  The rise of algorithmic pricing raises concerns of algorithmic collusion. We
conduct experiments with algorithmic pricing agents based on Large Language
Models (LLMs), and specifically GPT-4. 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. These results extend to auction settings. Our findings underscore
the need for antitrust regulation regarding algorithmic pricing, and uncover
regulatory challenges unique to LLM-based pricing agents.
