A Novel Ordinal Consensus Model for Multiple Attribute Group Decision Making With Incomplete Social Network Trust Preference Relations

multiple attribute group decision making (MAGDM) aims to assist a group in evaluating multiattribute alternatives for seeking the most satisfactory one(s). To improve the decision quality of MAGDM, various consensus models were suggested to deal with opinion differences among experts and achieve consensual decision outcomes. This study proposes a novel ordinal consensus framework for MAGDM with incomplete social network trust preference relations (TPRs). In this framework, incomplete linguistic preference relation are first utilized to represent experts' social network TPRs. After that, a consistency-driven two-stage optimization approach is designed to deal with incomplete TPRs for obtaining individual trust levels and expert weight information. Then, a distance-based ordinal consensus measure is designed with the integration of the obtained expert weight information and the basic idea that the higher-ranked alternatives should have greater importance than the lower-ranked ones. When the consensus degree among experts is unacceptable, an opinion dynamic-based feedback adjustment mechanism is devised by integrating the obtained individual trust levels to provide reasonable opinion modification suggestions for accelerating the consensus reaching in MAGDM. Otherwise, the selection process is used to make a selection. A simulation experiment is designed to investigate the effect of key parameters on consensus efficiency. Next, examples of project investment and software supplier selection demonstrate the usability of the proposed consensus framework. Meanwhile, a comparison analysis and in-depth discussions are presented to justify our proposal. The main contributions of this study are twofold. First, a new perspective on managing incomplete social network TPRs is suggested for MAGDM. Second, a novel ordinal consensus process is designed to enhance the effectiveness of the consensual decision outcome. These results can offer new insights into the consensus building for practice social network MAGDM problems.