Judgment under Uncertainty: Heuristics and Biases
Abstract
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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The fact that cognitive psychologists are able to recognize the frailties of human reasoning, and that subjects in psychological experiments perform better when their errors have been pointed out to them, suggests that biases and heuristics are not so pervasive as to cloud our reasoning completely.
“The very fact that cognitive psychologists (e.g., Kahneman et al., 1982) are able to recognize the frailties of human reasoning, and that subjects in psychological experiments perform better when their errors have been pointed out to them, suggests that biases and heuristics are not so pervasive as to cloud our reasoning completely”
From Evolutionary Approaches to Epistemic Justification (2011) · cites this paper as Kahneman et al., 1982
Diagnostic errors are often attributed to cognitive and/or systems-related factors.
“Although diagnostic errors may occur when signs of a disease are atypical or absent, diagnostic errors are often attributed to cognitive (e.g., faulty clinical reasoning) and/or systems-related factors (e.g., inefficient processes and poor communication7,9–21).”
From Reducing Diagnostic Errors through Effective Communication: Harnessing the Power of Information Technology (2008) · cites this paper as [13]
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- Cambridge University Press eBooks
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- Cambridge University Press
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- 1982-04-30
- OpenAlex
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- 2026: 165 · 2025: 249 · 2024: 354 · 2023: 292 · 2022: 295 · 2021: 335 · 2020: 304 · 2019: 292 · 2018: 262 · 2017: 257 · 2016: 213 · 2015: 246 · 2014: 249 · 2013: 256 · 2012: 279
- FWCI
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- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Social Sciences › Decision Sciences › Management Science and Operations Research › Leadership, Behavior, and Decision-Making Studies
- Keywords
- judgment under uncertainty, heuristics, cognitive biases, representativeness heuristic, availability heuristic, overconfidence, social perception, diagnosis, risk perception
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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