Game-theoretic LLM: Agent Workflow for Negotiation Games
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.
6732ec4ad7e101ba3713531d
Wenyue Hua
claimed_verified
2025-08-06T19:44:36.153Z
639a25aba2b0b1c9d85a51e8
https://cdn-avatars.huggingface.co/v1/production/uploads/639a25aba2b0b1c9d85a51e8/pphz-MK62hPNbBkMHAkeR.jpeg
Wenyue Hua
user
wenyueH
6732ec4ad7e101ba3713531e
Ollie Liu
claimed_verified
2025-01-07T08:43:03.584Z
66197a8afeb55cbe39e50ae8
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/pcSS1TsCoHxRcAEkcMNm0.png
Ollie Liu
user
oliu-io
6732ec4ad7e101ba3713531f
Lingyao Li
6732ec4ad7e101ba37135320
Alfonso Amayuelas
claimed_verified
2025-03-24T08:56:10.732Z
634899b203b5a62a0d1ef9ee
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Alfonso Amayuelas
user
amayuelas
6732ec4ad7e101ba37135321
Julie Chen
6732ec4ad7e101ba37135322
Lucas Jiang
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Mingyu Jin
6732ec4ad7e101ba37135324
Lizhou Fan
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Fei Sun
6732ec4ad7e101ba37135326
William Wang
6732ec4ad7e101ba37135327
Xintong Wang
6732ec4ad7e101ba37135328
Yongfeng Zhang
Game-theoretic LLM: Agent Workflow for Negotiation Games
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.
Game-theoretic workflows enhance the rationality of large language models in strategic decision-making tasks and negotiations, improving their performance and reducing susceptibility to exploitation.
large language models game theory complete-information games incomplete-information games Nash Equilibria optimal strategies negotiation meta-strategic considerations