English

Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers

Artificial Intelligence 2025-08-06 v2 Computer Science and Game Theory Machine Learning

Abstract

Game theory is a foundational framework for analyzing strategic interactions, and its intersection with large language models (LLMs) is a rapidly growing field. However, existing surveys mainly focus narrowly on using game theory to evaluate LLM behavior. This paper provides the first comprehensive survey of the bidirectional relationship between Game Theory and LLMs. We propose a novel taxonomy that categorizes the research in this intersection into four distinct perspectives: (1) evaluating LLMs in game-based scenarios; (2) improving LLMs using game-theoretic concepts for better interpretability and alignment; (3) modeling the competitive landscape of LLM development and its societal impact; and (4) leveraging LLMs to advance game models and to solve corresponding game theory problems. Furthermore, we identify key challenges and outline future research directions. By systematically investigating this interdisciplinary landscape, our survey highlights the mutual influence of game theory and LLMs, fostering progress at the intersection of these fields.

Keywords

Cite

@article{arxiv.2502.09053,
  title  = {Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers},
  author = {Haoran Sun and Yusen Wu and Peng Wang and Wei Chen and Yukun Cheng and Xiaotie Deng and Xu Chu},
  journal= {arXiv preprint arXiv:2502.09053},
  year   = {2025}
}

Comments

A shorter conference version is published in IJCAI 2025, titled 'Game Theory Meets Large Language Models: A Systematic Survey'