representativeness
When people are asked to judge the probability that an object or event A belongs to
class or process B, probabilities are evaluated by the degree to which A is representative of B, that
is, by the degree to which A resembles B.
Representativeness has been replaced by attribution-substitution (prototype heuristic and similarity heuristic).
Abstract: "This paper explores a heuristic—representativeness—according to which the subjective probability of an event, or a sample, is determined by the degree to which it: (i) is similar in essential characteristics to its parent population; and (ii) reflects the salient features of the process by which it is generated. This heuristic is explicated in a series of empirical examples demonstrating predictable and systematic errors in the evaluation of uncertain events. In particular, since sample size does not represent any property of the population, it is expected to have little or no effect on judgment of likelihood. This prediction is confirmed in studies showing that subjective sampling distributions and posterior probability judgments are determined by the most salient characteristic of the sample (e.g., proportion, mean) without regard to the size of the sample. The present heuristic approach is contrasted with the normative (Bayesian) approach to the analysis of the judgment of uncertainty."
Kahneman and Tversky (1972)
"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 originate from process B? What is the probability that process B will 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 is highly representative of B, the probability 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."
Tversky and Kahneman (1974)
"Daniel Kahneman and Amos Tversky have proposed that when judging the probability of some uncertain event people often resort to heuristics, or rules of thumb, which are less than perfectly correlated (if, indeed, at all) with the variables that actually determine the event’s probability. One such heuristic is representativeness, defined as a subjective judgment of the extent to which the event in question “is similar in essential properties to its parent population” or “reflects the salient features of the process by which it is generated” (Kahneman & Tversky, 1972b, p. 431, 3)."
Bar-Hillel, In: Kahneman, Slovic and Tversky (eds.) (1982)
"Representativeness is an assessment of the degree of corresponence between a sample and a population, an instance and a cateory, an act and an actor or, more generally, between an outcome and a model."
Tversky and Kahneman (1984), In: Gilovich, Griffin and Kahneman (2002)
"The best explanation to date of the misperception of random sequences is offered by psychologists Daniel Kahneman and Amos Tversky, who attribute it to people’s tendency to be overly inflenced by judgments of “representativeness.”8 Representativeness can be thought of as the reflexive tendency to assess the similarity of outcomes, instances, and categories on relatively salient and even superficial features, and then to use these assessments of similarity as a basis of judgment. People assume that “like goes with like”: Things that go together should look as though they go together. We expect instances to look like the categories of which they are members; thus, we expect someone who is a librarian to resemble the prototypical librarian. We expect effects to look like their causes; thus we are more likely to attribute a case of heartburn to spicy rather than bland food, and we are more inclined to see jagged handwriting as a sign of a tense rather than a relaxed personality."
Gilovich (1991), page 18
"According to Tversky and Kahneman (1974, p. 1124), people often judge probabilities “by the degree to which A is representative of B, that is, by the degree to which A resembles B.” Tversky and Kahneman called this rule of thumb the “representativeness heuristic.”"
Plous (1993) [book], page 109
Representativeness - entails looking at an event and making a judgment as to how closely it corresponds to other events as found in the general population.
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"...representativeness as a cause of the winner-loser effect..."
Shefrin (2000)
"One of the most important principles affecting financial decisions is known as representativeness. Representativeness refers to judgnents based on stereotypes."
Shefrin (2000)
"Representativeness is about reliance on stereotypes."
Shefrin (2000)
"Another aspect of overconfidence is that people tend to make judgments in uncertain situations by looking for familiar patterns and assuming that future patterns will resemble past ones, often without sufficient consideration of the reasons for the pattern or the probability of the pattern repeating itself. This anomaly of human judgment, called the representativeness heuristic, was demonstrated in a number of experiments by psychologists Tversky and Kahneman."
Shiller (2000) [book], page 144
"For example, people often predict future uncertain events by taking a short history of data and asking what broader picture this history is representative of. In focusing on such representativeness, they often do not pay enogh attention to the possibility that the recent history is generated by chance rather than by the ‘model’ they are constructing. Such heuristics are useful in many life situations—they help people to identify patterns in the data as well as to save on computation—but they may lead investors seriously astray. For example, investors may extrapolate short past histories of rapid earnings growth of some companies too far into the future and therefore overprice these glamorous companies without a recognition that, statistically speaking, trees do not grow to the sky."
Shleifer (2000) [book] page 11
"It then presents one such model motivated by the idea that, in forecasting future earnings, investors interpret the data on recent past earnings using the representativeness heuristic."
Shleifer (2000) [book] page 26
Our specification is motivated by the results of Tversky and Kahneman (1974) on the important behavioral heuristic known as representativeness, or the tendency of experimental subjects to view events as typical or representative of some specific class and to ignore the laws of probability in the process. An important manifestation of the representativeness heuristic is that people think that they see patterns in truly random sequences."
Shleifer (2000) [book] page 113
A second important phenomenon documented by psychologists is the representativeness heuristic (Tversky and Kahneman 1974): ‘A person who follows this heuristic evaluates the probability of an uncertain event, or a sample, by the degree to which it is (i) similar in its essential properties to the parent population, (ii) reflects the salient features of the process by which it is generated’ (p. 33). For example, if a detailed description of an individual’s personality matches up well with the subject’s experiences with people of a particular profession, the subject tends to significantly overestimate the actual probability that the given individual belongs to that profession. In overweighting the representative description, the subject underweighs the statistical base rate evidence of the small fraction of the population belonging to that profession.
An important manifestation of the representativeness heuristic, discussed in detail by Tversky and Kahneman, is that people think they see patterns in tryly random sequences. This aspect of the representativeness heuristic is suggestive of the overreaction evidence described above. When a company has a consistent history of earnings growth over several years, accompanied as it may be by salient and enthusiastic descriptions of its products and management, investos might conclude that the past history is representative of an underlying earnings growth potential. While a consistent pattern of high growth may be nothing more than a random draw for a few lucky firms, investos see ‘order in chaos’ and infer from the in-sample growth path that the firm belongs to a small and distinct population of firms whose earnings just keep grwing. As a consequence, investors using the representativeness heuristic might disregard the reality that a history of high earnings growth is unlikely to repeat itself, over-value the company, and become disappointed in the future when the forecasted earnings growth fails to materialize. This, of course, is what overreaction is all about."
Shleifer (2000) [book] page 128-129
"A cognitive heuristic through which people estimate the probability that A belongs to (or originates from) a particular class B by judging the degree to which A is representative or typical of B."
Colman (2001), Oxford Dictionary of Psychology
"A COGNITIVE HEURISTIC in which decisions are made based on how representative a given individual case appears to be independent of other information about its actual likelihood."
Reber and Reber (2001), The Penguin Dictionary of Psychology
"To explain the judgments they had observed, Tversky and Kahneman conjectured that observers expect the statisics of a sample to closely resemble (or “represent”) the corresponding population parameters, even when the sample is small. This “representation hypothesis” soon led to the idea of a “representativeness heuristic,” according to which some probability judgments (the likelihood that X is Y) are mediated by assessments of resemblance (the degree to which X “looks like” a Y)."
Kahneman and Frederick, In: Gilovich, Griffin and Kahneman (2002)
"Representativeness refers to our tendency to evaluate how likely something is with reference to how closely it resembles something rather than using probabilities."
Montier (2002) page 9
"The representativeness heuristic is a heuristic wherein we assume commonality between objects of similar appearance."
Wikipedia (2006)
"People tend to judge the probability of an event by finding a ‘comparable known’ event and assuming that the probabilities will be similar."
ChangingMinds.org (2006)
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