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Meaningful Information

Author

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  • Braghieri, Luca

Abstract

I study how to measure information transmission when agents have misspecified priors, misinterpret signals, or update beliefs in a non-Bayesian way. I axiomatize and characterize measures of \emph{meaningful information transmission} that account for both the objective features of a signal structure and agents' (mis)interpretations of it. Meaningful information measures coincide with canonical ones under correct Bayesian beliefs, but otherwise extend them in a disciplined way grounded in the same underlying decision geometry. I then estimate meaningful information in three empirical applications and show that it can diverge sharply from canonical measures.

Suggested Citation

  • Braghieri, Luca, 2026. "Meaningful Information," CEPR Discussion Papers 21483, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:21483
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    File URL: https://cepr.org/publications/DP21483
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    More about this item

    JEL classification:

    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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