Paper: arXiv 2410.00031

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced economic theory (Cournot competition, Folk Theorem, game-theoretic equilibria) and quantitative modeling, but its empirical component relies on simulation of LLM agents in controlled environments without backtesting on real market data or providing executable code and datasets.

Research Flowchart

  flowchart TD
  A["Research Goal: Assess Strategic Collusion of LLMs in Market Division"] --> B["Methodology: Multi-Commodity Cournot Competition"]
  B --> C["Input: LLM Agents & Market Parameters"]
  C --> D["Process: Dynamic Pricing & Quantity Adjustment"]
  D --> E["Outcome: LLM-Generated Market Division"]
  E --> F["Finding: Emergent Anti-Competitive Behavior"]
  F --> G["Implication: Regulatory & Business Strategy Challenges"]

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0 of 45 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.