Judgment under Uncertainty: Heuristics and BiasesThe 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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Inhalt
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 | |
Debiasing | |
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 | |
Problems | |
Learning from experience and suboptimal rules in decision | |
Improving inductive inference | |
Understanding perceived risk | |
On the study of statistical intuitions | |
Variants of uncertainty | |
References | |
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Judgment Under Uncertainty: Heuristics and Biases Daniel Kahneman,Stewart Paul Slovic,Paul Slovic,Amos Tversky,Cambridge University Press Eingeschränkte Leseprobe - 1982 |
Häufige Begriffe und Wortgruppen
accuracy addicts Amos Tversky analysis answer asked assessments attribution attribution theory availability availability heuristic average base rate base-rate behavior beliefs bias biases biopsy calibration cancer causal clinical clinicians cognitive condition confidence consensus information correct correlation covariation Daniel Kahneman decision diagnostic distribution effect errors estimate evaluation evidence example expected experience experimental factors feedback Fischhoff forecasts fractiles frequency given grade heuristic hindsight bias hypothesis illusory correlation individual inferences interquartile range intuitive Journal of Personality judged judgments Kahneman & Tversky Lichtenstein likelihood linear models mammogram mammography Nisbett observed odds one’s outcomes overconfidence patient people’s perception performance population posterior probability prediction presented prior probabilities probabilistic problem procedure proper linear model question random reasoning regression relevant reported representativeness representativeness heuristic response retrieval risk Ross rule sample score significant similar situations Slovic Social Psychology statistical strategies subjective probability target task theory uncertainty validity variables