@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://preview.aclanthology.org/fix-sig-urls/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 ``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."
}