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arXiv 1609.02795cs.GT
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不确定偏好下的帕累托最优分配

Pareto Optimal Allocation under Uncertain Preferences

Haris Aziz, Ronald de Haan, Baharak Rastegari

摘要:

分配问题是社会选择、匹配和离散分配中研究最多的问题之一。我们考虑的问题的附加功能,代理人的偏好涉及不确定性。不确定性的设置导致了一些有趣的问题,包括以下问题。如何计算一个具有最高概率的帕累托最优分配?计算一个给定的分配是帕累托最优的概率的复杂性是多少?是否存在概率为1的帕累托最优分配?我们考虑这些问题下的两个自然的不确定性模型:(1)彩票模型,其中每个代理人有一个独立的概率分布的线性订单和(2)联合概率模型,涉及一个联合概率分布的偏好配置文件。对于这两个模型,我们提出了一些算法和复杂性的结果。

英文摘要:

The assignment problem is one of the most well-studied settings in social choice, matching, and discrete allocation. We consider the problem with the additional feature that agents' preferences involve uncertainty. The setting with uncertainty leads to a number of interesting questions including the following ones. How to compute an assignment with the highest probability of being Pareto optimal? What is the complexity of computing the probability that a given assignment is Pareto optimal? Does there exist an assignment that is Pareto optimal with probability one? We consider these problems under two natural uncertainty models: (1) the lottery model in which each agent has an independent probability distribution over linear orders and (2) the joint probability model that involves a joint probability distribution over preference profiles. For both of the models, we present a number of algorithmic and complexity results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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