Judgment under Uncertainty: Heuristics and Biases

by Edited by Daniel Kahneman , Paul Slovic , Amos Tversky

• ISBN13: 9780521284141
• ISBN10: 0521284147
• Format: Paperback
• Copyright: 1982-04-30
• Publisher: Cambridge University Press

Summary

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.

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.