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A paired-comparison approach for fusing preference orderings from rank-ordered agents

机译:一种配对比较方法,用于融合来自排名排序的座席的偏好排序

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摘要

The problem of aggregating multi-agent preference orderings has received considerable attention in many fields of research, such as multi-criteria decision aiding and social choice theory; nevertheless, the case in which the agents' importance is expressed in the form of a rank-ordering, instead of a set of weights, has not been much debated. The aim of this article is to present a novel algorithm - denominated as ‘‘Ordered Paired-Comparisons Algorithm'' (OPCA), which addresses this decision-making problem in a relatively simple and practical way. The OPCA is organized into three main phases: (i) turning multi- agent preference orderings into sets of paired comparisons, (ii) synthesizing the paired-comparison sets, and (iii) constructing a fused (or consensus) ordering. Particularly interesting is phase two, which introduces a new aggregation process based on a priority sequence, obtained from the agents' importance rank-ordering. A detailed description of the new algorithm is supported by practical examples
机译:在多研究决策辅助和社会选择理论等许多研究领域中,聚集多主体偏好顺序的问题已引起相当多的关注。但是,关于代理人的重要性以等级排序而不是权重的形式表达的情况尚未引起太多争议。本文的目的是提出一种新颖的算法-称为“有序配对比较算法”(OPCA),该算法以相对简单和实用的方式解决了这一决策问题。 OPCA分为三个主要阶段:(i)将多主体偏好排序变成成对的比较集;(ii)合成成对的比较集;(iii)构建融合(或共识)排序。特别有趣的是第二阶段,该阶段引入了一种基于优先级序列的新聚合过程,该过程是从代理的重要性等级排序中获得的。实际示例支持对新算法的详细描述

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