STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making

Chuanhao Li, Runhan Yang, Tiankai Li, Milad Bafarassat, Kourosh Sharifi, Dirk Bergemann and Zhuoran Yang
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Chuanhao Li: Yale University
Runhan Yang: The Chinese University of Hong Kong
Tiankai Li: University of Science and Technology of China
Milad Bafarassat: Sabanci University
Kourosh Sharifi: Sabanci University
Zhuoran Yang: Yale University

No 2393, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: Large Language Models (LLMs) like GPT-4 have revolutionized natural language processing, showing remarkable linguistic proficiency and reasoning capabilities. However, their application in strategic multi-agent decision-making environments is hampered by significant limitations including poor mathematical reasoning, difficulty in following instructions, and a tendency to generate incorrect information. These deficiencies hinder their performance in strategic and interactive tasks that demand adherence to nuanced game rules, long-term planning, exploration in unknown environments, and anticipation of opponentsÕ moves. To overcome these obstacles, this paper presents a novel LLM agent framework equipped with memory and specialized tools to enhance their strategic decision-making capabilities. We deploy the tools in a number of economically important environments, in particular bilateral bargaining and multi-agent and dynamic mechanism design. We employ quantitative metrics to assess the frameworkÕs performance in various strategic decision-making problems. Our findings establish that our enhanced framework significantly improves the strategic decision-making capability of LLMs. While we highlight the inherent limitations of current LLM models, we demonstrate the improvements through targeted enhancements, suggesting a promising direction for future developments in LLM applications for interactive environments.

Pages: 39 pages
Date: 2024-05
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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