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arXiv · 2503.08004

Multiplayer Information Asymmetric Bandits in Metric Spaces

William Chang·Aditi Karthik
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Abstract

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.

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William Chang, Aditi Karthik. 2025-03-11. Multiplayer Information Asymmetric Bandits in Metric Spaces. https://arxiv.org/abs/2503.08004

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