Knowledge base Updated 2 November 2026 1 views

Heuristics and Biases

The research programme showing that people judge probability and frequency with mental shortcuts that are usually useful but produce predictable errors. Its best-known heuristics are availability, representativeness, and anchoring and adjustment.

Definition

A heuristic is a rule of thumb that simplifies a judgement. The three Tversky and Kahneman described in 1974:

Availability: judging how common something is by how easily examples come to mind. Dramatic, recent or heavily reported events are overestimated.
Representativeness: judging probability by resemblance to a prototype, which leads people to neglect base rates and sample size.
Anchoring and adjustment: estimates start from an initial value — even an irrelevant one — and are adjusted too little.

A bias is the systematic error that results when a heuristic is applied where it does not fit.

History

Amos Tversky and Daniel Kahneman began the programme in the late 1960s, challenging the assumption, common in economics, that people reason like intuitive statisticians.

Their 1974 paper in Science became one of the most cited in the social sciences. Kahneman received the Nobel Memorial Prize in Economic Sciences in 2002, partly for this work and for prospect theory; Tversky had died in 1996.

Theory

Heuristics are usually described today within dual-process accounts: fast, automatic judgement that can be corrected, with effort, by slower deliberate reasoning.

An important rival view, from Gigerenzer and colleagues, holds that heuristics are ecologically rational: in the environments they evolved or were learned for, simple rules often perform as well as or better than complex calculation. On this view many 'biases' are artefacts of how problems are presented, for example as single-event probabilities rather than natural frequencies.

Research

Classic demonstrations — the Linda problem, base-rate neglect, anchoring on a spun number — replicate reliably in their basic form.

Presentation matters a great deal. Expressing the same problem in natural frequencies rather than percentages sharply reduces base-rate neglect, including among doctors interpreting test results.

Many secondary effects in the wider biases literature have fared worse in replication than the original heuristics, and some well-known 'nudges' built on them show much smaller effects once publication bias is accounted for.

Applications

Risk communication (use natural frequencies and absolute risks), clinical reasoning and diagnostic error, forecasting, jury instructions, and the design of choices and defaults in policy.

Checklists and structured decision aids are practical ways of protecting judgement where a known bias is costly.

Criticism

Artificial problems. Many tasks are puzzles with a trick, far from real decisions.
Ecological rationality. Heuristics can be the smart choice, and calling their outputs errors depends on the standard chosen.
Bias lists have multiplied into catalogues of hundreds of named effects of very uneven evidence.
Debiasing is hard. Knowing about a bias does little to reduce it.

References

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451-482.
Hoffrage, U., Lindsey, S., Hertwig, R., & Gigerenzer, G. (2000). Communicating statistical information. Science, 290(5500), 2261-2262.

Written for PsychologyOrbit and reviewed before publishing. It is a summary for professionals and students, not clinical guidance, and it is not a substitute for the primary sources listed above.