Linguistic stochastic dominance to support consensus reaching in group decision making with linguistic distribution assessments
Linguistic stochastic dominance to support consensus reaching in group decision making with linguistic distribution assessments
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DOI：
10.1016/j.inffus.2021.05.003
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Haiming Liang;Xia Chen;Congcong Li;Hengjie Zhang
中科院分区：
文献类型：
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作者：
Haiming Liang;Xia Chen;Congcong Li;Hengjie Zhang
语言分布评估的复杂性增加了决策者处理这些评估的难度。最近，随机优势已被改变为一个有用的工具来比较两个随机变量。受此启发，本文致力于利用随机优势来比较语言分布评估，并进一步讨论GDM中的共识达成问题与语言分布评估。首先，我们介绍了三种类型的个体的语义敏感度。在此基础上，我们分别定义了不同语义敏感性语境下的语言随机优势度，并给出了一些令人满意的性质。然后，我们设计了一个基于语言随机优势的共识达成解决框架（CRRF-LSD）。最后，通过一个实例说明了CRRF-LSD的应用价值，并通过两个对比分析进一步说明了语言随机优势和CRRF-LSD的优势。比较结果表明，所提出的语言随机优势的方法有明显的优势，在比较两个语言分布评估的几个经典的现有方法。同时，对比结果表明，只有CRRF-LSD方法考虑了PIS和语义敏感性，这有助于确定更准确的个人排名结果。
The complexity of linguistic distribution assessments increases the difficulty for the decision makers dealing with them. Recently, stochastic dominance has been varied to be a useful tool to compare two stochastic variables. Inspired by this, in this paper we dedicate to utilizing the stochastic dominance to compare the linguistic distribution assessments and further discuss the consensus reaching issue in GDM with linguistic distribution assessments. First, we introduce three types of individuals’ semantic sensitivity. Based on this, we define the linguistic stochastic dominances respectively under different semantic sensitivity contexts, and then provide several desirable properties. Then, we design a consensus reaching resolution framework based on linguistic stochastic dominance (CRRF-LSD). Finally, a case study is provided to show the application value of the CRRF-LSD, and two comparison analyses are further conducted to show the advantages of the linguistic stochastic dominance and the CRRF-LSD. The comparison results show that the proposed linguistic stochastic dominances method has clear advantages over several classical existing methods in comparing two linguistic distribution assessments. Meanwhile, the comparison results show that only the CRRF-LSD method takes the PIS and semantic sensitivity into account, which is helpful to determine more accurate individual ranking results.