Judgment Under Uncertainty: Heuristics and Biases

Front Cover
Daniel Kahneman, Paul Slovic, Amos Tversky
Cambridge University Press, Apr 30, 1982 - Psychology - 555 pages
11 Reviews
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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Review: Judgment Under Uncertainty: Heuristics and Biases

User Review  - Dave Peticolas - Goodreads

A collection of research papers investigating the ways the human mind estimates probability. A lot of intriguing material, but also lots of dry writing. I must confess, I did a lot of skimming. Read full review

Review: Judgment Under Uncertainty: Heuristics and Biases

User Review  - Usman - Goodreads

Very insightful book. It shows deviations from mathematical thinking and leans towards descriptive research. It is self contained and accessible. Recommended to people who want a strong grasp on decision sciences. Read full review

Selected pages

Contents

Judgment under uncertainty Heuristics and biases
3
Belief in the law of small numbers
23
Subjective probability A judgment of representativeness
32
On the psychology of prediction
48
Studies of representativeness
69
Judgments of and by representativeness
84
Popular induction Information is not necessarily informative
101
Causal schemas in judgments under uncertainty
117
Overconfidence in casestudy judgments
287
A progress report on the training of probability assessors
294
Calibration of probabilities The state of the art to 1980
306
For those condemned to study the past Heuristics and biases in hindsight
335
Evaluation of compound probabilities in sequential choice
355
Conservatism in human information processing
359
The bestguess hypothesis in multistage inference
370
Inferences of personal characteristics on the basis of information retrieved from ones memory
378

Shortcomings in the attribution process On the origins and maintenance of erroneous social assessments
129
Evidential impact of base rates
153
Availability A heuristic for judging frequency and probability
163
Egocentric biases in availability and attribution
179
The availability bias in social perception and interaction
190
The simulation heuristic
201
Informal covariation assessment Databased versus theorybased judgments
211
The illusion of control
231
Test results are what you think they are
239
Probabilistic reasoning in clinical medicine Problems and opportunities
249
Learning from experience and suboptimal rules in decision making
268
The robust beauty of improper linear models in decision making
391
The vitality of mythical numbers
408
Intuitive prediction Biases and corrective procedures
414
Debiasing
422
Improving inductive inference
445
Facts versus fears Understanding perceived risk
463
On the study of statistical intuitions
493
Variants of uncertainty
509
References
521
Index
553
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About the author (1982)

Daniel Kahneman received the 2002 Nobel Prize in Economic Sciences for his pioneering work with Amos Tversky on decision-making.

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