The representativeness heuristic is used when making judgments about the probability of an event being representional in character and essence of known protyical 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". 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. This is because the person's appearance and behavior are more representative of the stereotype of a poet than an accountant.
The representativeness heuristic can be a useful shortcut in some cases, but it can also lead to errors in judgment. For example, if we only see a small sample of people from a particular group, we might overestimate the degree to which they are representative of the entire group. 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." 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). This heuristic is used because it is an easy computation. The problem is that people overestimate its ability to accurately predict the likelihood of an event. Thus, it can result in neglect of relevant base rates and other cognitive biases.
Determinants of representativeness
The representativeness heuristic is more likely to be used when the judgement or decision to be made has certain factors.
Similarity
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. It is also important that those features be salient. 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. Several examples of similarity have been described in the representativeness heuristic literature. This research has focused on medical beliefs. People often believe that medical symptoms should resemble their causes or treatments. For example, people have long believed that ulcers were caused by stress, due to the representativeness heuristic, when in fact bacteria cause ulcers. 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. Use of the representativeness heuristic can be seen in even simpler beliefs, such as the belief that eating fatty foods makes one fat. Even physicians may be swayed by the representativeness heuristic when judging similarity, in diagnoses, for example. 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.
Randomness
Irregularity and local representativeness affect judgments of randomness. Things that do not appear to have any logical sequence are regarded as representative of randomness and thus more likely to occur. For example, THTHTH as a series of coin tosses would not be considered representative of randomly generated coin tosses as it is too well ordered.