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Deep Q-learning of Prices in Oligopolies: The Number of Competitors Matters

Author

Listed:
  • Herbert Dawid

    (Bielefeld University, Germany)

  • Philipp Harting

    (Université Côte d'Azur, CNRS, GREDEG, France
    Bielefeld University, Germany)

  • Michal Neugart

    (Technical University of Darmstadt, Germany)

Abstract

Artificial intelligence algorithms are increasingly used for online pricing and are seen as a major threat to competitive markets. We show that if firms use a deep Q-network (DQN) as an example of a state-of-the-art machine learning algorithm, prices are supra-competitive in duopoly but quickly move to competitive prices as the number of competitors in an oligopoly increases. This finding is very robust concerning variations of the exploration and learning rate used in the DQN algorithm.

Suggested Citation

  • Herbert Dawid & Philipp Harting & Michal Neugart, 2024. "Deep Q-learning of Prices in Oligopolies: The Number of Competitors Matters," GREDEG Working Papers 2024-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    • Handle: RePEc:gre:wpaper:2024-32
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    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. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    3. Matthias Hettich, 2021. "Algorithmic Collusion: Insights from Deep Learning," CQE Working Papers 9421, Center for Quantitative Economics (CQE), University of Muenster.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    algorithmic price setting; deep Q-network; oligopoly; supracompetitive prices;
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