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Welfare Comparisons for Biased Learning

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Abstract

We study robust welfare comparisons of learning biases, i.e., deviations from correct Bayesian updating. Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static (one signal) and dynamic (many signals) settings. While the static characterization compares posteriors signal-by-signal, the dynamic characterization employs an "efficiency index" quantifying the speed of belief convergence. Our results yield welfare-founded quantifications of the severity of well-documented biases. Moreover, the static and dynamic rankings can disagree, and "smaller" biases can be worse in dynamic settings.

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  • Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Welfare Comparisons for Biased Learning," Cowles Foundation Discussion Papers 2274R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    • Handle: RePEc:cwl:cwldpp:2274r
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    Cited by:

    1. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Learning Efficiency of Multi-Agent Information Structures," Cowles Foundation Discussion Papers 2299R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    2. Steiner, Jakub & Netzer, Nick & Robson, Arthur & Kocourek, Pavel, 2021. "Endogenous Risk Attitudes," CEPR Discussion Papers 16190, C.E.P.R. Discussion Papers.
    3. Enrique Urbano Arellano & Xinyang Wang, 2023. "Social Learning of General Rules," Papers 2310.15861, arXiv.org.

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    More about this item

    Keywords

    Learning biases; Misspecification; Welfare comparisons;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General

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