Judgment under Uncertainty
Heuristics and Biases

Table of Contents

Preface
Part I. Introduction:
1. Judgment under uncertainty: heuristics and biases Amos Tversky and Daniel Kahneman
Part II. Representativeness:
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 presiction 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
References
Index.

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• Table of Contents (132 KB)
• Index (169 KB)
• Front Matter (399 KB)
• Marketing Excerpt (637 KB)
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