A social ties-based approach for group decision-making problems with incomplete additive preference relations

With the rapid growth of Web 2.0 technology, a new paradigm has been developed that allows many users to participate in decision-making processes within online social networks. The social information (i.e., social ties and social influence) of the members that is stored in online social networks provides a new perspective for investigating group decision-making (GDM) problems. In this paper, a new interactive GDM approach, based on online social networks, is proposed to address a ranking problem with incomplete additive preference relations (IAPRs). This approach incorporates the strength of social ties and social influence calculated by social network analysis methods regarding the decision-making process. After decision makers (DMs) provide IAPRs, a searching algorithm is developed to identify the optimal preference information transfer path from DMs to the decision supporters who can provide the corresponding preference information. Next, a linear programming model is constructed to complete the missing preference values of the IAPRs. The main features of the linear programming model include its ability to account for other DMs preference information and to maintain consistency. To help the group reach an agreement on the ranking of alternatives, a consensus reaching process is proposed. The strength of social ties and social influence are used to calculate the acceptable adjustment coefficients for DMs in the feedback mechanism. Finally, an illustrative example and further discussion demonstrate the validity of the proposed approach.