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arXiv · 2408.12845

Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

Arun Verma·Indrajit Saha·Makoto Yokoo·Bryan Kian Hsiang Low
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Abstract

This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an indivisible item that must be irrevocably allocated to one of the agents to achieve a desired balance between fairness and efficiency. Existing algorithms assume a small number of items with a sufficiently large number of copies, which ensures a good utility estimation for all item-agent pairs from noisy observed utilities. However, this assumption may not hold in many real-life applications, e.g., an online platform with a large number of users (items) who use the platform's service providers (agents) only a few times (a few copies of items), making it difficult to accurately estimate utilities for all item-agent pairs. To address this limitation, we assume utility is an unknown function of item-agent features. We propose algorithms that model online fair division as a contextual bandit problem and achieve provable sublinear regret. Our experimental results further validate the effectiveness of the proposed algorithms.

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Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low. 2024-08-23. Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies. https://arxiv.org/abs/2408.12845

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