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Algorithmic collusion: Genuine or spurious?

Author

Listed:
  • Calvano, Emilio
  • Calzolari, Giacomo
  • Denicolò, Vincenzo
  • Pastorello, Sergio

Abstract

Reinforcement-learning pricing algorithms sometimes converge to supra-competitive prices even in markets where collusion is impossible by design or cannot be an equilibrium outcome. We analyze when such spurious collusion may arise, and when instead the algorithms learn genuinely collusive strategies, focusing on the role of the rate and mode of exploration.

Suggested Citation

  • Calvano, Emilio & Calzolari, Giacomo & Denicolò, Vincenzo & Pastorello, Sergio, 2023. "Algorithmic collusion: Genuine or spurious?," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:indorg:v:90:y:2023:i:c:s0167718723000541
    DOI: 10.1016/j.ijindorg.2023.102973
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    References listed on IDEAS

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    1. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    2. 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.
    3. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    4. 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.
    5. Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
    Full references (including those not matched with items on IDEAS)

    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. Zhang Xu & Wei Zhao, 2024. "On Mechanism Underlying Algorithmic Collusion," Papers 2409.01147, arXiv.org.
    3. Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).
    4. Evangelos Katsamakas & J. Manuel Sanchez-Cartas, 2024. "Generative Artificial Intelligence, Content Creation, and Platforms," Journal of Industry, Competition and Trade, Springer, vol. 24(1), pages 1-20, December.

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    More about this item

    Keywords

    Artificial intelligence; reinforcement learning; collusion; exploration;
    All these keywords.

    JEL classification:

    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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