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 autonomously collude in oligopoly settings to the detriment of consumers, and (3) variation in seemingly innocuous phrases in LLM instructions ("prompts") may increase collusion. Novel off-path analysis techniques uncover 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 black-box pricing agents more broadly.

Date: 2024-03, Revised 2024-11
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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