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Robust Algorithmic Collusion

Author

Listed:
  • Nicolas Eschenbaum
  • Filip Mellgren
  • Philipp Zahn

Abstract

This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms' strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.

Suggested Citation

  • Nicolas Eschenbaum & Filip Mellgren & Philipp Zahn, 2022. "Robust Algorithmic Collusion," Papers 2201.00345, arXiv.org, revised Jan 2022.
    • Handle: RePEc:arx:papers:2201.00345
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    File URL: http://arxiv.org/pdf/2201.00345
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    References listed on IDEAS

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    1. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    2. Nicolas Bondoux & Anh Quan Nguyen & Thomas Fiig & Rodrigo Acuna-Agost, 2020. "Reinforcement learning applied to airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 332-348, October.
    3. Rodrigo Acuna-Agost & Eoin Thomas & Alix Lhéritier, 2021. "Price elasticity estimation for deep learning-based choice models: an application to air itinerary choices," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 213-226, June.
    4. Mailath, George J. & Samuelson, Larry, 2006. "Repeated Games and Reputations: Long-Run Relationships," OUP Catalogue, Oxford University Press, number 9780195300796.
    5. 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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    Cited by:

    1. John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.

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