Representativeness heuristic

[1] 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".

[1] The representativeness heuristic works by comparing an event to a prototype or stereotype that we already have in mind.

For example, if we see a person who is dressed in eccentric clothes and reading a poetry book, we might be more likely to think that they are a poet than an accountant.

Heuristics are described as "judgmental shortcuts that generally get us where we need to go – and quickly – but at the cost of occasionally sending us off course.

[4] 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).

[4] The problem is that people overestimate its ability to accurately predict the likelihood of an event.

[1] Nilsson, Juslin, and Olsson (2008) found this to be influenced by the exemplar account of memory (concrete examples of a category are stored in memory) so that new instances were classified as representative if highly similar to a category as well as if frequently encountered.

[2] In a similar line of thinking, in some alternative medicine beliefs patients have been encouraged to eat organ meat that corresponds to their medical disorder.

[2] Even physicians may be swayed by the representativeness heuristic when judging similarity, in diagnoses, for example.

Things that do not appear to have any logical sequence are regarded as representative of randomness and thus more likely to occur.

[1] Local representativeness is an assumption wherein people rely on the law of small numbers, whereby small samples are perceived to represent their population to the same extent as large samples (Tversky & Kahneman 1971).

Conversely, a small sample with a skewed distribution would weaken this belief.If a coin toss is repeated several times and the majority of the results consists of "heads", the assumption of local representativeness will cause the observer to believe the coin is biased toward "heads".

[10] In another study done by Tversky and Kahneman, subjects were given the following problem:[4] A cab was involved in a hit and run accident at night.

[13] Some research has explored base rate neglect in children, as there was a lack of understanding about how these judgment heuristics develop.

The authors found that the use of the representativeness heuristic as a strategy begins early on and is consistent.

There is evidence that even children use the representativeness heuristic, commit the conjunction fallacy, and disregard base rates.

[21] A group of undergraduates were provided with a description of Linda, modelled to be representative of an active feminist.

[22] Some research suggests that the conjunction error may partially be due to subtle linguistic factors, such as inexplicit wording or semantic interpretation of "probability".

[23][24] The authors argue that both logic and language use may relate to the error, and it should be more fully investigated.

[22] Evidence that the representativeness heuristic may cause the disjunction fallacy comes from Bar-Hillel and Neter (1993).

These incorrect appraisals remained even in the face of losing real money in bets on probabilities.

[22] Representativeness heuristic is also employed when subjects estimate the probability of a specific parameter of a sample.

A concept proposed by Tversky and Kahneman provides an example of this bias in a problem about two hospitals of differing size.

[25] The results show that more than half the respondents selected the wrong answer (third option).

The respondents selected the third option most likely because the same statistic represents both the large and small hospitals.

[25] Therefore, the large hospital would have a higher probability to stay close to the nominal value of 50%.