Algorithmic Collusion by Large Language Models

• URL: http://arxiv.org/abs/2404.00806v1
• Date: Sun, 31 Mar 2024 21:43:05 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) and GPT-4. We find that 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 may increase collusion. Our findings underscore the need for antitrust regulation regarding algorithmic pricing, and uncover regulatory challenges unique to LLM-based pricing agents.
• Score: 0.08192907805418582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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