1. Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
    The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important...
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  2. Amos Tversky & Daniel Kahneman (1993). Probabilistic Reasoning. In Alvin Goldman (ed.), Readings in Philosophy and Cognitive Science. Cambridge: MIT Press 43--68.
     
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    Daniel Kahneman & Amos Tversky (1979). On the Interpretation of Intuitive Probability: A Reply to Jonathan Cohen. Cognition 7 (December):409-11.
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    Daniel Kahneman & Amos Tversky (1982). Variants of Uncertainty. Cognition 11 (2):143-157.
  5. David Krantz, Duncan Luce, Patrick Suppes & Amos Tversky (eds.) (1971). Foundations of Measurement, Vol. I: Additive and Polynomial Representations. New York Academic Press.
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    Amos Tversky (1967). Utility Theory and Additivity Analysis of Risky Choices. Journal of Experimental Psychology 75 (1):27.
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    Daniel Kahneman & Amos Tversky (1982). On the Study of Statistical Intuitions. Cognition 11 (2):123-141.
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    Amos Tversky (1981). L. J. Cohen, Again: On the Evaluation of Inductive Intuitions. Behavioral and Brain Sciences 4 (3):354-356.
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    Amos Tversky (1975). A Critique of Expected Utility Theory: Descriptive and Normative Considerations. Erkenntnis 9 (2):163 - 173.
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    Glenn Shafer & Amos Tversky (1985). Languages and Designs for Probability Judgment. Cognitive Science 9 (3):309-339.
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    Amos Tversky & Itamar Gati (1978). Studies of Similarity. In Eleanor Rosch & Barbara Lloyd (eds.), Cognition and Categorization. Lawrence Elbaum Associates 1--1978.
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  12. Patrick Suppes, David Krantz, Duncan Luce & Amos Tversky (eds.) (1989). Foundations of Measurement, Vol. II: Geometrical, Threshold, and Probabilistic Representations. New York Academic Press.
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  13. Daniel Kahneman & Amos Tversky (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica: Journal of the Econometric Society:263--291.
    The following values have no corresponding Zotero field: PB - JSTOR.
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  14. Duncan Luce, David Krantz, Patrick Suppes & Amos Tversky (eds.) (1990). Foundations of Measurement, Vol. III: Representation, Axiomatization, and Invariance. New York Academic Press.
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    Amos Tversky & Ward Edwards (1966). Information Versus Reward in Binary Choices. Journal of Experimental Psychology 71 (5):680.
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    Daniel Kahneman & Amos Tversky (1983). Can Irrationality Be Intelligently Discussed? Behavioral and Brain Sciences 6 (3):509.
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    Daniel Kahneman & Amos Tversky (1982). A Reply to Evans. Cognition 12 (3):325-326.
  18. Philip N. Johnson-Laird, Eldar Shafir, Itamar Simonson, Amos Tversky, P. Legrenzi, V. Girotto, Pn Johnson-Laird, Edward E. Smith, Daniel Osherson & Nancy Pennington (1993). Numbers L-2. Cognition 49 (297):297.
     
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  19. Amos Tversky & Daniel Kahneman (2000). The Notion of Cognitive Bias. In Raymond Boudon & Mohamed Cherkaoui (eds.), Central Currents in Social Theory. Sage Publications 8--349.
  20. Daniel Kahneman & Amos Tversky (1973). On the Psychology of Prediction. Psychological Review 80 (4):237-251.
    Considers that intuitive predictions follow a judgmental heuristic-representativeness. By this heuristic, people predict the outcome that appears most representative of the evidence. Consequently, intuitive predictions are insensitive to the reliability of the evidence or to the prior probability of the outcome, in violation of the logic of statistical prediction. The hypothesis that people predict by representativeness was supported in a series of studies with both naive and sophisticated university students. The ranking of outcomes by likelihood coincided with the ranking by (...)
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  21. Amos Tversky & Daniel Kahneman (1973). Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology 5 (2):207-232.
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  22. Amos Tversky (1974). Assessing Uncertainty. Journal of the Royal Statistical Society. Series B 36 (2):148-159.
    Intuitive judgments of probability are based on a limited number of heuristics that are usually effective but sometimes lead to severe and systematic errors. Research shows, for example, that people judge the probability of a hypothesis by the degree to which it represents the evidence, with little or no regard for its prior probability. Other heuristics lead to an overestimation of the probabilities of highly available or salient events, and to overconfidence in the assessment of subjective probability distributions. These biases (...)
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  23. Amos Tversky & Daniel Kahneman (1974). Judgment Under Uncertainty: Heuristics and Biases. Science 185 (4157):1124-1131.
    This article described three heuristics that are employed in making judgements under uncertainty: 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; 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 adjustment from an anchor, which is usually employed in numerical prediction when a relevant value (...)
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