From collective opinion dependence to personalization: The impacts of social trust relationship in consensus formation

Abstract

Social network group decision-making (SNGDM) has become popular recently in the area of decision analysis as the social network can effectively model the relationship among decision makers. In the SNGDM, decision makers will present conflicting assessments due to individual differences in education backgrounds and cognitive levels. This study aims to develop a personalized consensus reaching framework considering the impact of social trust network on assessments-modifications willingness. A personalized feedback is designed for guiding assessments-modifications, which is based on the assumption that a decision maker will accept his\\her trusted decision makers’ assessments when modifying assessment in a social trust network. Then, a dual optimization-based personalized feedback is proposed for supporting consensus formation, where the first personalized feedback seeks to minimize the assessments-modifications and the second aims to minimize network-modifications. Particularly, two ways are used to measure network-modifications, referred to as trust values modifications with fixed network structure and network structure modifications, respectively. Further, an interactive dual optimization-based personalized consensus reaching process is constructed. With the aim of justifying the effectiveness of the proposed model, it is applied to solve the green supplier evaluation problem. Finally, it is illustrated that our proposal can improve consensus efficiency compared with collective opinion dependence feedback.