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Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms

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  • Pai, Mallesh
  • Hansen, Karsten

Abstract

Motivated by their increasing prevalence, we study outcomes when competing sellers use machine learning algorithms to run real-time dynamic price experiments. These algorithms are often misspecified, ignoring the effect of factors outside their control, e.g. competitors' prices. We show that the long-run prices depend on the informational value (or signal to noise ratio) of price experiments: if low, the long-run prices are consistent with the static Nash equilibrium of the corresponding full information setting. However, if high, the long-run prices are supra-competitive---the full information joint-monopoly outcome is possible. We show this occurs via a novel channel: competitors' algorithms’ prices end up running correlated experiments. Therefore, sellers’ misspecified models overestimate own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.

Suggested Citation

  • Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:14372
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    References listed on IDEAS

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    Cited by:

    1. Berman, Ron & Heller, Yuval, 2020. "Naive Analytics Equilibrium," MPRA Paper 103824, University Library of Munich, Germany.
    2. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    3. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    4. Kshitija Taywade & Brent Harrison & Judy Goldsmith, 2022. "Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand," Papers 2201.00486, arXiv.org.
    5. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    6. Bingyan Han, 2021. "Understanding algorithmic collusion with experience replay," Papers 2102.09139, arXiv.org, revised Mar 2021.
    7. Davide Proserpio & John R. Hauser & Xiao Liu & Tomomichi Amano & Alex Burnap & Tong Guo & Dokyun (DK) Lee & Randall Lewis & Kanishka Misra & Eric Schwarz & Artem Timoshenko & Lilei Xu & Hema Yoganaras, 2020. "Soul and machine (learning)," Marketing Letters, Springer, vol. 31(4), pages 393-404, December.
    8. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    9. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
    10. Leif Nelson & Duncan Simester & K. Sudhir, 2020. "Introduction to the Special Issue on Marketing Science and Field Experiments," Marketing Science, INFORMS, vol. 39(6), pages 1033-1038, November.
    11. Bingyan Han, 2022. "Cooperation between Independent Market Makers," Papers 2206.05410, arXiv.org.

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    Keywords

    Algorithmic pricing; Collusion; Misspecified models; Bandit algorithms;
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