence (see, e.g., Brunswik, 1956; Chapanis, 1961, 1967; Cronbach, 1975; Ebbesen & Konečni, 1980; Hammond, 1978; Hogarth, 1986; Lipshitz et al., 2001). I do not propose to add to this debate. Instead, the contribution is to demonstrate how the use of readily available technology can greatly facilitate random sampling of decision behavior outside the psychological laboratory. Indeed, as I shall argue in the following, obtaining appropriate samples of human decision behavior is not as difficult as might be imagined by researchers trained within experimental paradigms.

INTRODUCTION

Why are normative theories so prevalent in the study of judgment and choice, yet virtually absent in other branches of science? For example, imagine that atoms and molecules failed to follow the laws supposed to describe their behavior. Few would call such behavior irrational or suboptimal. However, if people violate expected utility axioms or do not revise probabilities in accord with Bayes's theorem, such behavior is considered suboptimal and perhaps irrational. What is the difference, if any, between the two situations? In the latter we implicitly assume that behavior is purposive and goal-directed while this is less (if at all) obvious in the former. (It is problematic how one might treat plant and animal behavior according to a descriptive–normative dichotomy.) Therefore, if one grants that behavior is goal-directed, it seems reasonable to assume that some ways of getting to the goal are better, in the sense of taking less time, making fewer errors, and so on, than others. Indeed, much of decision research concerns evaluating and developing ways for improving behavior, thereby reflecting a strong engineering orientation (Edwards, 1977; Hammond, Mumpower and Smith, 1977; Keeney and Raiffa, 1976). Moreover, comparison of actual behavior with normative models has been important in focusing attention on the discrepancies between them, and this in turn has raised important questions about the causes of such discrepancies.

Jacob Bronowski (1978, pp. 78–9) tells the following story about Bertrand Russell, who is reputed once to have said at a dinner party:

Now consider the following from Emerson's essay, “Self-reliance”:

The above passages reflect the conflict that lies at the heart of current research in behavioral decision theory – namely, the importance of consistency in following rules, axioms, and the like, versus abandoning rules in particular cases when judgments and choices seem to imply a “foolish consistency.” The present addendum to our review of behavioral decision theory considers this issue briefly (for more details, see Einhorn and Hogarth, 1981). We begin by examining one example of the recent work on “cognitive illusions” (Tversky and Kahneman, 1981).