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Artificial Intelligence and Spontaneous Collusion

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  • Martino Banchio
  • Giacomo Mantegazza

Abstract

We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism responsible for the emergence of algorithmic collusion. We observe that algorithms periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel relies on an endogenous statistical linkage in the algorithms' estimates which we call spontaneous coupling. The model's parameters predict whether the statistical linkage will appear, and what market structures facilitate algorithmic collusion. We show that spontaneous coupling can sustain collusion in prices and market shares, complementing experimental findings in the literature. Finally, we apply our results to design algorithmic markets.

Suggested Citation

  • Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    • Handle: RePEc:arx:papers:2202.05946
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    References listed on IDEAS

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    1. Fudenberg Drew & Kreps David M., 1993. "Learning Mixed Equilibria," Games and Economic Behavior, Elsevier, vol. 5(3), pages 320-367, July.
    2. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    3. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-881, September.
    4. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
    5. Hopkins, Ed & Posch, Martin, 2005. "Attainability of boundary points under reinforcement learning," Games and Economic Behavior, Elsevier, vol. 53(1), pages 110-125, October.
    6. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    7. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," Papers 2202.05947, arXiv.org.
    8. Maximilian Kasy & Anja Sautmann, 2021. "Adaptive Treatment Assignment in Experiments for Policy Choice," Econometrica, Econometric Society, vol. 89(1), pages 113-132, January.
    9. Hal Varian, 2018. "Artificial Intelligence, Economics, and Industrial Organization," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 399-419, National Bureau of Economic Research, Inc.
    10. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    11. Panayotis Mertikopoulos & William H. Sandholm, 2016. "Learning in Games via Reinforcement and Regularization," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1297-1324, November.
    12. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    13. Lawrence M. Wein, 1992. "Dynamic Scheduling of a Multiclass Make-to-Stock Queue," Operations Research, INFORMS, vol. 40(4), pages 724-735, August.
    14. Harris, Christopher, 1998. "On the Rate of Convergence of Continuous-Time Fictitious Play," Games and Economic Behavior, Elsevier, vol. 22(2), pages 238-259, February.
    15. 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.
    16. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
    17. Shlomo Halfin & Ward Whitt, 1981. "Heavy-Traffic Limits for Queues with Many Exponential Servers," Operations Research, INFORMS, vol. 29(3), pages 567-588, June.
    18. John Asker & Chaim Fershtman & Ariel Pakes, 2022. "Artificial Intelligence, Algorithm Design, and Pricing," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 452-456, May.
    19. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    20. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
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    Cited by:

    1. Pranjal Rawat, 2023. "Designing Auctions when Algorithms Learn to Bid: The critical role of Payment Rules," Papers 2306.09437, arXiv.org.
    2. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    3. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," Papers 2202.05947, arXiv.org.
    4. Maximilian Schaefer, 2022. "On the Emergence of Cooperation in the Repeated Prisoner's Dilemma," Papers 2211.15331, arXiv.org, revised Feb 2023.
    5. Olivier Compte, 2023. "Q-learning with biased policy rules," Papers 2304.12647, arXiv.org, revised Oct 2023.
    6. Ludovico Crippa & Yonatan Gur & Bar Light, 2022. "Equilibria in Repeated Games under No-Regret with Dynamic Benchmarks," Papers 2212.03152, arXiv.org, revised Jul 2023.
    7. Martino Banchio & Andrzej Skrzypacz, 2022. "Artificial Intelligence and Auction Design," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.

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