mlsanityData to 7 Oct 2026

Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis

arXiv 2312.05488 ↗First posted 9 Dec 2023cs.AI cs.CL cs.GTcited by 91

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Cited by 49 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Models (LLMs) and humans, a promising research direction is to employ LLMs as substitutes for humans in game experiments, enabling social science research. However, despite numerous empirical researches on the combination of LLMs and game theory, the capability boundaries of LLMs in game theory remain unclear. In this research, we endeavor to systematically analyze LLMs in the context of game theory. Specifically, rationality, as the fundamental principle of game theory, serves as the metric for evaluating players' behavior -- building a clear desire, refining belief about uncertainty, and taking optimal actions. Accordingly, we select three classical games (dictator game, Rock-Paper-Scissors, and ring-network game) to analyze to what extent LLMs can achieve rationality in these three aspects. The experimental results indicate that even the current state-of-the-art LLM (GPT-4) exhibits substantial disparities compared to humans in game theory. For instance, LLMs struggle to build desires based on uncommon preferences, fail to refine belief from many simple patterns, and may overlook or modify refined belief when taking actions. Therefore, we consider that introducing LLMs into game experiments in the field of social science should be approached with greater caution.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). Builds on 4 of its 32 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

4 of 32 references.

References · 4

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 737
  3. 2022Social Simulacra: Creating Populated Prototypes for Social Computing Systemscited by 300
  4. 2023Evidence of behavior consistent with self-interest and altruism in an artificially intelligent agentcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 33; 2025: 30; 2026: 27.

91 papers.

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  1. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
  2. 2024A Survey on Large Language Model-Based Game Agentscited by 106
  3. 2024LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Modelscited by 97
  4. 2024Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimizationcited by 93
  5. 2025MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learningcited by 80
  6. 2024How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environmentscited by 66
  7. 2024Can Large Language Model Agents Simulate Human Trust Behavior?cited by 62
  8. 2024Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?cited by 62
  9. 2024Game-theoretic LLM: Agent Workflow for Negotiation Gamescited by 55
  10. 2024Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Contextcited by 41
  11. 2024Moral Alignment for LLM Agentscited by 39
  12. 2025Multi-Agent Coordination across Diverse Applications: A Surveycited by 37
38 more
  1. 2024LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environmentscited by 35
  2. 2024Do LLM Agents Have Regret? A Case Study in Online Learning and Gamescited by 33
  3. 2024Position: Theory of Mind Benchmarks are Broken for Large Language Modelscited by 30
  4. 2024A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarioscited by 23
  5. 2024STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Makingcited by 22
  6. 2024Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboardcited by 22
  7. 2025Will Systems of LLM Agents Cooperate: An Investigation into a Social Dilemmacited by 22
  8. 2024TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMscited by 21
  9. 2024GLEE: A Unified Framework and Benchmark for Language-based Economic Environmentscited by 19
  10. 2024Toward LLM-Agent-Based Modeling of Transportation Systems: A Conceptual Frameworkcited by 19
  11. 2025Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontierscited by 14
  12. 2025When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmascited by 14
  13. 2024Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmascited by 12
  14. 2025Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agentscited by 11
  15. 2025Towards Transparent and Incentive-Compatible Collaboration in Decentralized LLM Multi-Agent Systems: A Blockchain-Driven Approachcited by 11
  16. 2026AgenticPay: A Multi-Agent LLM Negotiation System for Buyer-Seller Transactionscited by 11
  17. 2024EgoSocialArena: Benchmarking the Social Intelligence of Large Language Models from a First-person Perspectivecited by 10
  18. 2025Humanizing LLMs: A Survey of Psychological Measurements with Tools, Datasets, and Human-Agent Applicationscited by 10
  19. 2025Distributive Fairness in Large Language Models: Evaluating Alignment with Human Valuescited by 9
  20. 2024Do Large Language Models Learn Human-Like Strategic Preferences?cited by 8
  21. 2025Aligning LLM with human travel choices: a persona-based embedding learning approachcited by 7
  22. 2025Towards Strategic Persuasion with Language Modelscited by 7
  23. 2024Can LLMs Understand Social Norms in Autonomous Driving Games?cited by 6
  24. 2025From Natural Language to Extensive-Form Game Representationscited by 6
  25. 2025HARBOR: Exploring Persona Dynamics in Multi-Agent Competitioncited by 6
  26. 2025Evolution of Cooperation in LLM-Agent Societies: A Preliminary Study Using Different Punishment Strategiescited by 6
  27. 2026Evaluating Collective Behaviour of Hundreds of LLM Agentscited by 6
  28. 2023Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selectioncited by 5
  29. 2024Towards Logically Sound Natural Language Reasoning with Logic-Enhanced Language Model Agentscited by 5
  30. 2024Autoformalization of Game Descriptions using Large Language Modelscited by 5
  31. 2024SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMscited by 5
  32. 2024Generative Agents for Multi-Agent Autoformalization of Interaction Scenarioscited by 5
  33. 2025AI Agent Behavioral Sciencecited by 5
  34. 2025Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Makingcited by 5
  35. 2025DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humanscited by 5
  36. 2025Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmapcited by 5
  37. 2024Normative Modules: A Generative Agent Architecture for Learning Norms that Supports Multi-Agent Cooperationcited by 4
  38. 2024DiverseDialogue: A Methodology for Designing Chatbots with Human-Like Diversitycited by 4

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