{"title":"The Price of Unfairness","authors":"M. Köppen, Kaori Yoshida","doi":"10.1109/INCoS.2016.71","DOIUrl":null,"url":null,"abstract":"Here, measures for unfairness in resource allocation problems are studied. The extremity of such measures should match with an intuitive concept of an unfair allocation. To achieve a computational model for unfairness, various fairness models are extended to cover a related meaning of unfairness as well. In addition to the lexicographic maxmax relation, a model based on exponential utilities is introduced. Furthermore, a new mean, the symmetric Lehmer mean is identified as being able to favour allocations restricted to a subset of users only. Case examples of network flow control problems show feasibility of the proposed unfairness measures, as well as specific differences. Especially the symmetric Lehmer mean appears capable to handle unfairness in a much more nuanced way, while lexicographic maxmax appears as the computationally most convenient and also most intuitive measure. In all cases it can be shown that unfair allocations are not always efficient and that there is a price of unfairness as well.","PeriodicalId":102056,"journal":{"name":"2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INCoS.2016.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Here, measures for unfairness in resource allocation problems are studied. The extremity of such measures should match with an intuitive concept of an unfair allocation. To achieve a computational model for unfairness, various fairness models are extended to cover a related meaning of unfairness as well. In addition to the lexicographic maxmax relation, a model based on exponential utilities is introduced. Furthermore, a new mean, the symmetric Lehmer mean is identified as being able to favour allocations restricted to a subset of users only. Case examples of network flow control problems show feasibility of the proposed unfairness measures, as well as specific differences. Especially the symmetric Lehmer mean appears capable to handle unfairness in a much more nuanced way, while lexicographic maxmax appears as the computationally most convenient and also most intuitive measure. In all cases it can be shown that unfair allocations are not always efficient and that there is a price of unfairness as well.
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不公平的代价
本文研究了资源分配问题中不公平的度量方法。这种措施的极端程度应符合不公平分配的直观概念。为了获得不公平的计算模型,我们对各种公平模型进行了扩展,以涵盖不公平的相关含义。除了字典学的最大关系外,还引入了一个基于指数效用的模型。此外,一个新的平均,对称Lehmer平均被确定为能够支持分配仅限于用户的子集。网络流量控制问题的实例表明了所提出的不公平措施的可行性,以及具体的区别。特别是对称Lehmer平均值似乎能够以一种更微妙的方式处理不公平,而字典最大似乎是计算上最方便,也是最直观的度量。在所有情况下都可以证明,不公平的分配并不总是有效的,而且也有不公平的代价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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