PurposeThe development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.Design/methodology/approachFirstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.FindingsThe proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.Originality/valueThis study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

Social networks (SNs) have become popular as a medium for disseminating information and connecting like-minded people. They play a central role in decision-making by correlating the behaviors and preferences of connected agents. However, it is difficult to identify social influence effects in decision-making. In this article, we propose a framework of how to describe the uncertain nature of the social network group decision-making (SN-GDM) process. Social networks analysis (SNA) and quantum probability theory (QPT) are combined to construct a decision framework considering superposition and interference effects in SN-GDM scenarios. For the first time, we divide interference effects into symmetry and asymmetry. We construct an influence diagram, which is a quantum-like Bayesian network (QLBN), to model group decisions with interactions. We identify symmetry interference terms from Shapley value and asymmetry interference terms from trust value, respectively. The probability of an alternative is calculated through quantum probability theory in our influence diagram. The combination of QLBN model and social network could gain an understanding of how the group preferences evolve within SN-GDM scenarios, and provide new insights into SNA. Finally, an overall comparative analysis is performed with traditional SNA and other quantum decision models.

No abstract

No abstract

Due to people's increasing dependence on social networks, it is essential to develop a consensus model considering not only their own factors but also the interaction between people. Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making. This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making (SN-GDM). A concept named matching degree is proposed to measure expert reliability. Meanwhile, linguistic information is applied to manage the imprecise and vague information. Matching degree is expressed by a 2-tuple linguistic model, and experts' preferences are measured by a probabilistic linguistic term set (PLTS). Subsequently, a hybrid weight is explored to weigh experts ' importance in a group. Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus. Finally, a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.