IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2610.11675.html

Delegate Pricing to Algorithms: When Slow and Steady Wins the Race

Author

Listed:
  • Galit Ashkenazi-Golan
  • Edward Plumb
  • Clemens Possnig
  • Yufei Zhang

Abstract

We study the strategic design of learning algorithms in a canonical continuous-time pricing game. We introduce a meta-game in which firms select learning rates for a gradient dynamic, evaluating payoffs as the discounted sum of profits accrued along the entire learning path. We uncover a fundamental dichotomy driven by stage-game incentives: if the stage game exhibits strategic substitutability, each firm strictly prefers faster learning. Under strategic complementarity, however, an excessively fast algorithm accelerates the rival's competitive response, destroying transitional profit margins. Even though the competitive price is a dominant action in the stage game and convergence to this price is guaranteed, we prove that firms design sluggish algorithms in equilibrium.

Suggested Citation

  • Galit Ashkenazi-Golan & Edward Plumb & Clemens Possnig & Yufei Zhang, 2026. "Delegate Pricing to Algorithms: When Slow and Steady Wins the Race," Papers 2610.11675, arXiv.org, revised Oct 2026.
  • Handle: RePEc:arx:papers:2610.11675
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2610.11675
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    2. Rohit Lamba & Sergey Zhuk, 2026. "Pricing with Algorithms," American Economic Review: Insights, American Economic Association, vol. 8(3), pages 320-340, September.
    3. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    4. Lahkar, Ratul & Sandholm, William H., 2008. "The projection dynamic and the geometry of population games," Games and Economic Behavior, Elsevier, vol. 64(2), pages 565-590, November.
    5. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2024. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 723-771.
    6. Chaim Fershtman & Kenneth L. Judd, 2006. "Equilibrium Incentives in Oligopoly: Corrigendum," American Economic Review, American Economic Association, vol. 96(4), pages 1367-1367, September.
    7. Fudenberg, Drew & Tirole, Jean, 1984. "The Fat-Cat Effect, the Puppy-Dog Ploy, and the Lean and Hungry Look," American Economic Review, American Economic Association, vol. 74(2), pages 361-366, May.
    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. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," NBER Chapters, in: The Economics of Transformative AI, pages 119-138, National Bureau of Economic Research, Inc.
    2. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," Papers 2509.01063, arXiv.org.
    3. Shengyu Cao & Ming Hu, 2026. "Supracompetitive Pricing Under AI Monoculture," Papers 2601.01279, arXiv.org, revised Jun 2026.
    4. repec:ecb:ecbdps:202632 is not listed on IDEAS
    5. Ballestero, Gonzalo, 2026. "Algorithmic collusion under sequential pricing and stochastic costs," International Journal of Industrial Organization, Elsevier, vol. 106(C).
    6. Hangcheng Zhao & Ron Berman, 2025. "Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms," Papers 2508.08325, arXiv.org, revised Oct 2025.
    7. Yuhang Wu & Assaf Zeevi, 2026. "Oblivious Learning and Collusive Pricing," Papers 2606.05363, arXiv.org, revised Oct 2026.
    8. Adams, Jonathan J. & Fang, Min & Liu, Zheng & Wang, Yajie, 2026. "The rise of AI pricing: Trends, driving forces, and implications for firm performance," Journal of Monetary Economics, Elsevier, vol. 157(C).
    9. Spann, Martin & Bertini, Marco & Koenigsberg, Oded & Zeithammer, Robert & Aparicio, Diego & Chen, Yuxin & Fantini, Fabrizio & Jin, Ginger Zhe & Morwitz, Vicki G. & Popkowski Leszczyc, Peter & Vitorino, 2026. "Algorithmic pricing: Implications for marketing strategy and regulation," International Journal of Research in Marketing, Elsevier, vol. 43(2), pages 560-579.
    10. Martin Bichler & Julius Durmann & Matthias Oberlechner, 2025. "Algorithmic Pricing and Algorithmic Collusion," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 67(6), pages 971-979, December.
    11. Harrington, Joseph E., 2024. "The effect of demand variability on the adoption and design of a third party’s pricing algorithm," Economics Letters, Elsevier, vol. 244(C).
    12. Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
    13. Janusz M. Meylahn & Maximilian Schafer, 2026. "Equilibrium stability as a driver of cooperation among Q-learners," Papers 2607.13607, arXiv.org.
    14. Zexin Ye, 2025. "Algorithmic Collusion under Observed Demand Shocks," Papers 2502.15084, arXiv.org, revised Dec 2025.
    15. Lambin, Xavier & Raizonville, Adrien, 2025. "From black box to glass box: algorithmic explainability as a strategic decision," Information Economics and Policy, Elsevier, vol. 71(C).
    16. Berman, Ron & Heller, Yuval, 2025. "Naive analytics: The strategic advantage of algorithmic heuristics," Games and Economic Behavior, Elsevier, vol. 154(C), pages 62-78.
    17. Kaede Hanazawa, 2025. "Welfare Effects of Self-Preferencing by a Platform: Empirical Evidence from Airbnb," Papers 2503.04489, arXiv.org.
    18. Jackie Baek & Vivek F. Farias & Farrell Wu, 2026. "Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices," Papers 2605.16064, arXiv.org, revised Jun 2026.
    19. Corts, Kenneth S. & Neher, Darwin V., 2003. "Credible delegation," European Economic Review, Elsevier, vol. 47(3), pages 395-407, June.
    20. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    21. Silvester Koten, 2013. "Legal unbundling and auctions in vertically integrated (utilities) markets," European Journal of Law and Economics, Springer, vol. 36(3), pages 543-573, December.

    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:2610.11675. 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: https://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.