IDEAS home Printed from https://ideas.repec.org/a/wly/emetrp/v87y2019i2p653-675.html
   My bibliography  Save this article

The Interval Structure of Optimal Disclosure

Author

Listed:
  • Yingni Guo
  • Eran Shmaya

Abstract

A sender persuades a receiver to accept a project by disclosing information about a payoff‐relevant quality. The receiver has private information about the quality, referred to as his type. We show that the sender‐optimal mechanism takes the form of nested intervals: each type accepts on an interval of qualities and a more optimistic type's interval contains a less optimistic type's interval. This nested‐interval structure offers a simple algorithm to solve for the optimal disclosure and connects our problem to the monopoly screening problem. The mechanism is optimal even if the sender conditions the disclosure mechanism on the receiver's reported type.

Suggested Citation

  • Yingni Guo & Eran Shmaya, 2019. "The Interval Structure of Optimal Disclosure," Econometrica, Econometric Society, vol. 87(2), pages 653-675, March.
  • Handle: RePEc:wly:emetrp:v:87:y:2019:i:2:p:653-675
    DOI: 10.3982/ECTA15668
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/ECTA15668
    Download Restriction: no

    File URL: https://libkey.io/10.3982/ECTA15668?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zeng, Yishu, 2023. "Derandomization of persuasion mechanisms," Journal of Economic Theory, Elsevier, vol. 212(C).
    2. Anton Kolotilin & Andriy Zapechelnyuk, 2018. "Persuasion Meets Delegation," Discussion Papers 2018-06, School of Economics, The University of New South Wales.
    3. Carl Heese & Stephan Lauermann, 2021. "Persuasion and Information Aggregation in Elections," ECONtribute Discussion Papers Series 112, University of Bonn and University of Cologne, Germany.
    4. Ju Hu & Xi Weng, 2021. "Robust persuasion of a privately informed receiver," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 909-953, October.
    5. Pavan, Alessandro & Dworczak, Piotr, 2020. "Preparing for the Worst But Hoping for the Best: Robust (Bayesian) Persuasion," CEPR Discussion Papers 15017, C.E.P.R. Discussion Papers.
    6. Yaron Leitner & Basil Williams, 2023. "Model Secrecy and Stress Tests," Journal of Finance, American Finance Association, vol. 78(2), pages 1055-1095, April.
    7. Lee, Logan M. & Waddell, Glen R., 2021. "Diversity and the timing of preference in hiring decisions," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 432-459.
    8. Azarmsa, Ehsan & Cong, Lin William, 2020. "Persuasion in relationship finance," Journal of Financial Economics, Elsevier, vol. 138(3), pages 818-837.
    9. Terstiege, Stefan & Wasser, Cédric, 2020. "Buyer-optimal extensionproof information," Journal of Economic Theory, Elsevier, vol. 188(C).
    10. Pham, Hien, "undated". "a reprendre_ WP annulé," TSE Working Papers 21-1263, Toulouse School of Economics (TSE).
    11. Alonso, Ricardo & Câmara, Odilon, 2021. "Organizing Data Analytics," CEPR Discussion Papers 16768, C.E.P.R. Discussion Papers.
    12. Lily Ling Yang, 2024. "Information Design with Costly State Verifi cation," CRC TR 224 Discussion Paper Series crctr224_2024_502, University of Bonn and University of Mannheim, Germany.
    13. Ozan Candogan & Philipp Strack, 2021. "Optimal Disclosure of Information to a Privately Informed Receiver," Papers 2101.10431, arXiv.org, revised Jan 2022.
    14. Meng, Delong, 2021. "Learning from like-minded people," Games and Economic Behavior, Elsevier, vol. 126(C), pages 231-250.
    15. Shih-Tang Su & Vijay G. Subramanian & Grant Schoenebeck, 2021. "Bayesian Persuasion in Sequential Trials," Papers 2110.09594, arXiv.org, revised Nov 2021.
    16. Babichenko, Yakov & Talgam-Cohen, Inbal & Xu, Haifeng & Zabarnyi, Konstantin, 2022. "Regret-minimizing Bayesian persuasion," Games and Economic Behavior, Elsevier, vol. 136(C), pages 226-248.
    17. Candogan, Ozan & Strack, Philipp, 2023. "Optimal disclosure of information to privately informed agents," Theoretical Economics, Econometric Society, vol. 18(3), July.
    18. Arieli, Itai & Babichenko, Yakov & Smorodinsky, Rann & Yamashita, Takuro, 2023. "Optimal persuasion via bi-pooling," Theoretical Economics, Econometric Society, vol. 18(1), January.
    19. Maryam Saeedi & Ali Shourideh, 2020. "Optimal Rating Design under Moral Hazard," Papers 2008.09529, arXiv.org, revised Jul 2023.
    20. Terstiege, Stefan & Wasser, Cédric, 2023. "Experiments versus distributions of posteriors," Mathematical Social Sciences, Elsevier, vol. 125(C), pages 58-60.
    21. Clement Minaudier, 2022. "The Value of Confidential Policy Information: Persuasion, Transparency, and Influence," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 38(2), pages 570-612.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:emetrp:v:87:y:2019:i:2:p:653-675. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.