The advancement of science and technology and the development of network environments have made social network multi-criteria group decision making (SN-MCGDM) an interesting research topic. A satisfaction-driven consensus model that can be applied to incomplete information under probabilistic linguistic trust in SN-MCGDM is presented. First, to model the trust relationships among group experts more flexibly and accurately, the concept of a probabilistic linguistic trust function is defined. Based on this concept, a t-norm-based probabilistic linguistic trust propagation operator and a path-weighted averaging operator are proposed to construct the complete trust relationships among group experts. Then, the incomplete evaluation information in the decision matrix is estimated based on the trust relationships. To identify inconsistent experts, a new consensus measure is provided. To achieve the individual aims as well as to retain the initial opinions of the experts to the greatest extent, identification rules based on satisfaction along with suggestion rules with local modifications are then proposed to help experts reach consensus. Finally, an example followed by comparative analyses is provided to verify the effectiveness of the proposed consensus-reaching model.

随着科学技术的进步和网络环境的发展，社会网络多准则群体决策（SN-MCGDM）成为一个有趣的研究课题。提出了一种满意度驱动的共识模型，该模型可以应用于SN-MCGDM中概率语言信任下的不完全信息。首先，为了更灵活，更准确地建模小组专家之间的信任关系，定义了概率语言信任功能的概念。基于这样的理念，一个牛逼提出了基于范数的概率语言信任传播算子和路径加权平均算子，以构建群体专家之间完整的信任关系。然后，基于信任关系估计决策矩阵中的不完整评估信息。为了确定不一致的专家，提供了一种新的共识措施。为了实现个人目标并最大程度地保留专家的初步意见，然后提出了基于满意度的识别规则以及经过局部修改的建议规则，以帮助专家达成共识。最后，提供一个示例，然后进行比较分析，以验证所提出的达成共识模型的有效性。