title: Game-theoretic LLM: Agent Workflow for Negotiation Games
summary: This paper investigates the rationality of large language models (LLMs) in
  strategic decision-making contexts, specifically within the framework of game
  theory. We evaluate several state-of-the-art LLMs across a spectrum of
  complete-information and incomplete-information games. Our findings reveal that
  LLMs frequently deviate from rational strategies, particularly as the
  complexity of the game increases with larger payoff matrices or deeper
  sequential trees.
    To address these limitations, we design multiple game-theoretic workflows
  that guide the reasoning and decision-making processes of LLMs. These workflows
  aim to enhance the models' ability to compute Nash Equilibria and make rational
  choices, even under conditions of uncertainty and incomplete information.
  Experimental results demonstrate that the adoption of these workflows
  significantly improves the rationality and robustness of LLMs in game-theoretic
  tasks. Specifically, with the workflow, LLMs exhibit marked improvements in
  identifying optimal strategies, achieving near-optimal allocations in
  negotiation scenarios, and reducing susceptibility to exploitation during
  negotiations. Furthermore, we explore the meta-strategic considerations of
  whether it is rational for agents to adopt such workflows, recognizing that the
  decision to use or forgo the workflow constitutes a game-theoretic issue in
  itself.
    Our research contributes to a deeper understanding of LLMs' decision-making
  capabilities in strategic contexts and provides insights into enhancing their
  rationality through structured workflows. The findings have implications for
  the development of more robust and strategically sound AI agents capable of
  navigating complex interactive environments. Code and data supporting this
  study are available at https://github.com/Wenyueh/game_theory.
paper_authors._id: 6732ec4ad7e101ba3713531d
paper_authors.name: Wenyue Hua
paper_authors._id: 6732ec4ad7e101ba3713531e
paper_authors.name: Ollie Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-07T08:43:03.584Z
paper_authors._id: 66197a8afeb55cbe39e50ae8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pcSS1TsCoHxRcAEkcMNm0.png
paper_authors.fullname: Ollie Liu
paper_authors.type: user
paper_authors.user: oliu-io
paper_authors._id: 6732ec4ad7e101ba3713531f
paper_authors.name: Lingyao Li
paper_authors._id: 6732ec4ad7e101ba37135320
paper_authors.name: Alfonso Amayuelas
paper_authors._id: 6732ec4ad7e101ba37135321
paper_authors.name: Julie Chen
paper_authors._id: 6732ec4ad7e101ba37135322
paper_authors.name: Lucas Jiang
paper_authors._id: 6732ec4ad7e101ba37135323
paper_authors.name: Mingyu Jin
paper_authors._id: 6732ec4ad7e101ba37135324
paper_authors.name: Lizhou Fan
paper_authors._id: 6732ec4ad7e101ba37135325
paper_authors.name: Fei Sun
paper_authors._id: 6732ec4ad7e101ba37135326
paper_authors.name: William Wang
paper_authors._id: 6732ec4ad7e101ba37135327
paper_authors.name: Xintong Wang
paper_authors._id: 6732ec4ad7e101ba37135328
paper_authors.name: Yongfeng Zhang
paper_title: Game-theoretic LLM: Agent Workflow for Negotiation Games
paper_summary: This paper investigates the rationality of large language models (LLMs) in
  strategic decision-making contexts, specifically within the framework of game
  theory. We evaluate several state-of-the-art LLMs across a spectrum of
  complete-information and incomplete-information games. Our findings reveal that
  LLMs frequently deviate from rational strategies, particularly as the
  complexity of the game increases with larger payoff matrices or deeper
  sequential trees.
    To address these limitations, we design multiple game-theoretic workflows
  that guide the reasoning and decision-making processes of LLMs. These workflows
  aim to enhance the models' ability to compute Nash Equilibria and make rational
  choices, even under conditions of uncertainty and incomplete information.
  Experimental results demonstrate that the adoption of these workflows
  significantly improves the rationality and robustness of LLMs in game-theoretic
  tasks. Specifically, with the workflow, LLMs exhibit marked improvements in
  identifying optimal strategies, achieving near-optimal allocations in
  negotiation scenarios, and reducing susceptibility to exploitation during
  negotiations. Furthermore, we explore the meta-strategic considerations of
  whether it is rational for agents to adopt such workflows, recognizing that the
  decision to use or forgo the workflow constitutes a game-theoretic issue in
  itself.
    Our research contributes to a deeper understanding of LLMs' decision-making
  capabilities in strategic contexts and provides insights into enhancing their
  rationality through structured workflows. The findings have implications for
  the development of more robust and strategically sound AI agents capable of
  navigating complex interactive environments. Code and data supporting this
  study are available at https://github.com/Wenyueh/game_theory.
paper_ai_keywords: large language models (LLMs) strategic decision-making game theory complete-information games incomplete-information games rational strategies payoff conventions sequential trees Nash Equilibria decision-making processes game-theoretic tasks negotiation scenarios meta-strategic considerations iterative allocations robust AI agents interactive environments