"Fairness Maximization among Offline Agents in Online-Matching Markets" by Will Ma, Pan Xu et al.
 

Fairness Maximization among Offline Agents in Online-Matching Markets

Document Type

Conference Proceeding

Publication Date

4-5-2023

Abstract

Online matching markets (OMMs) are commonly used in today's world to pair agents from two parties (whom we will call offline and online agents) for mutual benefit. However, studies have shown that the algorithms making decisions in these OMMs often leave disparities in matching rates, especially for offline agents. In this article, we propose online matching algorithms that optimize for either individual or group-level fairness among offline agents in OMMs. We present two linear-programming (LP) based sampling algorithms, which achieve competitive ratios at least 0.725 for individual fairness maximization and 0.719 for group fairness maximization. We derive further bounds based on fairness parameters, demonstrating conditions under which the competitive ratio can increase to 100%. There are two key ideas helping us break the barrier of 1-1/∼ 63.2% for competitive ratio in online matching. One is boosting, which is to adaptively re-distribute all sampling probabilities among only the available neighbors for every arriving online agent. The other is attenuation, which aims to balance the matching probabilities among offline agents with different mass allocated by the benchmark LP. We conduct extensive numerical experiments and results show that our boosted version of sampling algorithms are not only conceptually easy to implement but also highly effective in practical instances of OMMs where fairness is a concern.

Identifier

85152619442 (Scopus)

Publication Title

ACM Transactions on Economics and Computation

External Full Text Location

https://doi.org/10.1145/3569705

e-ISSN

21678383

ISSN

21678375

Issue

4

Volume

10

Grant

IIS-1948157

Fund Ref

National Science Foundation

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