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Analysis of the Final Ranking Decisions Made by Experts After a Consensus has Been Reached in Group Decision Making

机译:群体决策中达成共识后专家做出的最终排名决策分析

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摘要

Traditional approaches to group decision making (GDM) problems for ranking a finite set of alternatives terminate when the experts involved in the GDM process reach a consensus. This paper proposes ways for analyzing the final results after a consensus has been reached in GDM. Results derived from this last step can be used to further enhance the understanding of possible hidden dynamics of the problem under consideration. The proposed approach for post-consensus analysis is in part based on a novel idea, known as preference maps (PMs) introduced recently in the literature on how rankings should be described when ties in the rankings are allowed. An original contribution of this paper is how to define the difference between two PMs. This is achieved by using a metric known as the Marczewski-Steinhaus distance. Approaches for analyzing the final results of a GDM process after consensus has been reached may reveal hidden but crucial insights in the way the experts reached the consensus and also new insights related to the alternatives. These approaches rely on the concept of differences in the rankings, defined by traditional means or as the difference between two PMs as defined in this paper. This is the second group of original contributions made in this paper. The various issues are illustrated with numerical examples and an application inspired from a real-world problem described in the literature. The new contributions described in this study offer an exciting potential to enrich the group decision making process considerably.
机译:当参与GDM流程的专家达成共识时,用于对一组有限的备选方案进行排名的传统组决策(GDM)问题的方法就会终止。本文提出了在GDM中达成共识后分析最终结果的方法。从最后一步得出的结果可用于进一步增强对所考虑问题的可能隐藏动态的理解。提议的共识后分析方法部分基于一种新颖的想法,即最近在文献中引入的关于偏好排名时应如何描述排名的文献偏好地图(PMs)。本文的原始贡献是如何定义两个PM之间的差异。这可以通过使用称为Marczewski-Steinhaus距离的度量来实现。在达成共识后,分析GDM流程最终结果的方法可能会揭示专家达成共识的方式中隐藏但至关重要的见解,以及与替代方案有关的新见解。这些方法依赖于等级差异的概念,该差异是通过传统方式定义的,或者是本文定义的两个PM之间的差异。这是本文的第二组原创性贡献。各种问题均通过数值示例进行说明,并从文献中描述的实际问题中启发了应用程序。这项研究中描述的新贡献提供了令人兴奋的潜力,可以极大地丰富团队决策过程。

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