Safety
Fair Stable Matching: A Nash Social Welfare Approach
arXiv:2609.02354v1 Announce Type: cross Abstract: While traditional stable matching algorithms, such as the Gale-Shapley algorithm, prioritize stability, they may fall short of achieving equitable out
arXiv:2609.02354v1 Announce Type: cross Abstract: While traditional stable matching algorithms, such as the Gale-Shapley algorithm, prioritize stability, they may fall short of achieving equitable outcomes among participants. We study the role of Nash social welfare (NSW) as a fairness objective in the classic stable marriage problem. We develop exttt{SNSW-Alg} that finds a stable matching that maximizes Nash social welfare under rank-induced utilities in ilde{O}(n^4) time, where n is the number of men or women. We demonstrate that exttt{SNSW-Alg} balances equity while preserving stability. We empirically evaluate our methods across diverse preference distributions, demonstrating significant gains in fairness without substantial losses in other key measures such as regret, egalitarian criterion, and sex equality. Our findings suggest that the stable matching produced by exttt{SNSW-Alg} is statistically Pareto-undominated by stable matchings based on other fairness measures - regret, egalitarian, and sex equality. This study offers compelling insights for designing fair-stable matching.
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Source: arXiv cs.AI | 2026-09-03