Algorithmic Collusion by Large Language Models

Sara Fish, Yannai A. Gonczarowski and Ran I. Shorrer

Papers from arXiv.org

Abstract: The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). We find that (1) LLM-based agents are adept at pricing tasks, (2) LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits in oligopoly settings, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may substantially influence the degree of supracompetitive pricing. Off-path analysis using novel techniques uncovers price-war concerns as contributing to these phenomena. Our results extend to auction settings. Our findings uncover unique challenges to any future regulation of LLM-based pricing agents, and generative AI pricing agents more broadly.

Date: 2024-03, Revised 2025-05
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-com, nep-ind and nep-reg
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Citations: View citations in EconPapers (3)

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