  This paper introduces Alympics (Olympics for Agents), a systematic simulation
framework utilizing Large Language Model (LLM) agents for game theory research.
Alympics creates a versatile platform for studying complex game theory
problems, bridging the gap between theoretical game theory and empirical
investigations by providing a controlled environment for simulating human-like
strategic interactions with LLM agents. In our pilot case study, the "Water
Allocation Challenge," we explore Alympics through a challenging strategic game
focused on the multi-round auction on scarce survival resources. This study
demonstrates the framework's ability to qualitatively and quantitatively
analyze game determinants, strategies, and outcomes. Additionally, we conduct a
comprehensive human assessment and an in-depth evaluation of LLM agents in
strategic decision-making scenarios. Our findings not only expand the
understanding of LLM agents' proficiency in emulating human strategic behavior
but also highlight their potential in advancing game theory knowledge, thereby
enriching our understanding of both game theory and empowering further research
into strategic decision-making domains with LLM agents. Codes, prompts, and all
related resources are available at https://github.com/microsoft/Alympics.

Computation and Language;Artificial Intelligence;Computer Science and Game Theory