A two-stage dynamic influence model-achieving decision-making consensus within large scale groups operating with incomplete information

Abstract

The decision making environment has been dramatically affected by rapid developments in society and the economy. Large-scale group decision making (LSGDM) based on social network has become a vital research topic in the field of decision making science. In this paper, we propose a novel framework based on social network to manage the consensus reaching process (CRP) for LSGDM faced with incomplete information. In this framework, the large-scale group is first classified into several smaller sub-groups using a sub-group detection algorithm, based on the social network. Then, we propose an estimating method based on a collaborative filtering algorithm for estimating the missing preference information of the opinion leaders in each sub-group. The two-stage dynamic influence model for handling the consensus reaching process in LSGDM begins when the LSGDM is transformed into several smaller sub-group decision processes. In the first stage, a consensus model, based on opinion evolution, is proposed for the CRP within each sub-group. In the second stage, we consider each sub-group as a decision making unit. By focusing on the consensus problem across the sub-groups, we develop a novel opinion-leaders feedback strategy in order to help the sub-groups revise their opinions, working toward consensus. We provide an example of an application of our process to illustrate the validity of the proposed model for managing the CRP in LSGDM.