This paper explores a limited trust propagation-based consensus model considering individual attitude for preference modification in a social networked setting with uncertain preference information. To examine the construction of complete linkages, and the status of decision makers in group decision making, it is assumed that the group size and network density will affect the scale of mediators in the propagation process, then a definition of limited trust propagation is proposed and the propagation efficiency can be introduced. On this basis, we obtain missing trust relationships and individual centrality in network. In the process of consensus reaching, both the decision maker’s original preference and recommendation advice are considered for flexibly modeling the preference modification process: the individual attitude toward modification is determined by a newly introduced measure of comprehensive relative out-degree centrality, showing the degree of willingness to adjust assessments. When the willingness is too low to reach the preset consensus level, a multi-objective programming model is designed to improve the consensus as much as possible. Moreover, the proposed feedback mechanism narrows the individual acceptable modification range based on the previous adjustment rule, so as to simulate the personalized and targeted decision behavior. To guarantee obtaining a collective aggregated preference in a logical and precise manner, a two-stage optimization model composing of comprehensive relative in-degree centrality-based information aggregation and best consistency-based uncertainty elimination, is proposed. A numerical example and comparative analyses are performed to show the validity and feasibility of the proposed model.

本文探讨了一种基于有限信任传播的共识模型，该模型考虑了偏好信息不确定的社交网络环境中个人对偏好修改的态度。为了考察完全联系的构建，以及决策者在群体决策中的地位，假设群体规模和网络密度会影响传播过程中中介的规模，然后提出有限信任传播的定义和可以引入传播效率。在此基础上，我们获得了网络中缺失的信任关系和个体中心性。在达成共识的过程中，同时考虑了决策者的原始偏好和推荐建议，以灵活建模偏好修改过程：个人对修改的态度由新引入的综合相对外度中心性度量决定，显示了调整评估的意愿程度。当意愿过低而无法达到预设的共识水平时，设计多目标规划模型，尽可能提高共识。此外，所提出的反馈机制基于先前的调整规则缩小了个人可接受的修改范围，从而模拟个性化和有针对性的决策行为。为了保证以逻辑和精确的方式获得集体聚合偏好，提出了一个由综合相对度中心性信息聚合和基于最佳一致性的不确定性消除组成的两阶段优化模型。