A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

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.

Extension of Subjective Logic for Time-Related Trust

To describe the dynamic propcrty of trust relationship, wt propose atime-related trust model and extend Joang's subjective logic to fit for time-related trust model.The extension includes prepositional conjunction, disjunction and negation for traditional logic anddiscounting and consensus operators that are evidential operators specially designed for thepropagation and computation of trust relationships. With the extension of subjective logic fortime-related trust, our time-related trust modelis suitable to model the dynamic trust relationshipin practice. Finally an example of reputation assessment is offered to demonstrate the usage of ourtrust model.

Study on the Factors Driving the Loyalty of the Customers of a Third Party Mobile Payment Platform

. In this paper, the main driving factors affecting the customer loyalty of a third party mobile payment service were chosen by combining with the actual situation of the third party mobile payment service and customer spending habits in China. The assumed relations between all affecting factors and the customer loyalty of a third party mobile payment platform were proposed, and a model for studying customer loyalty was established and also a conclusion was made through the questionnaire survey analysis data. Finally, suggestions on improving the loyalty of the customers of a third party mobile payment platform are presented.