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

arXiv: https://arxiv.org/abs/2410.00031 · HTML: https://mlsanity.com/paper/2410.00031/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 6 of 6)

- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Artificial Intelligence and Auction Design](https://mlsanity.com/paper/2202.05947/) (2022)
- [Multi-Agent Reinforcement Learning in Cournot Games](https://mlsanity.com/paper/2009.06224/) (2020)

## Built on by (most cited first, 26 of 26)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process](https://mlsanity.com/paper/2506.01080/) (2025)
- [Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphs](https://mlsanity.com/paper/2601.11369/) (2026)
- [Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage](https://mlsanity.com/paper/2601.01685/) (2026)
- [Colosseum: Auditing Collusion in Cooperative Multi-Agent Systems](https://mlsanity.com/paper/2602.15198/) (2026)
- [Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective](https://mlsanity.com/paper/2504.03255/) (2025)
- [Evaluating LLM Agent Collusion in Double Auctions](https://mlsanity.com/paper/2507.01413/) (2025)
- [Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothing](https://mlsanity.com/paper/2507.04105/) (2025)
- [Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment](https://mlsanity.com/paper/2510.14008/) (2025)
- [Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents](https://mlsanity.com/paper/2604.17774/) (2026)
- [Agentic Microphysics: A Manifesto for Generative AI Safety](https://mlsanity.com/paper/2604.15236/) (2026)
- [Solipsistic Superintelligence is Unlikely to be Cooperative](https://mlsanity.com/paper/2606.03237/) (2026)
- [Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectives](https://mlsanity.com/paper/2607.22188/) (2026)
- [Competitive Market Behavior of LLMs](https://mlsanity.com/paper/2609.02580/) (2026)
- [Mitigating Retaliatory Algorithmic Collusion in Repeated Games](https://mlsanity.com/paper/2609.20548/) (2026)
- [When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Markets](https://mlsanity.com/paper/2512.04988/) (2025)
- [Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI](https://mlsanity.com/paper/2603.13244/) (2026)
- [Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems](https://mlsanity.com/paper/2604.15078/) (2026)
- [SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulation](https://mlsanity.com/paper/2606.24694/) (2026)
- [Scaffolding the Strategist: Architecture-Dependent Reasoning Interventions in Hotelling Spatial Markets](https://mlsanity.com/paper/2607.09743/) (2026)
- [Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction](https://mlsanity.com/paper/2607.26385/) (2026)
- [From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models](https://mlsanity.com/paper/2608.06020/) (2026)
- [Learning under Opponent Unawareness in Linear-Quadratic Stochastic Games](https://mlsanity.com/paper/2608.08268/) (2026)
- [Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competition](https://mlsanity.com/paper/2609.18346/) (2026)
- [Disentangling Models from Personas in Heterogeneous LLM Simulations](https://mlsanity.com/paper/2610.07535/) (2026)

## Abstract (arXiv)

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.