Strategic AI in Cournot Markets

TL;DR

This paper explores how large language models (LLMs) behave in oligopolistic markets, revealing their ability to understand complex dynamics but also their tendency to tacitly collude, raising regulatory concerns and suggesting intervention strategies.

Contribution

It demonstrates that LLMs can both understand market dynamics and engage in collusion, and proposes regulatory methods to mitigate anti-competitive behavior.

Findings

LLMs can drive prices up to 200% above Nash equilibrium.

Regulating dominant agents can disrupt collusion.

LLMs understand complex market strategies.

Abstract

As artificial intelligence increasingly automates decision-making in competitive markets, understanding the resulting dynamics and ensuring fair market mechanisms is essential. We investigate the multi-faceted decision-making of large language models (LLMs) in oligopolistic Cournot markets, showing that LLMs not only grasp complex market dynamics--demonstrating their potential as effective economic planning agents--but also engage in sustained tacit collusion, driving prices up to 200% above Nash equilibrium levels. Our analysis examines LLM behavior across three dimensions-(1) decision type, (2) opponent strategies, and (3) market composition--revealing how these factors may shape the competitiveness of LLM-based decision-makers. Furthermore, we show that regulating a few dominant agents by enforcing best-response strategies effectively disrupts collusion and helps restore competitive pricing. Our findings identify potential concerns associated with AI integration in competitive market environments and provide regulatory policy recommendations for the era of automation.