Fuzzy inference based Hegselmann–Krause opinion dynamics for group decision-making under ambiguity

Abstract

This paper considers a group decision-making mechanism for a group of social agents with ambiguous interactions. First, a fuzzy inference approach is introduced to describe bounded confidence based interaction rules among social agents for a certain object in a social network platform. Second, an influence graph is introduced to model the communication network topology associated with the group of agents. A fuzzy inference based opinion dynamics model is built when the defuzzified interaction weights among agents are used in the opinion update scheme for each agent. Third, the patterns of the collective final opinions are analyzed under the proposed fuzzy opinion dynamics model. The fuzziness of opinion gaps and interaction weights among agents are respectively investigated in the collective opinion evolution. Simulation results show the influence of fuzzy values of opinion gaps and interaction weights on the pattern of the collective final opinions and reveal the quantitative relationships of opinion gap and interaction weights between the original HK opinion dynamics model and the fuzzy HK opinion dynamics model.