Нақтылық жоқ жағдайдағы шешімдерге көмектесетін құрал

Tool for assisting judgement in uncertainty
The representativeness heuristic is used when making judgments about the probability of an event being representional in character and essence of a known prototypical event. It is one of a group of heuristics (simple rules governing judgment or decision making) proposed by psychologists Amos Tversky and Daniel Kahneman in the early 1970s as "the degree to which [an event] (i) is similar in essential characteristics to its parent population, and (ii) reflects the salient features of the process by which it is generated". Heuristics are useful because they use effort reduction and simplification in decision making. When people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely. The representativeness heuristic is simply described as assessing similarity of objects and organizing them based around the category prototype (e. g., like goes with like, and causes and effects should resemble each other). Thus, it can result in neglect of relevant base rates and other cognitive biases.

The representativeness heuristic is more likely to be used when the judgement or decision to be made has certain factors.

When judging the representativeness of a new stimulus/event, people usually pay attention to the degree of similarity between the stimulus/event and a standard/process. Several examples of similarity have been described in the representativeness heuristic literature. This research has focused on medical beliefs. The researcher found that clinicians use the representativeness heuristic in making diagnoses by judging how similar patients are to the stereotypical or prototypical patient with that disorder. However, judgments by representativeness only look at the resemblance between the hypothesis and the data, thus inverse probabilities are equated: This was explicitly tested by Dawes, Mirels, Gold and Donahue (1993) who had people judge both the base rate of people who had a particular personality trait and the probability that a person who had a given personality trait had another one. Some research has explored base rate neglect in children, as there was a lack of understanding about how these judgment heuristics develop. The authors of one such study wanted to understand the development of the heuristic, if it differs between social judgments and other judgments, and whether children use base rates when they are not using the representativeness heuristic. Base rates may be neglected more often when the information presented is not causal. Base rates are used less if there is relevant individuating information. Groups have been found to neglect base rate more than individuals do. Use of base rates differs based on context. Research on use of base rates has been inconsistent, with some authors suggesting a new model is necessary.

A group of undergraduates were provided with a description of Linda, modelled to be representative of an active feminist. Then participants were then asked to evaluate the probability of her being a feminist, the probability of her being a bank teller, or the probability of being both a bank teller and feminist. Probability theory dictates that the probability of being both a bank teller and feminist (the conjunction of two sets) must be less than or equal to the probability of being either a feminist or a bank teller. A conjunction cannot be more probable than one of its constituents. However, participants judged the conjunction (bank teller and feminist) as being more probable than being a bank teller alone. Some research suggests that the conjunction error may partially be due to subtle linguistic factors, such as inexplicit wording or semantic interpretation of "probability". The authors argue that both logic and language use may relate to the error, and it should be more fully investigated. They found that people judge a person who is highly representative of being a statistics major (e. g., highly intelligent, does math competitions) as being more likely to be a statistics major than a social sciences major (superset of statistics), but they do not think that he is more likely to be a Hebrew language major than a humanities major (superset of Hebrew language). Thus, only when the person seems highly representative of a category is that category judged as more probable than its superordinate category. These incorrect appraisals remained even in the face of losing real money in bets on probabilities.

Representativeness heuristic is also employed when subjects estimate the probability of a specific parameter of a sample. If the parameter highly represents the population, the parameter is often given a high probability. This estimation process usually ignores the impact of the sample size. A concept proposed by Tversky and Kahneman provides an example of this bias in a problem about two hospitals of differing size. The values shown in parentheses are the number of students choosing each answer. The results show that more than half the respondents selected the wrong answer (third option). This is due to the respondents ignoring the effect of sample size. The respondents selected the third option most likely because the same statistic represents both the large and small hospitals. According to statistical theory, a small sample size allows the statistical parameter to deviate considerably compared to a large sample. Therefore, the large hospital would have a higher probability to stay close to the nominal value of 50%.