CONCEPT
The Representativeness Heuristic
Tversky and Kahneman's shortcut by which people judge probability through resemblance to a prototype — the mechanism that makes fluent AI output feel correct before it is verified.
The representativeness heuristic is the cognitive operation by which probability is judged through similarity to a prototype rather than through base-rate statistics. Asked whether a quiet, detail-oriented individual is more likely to be a librarian or a farmer, subjects choose librarian — because the description matches the stereotype, despite farmers vastly outnumbering librarians. The heuristic is efficient and usually adequate, but produces systematic errors when prototype-match and actual-frequency diverge. In the AI era, the heuristic is exploited by large language models whose output matches the surface features of good analysis — fluent prose, structured reasoning, appropriate hedging — regardless of whether the underlying claims are accurate. The output looks like correctness, and the representativeness heuristic reads this resemblance as evidence of correctness.
In The You On AI Field Guide
The 1972 and 1973 papers by Kahneman and Tversky established representativeness as a foundational shortcut, demonstrating through experiments on the conjunction fallacy, base rate neglect, and insensitivity to sample size that subjects systematically