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Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?

Author

Listed:
  • Ibrahim Abada

    (Grenoble Ecole de Management, 38000 Grenoble, France; ENGIE Impact, 92400 Paris, France)

  • Xavier Lambin

    (ESSEC Business School and THEMA, Cergy 95021, France)

Abstract

Strategic decisions are increasingly delegated to algorithms. We extend previous results of the algorithmic collusion literature to the context of dynamic optimization with imperfect monitoring by analyzing a setting where a limited number of agents use simple and independent machine-learning algorithms to buy and sell a storable good. No specific instruction is given to them, only that their objective is to maximize profits based solely on past market prices and payoffs. With an original application to battery operations, we observe that the algorithms learn quickly to reach seemingly collusive decisions, despite the absence of any formal communication between them. Building on the findings of the existing literature on algorithmic collusion, we show that seeming collusion could originate in imperfect exploration rather than excessive algorithmic sophistication. We then show that a regulator may succeed in disciplining the market to produce socially desirable outcomes by enforcing decentralized learning or with adequate intervention during the learning process.

Suggested Citation

  • Ibrahim Abada & Xavier Lambin, 2023. "Artificial Intelligence: Can Seemingly Collusive Outcomes Be Avoided?," Management Science, INFORMS, vol. 69(9), pages 5042-5065, September.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5042-5065
    DOI: 10.1287/mnsc.2022.4623
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    Citations

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    Cited by:

    1. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2025. "Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning," Papers 2503.11270, arXiv.org.
    2. Cesare Carissimo & Jan Nagler & Heinrich Nax, 2025. "Cycles and collusion in congestion games under Q-learning," Papers 2502.18984, arXiv.org.

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