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Learning and Tacit Collusion by Artificial Agents in Cournot Duopoly Games

In: Formal Modelling in Electronic Commerce

Author

Listed:
  • Steven O. Kimbrough

    (University of Pennsylvania)

  • Ming Lu

    (University of Pennsylvania)

  • Frederic Murphy

    (Temple University)

Abstract

We examine learning by artificial agents in repeated play of Cournot duopoly games. Our learning model is simple and cognitively realistic. The model departs from standard reinforcement learning models, as applied to agents in games, in that it credits the agent with a form of conceptual ascent, whereby the agent is able to learn from a consideration set of strategies spanning more than one period of play. The resulting behavior is markedly different from behavior predicted by classical economics for the single-shot (unrepeated) Cournot duopoly game. In repeated play under our learning regime, agents are able to arrive at a tacit form of collusion and set production levels near to those for a monopolist. We note that Cournot duopoly games are reasonable approximations for many real-world arrangements, including hourly spot markets for electricity.

Suggested Citation

  • Steven O. Kimbrough & Ming Lu & Frederic Murphy, 2005. "Learning and Tacit Collusion by Artificial Agents in Cournot Duopoly Games," International Handbooks on Information Systems, in: Steven O. Kimbrough & D.J. Wu (ed.), Formal Modelling in Electronic Commerce, pages 477-492, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-26989-2_19
    DOI: 10.1007/3-540-26989-4_19
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    Cited by:

    1. Igor Sadoune & Andrea Lodi & Marcelin Joanis, 2022. "Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data," Papers 2207.12255, arXiv.org, revised Feb 2024.
    2. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.

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