IDEAS home Printed from https://ideas.repec.org/a/inm/oropre/v73y2025i5p2515-2529.html
   My bibliography  Save this article

Policy Learning with Competing Agents

Author

Listed:
  • Roshni Sahoo

    (Department of Computer Science, Stanford University, Stanford, California 94305)

  • Stefan Wager

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy. In this paper, we study capacity-constrained treatment assignments in the presence of such interference. We consider a dynamic model in which the decision maker allocates treatments at each time step and heterogeneous agents myopically best respond to the previous treatment assignment policy. When the number of agents is large but finite, we show that the threshold for receiving treatment under a given policy converges to the policy’s mean-field equilibrium threshold. Based on this result, we develop a consistent estimator for the policy gradient. In a semisynthetic experiment with data from the National Education Longitudinal Study of 1988, we demonstrate that this estimator can be used for learning capacity-constrained policies in the presence of strategic behavior.

Suggested Citation

  • Roshni Sahoo & Stefan Wager, 2025. "Policy Learning with Competing Agents," Operations Research, INFORMS, vol. 73(5), pages 2515-2529, September.
    • Handle: RePEc:inm:oropre:v:73:y:2025:i:5:p:2515-2529
    DOI: 10.1287/opre.2022.0687
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/opre.2022.0687
    Download Restriction: no
    File URL: https://libkey.io/10.1287/opre.2022.0687?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
    ---><---

    More about this item

    Keywords

    ;

    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:inm:oropre:v:73:y:2025:i:5:p:2515-2529. 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.