Judgment Under Uncertainty (Kahneman, Slovic, Tversky, 1982)

"The papers chosen for this volume are an excellent collection, generally well-written and fascinating." Journal of Economic Literature

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"...an important and well-written book." Journal of the American Statistical Association

"...a good collection of papers on an important topic." Quarterly Journal of Experimental Psychology

"Clearly, this is an important book. Anyone who undertakes judgment and decision research should own it." Contemporary Psychology

Book Description
The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important social, medical, and political situations as well. Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

xi Meehl's classic book, published in 1954 [Clinical versus statistical prediction: A theoretical analysis and a review of the evidence], summarized evidence for the conclusion that simple linear combinations of cues outdo the intuitive judgments of experts in predicting significant behavioral criteria.

p.49 The thesis of this paper is that people predict by representativeness, that is, they select or order outcomes by the degree to which the outcomes represent the essential features of the evidence. In many situations, representative outcomes are indeed more likely than others.

p.58 the representativeness hypothesis, however, entails that prediction and evaluation should coincide...Further evidence for the equivalence of evaluation and prediction was obtained in a master's thesis by Beyth (1972).

p.65 As demonstrated in the preceding sections, one predicts by selecting the outcome that is most representative of the input.

p.69 Daniel Kahneman and Amos Tversky have proposed that when judging the probability of some uncertain event people often resort to heuristics, or rules of thumb, which are less than perfectly correlated (if, indeed, at all) with the variables that actually determine the event's probability. One such heuristic is representativeness, defined as a subjective judgment of the extent to which the event in question "is similar in essential properties to its parent population" or "reflects the salient features of the process by which it is generated" (Kahneman & Tversky, 1972b, p. 431,3).

p.72-73 Olson (1976) pointed out that although "the notion of judgment based on representativeness enjoys considerable support, both experimental and introspective, in a wide range of judgmental situations," it is not complete until we can determine "the factors that make particular task and problem characteristics the salient ones with respect to which representativeness is judged" (p.608)... these characteristics can be discovered by asking people to render probability judgments for a suitably selected set of samples... probability judgments are used not to confirm representativeness but to infer representativeness.

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