“Algorithmic Collusion by Large Language Models”, 2024-03-31 (; similar):
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 [game-theoretical] ‘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.