Risk as feelings.
Abstract
Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at a decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfields of psychology, they show that emotional reactions to risky situations often diverge from cognitive assessments of those risks. When such divergence occurs, emotional reactions often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.
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Much evidence demonstrates the significant role of affect in human judgment.
“Much evidence demonstrates the significant role of affect in human judgment (Lerner et al., 2015; Loewenstein et al., 2001; Slovic et al., 2007).”
From Everyone I Don’t Like Is Biased: Affective Evaluations and the Bias Blind Spot (2026) · cites this paper as Loewenstein et al., 2001
Under the risk-as-feelings hypothesis, affective responses to vivid outcome representations drive decision behavior by increasing motivational engagement.
“Under the alternative risk-as-feelings hypothesis (Loewenstein et al. 2001;Slovic et al. 2004), affective responses to vivid outcome representations drive decision behavior by increasing motivational engagement.”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Loewenstein et al. 2001
Under an attentional reframing account, consequence framing shifts the decision-maker's frame from responding to a recommendation to predicting an outcome, preserving independent judgment without requiring additional cognitive effort.
“Under an attentional reframing account, consequence framing shifts the decision-maker's frame from responding to a recommendation to predicting an outcome, preserving independent judgment without requiring additional cognitive effort (Loewenstein et al. 2001;Lerner and Keltner 2001).”
From Limits of Interventions in AI-Assisted Decision Making (2026) · cites this paper as Loewenstein et al. 2001
Participants in our study anticipated their responses to gains and losses and chose accordingly.
“Just as recent work demonstrating that individuals' anticipation of loss may shift their choices (38), it appears that participants in our study similarly anticipated their responses to gains and losses and chose accordingly (39).”
From Thinking like a trader selectively reduces individuals' loss aversion (2009) · cites this paper as [39]
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- Authors
- Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Psychological Bulletin
- Published
- 2001
- Volume
- 127
- Issue
- 2
- Pages
- 267-286
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W3124896223
- Cited by
- 5,955
- References
- 224
- Citations by year
- 2026: 287 · 2025: 261 · 2024: 276 · 2023: 260 · 2022: 330 · 2021: 393 · 2020: 316 · 2019: 313 · 2018: 264 · 2017: 257 · 2016: 265 · 2015: 283 · 2014: 303 · 2013: 277 · 2012: 304
- FWCI
- 56.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Bulletin (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Sociology and Political Science › Risk Perception and Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Leadership, Behavior, and Decision-Making Studies
- Keywords
- risk perception, decision making under risk, uncertainty, emotional decision-making
- MeSH terms
- Cognition, Decision Making, Emotions, Humans, Models, Psychological, Risk-Taking
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.
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