Nash Equilibrium and Learning Dynamics in Three-Player Matching $m$-Action Games
<|reference_start|>Nash Equilibrium and Learning Dynamics in Three-Player Matching $m$-Action Games: Learning in games discusses the processes where multiple players learn their optimal strategies through the repetition of game plays. The dynamics of learning between two players in zero-sum games, such as matching pennies, where their benefits are competitive, have already been well analyzed. However, it is still unexplored and challenging to analyze the dynamics of learning among three players. In this study, we formulate a minimalistic game where three players compete to match their actions with one another. Although interaction among three players diversifies and complicates the Nash equilibria, we fully analyze the equilibria. We also discuss the dynamics of learning based on some famous algorithms categorized into Follow the Regularized Leader. From both theoretical and experimental aspects, we characterize the dynamics by categorizing three-player interactions into three forces to synchronize their actions, switch their actions rotationally, and seek competition.<|reference_end|>
@article{fujimoto2024nash,
    title={Nash Equilibrium and Learning Dynamics in Three-Player Matching
  $m$-Action Games},
    author={Yuma Fujimoto, Kaito Ariu, and Kenshi Abe},
    journal={arXiv preprint arXiv:2402.10825},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10825},
    primaryClass={cs.GT cs.MA math.OC nlin.CD}
}