Abstract The efficient and fair allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who gets what—and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.

中文翻译：

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调整肾脏交换算法以符合人类价值观

摘要 有限资源的高效公平分配是经济学和计算机科学中的经典问题。在肾脏交易中，中央做市商将活体肾脏捐赠者分配给需要器官的患者。肾脏交换中的患者和捐赠者使用委员会决定的临时权重进行优先排序，然后输入分配算法，确定谁得到什么，谁没有。在本文中，我们提供了一种端到端的方法来估计肾脏交换中个体参与者配置文件的权重。我们首先从人类受试者中引出他们认为可接受的患者属性列表，以便优先考虑患者（例如，医学特征、生活方式选择等）。然后，我们询问受试者患者档案之间的比较查询，并以原则性的方式从他们的回答中估计权重。我们展示了如何在肾脏交易市场清算算法中使用这些权重。然后，我们评估权重在模拟中的影响，发现我们计算的权重的精确数值无关紧要，除了它们所暗示的配置文件的顺序。然而，与根本不优先考虑患者相比，有一个显着的效果，根据人类引发的价值判断，某些类别的患者被（去）优先化。除了它们所暗示的配置文件的顺序。然而，与根本不优先考虑患者相比，有一个显着的效果，根据人类引发的价值判断，某些类别的患者被（去）优先化。除了它们所暗示的配置文件的顺序。然而，与根本不优先考虑患者相比，有一个显着的效果，根据人类引发的价值判断，某些类别的患者被（去）优先化。