Representativeness Heuristic
Updated 2026-08-02
INTRODUCTION
English translation pending.
CORE DEFINITION
Identified by Amos Tversky and Daniel Kahneman, the representativeness heuristic describes judging likelihood by resemblance to a category rather than by statistical frequency. In their well-known description of a shy, orderly man, most respondents guessed librarian over farmer, ignoring that farmers vastly outnumber librarians. The heuristic produces systematic errors including base-rate neglect, insensitivity to sample size, and stereotyping. Key qualification: representativeness is often a useful fast approximation, and it fails predictably when base rates are extreme or samples are small.
SCAFFOLDING EFFECT
Reduce cognitive load
- Check the base rate: ask how common the outcome is before assessing how typical it looks. - Separate similarity from probability: note when your judgment rests on resemblance rather than frequency. - Weight sample size: discount strong conclusions drawn from a handful of observations.
Anchor fast decisions
Similarity is fast to compute and usually correlates with likelihood, so the mind substitutes one for the other. But similarity ignores how many cases exist in each category and how noisy small samples are. When base rates are extreme or evidence is thin, the substitution produces confident judgments that are systematically wrong in a predictable direction.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Representativeness_heuristicverified
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