CoChat
Sign Up
Cited by 1,167Decision-Making and Behavioral EconomicsBayesian Modeling and Causal InferenceCategorization, perception, and language

The DOI matches its Crossref record; 1,086 of the 1,090 references it cites were found in a registry or an index.

Support theory: a nonextensional representation of subjective probability

Tversky, Amos, Koehler, Derek J.·1994·Psychological Review
DOI 10.1037/0033-295x.101.4.547
Open this paper in CoChat — free account

Abstract

This article presents a new theory of subjective probability according to which different descriptions of the same event can give rise to different judgments. The experimental evidence confirms the major predictions of the theory. First, judged probability increases by unpacking the focal hypothesis and decreases by unpacking the alternative hypothesis. Second, judged probabilities are complementary in the binary case and subadditive in the general case, contrary to both classical and revisionist models of belief. Third, subadditivity is more pronounced for probability judgments than for frequency judgments and is enhanced by compatible evidence. The theory provides a unified treatment of a wide range of empirical findings. It is extended to ordinal judgments and to the assessment of upper and lower probabilities. Both laypeople and experts are often called upon to evaluate the probability of uncertain events such as the outcome of a trial, the result of a medical operation, the success of a business venture, or the winner of a football game. Such assessments play an important role in deciding, respectively, whether to go to court, undergo surgery, invest in the venture, or bet on the home team. Uncertainty is usually expressed in verbal terms (e.g., unlikely or probable), but numerical estimates are also common. Weather forecasters, for example, often report the probability of rain (Murphy, 1985), and economists are sometimes required to estimate the chances of recession (Zarnowitz, 1985). The theoretical and practical significance of subjective probability has inspired psychologists, philosophers, and statisticians to investigate this notion from both descriptive and prescriptive standpoints.

Overview

Integrity signals

Registry

Crossref · 10.1037/0033-295x.101.4.547

Cites

1086 of 1086 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Items 601–800 of 1,086

Cited by

Sorted by citations

See all 1,167

Related

See all

Metadata

Authors
Tversky, A., & Koehler, D. J.
Affiliations
Stanford University (US)
ORCID
Koehler, Derek J.
Venue
Psychological Review
Publisher
American Psychological Association (APA)
Published
1994-10
Volume
101
Issue
4
Pages
547-567
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
DOI
10.1037/0033-295x.101.4.547
OpenAlex
W2054875343
Cited by
1,167
References
1,090
Citations by year
2026: 15 · 2025: 27 · 2024: 40 · 2023: 42 · 2022: 35 · 2021: 44 · 2020: 39 · 2019: 34 · 2018: 31 · 2017: 67 · 2016: 44 · 2015: 49 · 2014: 49 · 2013: 28 · 2012: 52
FWCI
10.46
Type
journal article
Language
en
Open access
closed
Locations
Psychological Review (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords
subjective probability, support theory, subadditivity, alternative hypothesis, lower and upper probabilities, probability judgment, frequency judgment, belief revision

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review