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This paper introduces Alympics (Olympics for Agents), a systematic simulationframework utilizing Large Language Model (LLM) agents for game theory research.Alympics creates a versatile platform for studying complex game theoryproblems, bridging the gap between theoretical game theory and empiricalinvestigations by providing a controlled environment for simulating human-likestrategic interactions with LLM agents. In our pilot case study, the "WaterAllocation Challenge," we explore Alympics through a challenging strategic gamefocused on the multi-round auction on scarce survival resources. This studydemonstrates the framework's ability to qualitatively and quantitativelyanalyze game determinants, strategies, and outcomes. Additionally, we conduct acomprehensive human assessment and an in-depth evaluation of LLM agents instrategic decision-making scenarios. Our findings not only expand theunderstanding of LLM agents' proficiency in emulating human strategic behaviorbut also highlight their potential in advancing game theory knowledge, therebyenriching our understanding of both game theory and empowering further researchinto strategic decision-making domains with LLM agents. Codes, prompts, and allrelated resources are available at https://github.com/microsoft/Alympics.

ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents