mlsanityData to 7 Oct 2026

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

arXiv 2410.00031 ↗First posted 19 Sep 2024cs.GT cs.AI cs.CLcited by 25

On the map: LLM agents and tool use → Financial trading and forecasting agents

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.

Key facts. Cited by 25 papers in the map (data through 7 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.

Builds on

6 of 36 references.

References · 6

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  4. 2024Algorithmic Collusion by Large Language Modelscited by 54
  5. 2022Artificial Intelligence and Auction Designcited by 20
  6. 2020Multi-Agent Reinforcement Learning in Cournot Gamescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 17.

25 papers.

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  1. 2025From LLM Reasoning to Autonomous AI Agents: A Comprehensive Reviewcited by 146
  2. 2025Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systemscited by 125
  3. 2025The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Processcited by 21
  4. 2026Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montagecited by 17
  5. 2026Institutional AI: Governing LLM Collusion in Multi-Agent Cournot Markets via Public Governance Graphscited by 17
  6. 2026Colosseum: Auditing Collusion in Cooperative Multi-Agent Systemscited by 17
  7. 2025Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspectivecited by 12
  8. 2025Evaluating LLM Agent Collusion in Double Auctionscited by 11
  9. 2025Enhancing Robustness of LLM-Driven Multi-Agent Systems through Randomized Smoothingcited by 9
  10. 2025Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignmentcited by 7
  11. 2026Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agentscited by 4
  12. 2026Agentic Microphysics: A Manifesto for Generative AI Safetycited by 2
13 more
  1. 2026Solipsistic Superintelligence is Unlikely to be Cooperativecited by 1
  2. 2026Draining the Energy Commons: Self-Defeating Over-Appropriation as a Coordination Failure in Agentic LLM Collectivescited by 1
  3. 2026Mitigating Retaliatory Algorithmic Collusion in Repeated Gamescited by 1
  4. 2025When AI Agents Compete for Jobs: Strategic Capabilities and Economic Dynamics of AI Labour Marketscited by 0
  5. 2026Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGIcited by 0
  6. 2026Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systemscited by 0
  7. 2026SupplyNet: Supporting Visual Exploratory Learning in Supply Chain via Contextual Multi-Agent Simulationcited by 0
  8. 2026Scaffolding the Strategist: Architecture-Dependent Reasoning Interventions in Hotelling Spatial Marketscited by 0
  9. 2026Collusion with Competitive Marginals: Price-Level Audits Are Blind by Constructioncited by 0
  10. 2026From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Modelscited by 0
  11. 2026Learning under Opponent Unawareness in Linear-Quadratic Stochastic Gamescited by 0
  12. 2026Competitive Market Behavior of LLMscited by 0
  13. 2026Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competitioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025LLM-based Multi-Agent System for Simulating Strategic and Goal-Oriented Data Marketplaces
  2. 2026On the Fragility of AI Agent Collusion
  3. 2026Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets
  4. 2026Competition and Cooperation of LLM Agents in Games
  5. 2025From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
  6. 2026Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
  7. 2026Market-Bench: Benchmarking Large Language Models on Economic and Trade Competition
  8. 2023Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena