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Support theory: a nonextensional representation of subjective probability
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.
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“In the unpacking effect, participants judge, say, 'being a lawyer', to be less probable than the 'unpacked' description 'being a tax, corporate, patent, or other type of lawyer' [44][45][46].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [45]
In the citing paper
“when guided to where the peaks are (i.e., starting the android from a different location), for example, by being asked about the pattern _ _ _ _ ing in the more detailed description, then large peaks are found and probability estimates are higher [31,45].”
From Bayesian Brains without Probabilities (2016) · cites this paper as [45]
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Metadata
- Authors
- Tversky, A., & Koehler, D. J.
- Affiliations
- Stanford University (US)
- ORCID
- Koehler, Derek J.
- Venue
- Psychological Review
- Published
- 1994-10
- Volume
- 101
- Issue
- 4
- Pages
- 547-567
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- 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
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