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Cited by 913

The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.

Weighing risk and uncertainty.

Tversky, Amos, Fox, Craig R.·1995·Psychological Review

Abstract

Decision theory distinguishes between risky prospects, where the probabilities associated with the possible outcomes are assumed to be known, and uncertain prospects, where these probabilities are not assumed to be known. Studies of choice between risky prospects have suggested a nonlinear transformation of the probability scale that overweights low probabilities and underweights moderate and high probabilities. The present article extends this notion from risk to uncertainty by invoking the principle of bounded subadditivity: An event has greater impact when it turns impossibility into possibility, or possibility into certainty, than when it merely makes a possibility more or less likely. A series of studies provides support for this principle in decision under both risk and uncertainty and shows that people are less sensitive to uncertainty than to risk. Finally, the article discusses the relationship between probability judgments and decision weights and distinguishes relative sensitivity from ambiguity aversion.

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Crossref · 10.1037/0033-295x.102.2.269

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Metadata

Authors
Tversky, A., & Fox, C. R.
Venue
Psychological Review
Published
1995-04
Volume
102
Issue
2
Pages
269-283
ISSN
  • 0033-295X (ISSN-L)
  • 1939-1471
OpenAlex
W2155513791
Cited by
913
References
35
FWCI
8.68
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Weighing risk and uncertainty. https://app.cochat.ai/papers/10.1037/0033-295x.102.2.269
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Tversky, A., & Fox, C. R. (1995). Weighing risk and uncertainty. Psychological Review. https://doi.org/10.1037/0033-295x.102.2.269

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.