Fair Algorithms for Multi-Agent Multi-Armed Bandits

Safwan Hossain; Evi Micha; Nisarg Shah

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arXiv · 2007.06699

Fair Algorithms for Multi-Agent Multi-Armed Bandits

Safwan Hossain·Evi Micha·Nisarg Shah

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Abstract

We propose a multi-agent variant of the classical multi-armed bandit problem, in which there are $N$ agents and $K$ arms, and pulling an arm generates a (possibly different) stochastic reward for each agent. Unlike the classical multi-armed bandit problem, the goal is not to learn the "best arm"; indeed, each agent may perceive a different arm to be the best for her personally. Instead, we seek to learn a fair distribution over the arms. Drawing on a long line of research in economics and computer science, we use the Nash social welfare as our notion of fairness. We design multi-agent variants of three classic multi-armed bandit algorithms and show that they achieve sublinear regret, which is now measured in terms of the lost Nash social welfare.

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BibTeX RIS

Safwan Hossain, Evi Micha, Nisarg Shah. 2020-07-13. Fair Algorithms for Multi-Agent Multi-Armed Bandits. https://arxiv.org/abs/2007.06699

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