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Communication With Unknown Perspectives

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  • Rajiv Sethi
  • Muhamet Yildiz

Abstract

Consider a group of individuals with unobservable perspectives (subjective prior beliefs) about a sequence of states. In each period, each individual receives private information about the current state and forms an opinion (a posterior belief). She also chooses a target individual and observes the target's opinion. This choice involves a trade‐off between well‐informed targets, whose signals are precise, and well‐understood targets, whose perspectives are well known. Opinions are informative about the target's perspective, so observed individuals become better understood over time. We identify a simple condition under which long‐run behavior is history independent. When this fails, each individual restricts attention to a small set of experts and observes the most informed among these. A broad range of observational patterns can arise with positive probability, including opinion leadership and information segregation. In an application to areas of expertise, we show how these mechanisms generate own field bias and large field dominance.

Suggested Citation

  • Rajiv Sethi & Muhamet Yildiz, 2016. "Communication With Unknown Perspectives," Econometrica, Econometric Society, vol. 84, pages 2029-2069, November.
    • Handle: RePEc:wly:emetrp:v:84:y:2016:i::p:2029-2069
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    Cited by:

    1. Cipullo, Davide & Reslow, André, 2019. "Biased Forecasts to Affect Voting Decisions? The Brexit Case," Working Paper Series 364, Sveriges Riksbank (Central Bank of Sweden).
    2. V. Bhaskar & Caroline Thomas, 2019. "The Culture of Overconfidence," American Economic Review: Insights, American Economic Association, vol. 1(1), pages 95-110, June.
    3. Ergun, Lerby & Uthemann, Andreas, 2020. "Higher-order uncertainty in financial markets: evidence from a consensus pricing service," LSE Research Online Documents on Economics 118893, London School of Economics and Political Science, LSE Library.
    4. George J. Mailath & Larry Samuelson, 2020. "Learning under Diverse World Views: Model-Based Inference," American Economic Review, American Economic Association, vol. 110(5), pages 1464-1501, May.
    5. Cheng, Ing-Haw & Hsiaw, Alice, 2022. "Distrust in experts and the origins of disagreement," Journal of Economic Theory, Elsevier, vol. 200(C).
    6. Jeanne Hagenbach & Frédéric Koessler, 2017. "Simple versus rich language in disclosure games," Review of Economic Design, Springer;Society for Economic Design, vol. 21(3), pages 163-175, September.
    7. Jacopo Bizzotto & Davide Cipullo & André Reslow, 2024. "Biased Forecasts and Voting: The Brexit Referendum Case," CESifo Working Paper Series 11221, CESifo.
    8. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Feb 2024.
    9. Chen, Wanyi, 2021. "Dynamic survival bias in optimal stopping problems," Journal of Economic Theory, Elsevier, vol. 196(C).
    10. Abhijit Banerjee & Olivier Compte, 2024. "Consensus and Disagreement: Information Aggregation under (Not So) Naive Learning," Journal of Political Economy, University of Chicago Press, vol. 132(8), pages 2790-2829.
    11. Ricardo J. Caballero & Alp Simsek, 2022. "Monetary Policy with Opinionated Markets," American Economic Review, American Economic Association, vol. 112(7), pages 2353-2392, July.
    12. Lin Hu & Anqi Li & Xu Tan, 2021. "A Rational Inattention Theory of Echo Chamber," Papers 2104.10657, arXiv.org, revised Jun 2024.
    13. Annie Liang & Xiaosheng Mu, 2018. "Overabundant Information and Learning Traps," PIER Working Paper Archive 18-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 27 Mar 2018.
    14. Meng, Delong, 2021. "Learning from like-minded people," Games and Economic Behavior, Elsevier, vol. 126(C), pages 231-250.
    15. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..

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