发表机构
Brown University（布朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究匹配市场中的优先级违规强度，提出非常正当的嫉妒概念，证明帕累托效率与其消除不相容，并推广延迟接受和即时接受算法以消除之，模拟显示适度放宽可显著改善分配排名。
AI 中文摘要
本文研究了匹配市场中优先级违规的强度。如果某个代理人更偏好另一个代理人的匹配结果而非自己的，并且她对该对象的优先级优势超过$k$个排名，则称其具有非常正当的嫉妒。我证明了除非$k$过大，否则帕累托效率与消除非常正当的嫉妒是不相容的。本文提出了一种对延迟接受和即时接受的简单推广，并证明该推广对于任意$k$都能消除非常正当的嫉妒。在随机匹配市场的模拟中，我发现对正当嫉妒的适度放宽可以显著改善分配排名。
英文摘要
This paper studies the intensity of priority violations in matching markets. An agent is said to have very justified envy if she prefers another agent's match to her own and her priority advantage for the object exceeds $k$ ranks. I show that unless $k$ is prohibitively large, Pareto efficiency is incompatible with the elimination of very justified envy. A simple generalization of Deferred Acceptance and Immediate Acceptance is shown to eliminate very justified envy for any $k$. In simulations of random matching markets, I find that modest relaxations to justified envy can yield significant improvements in assignment ranks.
