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.

语言分布评估的复杂性增加了决策者处理它们的难度。最近，随机优势已成为比较两个随机变量的有用工具。受此启发，在本文中，我们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 与语言分布评估的共识达成问题。首先，我们介绍三种类型的个体语义敏感性。在此基础上，我们分别定义了不同语义敏感性上下文下的语言随机优势，然后提供了几个理想的属性。然后，我们设计了一个基于语言随机优势（CRRF-LSD）的共识达成解决框架。最后，提供了一个案例研究来展示CRRF-LSD的应用价值，并进一步进行了两次比较分析以展示语言随机优势和CRRF-LSD的优势。比较结果表明，所提出的语言随机优势方法在比较两种语言分布评估方面比现有的几种经典方法具有明显的优势。同时，对比结果表明只有CRRF-LSD方法考虑了PIS和语义敏感性，有助于确定更准确的个体排名结果。比较结果表明，所提出的语言随机优势方法在比较两种语言分布评估方面比现有的几种经典方法具有明显的优势。同时，对比结果表明只有CRRF-LSD方法考虑了PIS和语义敏感性，有助于确定更准确的个体排名结果。比较结果表明，所提出的语言随机优势方法在比较两种语言分布评估方面比现有的几种经典方法具有明显的优势。同时，对比结果表明只有CRRF-LSD方法考虑了PIS和语义敏感性，有助于确定更准确的个体排名结果。