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Reformulating Arrow's Conditions in Terms of Cardinal Pairwise Comparison Matrices Defined Over a General Framework

机译:根据通用框架上定义的基数成对比较矩阵重新制定Arrow的条件

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In the paper, we deal with cardinal preferences of experts when these are expressed by means of Pairwise Comparison Matrices (PCMs). In order to obtain general results, suitable for several kinds of PCMs proposed in literature, we focus on PCMs defined over a general unifying framework, that is an Abelian linearly ordered group. In this framework, firstly, we aggregate several PCMs and we analyse how the aggregated PCM preserves some coherence levels, such as transitivity, weak consistency and consistency. Then, we reformulate Arrow's conditions in terms of PCMs, and we provide two preference aggregation procedures for representing group preferences that give a social PCM and a social cardinal ranking, respectively. Finally, we analyse how these preference aggregation procedures satisfy reformulated Arrow's conditions.
机译:在本文中,我们通过成对比较矩阵(PCM)来表达专家的基本偏好。为了获得适合于文献中提出的几种PCM的一般结果,我们关注于在通用统一框架(即Abelian线性有序组)上定义的PCM。在此框架中,首先,我们汇总了几个PCM,然后分析了汇总的PCM如何保留某些一致性级别,例如可传递性,弱一致性和一致性。然后,我们根据PCM重新制定了Arrow的条件,并提供了两种偏好汇总程序来表示分别代表社交PCM和社交基数排名的群体偏好。最后,我们分析了这些偏好聚合过程如何满足重新制定的Arrow条件。

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