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Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement

Author

Listed:
  • Frédéric Marty
  • Thierry Warin
Abstract
This paper examines algorithmic collusion from legal and economic perspectives, highlighting the growing role of algorithms in digital markets and their potential for anti-competitive behavior. Using bandit algorithms as a model, traditionally applied in uncertain decision-making contexts, we illuminate the dynamics of implicit collusion without overt communication. Legally, the challenge is discerning and classifying these algorithmic signals, especially as unilateral communications. Economically, distinguishing between rational pricing and collusive patterns becomes intricate with algorithm-driven decisions. The paper emphasizes the imperative for competition authorities to identify unusual market behaviors, hinting at shifting the burden of proof to firms with algorithmic pricing. Balancing algorithmic transparency and collusion prevention is crucial. While regulations might address these concerns, they could hinder algorithmic development. As this form of collusion becomes central in antitrust, understanding through models like bandit algorithms is vital, since these last ones may converge faster towards an anticompetitive equilibrium. Cet article examine la collusion algorithmique du point de vue juridique et économique, mettant en évidence le rôle croissant des algorithmes dans les marchés numériques et leur potentiel comportement anticoncurrentiel. En utilisant les algorithmes de bandit comme modèle, traditionnellement appliqués dans des contextes de prise de décision incertaine, nous mettons en lumière la dynamique de la collusion implicite sans communication explicite. Sur le plan juridique, le défi réside dans le discernement et la classification de ces signaux algorithmiques, en particulier en tant que communications unilatérales. Sur le plan économique, la distinction entre une tarification rationnelle et des schémas collusifs devient complexe avec les décisions pilotées par des algorithmes. L'article met l'accent sur l'impératif pour les autorités de la concurrence d'identifier les comportements de marché inhabituels, laissant entendre un transfert du fardeau de la preuve aux entreprises pratiquant la tarification algorithmique. Équilibrer la transparence algorithmique et la prévention de la collusion est crucial. Bien que la réglementation puisse traiter ces préoccupations, elle pourrait entraver le développement des algorithmes. À mesure que cette forme de collusion devient centrale dans le domaine de la concurrence, la compréhension à travers des modèles tels que les algorithmes de bandit est essentielle, car ces derniers peuvent converger plus rapidement vers un équilibre anticoncurrentiel.

Suggested Citation

  • Frédéric Marty & Thierry Warin, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," CIRANO Working Papers 2023s-26, CIRANO.
    • Handle: RePEc:cir:cirwor:2023s-26
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    File URL: https://cirano.qc.ca/files/publications/2023s-26.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Algorithmic Collusion; Bandit Algorithms; Antitrust Enforcement; Unilateral Signals; Pricing Strategies; Collusion algorithmique; algorithmes de bandits; Application du droit de la concurrence; signaux unilatéraux; Stratégies de tarification;
    All these keywords.

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law

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