# Group-level Fairness Maximization in Online Bipartite Matching.

International Joint Conference on Autonomous Agents and Multi-agent Systems（2022）

Abstract

In typical online matching problems, the goal is to maximize the number of matches. This paper studies online bipartite matching from the perspective of group-level fairness, and the goal is to balance the number of matches made across different groups of online nodes. We assume that an online node's group belongings are revealed upon arrival, and measure performance based on the group with the smallest fraction of its nodes matched at the end. We distinguish between two different objectives: long-run fairness, where the algorithm is audited for its fairness over many realizations from the same instance; and short-run fairness, where the algorithm is audited for its fairness on a single realization. We focus on the known-IID model of online arrivals and analyze, under both objectives, the competitive ratio for two classes of algorithms: non-rejecting algorithms, which must match an online node as long as a neighbor is available; and general online algorithms, which are allowed to reject online nodes to improve fairness. For long-run fairness, we analyze two online algorithms (sampling and pooling) which establish asymptotic optimality across many different regimes (no specialized supplies, no rare demand types, or imbalanced supply/demand). By contrast, outside all of these regimes, we show that the competitive ratio for online algorithms is between 0.632 and 0.732. For short-run fairness, we focus on the case of a complete bipartite graph and show that the competitive ratio for online algorithms is between 0.863 and 0.942; we also derive a probabilistic rejection algorithm which is asymptotically optimal as total demand increases.

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