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Regulation of Algorithmic Collusion

Author

Listed:
  • Jason D. Hartline
  • Sheng Long
  • Chenhao Zhang
Abstract
Consider sellers in a competitive market that use algorithms to adapt their prices from data that they collect. In such a context it is plausible that algorithms could arrive at prices that are higher than the competitive prices and this may benefit sellers at the expense of consumers (i.e., the buyers in the market). This paper gives a definition of plausible algorithmic non-collusion for pricing algorithms. The definition allows a regulator to empirically audit algorithms by applying a statistical test to the data that they collect. Algorithms that are good, i.e., approximately optimize prices to market conditions, can be augmented to contain the data sufficient to pass the audit. Algorithms that have colluded on, e.g., supra-competitive prices cannot pass the audit. The definition allows sellers to possess useful side information that may be correlated with supply and demand and could affect the prices used by good algorithms. The paper provides an analysis of the statistical complexity of such an audit, i.e., how much data is sufficient for the test of non-collusion to be accurate.

Suggested Citation

  • Jason D. Hartline & Sheng Long & Chenhao Zhang, 2024. "Regulation of Algorithmic Collusion," Papers 2401.15794, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2401.15794
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    References listed on IDEAS

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    1. Foster, Dean P. & Vohra, Rakesh V., 1997. "Calibrated Learning and Correlated Equilibrium," Games and Economic Behavior, Elsevier, vol. 21(1-2), pages 40-55, October.
    2. James W. Friedman, 1971. "A Non-cooperative Equilibrium for Supergames," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 38(1), pages 1-12.
    3. Gautier, Axel & Ittoo, Ashwin & Van Cleynenbreugel, Pieter, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," LIDAM Reprints CORE 3138, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Sylvain Chassang & Juan Ortner, 2023. "Regulating Collusion," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 177-204, September.
    5. Aumann, Robert J., 1974. "Subjectivity and correlation in randomized strategies," Journal of Mathematical Economics, Elsevier, vol. 1(1), pages 67-96, March.
    6. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    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 E. Kovacic & Carl Shapiro, 2000. "Antitrust Policy: A Century of Economic and Legal Thinking," Journal of Economic Perspectives, American Economic Association, vol. 14(1), pages 43-60, Winter.
    9. 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.
    10. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    11. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Working Paper 1438, Economics Department, Queen's University.
    12. Benoit, Jean-Pierre & Krishna, Vijay, 1985. "Finitely Repeated Games," Econometrica, Econometric Society, vol. 53(4), pages 905-922, July.
    13. 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.
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