Multiplayer Information Asymmetric Bandits in Metric Spaces

In recent years the information asymmetric Lipschitz bandits In this paper we studied the Lipschitz bandit problem applied to the multiplayer information asymmetric problem studied in \cite{chang2022online, chang2023optimal}. More specifically we consider information asymmetry in rewards, actions, or both. We adopt the CAB algorithm given in \cite{kleinberg2004nearly} which uses a fixed discretization to give regret bounds of the same order (in the dimension of the action) space in all 3 problem settings. We also adopt their zooming algorithm \cite{ kleinberg2008multi}which uses an adaptive discretization and apply it to information asymmetry in rewards and information asymmetry in actions.

@article{chang2025_2503.08004,
  title={ Multiplayer Information Asymmetric Bandits in Metric Spaces },
  author={ William Chang and Aditi Kartik },
  journal={arXiv preprint arXiv:2503.08004},
  year={ 2025 }
}