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Algorithmic Collusion by Large Language Models

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  • Sara Fish
  • Yannai A. Gonczarowski
  • Ran I. Shorrer

Abstract

The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs), and specifically GPT-4. We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may increase collusion. These results extend to auction settings. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.

Suggested Citation

  • Sara Fish & Yannai A. Gonczarowski & Ran I. Shorrer, 2024. "Algorithmic Collusion by Large Language Models," Papers 2404.00806, arXiv.org.
    • Handle: RePEc:arx:papers:2404.00806
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    References listed on IDEAS

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    1. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
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    7. 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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