General Evaluability Theory
Abstract
A central question in psychology and economics is the determination of whether individuals react differently to different values of a cared-about attribute (e.g., different income levels, different gas prices, and different ambient temperatures). Building on and significantly extending our earlier work on preference reversals between joint and separate evaluations, we propose a general evaluability theory (GET) that specifies when people are value sensitive and when people mispredict their own or others' value sensitivity. The GET can explain and unify many seemingly unrelated findings, ranging from duration neglect to affective forecasting errors and can generate many new research directions on topics ranging from temporal discounting to subjective well-being.
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Culture and Judgment and Decision Making
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Meeting Three Challenges in Risk Communication
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The Psychology of Marginal Utility
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Self-citationValuations of human lives: normative expectations and psychological mechanisms of (ir)rationality
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The Visual Complexity = Higher Production Cost Lay Belief
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Forecasting forecasts: The trend effect
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Bang for the Buck: Gain-Loss Ratio as a Driver of Judgment and Choice
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Options as information: Rational reversals of evaluation and preference.
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Putting a Price Tag on Personal Information - A Literature Review
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More than 50% or Less than 70% Chance: Pragmatic Implications of Single‐Bound Probability Estimates
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Financial and symbolic incentives promote ‘green’ charging choices
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Realistic affective forecasting: The role of personality
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Relevance insensitivity: A new look at some old biases
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Self-citationToward a Process-Transfer Model of the Endorser Effect
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Less is more: Why some domains are more positional than others
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The role of evaluation mode on the unit effect
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The effect of paternalistic alternatives on attitudes toward default nudges
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Winners Love Winning and Losers Love Money
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Much ado about nothing: The zero effect in life‐saving decisions
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Less Information, More Comparison, and Better Performance: Evidence from a Field Experiment
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Do Investors Care About Impact?Preprint
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Moral preferences in helping dilemmas expressed by matching and forced choice
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Selling Hope: The Role of Affect-Laden Health Care Advertising in Consumer Decision Making
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This way, please: Uncovering the directional effects of attribute translations on decision making
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Product Attributes, Evaluability, and Consumer Satisfaction
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Incomparability and Incommensurability in Choice: No Common Currency of Value?
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Risky business: rhesus monkeys exhibit persistent preferences for risky options
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Is a .51 validity coefficient good? Value sensitivity for interview validity
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Self-citationType and amount of help as predictors for impression of helpers
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Choosing between options associated with past and future regret
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- Authors
- Hsee, C. K., & Zhang, J.
- Affiliations
- University of Chicago (US)
- University of Miami (US)
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2010-07
- Volume
- 5
- Issue
- 4
- Pages
- 343-355
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- OpenAlex
- W1997137767
- Cited by
- 410
- References
- 63
- Citations by year
- 2026: 20 · 2025: 18 · 2024: 25 · 2023: 68 · 2022: 33 · 2021: 31 · 2020: 22 · 2019: 30 · 2018: 28 · 2017: 26 · 2016: 18 · 2015: 23 · 2014: 24 · 2013: 20 · 2012: 14
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Perspectives on Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- John Templeton Foundation
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Keywords
- value sensitivity, preference reversal, delay discounting, subjective well-being
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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