English(EN) Multi-Dimensional Matching in Market Design

新的SVD方法简化了多维匹配市场

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-19 19:35

研究人员开发了一种新颖、计算效率高的多维匹配市场机制。这种新方法使用奇异值分解（SVD）将复杂的偏好匹配简化为一维问题，显著减少了计算时间。该机制旨在近似最大化纳什社会福利并确保分配真实性，提供鲁棒性保证，并以现有方法一小部分的速度实现近乎最优的福利。 AI

影响为复杂的匹配问题引入了更有效的方法，可能影响资源分配和市场设计中的AI应用。

排序理由该集群包含一篇详细介绍市场设计新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Irene Aldridge · 2026-05-19 19:35

市场设计中的多维度匹配

This paper proposes a computationally efficient mechanism for multi-dimensional matching markets where agents report preferences over object features rather than complete utility assessments. We use Singular Value Decomposition (SVD) to identify the principal direction of variati…