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Optimal Public Information Disclosure by Mechanism Designer

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  • Yamashita, Takuro

Abstract

We consider a mechanism design environment where a principal can partially control agents' information before they play a mechanism (e.g., a seller disclosing quality information). Assuming that the principal can ex ante commit to his disclosure policy, this is a Bayesian persuasion problem with an endogenous payoff function as a consequence of optimal mechanism design. Wefirst show that, if the principal's and agents' information are independent or affiliated, and if the implementable set of (non-monetary) allocation rules is invariant to disclosure policies, then it is optimal for the principal to disclose all the relevant information. In case of negative correlation or in case the set of implementable allocation rules varies with disclosure policies, then full disclosure is not necessarily optimal. We then characterize the optimal (non-full) disclosure policy under additional assumptions, which, viewed as a Bayesian persuasion problem, provides a solution to a novel class of environments.

Suggested Citation

  • Yamashita, Takuro, 2018. "Optimal Public Information Disclosure by Mechanism Designer," TSE Working Papers 18-936, Toulouse School of Economics (TSE).
  • Handle: RePEc:tse:wpaper:32879
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    References listed on IDEAS

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    Cited by:

    1. Bara Kim & Seung Han Yoo, 2022. "Grand Mechanism and Population Uncertainty," Discussion Paper Series 2204, Institute of Economic Research, Korea University.
    2. Yamashita, Takuro & Zhu, Shuguang, 2021. "Type-contingent Information Disclosure," TSE Working Papers 21-1242, Toulouse School of Economics (TSE).

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