Heuristics and Biases: The Psychology of Intuitive Judgment
Thomas Gilovich, Dale Griffin, Daniel Kahneman
Cambridge University Press, Jul 8, 2002 - Psychology - 857 pages
Is our case strong enough to go to trial? Will interest rates go up? Can I trust this person? Such questions - and the judgments required to answer them - are woven into the fabric of everyday experience. This book, first published in 2002, examines how people make such judgments. The study of human judgment was transformed in the 1970s, when Kahneman and Tversky introduced their 'heuristics and biases' approach and challenged the dominance of strictly rational models. Their work highlighted the reflexive mental operations used to make complex problems manageable and illuminated how the same processes can lead to both accurate and dangerously flawed judgments. The heuristics and biases framework generated a torrent of influential research in psychology - research that reverberated widely and affected scholarship in economics, law, medicine, management, and political science. This book compiles the most influential research in the heuristics and biases tradition since the initial collection of 1982 (by Kahneman, Slovic, and Tversky).
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accuracy adjustment affect heuristic affective forecasts alternative Amos Tversky anchoring anchoring and adjustment asked assessments attribute availability heuristic average bank teller base rate base-rate Bayesian Bayesian network behavior belief bias calibration causal choice cognitive compared condition conjunction fallacy conjunction rule consider contagion correlation counterfactual cues decision domain effect errors estimates evaluation evidence example expected experience explicit disjunction factors Fischhoff forecasts frequency frequentistic heuristics and biases hypothesis implies individual inferences influence interpretation intuitive judged probability law of large less Linda negative normative observed outcome overconfidence participants people's performance person perspective planning fallacy players positive posterior probability predictions prior probability probability judgments problem Psychology question rationality reasoning recall regression relevant representativeness representativeness heuristic response risk sample Schwarz similar Slovic statistical strategy subadditivity subjects suggests support theory target task tion Tversky & Kahneman unpacking variables weight