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English(EN) Fair Stable Matching: A Nash Social Welfare Approach

新算法平衡匹配问题中的公平性和稳定性

研究人员开发了一种新算法SNSW-Alg,旨在解决稳定匹配问题中的公平性问题。该算法旨在最大化纳什社会福利(一种公平性度量),同时保持Gale-Shapley等算法传统上优先考虑的稳定性。SNSW-Alg的运行时间约为O(n^4),并在各种偏好分布下在公平性方面取得了实证收益,而不会显著损害诸如遗憾或平均主义标准等其他指标。 AI

影响 为算法匹配中的公平性引入了一种新颖的方法,可能影响未来需要公平资源分配的AI系统。

排序理由 学术论文,详细介绍了一种解决理论问题的算法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新算法平衡匹配问题中的公平性和稳定性

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学术论文,详细介绍了一种解决理论问题的算法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Parth Desai, Rasheed M, Ganesh Ghalme, Sujit Gujar ·

    公平稳定匹配:一种纳什社会福利方法

    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 \emph{Nash social welfare} (NSW) …