博弈論 LLM：代理人工作流程用於談判遊戲

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

本文探討大型語言模型（LLMs）在戰略決策情境中的合理性，尤其是在博弈理論框架下。我們評估了幾種最先進的LLMs在完全信息和不完全信息博弈的範疇中。我們的研究發現，LLMs經常偏離理性策略，特別是在遊戲複雜度隨著更大的收益矩陣或更深的序列樹而增加時。 為了應對這些限制，我們設計了多個博弈理論工作流程，引導LLMs的推理和決策過程。這些工作流程旨在增強模型計算納什均衡和做出理性選擇的能力，即使在不確定和不完全信息的情況下也是如此。實驗結果表明，採用這些工作流程顯著提高了LLMs在博弈任務中的理性和韌性。具體而言，憑藉這些工作流程，LLMs在識別最佳策略、在談判情境中實現接近最佳的分配以及減少在談判過程中易受剝削方面均有顯著改善。此外，我們探討了元策略考量，即代理是否理性地採用這些工作流程，認識到使用或放棄工作流程本身就構成一個博弈理論問題。 我們的研究有助於更深入地了解LLMs在戰略情境中的決策能力，並提供了通過結構化工作流程增強其理性的見解。這些研究結果對於開發更強大和策略合理的AI代理，能夠在複雜的互動環境中進行導航具有重要意義。支持本研究的代碼和數據可在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.