Managing Consensus With Minimum Adjustments in Group Decision Making With Opinions Evolution

Nowadays, online social networks, such as Facebook and WeChat, facilitate the expression, diffusion, and interactions of individuals' opinions regarding various issues. In this environment, individuals' opinions are liable to be influenced by others and then evolve over the time. In this paper, we propose an approach based on minimum adjustments to manage the consensus in the group decision making (GDM) with opinions evolution. First, inspired by the idea proposed in DeGroot model, we establish a novel GDM model with opinions evolution, and then discuss its consensus conditions. Based on this, we propose an algorithm to achieve the network partition, and then provide a consensus model with minimum adjustments to obtain the optimal adjusted initial opinions and collective consensus opinion. Finally, we provide a numerical example to demonstrate the feasibility and effectiveness of the proposed theoretical results, and design comparative simulations to explore the effects of the opinions evolution on the final consensus solution.

Automated summary

This paper proposes an approach to manage consensus in group decision making with opinions evolution by establishing a novel GDM model and providing a consensus algorithm. The feasibility and effectiveness of the proposed theoretical results are demonstrated through a numerical example.