Measure-Based Group Decision-Making With Principle-Guided Social Interaction Influence for Incomplete Information: A Game Theoretic Perspective

The consideration of social influence in solving group decision-making (GDM) problems has caused widespread concern in recent years. In most cases, the interaction of experts and the association efforts of alternatives are often not to be ignored in the actual decision-making environment, especially for those situations with incomplete information in the process of opinion changes. In this study, a novel measure-based GDM model is proposed to more comprehensively consider such a factor. Also, a positive cooperation-first principle is proposed as a guide for situations with incomplete information. One of the main advantages of the proposed model is that the estimation of missing fuzzy preference relations is completed according to the modeling results of peer interactions, which makes the preference evolution results more reliable. With the utilization of the fuzzy measure framework, the Shapley function and interaction indicator, originated from the cooperative game theory, are also introduced to extract the interaction features. After several iterations, the global opinion can be eventually obtained via the social influence network technique. Furthermore, it is also demonstrated that the evolution of individual opinions converges to the ultimate collective opinion. A case study of supplier selection with the proposed model is implemented to illustrate its practicality and effectiveness. Related comparisons and discussion regarding related methods are also mentioned.