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Judgment Under Uncertainty: Heuristics and Biases

Page history last edited by PBworks 13 years, 1 month ago

Notes on Kahneman D., Slovic P., and Tversky, A. (Eds.) (1982) Judgment Under Uncertainty: Heuristics and Biases. New York: Cambridge University Press (Google preview)

 

Up: Key References

 

This collection of research papers is the seminal reference in what is known as the "Heuristics and Biases Research Programme" in psychology. In other words, the research comparing how judgements and decisions by real people differ from the predictions of rational choice theory.

 

If you just want a little paperback which summarises this research and explains its relevance to real-world applications in business, military, politics, medicine and the law, get Sutherland's Irrationality: the Enemy Within. If you want an integrated textbook treatment of the topic, go to Baron's Thinking and Deciding.

 

My personal interest is more in the "hot cognition" topics of cognitive dissonance and self-enhancement, but this book is essential for any scholar of how human thinking goes wrong, especially if intrigued by the above books.

 

Outline of the Book

 

  • Part I. Introduction
  • Part II. Representativeness
    • 2. Belief in the law of small numbers (Amos Tversky and Daniel Kahneman) (online as a .doc)
    • 4. On the psychology of prediction (Daniel Kahneman and Amos Tversky) (online at APA PsycNET)
    • 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) (online at ScienceDirect: subscription required)
    • 12. Egocentric biases in availability and attribution (Michael Ross and Fiore Sicoly) (online at APA PsycNET)
    • 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) [see my notes on Illusion of Control]
    • 17. Test results are what you think they are (Loren J. Chapman and Jean Chapman) [pioneering paper on illusory correlation]
    • 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) (Online at APA PsycNET)
    • 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) (online at Nature.com)
    • 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) (online at ScienceDirect: subscription required)
    • 27. Inferences of personal characteristics on the basis of information retrieved from one’s memory (Yaacov Trope) (online at APA PsycNET)
  • Part VIII. Corrective Procedures
    • 28. The robust beauty of improper linear models in decision making (Robyn M. Dawes) (online at APA PsycNET)
    • 29. The vitality of mythical numbers (Max Singer) (reprinted in the Arizona Skeptic)
    • 30. Intuitive prediction: Biases and corrective procedures (Daniel Kahneman and Amos Tversky)
    • 31. Debiasing (Baruch Fischhoff)
    • 32. Improving inductive inference (Richard E. Nisbett, 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

 

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