Algorithmic Collusion by Large Language Models
This research highlights unique regulatory challenges for LLM-based and AI-based pricing agents, addressing concerns about algorithmic collusion in markets.
The study investigated algorithmic collusion by Large Language Model (LLM)-based pricing agents in oligopoly settings, finding that they autonomously achieve supracompetitive prices and profits, with prompt variations significantly influencing outcomes, and extended these results to auctions.
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 LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits in oligopoly settings and that 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 AI-based pricing agents more broadly.