Judgment under Uncertainty: Heuristics and BiasesAmos Tversky; Daniel KahnemanScience, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-1131.Stable URL:http://links.jstor.org/sici?sici=0036-8075%2819740927%293%3A185%3A4157%3C1124%3AJUUHAB%3E2.0.CO%3B2-MScience is currently published by American Association for the Advancement of Science.Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available athttp://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtainedprior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content inthe JSTOR archive only for your personal, non-commercial use.Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained athttp://www.jstor.org/journals/aaas.html.Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.JSTOR is an independent not-for-profit organization dedicated to and preserving a digital archive of scholarly journals. Formore information regarding JSTOR, please contact [email protected]://www.jstor.orgThu Apr 5 16:07:46 2007Judgment under Uncertainty: Heuristics and Biases Biases in judgments reveal some heuristics of thinking under uncertainty. Amos Tversky and Daniel Kahneman Many decisions are based on beliefs concerning the likelihood of uncertain events such as the outcome of an elec- tion, the guilt of a defendant, or the future value of the dollar. These beliefs are usually expressed in statements such as "I think that . . . ," "chances are . . . ," "it is unlikely that . . . ," and so forth. Occasionally, beliefs concern-ing iuncertain events are expressed in numerical form as odds or subjective probabilities. What determines such be- liefs? How do people assess the prob- ability of an uncertain event or the value of an uncertain quantity? This article shows that people rely on a limited number of heuristic principles which reduce the complex tasks of as-sessing probabilities and predicting val- ues to simpler judgmental operations. In general, these heuristics are quite useful, but sometimes they lead to severe and systematic errors. The subjective assessment of proba-bility resembles the subjective assess-ment of physical quantities such as distance or size. These judgments are all based on data of limited validity, which are processed according to heu- ristic rules. For example, the apparent distance of an object is determined in part by its clarity. The more sharply the object is seen, the closer it appears to be. This rule has some validity, hecaust: in any given scene the more distant objects are seen less sharply than nearer objects. However, the reliance on this rule leads to systematic errors in the estimation of distance. Specifically, dis- tances are often overestimated when visibility is poor because the contours of objects are blurred. On the other hand, distances are often underesti-The authors are tncmbers of the department of psychology at the Hebt'cw University, Jerusalem, I~rael. mated when visibility is good because the objects are seen sharply. Thus, the reliance on clarity as an indication of distance leads to common biases. Such biases are also found in the intuitive judgment of probability. This article describes three heuristics that are em-ployed to assess probabilities and to predict values. Biases to which these heuristics lead are enumerated, and the applied and theoretical implications of these observation5 are discussed. Representativeness Many of the probabilistic questions with which people are concerned belong to one of the following types: What is the probability that object A belongs to class B? What is the probability that event A originates from process B? What is the probability that process R w~ll generate event A? In answering such questions, people typically rely on the representativeness heuristic, in which probabilities are evaluated by the degree to which A is representative of B, that is, by the degree to which A resembles B. For example, when A ib highly representative of B, the proba- bility that A originates from B is judged to be high. On the other hand, if A is not similar to B, the probability that A originates from B is judged to be low. For an illustration of judgment by representativeness, consider an indi-vidual who has been described by a former neighbor as follows: "Steve is very shy and withdrawn, invariably helpful, but with little interest in peo- ple, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail." How do people assess the probability that Steve is engaged in a particular occupation from a list of possibilities (for example, farmer, salesman, airline pilot, librarian, or physician)? How do people order these occupat~oas from most to least likely? In the representa- tivenes> heuristic, the probability that Steve is a I~brarian, for example, IS assessed by the degree to which he is representative of, or similar to, the stereotype of a librarian. Indeed, re-search with problems of this type has shown that people order the occupa-tions by probability and by similarity in exactly the same way (I). This ap- proach to ofthe ji~dg~~ient probability leads to serious errors, because sim-ilarity, or representativeness, is not 1t1-fluenced by several factors that should affect judgments of probability. l1zrer7ritivity to prior probability of outcomer. One of the factors that have no effect on representat~veness but should have a major effect on probabil- ~ty is the prior probability, or base-rate frequency, of the outcomes. In the case of Steve, for example, the fact that there are many more farmers than li-brarians in the population should enter into any reasonable estimate of the probabil~ty that Steve is a librarian rather than a