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The Hazards and Benefits of Condescension in Social Learning

Author

Listed:
  • Itai Arieli
  • Yakov Babichenko
  • Stephan Muller
  • Farzad Pourbabaee
  • Omer Tamuz

Abstract

In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension.

Suggested Citation

  • Itai Arieli & Yakov Babichenko & Stephan Muller & Farzad Pourbabaee & Omer Tamuz, 2023. "The Hazards and Benefits of Condescension in Social Learning," Papers 2301.11237, arXiv.org, revised Feb 2024.
    • Handle: RePEc:arx:papers:2301.11237
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    File URL: http://arxiv.org/pdf/2301.11237
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    References listed on IDEAS

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    1. Antonio E. Bernardo & Ivo Welch, 2001. "On the Evolution of Overconfidence and Entrepreneurs," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 10(3), pages 301-330, September.
    2. Bohren, J. Aislinn, 2016. "Informational herding with model misspecification," Journal of Economic Theory, Elsevier, vol. 163(C), pages 222-247.
    3. He, Kevin, 2022. "Mislearning from censored data: The gambler's fallacy and other correlational mistakes in optimal-stopping problems," Theoretical Economics, Econometric Society, vol. 17(3), July.
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    Cited by:

    1. Wanying Huang & Philipp Strack & Omer Tamuz, 2024. "Learning in Repeated Interactions on Networks," Econometrica, Econometric Society, vol. 92(1), pages 1-27, January.

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