@inproceedings{mao-etal-2025-alympics,
title = "{ALYMPICS}: {LLM} Agents Meet Game Theory",
author = "Mao, Shaoguang and
Cai, Yuzhe and
Xia, Yan and
Wu, Wenshan and
Wang, Xun and
Wang, Fengyi and
Guan, Qiang and
Ge, Tao and
Wei, Furu",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.193/",
pages = "2845--2866",
abstract = "Game theory is a branch of mathematics that studies strategic interactions among rational agents. We propose Alympics (Olympics for Agents), a systematic framework utilizing Large Language Model (LLM) agents for empirical 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 {\textquotedblleft}Water Allocation Challenge{\textquotedblright}, we explore Alympics through a challenging strategic game focused on the multi-round auction of 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 rational strategic decision-making scenarios. Our findings highlight LLM agents' potential to advance game theory knowledge and expand the understanding of their proficiency in emulating human strategic behavior."
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%0 Conference Proceedings
%T ALYMPICS: LLM Agents Meet Game Theory
%A Mao, Shaoguang
%A Cai, Yuzhe
%A Xia, Yan
%A Wu, Wenshan
%A Wang, Xun
%A Wang, Fengyi
%A Guan, Qiang
%A Ge, Tao
%A Wei, Furu
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F mao-etal-2025-alympics
%X Game theory is a branch of mathematics that studies strategic interactions among rational agents. We propose Alympics (Olympics for Agents), a systematic framework utilizing Large Language Model (LLM) agents for empirical 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 of 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 rational strategic decision-making scenarios. Our findings highlight LLM agents’ potential to advance game theory knowledge and expand the understanding of their proficiency in emulating human strategic behavior.
%U https://aclanthology.org/2025.coling-main.193/
%P 2845-2866
Markdown (Informal)
[ALYMPICS: LLM Agents Meet Game Theory](https://aclanthology.org/2025.coling-main.193/) (Mao et al., COLING 2025)
ACL
- Shaoguang Mao, Yuzhe Cai, Yan Xia, Wenshan Wu, Xun Wang, Fengyi Wang, Qiang Guan, Tao Ge, and Furu Wei. 2025. ALYMPICS: LLM Agents Meet Game Theory. In Proceedings of the 31st International Conference on Computational Linguistics, pages 2845–2866, Abu Dhabi, UAE. Association for Computational Linguistics.