Managing heterogeneous preferences and multiple consensus behaviors with self-confidence in large-scale group decision making

With the rapid increase of experts, groups or organizations involved in decision making, the problem of large-scale group decision making (LSGDM) has attracted increasing attention in the whole research field. Behavioral management and heterogeneous preference representation structures are two fundamental aspects of LSGDM problems. However, psychological functioning has been less considered in existing consensus models to deal with the different behavioral styles of decision-makers. Therefore, this study proposes a novel consensus reaching framework to detect and manage multiple styles of behavior in LSGDM based on heterogeneous preferences with self-confidence. Specifically, an optimization-based selection process is introduced to obtain the individual and collective preference vectors. Next, a self-confidence driven consensus approach is proposed, which includes consensus measure, clustering, detection of multiple styles of behavior, and hybrid feedback adjustment mechanism. According to the consensus level and the self-confidence level, the proposed detection of multiple styles of behavior method identifies four different behavioral subgroup types: collaborating, accommodating, competing, and avoiding. The hybrid feedback adjustment mechanism generates different feedback adjustment opinions for the four identified behavioral type subgroups. The effectiveness and characteristics of the proposed consensus approach is demonstrated with an emergency management case study and the reporting of comprehensive simulation experiments.