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Algorithmic collusion with imperfect monitoring

Author

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

Abstract

We show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of “price wars” triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks.

Suggested Citation

  • Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:indorg:v:79:y:2021:i:c:s0167718721000059
    DOI: 10.1016/j.ijindorg.2021.102712
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    References listed on IDEAS

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    6. 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.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Aleksandar B. Todorov, 2022. "Algorithmic pricing and concerted behaviour – competitive challenges?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 90-107.
    2. Gonzalo Ballestero, 2022. "Collusion and Artificial Intelligence: A Computational Experiment with Sequential Pricing Algorithms under Stochastic Costs," Working Papers 118, Red Nacional de Investigadores en Economía (RedNIE).
    3. Normann, Hans-Theo & Sternberg, Martin, 2022. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," DICE Discussion Papers 392, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    4. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Young Researchers Working Papers 1, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    5. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    6. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    7. Hans-Theo Normann & Martin Sternberg, 2021. "Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_11, Max Planck Institute for Research on Collective Goods, revised 13 Apr 2022.
    8. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    9. Daehyeon Park & Doojin Ryu, 2022. "Supply chain ethics and transparency: An agent‐based model approach with Q‐learning agents," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3331-3337, December.
    10. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Asociación Argentina de Economía Política: Working Papers 4433, Asociación Argentina de Economía Política.
    11. Martino Banchio & Giacomo Mantegazza, 2022. "Artificial Intelligence and Spontaneous Collusion," Papers 2202.05946, arXiv.org, revised Sep 2023.
    12. Sinziana-Maria Rindasu & Ioan Dan Topor & Liliana Ionescu-Feleaga, 2023. "The Evolution of Management Accountants' Digital Skills in Industry 4.0: A Qualitative Approach," Oblik i finansi, Institute of Accounting and Finance, issue 1, pages 38-48, March.
    13. Matthias Hettich, 2021. "Algorithmic Collusion: Insights from Deep Learning," CQE Working Papers 9421, Center for Quantitative Economics (CQE), University of Muenster.
    14. Nicolas Eschenbaum & Filip Mellgren & Philipp Zahn, 2022. "Robust Algorithmic Collusion," Papers 2201.00345, arXiv.org, revised Jan 2022.
    15. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    16. W. Benedikt Schmal, 2022. "From Rules to Regs: A Structural Topic Model of Collusion Research," Papers 2210.02957, arXiv.org.

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    More about this item

    Keywords

    Artificial intelligence; Q-Learning; Imperfect monitoring; Collusion;
    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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