Representativeness heuristic

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.^[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. 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."^[2] Heuristics are useful because they use effort-reduction and simplification in decision-making.^[3]

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.^[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).^[2] This heuristic is used because it is an easy computation.^[4] The problem is that people overestimate its ability to accurately predict the likelihood of an event.^[5] Thus, it can result in neglect of relevant base rates and other cognitive biases.^[6][7]