Judgment under Uncertainty: Heuristics and Biases

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Daniel Kahneman, Paul Slovic, Amos Tversky
Cambridge University Press, 30 apr 1982
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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.

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A wealth of information on the subject. Looking forward to subsequent volume that adds 20 years of additional research Leggi recensione completa


List of contributors
Belief in the law of small numbers
A judgment of representativeness
Daniel Kahneman and Amos Tversky
Studies of representativeness
Judgments of and by representativeness
Information is not necessarily informative
Causal schemas in judgments under uncertainty
2O Overconfidence in casestudy judgments
The state of the art to 1980
Heuristics and biases
Evaluation of compound probabilities in sequential choice
The bestguess hypothesis in multistage inference
The robust beauty of improper linear models in decision making
The vitality of mythical numbers

On the origins
Evidential impact of base rates
A heuristic for judging frequency and probability
Egocentric biases in availability and attribution
The simulation heuristic
The illusion of control
Learning from experience and suboptimal rules in decision
Improving inductive inference
Understanding perceived risk
On the study of statistical intuitions
Variants of uncertainty

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