Judgment under Uncertainty¶
Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124-1131.
Cited by¶
13 citations across 13 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Anchoring
- Anchoring is the systematic influence of an initial numerical value or reference point on subsequent judgments, evaluations, and decisions, such that final answers are drawn toward the anchor stimulus even when the anchor is arbitrary, uninformative, or explicitly disclosed as irrelevant.
This sourceFounding paper of the heuristics-and-biases program; Section on adjustment-and-anchoring reports the wheel-of-fortune experiment (spinner landing on 10 vs. 65; median UN-Africa estimates 25% vs. 45%) and names anchoring as a systematic judgment bias.
- Anchoring is the systematic influence of an initial numerical value or reference point on subsequent judgments, evaluations, and decisions, such that final answers are drawn toward the anchor stimulus even when the anchor is arbitrary, uninformative, or explicitly disclosed as irrelevant.
- Belief Formation
- It enables path-sensitivity analysis: the process is path-dependent (order and framing of inputs shifts the final belief even with the same total evidence), so the reasoner can predict that two agents exposed to the same evidence in different sequences will end up at different posteriors — a finding Tversky and Kahneman (1974) embedded in their analysis of anchoring and order effects on judgment under uncertainty.
This sourceDocuments representativeness, availability, and anchoring-and-adjustment as systematic departures from coherent probabilistic reasoning
- It enables path-sensitivity analysis: the process is path-dependent (order and framing of inputs shifts the final belief even with the same total evidence), so the reasoner can predict that two agents exposed to the same evidence in different sequences will end up at different posteriors — a finding Tversky and Kahneman (1974) embedded in their analysis of anchoring and order effects on judgment under uncertainty.
- Bias
- These are not random errors; they push judgment the same way across people and occasions.
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic, reproducible departures from coherent probabilistic reasoning (including base-rate neglect) — directional distortions that push judgment the same way across people and occasions.
- These are not random errors; they push judgment the same way across people and occasions.
- Bounded Rationality
- Confirmation Bias
- .
This sourceFounding heuristics-and-biases paper documenting representativeness, availability, and anchoring as systematic DEPARTURES from coherent probabilistic reasoning. Does NOT establish that confirmation behavior is rational Bayesian updating — it is a non-supporting (indeed contrary) anchor for the specific T5 claim it sits on (see non-supporting flag). Live-verified (Science, DOI resolves).
- .
- Heuristic
- Cognitive heuristics canonical triad.
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic departures from coherent probabilistic reasoning, including base-rate neglect and inverse-fallacy errors.
- Cognitive heuristics canonical triad.
- Mental Model
- In both, mental-model diagnosis enables targeted intervention.
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic departures from coherent probabilistic reasoning, including base-rate neglect and inverse-fallacy errors.
- In both, mental-model diagnosis enables targeted intervention.
- Probability
- Medicine and public health apply it to diagnostic probabilities (sensitivity, specificity, positive and negative predictive value), epidemiological models, clinical-trial design, and risk stratification. Engineering reliability treats system failure as a probabilistic event, with Weibull and exponential lifetime models driving maintenance schedules and warranty design. Finance applies it to derivatives pricing under risk-neutral measures, value-at-risk computation, and stress testing. Cognitive science and behavioral economics measure how human probability judgment systematically departs from coherence — Tversky and Kahneman's (1974) heuristics-and-biases program documented base-rate neglect, conjunction errors, and representativeness substitution.
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic departures from coherent probabilistic reasoning, including base-rate neglect and inverse-fallacy errors.
- Medicine and public health apply it to diagnostic probabilities (sensitivity, specificity, positive and negative predictive value), epidemiological models, clinical-trial design, and risk stratification. Engineering reliability treats system failure as a probabilistic event, with Weibull and exponential lifetime models driving maintenance schedules and warranty design. Finance applies it to derivatives pricing under risk-neutral measures, value-at-risk computation, and stress testing. Cognitive science and behavioral economics measure how human probability judgment systematically departs from coherence — Tversky and Kahneman's (1974) heuristics-and-biases program documented base-rate neglect, conjunction errors, and representativeness substitution.
- Regression to the Mean
- Additional canonical references in this cluster: , , , , ,
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic departures from coherent probabilistic reasoning, including base-rate neglect and inverse-fallacy errors.
- Additional canonical references in this cluster: , , , , ,
- Stereotyping
- As Tversky and Kahneman (1974) demonstrated in their canonical analysis of heuristics and biases, the question is not whether to compress but how to monitor and correct for compression bias.
This sourceFounding paper of the heuristics-and-biases program; documents representativeness, availability, and anchoring as systematic departures from coherent probabilistic reasoning, including base-rate neglect and inverse-fallacy errors.
- As Tversky and Kahneman (1974) demonstrated in their canonical analysis of heuristics and biases, the question is not whether to compress but how to monitor and correct for compression bias.
Domain-specific¶
Mechanisms¶
- Duplicate or Blind Remeasurement Check
- It catches anchoring bias — the pull of a first answer on a second
This sourceDefines anchoring as estimates remaining biased toward an initial value.
- It catches anchoring bias — the pull of a first answer on a second
- Rolling Forecast Review
- Its most common quiet failure is anchoring: a "review" that merely tweaks last period's forecast a few percent rather than rebuilding it
This sourceDefines anchoring-and-adjustment as estimating from an initial value and shows that adjustment is often insufficient.
- Its most common quiet failure is anchoring: a "review" that merely tweaks last period's forecast a few percent rather than rebuilding it
Verification¶
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