Algorithmic Collusion by Large Language Models

• URL: http://arxiv.org/abs/2404.00806v2
• Date: Wed, 27 Nov 2024 00:19:55 GMT
• Title: Algorithmic Collusion by Large Language Models
• Authors: Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer,
• Abstract summary: We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs) We find that LLM-based agents are adept at pricing tasks, autonomously collude in oligopoly settings to the detriment of consumers, and variation in seemingly innocuous phrases in LLM instructions may increase collusion.
• Score: 0.08192907805418582
• License:
• 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.

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