IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v70y2022i6p3490-3510.html
   My bibliography  Save this article

Learning Manipulation Through Information Dissemination

Author

Listed:
  • Jussi Keppo

    (NUS Business School and Institute of Operations Research and Analytics, National University of Singapore, Singapore 119245, Singapore)

  • Michael Jong Kim

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

  • Xinyuan Zhang

    (Sauder School of Business, University of British Columbia, Vancouver, British Columbia V6T 1Z2, Canada)

Abstract

We study optimal manipulation of a Bayesian learner through adaptive provisioning of information. The problem is motivated by settings in which a firm can disseminate possibly biased information at a cost, to influence the public’s belief about a hidden parameter related to the firm’s payoffs. For example, firms advertise to sell products. We study a sequential optimization model in which the firm dynamically decides on the quantity and content of information sent to the public, aiming to maximize its expected total discounted profits over an infinite horizon. We solve the associated Bayesian dynamic programming equation and explicitly characterize the optimal manipulation policy in closed form. The explicit solution allows us to further characterize the evolution of the public’s posterior belief under such manipulation over time. We also extend our analysis to consider the public as partially Bayesian social learners who rely on public reviews to resist manipulation. We show that the public asymptotically learns the truth in this extended setting.

Suggested Citation

  • Jussi Keppo & Michael Jong Kim & Xinyuan Zhang, 2022. "Learning Manipulation Through Information Dissemination," Operations Research, INFORMS, vol. 70(6), pages 3490-3510, November.
    • Handle: RePEc:inm:oropre:v:70:y:2022:i:6:p:3490-3510
    DOI: 10.1287/opre.2021.2209
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2021.2209
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2021.2209?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
    ---><---

    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:inm:oropre:v:70:y:2022:i:6:p:3490-3510. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.