Kahneman, D., and Tversky, A. (1982). Judgment of and by representativeness. In Kahneman, D., Slovic, P., and Tversky, A. (Eds.),Judgment under Uncertainty: Heuristics and Biases. New York: Cambridge University Press. Home/Search Document Not in Database Summary Related Articles Check |
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Research on Forecasting: A Quarter-Century Review, 1960-1984 - Armstrong (1986) (Correct)
....and McKenzie 1985] 3 Expert Opinion: The growth of research on judgmental forecasting has been rapid. The number of publications in this area has been growing at about 14 percent per year over the past 30 years, a crude estimate based on my analysis of references in Kahneman, Slovic, and Tversky [1982]. For reviews of this literature see Armstrong [1985, Chapter 6] Hogarth [1980] and Hogarth and Makridakis [1981] The research by Kahneman and Tversky (for example, see Kahneman, Slovic, and Tversky [1982] has been especially influential. It points to numerous shortcomings in the way ....
.... 30 years, a crude estimate based on my analysis of references in Kahneman, Slovic, and Tversky [1982] For reviews of this literature see Armstrong [1985, Chapter 6] Hogarth [1980] and Hogarth and Makridakis [1981] The research by Kahneman and Tversky (for example, see Kahneman, Slovic, and Tversky [1982]) has been especially influential. It points to numerous shortcomings in the way forecasters use judgment. Sometimes, however, these shortcomings are caused by the questions posed to the forecaster being poorly worded [Tversky and Kahneman 1981; Kruglanski, Friedland, and Farkash 1984] Some ....
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Tversky, Amos and Kahneman, Daniel 1982, "Judgments of and by representativeness," in Daniel Kahneman, Paul Slovic and Amos Tversky (eds.) Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, pp. 84-98.
Rationality and Evolutionary Psychology - Oberlander (2000) (Correct)
....(wrongly) 5 many also respond (wrongly) don t bother with 4 This nding has been extensively replicated, although variants of the task (eg: using more content) can gain more accurate responses. Why jon ICS Lecture 18 March 9, 2000 Irrational behaviour 3 The conjunction fallacy Tversky and Kahneman 1982 presented subjects with the following task. Linda is 31 years old, single, outspoken and vert bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti nuclear demonstrations. Please rank the ....
Tversky, A. and Kahneman, D. (1982) Judgments of and by representativeness. In Kahneman et al. Judgment Under Uncertainty: Heuristics and Biases, Cambridge: CUP.
Inference by Believers in the Law of Small Numbers - Rabin (2000) (1 citation) (Correct)
....the mean IQ to be for the whole sample Tversky and Kahneman (1971) report that a surprisingly large number of subjects believe that the expected 5 IQ for the sample is still 100, and Abraham and Schulz (1984) found that 13 out of 22 subjects guessed 100, while only 3 said 101. Kahneman and Tversky (1982a, p. 44) illustrate how people expect close to the same probability distribution of types in small groups as they do in large groups, asking a group of undergraduates the following question: A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and ....
Tversky, A. and D. Kahneman (1982). "Judgments of and by Representativeness", in Judgment under Uncertainty: Heuristics and Biases. D. Kahneman, P. Slovic and A. Tversky, Cambridge 51 University Press: 84-98, Chap. 6.
A Cognitive Model of Argumentation - Korb, McConachy, Zukerman (1997) (2 citations) (Correct)
....model, for judging the persuasive effect of the same argument upon the user. The user model should ideally reflect all of the human cognitive heuristics and weaknesses that cognitive psychologists may establish as widespread, such as the failure to use base rate information in inductive reasoning (Tversky and Kahneman, 1982a) and overconfidence (Lichtenstein et al. 1982) The normative model should ideally incorporate as many items of knowledge as we can muster and the best evaluative tools for judging their relationships. Neither the user being modeled by the system nor NAG itself are unlimited cognitive agents, ....
....at once. 7 The factor is multiplied into the likelihood ratio during belief updates. Belief 1.0 0.5 0.0 Frequency 0.0 0.5 1. 0 Figure 5: Base Rate Fallacy Curve (2 outcomes) The base rate fallacy is the tendency humans have to ignore objective prior probability information when it is given (Tversky and Kahneman, 1982a) A striking example of this is the cab problem (Tversky and Kahneman, 1982b) A cab was involved in a hit and run accident at night. Two cab companies, the Green and the Blue, operate in the city. You are given the following data: ffl 85 of the cabs in the city are Green and 15 are Blue. ....
[Article contains additional citation context not shown here]
Tversky, A. and Kahneman, D. (1982a). Judgments of and by representativeness. In Kahneman et al. (1982) (pp. 84-98).
Research Writing in Computer Science - Korb (Correct)
....For example, if people have a particular image of what a stereotypical member of a class is like, then something matching that image will very likely be considered to be in the class regardless of any explicit prior probability of its being in the class. In a notorious example of this Tversky and Kahneman (1982) found that most people consider a woman described as a politically active feminist more likely to be a feminist bank teller than to be a bank teller. This is despite the fact that such an assertion is strictly incoherent. The reason for this incoherence, according to Tversky and Kahneman (and ....
....beliefs. 5.3.1 Bibliographic Notes Nisbett and Wilson (1977) had a major impact on thinking about thinking, revealing how unreliable humans are as witnesses to their own cognitive processes. Many other influential papers in the psychology of induction were collected in Kahneman, Slovic and Tversky (1982). Nisbett and Ross (1980) provide an excellent overview. Holland et al. 1986) present an interesting theory of induction for artificial intelligence with reference to the psychological work on induction. A general, and highly readable, review of the science of memory is Schachter s Searching for ....
Tversky, A. and Kahneman, D. (1982). Judgments of and by representativeness. In Kahneman et al. (1982), pp. 153-160.
A New Foundation for Support Theory - Narens (2004) (Correct)
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Kahneman, D., and Tversky, A. (1982). Judgment of and by representativeness. In Kahneman, D., Slovic, P., and Tversky, A. (Eds.),Judgment under Uncertainty: Heuristics and Biases. New York: Cambridge University Press.
Learning Human-like Knowledge by Singular Value.. - Thomas Landauer Darrell (1998) (8 citations) (Correct)
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Science, 185, 1124-1131. Tversky, A., & Kahneman, D. (1980). Judgments of and by representativeness. In D.
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