博弈论LLM：谈判游戏的代理工作流程

Game-theoretic LLM: Agent Workflow for Negotiation Games

November 8, 2024

作者: Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, Fei Sun, William Wang, Xintong Wang, Yongfeng Zhang

cs.AI

摘要

本文研究了大型语言模型（LLMs）在战略决策背景下的合理性，特别是在博弈论框架内。我们评估了几种最先进的LLMs在完全信息和不完全信息博弈的一系列情境中的表现。我们的研究发现，LLMs经常偏离理性策略，特别是在游戏复杂度随着更大的收益矩阵或更深的顺序树而增加时。 为了解决这些局限性，我们设计了多个博弈论工作流程，指导LLMs的推理和决策过程。这些工作流程旨在增强模型计算纳什均衡和做出理性选择的能力，即使在不确定性和不完全信息的情况下也能如此。实验结果表明，采用这些工作流程显著改善了LLMs在博弈任务中的理性和鲁棒性。具体而言，通过工作流程，LLMs在确定最佳策略、在谈判场景中实现接近最佳分配以及减少在谈判过程中易受剥削方面表现出明显改进。此外，我们探讨了关于是否对代理采用这些工作流程是理性的元战略考虑，认识到使用或放弃工作流程的决定本身构成一个博弈论问题。 我们的研究深入探讨了LLMs在战略背景下的决策能力，并提供了通过结构化工作流程增强它们理性的见解。这些发现对于开发更加强大和战略上可靠的人工智能代理，能够在复杂互动环境中进行导航具有重要意义。支持本研究的代码和数据可在https://github.com/Wenyueh/game_theory找到。

English

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.