A social network analysis trust–consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations

A social network analysis (SNA) trust–consensus based group decision making model with interval-valued fuzzy reciprocal preference relation (IFRPR) is investigated. The main novelty of this model is that it determines the importance degree of experts by combining two reliable resources: trust degree (TD) and consensus level (CL). To do that, an interval-valued fuzzy SNA methodology to represent and model trust relationship between experts and to compute the trust degree of each expert is developed. The multiplicative consistency property of IFRPR is also investigated, and the consistency indexes for the three different levels of an IFRPR are defined. Additionally, similarity indexes of IFRPR are defined to measure the level of agreement among the group of experts. The consensus level is derived by combining both the consistency index and similarity index, and it is used to guide a feedback mechanism to support experts in changing their opinions to achieve a consensus solution with a high degree of consistency. Finally, a quantifier guided non-dominance possibility degree (QGNDPD) based prioritisation method to derive the final trust–consensus based solution is proposed.