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Non-Bayesian Persuasion

Author

Listed:
  • Geoffroy de Clippel
  • Xu Zhang

Abstract

Following Kamenica and Gentzkow, this paper studies persuasion as an information design problem. We investigate how mistakes in probabilistic inference impact optimal persuasion. The concavification method is shown to extend naturally to a large class of belief updating rules, which we identify and characterize. This class comprises many non-Bayesian models discussed in the literature. We apply this new technique to gain insight into the revelation principle, the ranking of updating rules, when persuasion is beneficial to the sender, and when it is detrimental to the receiver. Our key result also extends to shed light on the question of robust persuasion.

Suggested Citation

  • Geoffroy de Clippel & Xu Zhang, 2022. "Non-Bayesian Persuasion," Journal of Political Economy, University of Chicago Press, vol. 130(10), pages 2594-2642.
    • Handle: RePEc:ucp:jpolec:doi:10.1086/720464
    DOI: 10.1086/720464
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    Cited by:

    1. Dirk Bergemann & Tan Gan & Yingkai Li, 2023. "Managing Persuasion Robustly: The Optimality of Quota Rules," Papers 2310.10024, arXiv.org.
    2. Kfir Eliaz & Ran Spiegler, 2024. "News Media as Suppliers of Narratives (and Information)," Papers 2403.09155, arXiv.org.
    3. Mark Whitmeyer, 2023. "Bayes = Blackwell, Almost," Papers 2302.13956, arXiv.org, revised Oct 2023.
    4. Maxim Senkov & Toygar T. Kerman, 2024. "Changing Simplistic Worldviews," Papers 2401.02867, arXiv.org.

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