Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions

Ryan Y. Lin, Siddhartha Ojha, Kevin Cai, Maxwell Chen

Keywords: Collusion, Large Language Models, Machine Learning, Algorithmic Game Theory

TL;DR: We find that LLM agents specialize and divide markets in a multi-commodity Cournot competition setting even without explicit instructions to collude.

Abstract: Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents.

Submission Number: 10