IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2508.19676.html

Dynamic Delegation with Reputation Feedback

Author

Listed:
  • Georgy Lukyanov
  • Anna Vlasova

Abstract

We study dynamic delegation with reputation feedback: a long-lived expert advises a sequence of implementers whose effort responds to current reputation, altering outcome informativeness and belief updates. We solve for a recursive, belief-based equilibrium and show that advice is a reputation-dependent cutoff in the expert's signal. A diagnosticity condition - failures at least as informative as successes - implies reputational conservatism: the cutoff (weakly) rises with reputation. Comparative statics are transparent: greater private precision or a higher good-state prior lowers the cutoff, whereas patience (value curvature) raises it. Reputation is a submartingale under competent types and a supermartingale under less competent types; we separate boundary hitting into learning (news generated infinitely often) versus no-news absorption. A success-contingent bonus implements any target experimentation rate with a plug-in calibration in a Gaussian benchmark. The framework yields testable predictions and a measurement map for surgery (operate vs. conservative care).

Suggested Citation

  • Georgy Lukyanov & Anna Vlasova, 2025. "Dynamic Delegation with Reputation Feedback," Papers 2508.19676, arXiv.org, revised Aug 2025.
    • Handle: RePEc:arx:papers:2508.19676
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2508.19676
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thomas, Caroline, 2019. "Experimentation with reputation concerns – Dynamic signalling with changing types," Journal of Economic Theory, Elsevier, vol. 179(C), pages 366-415.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chia-Hui Chen & Junichiro Ishida & Wing Suen, 2021. "Reputation Concerns in Risky Experimentation [Reputation and Survival: Learning in a Dynamic Signalling Model]," Journal of the European Economic Association, European Economic Association, vol. 19(4), pages 1981-2021.
    2. Chen, Wanyi, 2021. "Dynamic survival bias in optimal stopping problems," Journal of Economic Theory, Elsevier, vol. 196(C).
    3. Hwang, Ilwoo, 2023. "Policy experimentation with repeated elections," Games and Economic Behavior, Elsevier, vol. 142(C), pages 623-644.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2508.19676. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.