Judgment under Uncertainty

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 p...

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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.

Part I. Introduction:
1. Judgment under uncertainty: Heuristics and biases
Amos Tversky and Daniel Kahneman
Part II. Represent ativeness:
2. Belief in the law of small numbers
Amos Tversky and Daniel Kahneman
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Part I. Introduction:
1. Judgment under uncertainty: Heuristics and biases
Amos Tversky and Daniel Kahneman
Part II. Represent ativeness:
2. Belief in the law of small numbers
Amos Tversky and Daniel Kahneman
3. Subjective probability: A judgment of representativeness
Daniel Kahneman and Amos Tversky
4. On the psychology of prediction
Daniel Kahneman and Amos Tversky
5. Studies of representativeness
Maya Bar-Hillel
6. Judgments of and by representativeness Amos Tversky and Daniel Kahneman
Part III. Causality and Attribution:
7. Popular induction: Information is not necessarily informative
Richard E. Nisbett, Eugene Borgida, Rick Crandall and Harvey Reed
8. Causal schemas in judgments under uncertainty
Amos Tversky and Daniel Kahneman
9. Shortcomings in the attribution process: On the origins and maintenance of erroneous social assessments
Lee Ross and Craig A. Anderson
10. Evidential impact of base rates
Amos Tversky and Daniel Kahneman
Part IV. Availability:
11. Availability: A heuristic for judging frequency and probability
Amos Tversky and Daniel Kahneman
12. Egocentric biases in availability and attribution
Michael Ross and Fiore Sicoly
13. The availability bias in social perception and interaction
Shelley E. Taylor
14. The simulation heuristic
Daniel Kahneman and Amos Tversky
Part V. Covariation and Control:
15. Informal covariation asssessment: data-based versus theory-based judgments
Dennis L. Jennings, Teresa M. Amabile and Lee Ross
16. The illusion of control
Ellen J. Langer
17. Test results are what you think they are
Loren J. Chapman and Jean Chapman
18. Probabilistic reasoning in clinical medicine: problems and opportunities
David M. Eddy
19. Learning from experience and suboptimal rules in decision making
Hillel J. Einhorn
Part VI. Overconfidence:
20. Overconfidence in case-study judgments
Stuart Oskamp
21. A progress report on the training of probability assessors
Marc Alpert and Howard Raiffa
22. Calibration of probabilities: the state of the art to 1980
Sarah Lichtenstein, Baruch Fischhoff and Lawrence D. Phillips
23. For those condemned to study the past: heuristics and biases in hindsight
Baruch Fischhoff
Part VII. Multistage Evaluation:
24. Evaluation of compound probabilities in sequential choice
John Cohen, E. I. Chesnick and D. Haran
25. Conservatism in human information processing
Ward Edwards
26. The best-guess hypothesis in multistage inference
Charles F. Gettys, Clinton Kelly III and Cameron R. Peterson
27. Inferences of personal characteristics on the basis of information retrieved from one’s memory
Yaacov Trope
Part VIII. Corrective Procedures:
28. The robust beauty of improper linear models in decision making
Robyn M. Dawes
29. The vitality of mythical numbers
Max Singer
30. Intuitive prediction: biases and corrective procedures
Daniel Kahneman and Amos Tversky
31. Debiasing
Baruch Fischhoff
32. Improving inductive inference
Richard E. Nesbett, David H. Krantz, Christopher Jepson and Geoffrey T. Fong
Part IX. Risk Perception:
33. Facts versus fears: understanding perceived risk
Paul Slovic, Baruch Fischhoff and Sarah Lichtenstein
Part X. Postscript:
34. On the study of statistical intuitions
Daniel Kahneman and Amos Tversky
35. Variants of uncertainty
Daniel Kahneman and Amos Tversky
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