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Disambiguating Information Interventions: Recovering Beliefs and Ambiguity Attitudes from Virtual Twins

Author

Listed:
  • Aurelien Baillon
  • Francesco Capozza
  • Vahid Moghani

Abstract

Elicited probabilities conflate latent beliefs, ambiguity attitudes, and response er- ror. We develop a measurement method that identifies these components from noisy subjective-probability data using consequential bets on singleton and union events. Ambiguity weighting is parameterized by two indices (a,b), capturing insensitivity and elevation. A hierarchical Bayesian model places latent beliefs on the simplex, al- lows respondent-level (ai,bi), and treats elicited probabilities as noisy measurements. We implement the method in a randomized information experiment in the Dutch LISS panel, where respondents make incentivized forecasts about a demographically matched virtual twin’s GP utilization. The treatment effect on raw probabilities is imprecise and indistinguishable from zero; model-recovered beliefs show meaningful updating, improving predictive accuracy by 0.026–0.032 Brier points and lowering elevation-based ambiguity aversion, with a smaller and less robust change in likelihood insensitivity. Treatment effects on elicited probabilities need not identify treatment effects on beliefs.

Suggested Citation

  • Aurelien Baillon & Francesco Capozza & Vahid Moghani, 2026. "Disambiguating Information Interventions: Recovering Beliefs and Ambiguity Attitudes from Virtual Twins," CESifo Working Paper Series 12856, CESifo.
    • Handle: RePEc:ces:ceswps:_12856
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    References listed on IDEAS

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    1. Han Bleichrodt & Christophe Courbage & Béatrice Rey, 2019. "The value of a statistical life under changes in ambiguity," Journal of Risk and Uncertainty, Springer, vol. 58(1), pages 1-15, February.
    2. Mohammed Abdellaoui & Aurelien Baillon & Laetitia Placido & Peter P. Wakker, 2011. "The Rich Domain of Uncertainty: Source Functions and Their Experimental Implementation," American Economic Review, American Economic Association, vol. 101(2), pages 695-723, April.
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    Keywords

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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