On the psychology of prediction
The chapter examines the rules governing intuitive predictions and confidence in them, and compares them with the normative principles of statistical prediction, covering category prediction, such as an election winner, and numerical prediction, such as a student's grade point average. It argues that under uncertainty people do not follow the rules of probability but rely on a few heuristics that can give reasonable judgements and also severe systematic errors. It concentrates on representativeness, by which people predict by ranking outcomes according to how well each represents the essential features of the evidence.
The chapter examines the rules governing intuitive predictions and confidence in them, and compares them with the normative principles of statistical prediction, covering category prediction, such as an election winner, and numerical prediction, such as a student's grade point average. It argues that under uncertainty people do not follow the rules of probability but rely on a few heuristics that can give reasonable judgements and also severe systematic errors. It concentrates on representativeness, by which people predict by ranking outcomes according to how well each represents the essential features of the evidence.
Written from the abstract read 2026-10-27 via OpenAlex (the record carries the text of the original prediction paper).
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In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under Uncertainty: Heuristics and Biases Cambridge University Press. doi:10.1017/CBO9780511809477.005