AI Bibliography
AI Bibliography |
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Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. |
| Resource type: Journal Article BibTeX citation key: Tversky1974 View all bibliographic details  |
Categories: Cognitive Science, Complexity Science, Decision Theory, General Subcategories: Decision making, Game theory, Human decisionmaking, Human learning Creators: Kahneman, Tversky Publisher: Collection: Science |
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| Abstract |
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This article described three heuristics that are employed in making judgments under uncertainty: (i) representativeness, which is usually employed when people are asked to judge the probability that an object or event A belongs to class or process B; (ii) availability of instances or scenarios, which is often employed when people are asked to assess the frequency of a class or the plausibility of a particular development; and (iii) adjustment from an anchor, which is usually employed in numerical prediction when a relevant value is available. These heuristics are highly economical and usually effective, but they lead to systematic and predictable errors. A better understanding of these heuristics and of the biases to which they lead could improve judgments and decisions in situations of uncertainty.
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