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Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms

Author

Listed:
  • Arnoud V. den Boer

    (University of Amsterdam)

  • Janusz M. Meylahn

    (University of Twente)

  • Maarten Pieter Schinkel

    (University of Amsterdam)

Abstract

We examine recent claims that a particular Q-learning algorithm used by competitors ‘autonomously’ and systematically learns to collude, resulting in supracompetitive prices and extra profits for the firms sustained by collusive equilibria. A detailed analysis of the inner workings of this algorithm reveals that there is no immediate reason for alarm. We set out what is needed to demonstrate the existence of a colluding price algorithm that does form a threat to competition.

Suggested Citation

  • Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20220067
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    References listed on IDEAS

    as
    1. Matthias Hettich, 2021. "Algorithmic Collusion: Insights from Deep Learning," CQE Working Papers 9421, Center for Quantitative Economics (CQE), University of Muenster.
    2. Rothschild, Michael, 1974. "A two-armed bandit theory of market pricing," Journal of Economic Theory, Elsevier, vol. 9(2), pages 185-202, October.
    3. Fershtman, Chaim & Asker, John & Pakes, Ariel, 2021. "Artificial intelligence and Pricing: The Impact of Algorithm Design," CEPR Discussion Papers 15880, C.E.P.R. Discussion Papers.
    4. Daníelsson, Jón & Macrae, Robert & Uthemann, Andreas, 2022. "Artificial intelligence and systemic risk," Journal of Banking & Finance, Elsevier, vol. 140(C).
    5. Hongmin Li & Woonghee Tim Huh, 2011. "Pricing Multiple Products with the Multinomial Logit and Nested Logit Models: Concavity and Implications," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 549-563, October.
    6. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    7. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    8. William L. Cooper & Tito Homem-de-Mello & Anton J. Kleywegt, 2015. "Learning and Pricing with Models That Do Not Explicitly Incorporate Competition," Operations Research, INFORMS, vol. 63(1), pages 86-103, February.
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    Citations

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    Cited by:

    1. Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
    2. John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.
    3. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    4. Aleksei Pastushkov, 2024. "Market efficiency, informational asymmetry and pseudo-collusion of adaptively learning agents," Papers 2411.05032, arXiv.org.
    5. Sara Fish & Yannai A. Gonczarowski & Ran I. Shorrer, 2024. "Algorithmic Collusion by Large Language Models," Papers 2404.00806, arXiv.org, revised Nov 2024.
    6. Gagan Aggarwal & Anupam Gupta & Andres Perlroth & Grigoris Velegkas, 2024. "Randomized Truthful Auctions with Learning Agents," Papers 2411.09517, arXiv.org.
    7. 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.
    8. Diwas Paudel & Tapas K. Das, 2024. "Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game," Papers 2401.15108, arXiv.org, revised May 2024.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law

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