Algorithmic Collusion by Large Language Models

 [[{“value”:”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
The post Algorithmic Collusion by Large Language Models appeared first on Marginal REVOLUTION.”}]] 

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.

That is a new paper by Sara Fish, Yannai A. Gonczarowski, and Ran I. Shorrer.  The authors are running too quickly into their policy conclusion there (how about removing legal barriers to free entry in many cases? not worth a mention?), but nonetheless very interesting work.  Via Ethan Mollick.

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 Economics, Web/Tech