Social network group decision making: Managing self-confidence-based consensus model with the dynamic importance degree of experts and trust-based feedback mechanism

摘要

Due to the development of intelligent decision-making, social network group decision-making (SNGDM) has become increasingly valued. Generally, real SNGDM cases involve not only the mathematical formulation of the social network analysis but also the experts' psychological behaviors. Self-confidence, an expert's psychological implication of self-statement, is a significant topic in SNGDM problems, while it is overlooked in most existing research. To address this issue, this study takes experts' self-confidence into account in SNGDM. All experts use self-confident fuzzy preference relations (SC-FPRs) to express their opinions. Subsequently, we have developed a novel self-confidence-based consensus approach for SNGDM with SC-FPRs. A dynamic importance degree of experts which combines the external trust and internal self-confidence is proposed to determine their weights. A consensus index considering self-confidence is defined to assess the consensus level among experts. Meanwhile, a trust-based feedback mechanism is presented to improve the consensus efficiency. The rule of the feedback mechanism is that experts dynamically adjust their self-confidence levels while revising the preferences. Using a self-confidence score function, an alternative that has the highest self-confidence score can be selected as the best solution. An illustrative example and some comparisons are given to verify the feasibility and effectiveness of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.