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Misinterpreting Others and the Fragility of Social Learning

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Abstract

We study to what extent information aggregation in social learning environments is robust to slight misperceptions of others’ characteristics (e.g., tastes or risk attitudes). We consider a population of agents who obtain information about the state of the world both from initial private signals and by observing a random sample of other agents’ actions over time, where agents’ actions depend not only on their beliefs about the state but also on their idiosyncratic types. When agents are correct about the type distribution in the population, they learn the true state in the long run. By contrast, our first main result shows that even arbitrarily small amounts of misperception can generate extreme breakdowns of information aggregation, wherein the long run all agents incorrectly assign probability 1 to some fixed state of the world, regardless of the true underlying state. This stark discontinuous departure from the correctly specified benchmark motivates independent analysis of information aggregation under misperception. Our second main result shows that any misperception of the type distribution gives rise to a specific failure of information aggregation where agents’ long-run beliefs and behavior vary only coarsely with the state, and we provide systematic predictions for how the nature of misperception shapes these coarse long-run outcomes. Finally, we show that how sensitive information aggregation is to misperception depends on how rich agents’ payoff-relevant uncertainty is. A design implication is that information aggregation can be improved through interventions aimed at simplifying the agents’ learning environment.

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  • Mira Frick & Ryota Iijima & Yuhta Ishii, 2019. "Misinterpreting Others and the Fragility of Social Learning," Cowles Foundation Discussion Papers 2160, Cowles Foundation for Research in Economics, Yale University.
  • Handle: RePEc:cwl:cwldpp:2160
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    Cited by:

    1. Itai Arieli & Fedor Sandomirskiy & Rann Smorodinsky, 2020. "On social networks that support learning," Papers 2011.05255, arXiv.org.
    2. Navin Kartik & SangMok Lee & Daniel Rappoport, 2021. "Observational Learning with Ordered States," Papers 2103.02754, arXiv.org, revised Apr 2021.
    3. Sushil Bikhchandani & David Hirshleifer & Omer Tamuz & Ivo Welch, 2021. "Information Cascades and Social Learning," Papers 2105.11044, arXiv.org.
    4. Tristan Gagnon-Bartsch & Marco Pagnozzi & Antonio Rosato, 2020. "Projection of Private Values in Auctions," CSEF Working Papers 571, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    5. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Misinterpreting Others and the Fragility of Social Learning," Econometrica, Econometric Society, vol. 88(6), pages 2281-2328, November.
    6. Sadler, Evan, 2020. "Innovation adoption and collective experimentation," Games and Economic Behavior, Elsevier, vol. 120(C), pages 121-131.
    7. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Belief Convergence under Misspecified Learning: A Martingale Approach," Cowles Foundation Discussion Papers 2235R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    8. Drew Fudenberg & Giacomo Lanzani & Philipp Strack, 2021. "Limit Points of Endogenous Misspecified Learning," Econometrica, Econometric Society, vol. 89(3), pages 1065-1098, May.
    9. Mira Frick & Ryota Iijima & Yuhta Ishii, 2020. "Stability and Robustness in Misspecified Learning Models," Cowles Foundation Discussion Papers 2235, Cowles Foundation for Research in Economics, Yale University.
    10. 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.
    11. Ignacio Esponda & Demian Pouzo & Yuichi Yamamoto, 2019. "Asymptotic Behavior of Bayesian Learners with Misspecified Models," Papers 1904.08551, arXiv.org, revised Oct 2019.
    12. Takeshi Murooka & Yuichi Yamamoto, 2021. "Multi-Player Bayesian Learning with Misspecified Models," OSIPP Discussion Paper 21E001, Osaka School of International Public Policy, Osaka University.
    13. Mira Frick & Ryota Iijima & Yuhta Ishii, 2018. "Dispersed Behavior and Perceptions in Assortative Societies," Cowles Foundation Discussion Papers 2128R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2019.
    14. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Welfare Comparisons for Biased Learning," Cowles Foundation Discussion Papers 2274, Cowles Foundation for Research in Economics, Yale University.
    15. Andrew Ellis & Heidi Christina Thysen, 2021. "Subjective Causality in Choice," Papers 2106.05957, arXiv.org.

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    More about this item

    Keywords

    Misspecification; Social learning; Information aggregation; Fragility;
    All these keywords.

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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