Abstract

In real decision-making problems, decision makers (DMs) usually select the most potential project from several ones. However, they unconsciously show different confidence levels in decision-making process because they come from various backgrounds and have different experiences, etc., which affects the decision results. Moreover, the probabilistic linguistic term set, which not only includes the linguistic expressions used by DMs in their daily life but also contains the probability for each linguistic term, can well portray the real perceptions of DMs for the projects. Furthermore, large-scale consensus has gradually been a popular way to effectively solve complex decision-making problems. To sum up, in this paper, we are dedicated to constructing a large-scale consensus model considering the confidence levels of DMs under probabilistic linguistic circumstance. Firstly, the endo-confidence is defined and measured by DM’s probabilistic linguistic information. Then, the DMs are clustered according to the similarities of both evaluation information and the endo-confidence levels. Both evaluation of the non-consensus cluster and evaluation integrated by the clusters with higher endo-confidence level than this non-consensus cluster are used as the reference to adjust its evaluation information. Then, a case study and the comparative analysis are carried out. Finally, some conclusions and future work are given.

摘要

在实际的决策问题中，决策者（DM）通常会从几个项目中选择最有潜力的项目。然而，他们在决策过程中不知不觉地表现出不同的信心水平，因为他们来自不同的背景，拥有不同的经历等，从而影响决策结果。此外，概率语言术语集不仅包括DMs在日常生活中使用的语言表达，还包含每个语言术语的概率，可以很好地描绘DMs对项目的真实感知。此外，大规模共识已逐渐成为有效解决复杂决策问题的流行方式。综上所述，在本文​​中，我们致力于构建一个考虑概率语言环境下 DM 置信度的大规模共识模型。首先，内部置信度由 DM 的概率语言信息定义和测量。然后，根据评价信息和内信度水平的相似性对 DM 进行聚类。非共识集群的评价和比该非共识集群具有更高内部置信度的集群整合的评价都作为调整其评价信息的参考。然后，进行案例研究和比较分析。最后，给出了一些结论和未来的工作。根据评估信息和内部置信水平的相似性对 DM 进行聚类。非共识集群的评价和比该非共识集群具有更高内部置信度的集群整合的评价都作为调整其评价信息的参考。然后，进行案例研究和比较分析。最后，给出了一些结论和未来的工作。根据评估信息和内部置信水平的相似性对 DM 进行聚类。非共识集群的评价和比该非共识集群具有更高内部置信度的集群整合的评价都作为调整其评价信息的参考。然后，进行案例研究和比较分析。最后，给出了一些结论和未来的工作。