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

Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low

Keywords: Online fair division, Contextual bandits, Fairness and efficiency, Regret guarantees, Utility estimation

TL;DR: We propose a contextual bandit algorithm for online fair division problems involving multiple agents and a large number of items, each with only a few copies.

Abstract: This paper studies a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes indivisible items that must be irrevocably allocated to agents while satisfying a desired balance between fairness and efficiency. Existing algorithms assume a small number of items with a sufficiently large number of copies, which ensures an accurate estimation of utilities for all item-agent pairs from noisy utilities. However, this assumption may not hold in many real-life applications, for example, an online platform that has a large number of users (items) who use the platform's service providers (agents) only a few times (a few copies of items), which makes 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. Building on this assumption, we propose algorithms that model online fair division as a contextual bandit problem, with provable sub-linear regret upper bound guarantees. Our experimental results further validate the effectiveness of the proposed algorithms.

Track: Long Paper

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Submission Number: 113