Adapting a kidney exchange algorithm to align with human values

Adapting a kidney exchange algorithm to align with human values
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DOI:
10.1016/j.artint.2020.103261
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发表时间:
2020-06-01
影响因子:
14.4
通讯作者:
Conitzer, Vincent
Conitzer, Vincent
中科院分区:
计算机科学2区
文献类型:
--
作者:
Freedman, Rachel;Borg, Jana Schaich;Conitzer, Vincent

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有限资源的有效和公平分配是经济学和计算机科学中的经典问题。在肾脏交换中,一个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由委员会决定的特别权重进行优先排序,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方法来估计肾脏交换中个人参与者概况的权重。我们首先从人类受试者中引出一系列他们认为可接受的患者属性,以用于对患者进行优先排序(例如,医学特征、生活方式选择等)。然后,我们问受试者比较查询患者的个人资料和估计权重的原则性的方式从他们的反应。我们将展示如何使用这些权重在肾脏交易市场的清算算法。然后,我们评估了模拟中权重的影响,发现我们计算的权重的精确数值除了它们所暗示的配置文件的顺序之外几乎没有什么关系。然而,与根本不优先考虑患者相比,有一个显着的效果,某些类别的患者根据人类引发的价值判断被(去)优先考虑。(C)2020 Elsevier B.V.保留所有权利。
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. (C) 2020 Elsevier B.V. All rights reserved.