1. Noise-Based Control of Opinion Dynamics
- Author
-
Wei Su, Xianzhong Chen, Yongguang Yu, and Ge Chen
- Subjects
FOS: Computer and information sciences ,Physics - Physics and Society ,Computer science ,Control (management) ,FOS: Physical sciences ,Physics and Society (physics.soc-ph) ,Systems and Control (eess.SY) ,Task (project management) ,FOS: Electrical engineering, electronic engineering, information engineering ,FOS: Mathematics ,Computer Science - Multiagent Systems ,Fraction (mathematics) ,Electrical and Electronic Engineering ,Mathematics - Optimization and Control ,Social and Information Networks (cs.SI) ,Protocol (science) ,Computer Science - Social and Information Networks ,Nonlinear Sciences - Adaptation and Self-Organizing Systems ,Computer Science Applications ,Noise ,Social dynamics ,Intervention (law) ,Risk analysis (engineering) ,Optimization and Control (math.OC) ,Control and Systems Engineering ,Social system ,Computer Science - Systems and Control ,Adaptation and Self-Organizing Systems (nlin.AO) ,Multiagent Systems (cs.MA) - Abstract
Designing feasible control strategies for opinion dynamics in complex social systems has never been an easy task. It requires a control protocol which 1) is not enforced on all individuals in the society, and 2) does not exclusively rely on specific opinion values shared by the social system. Thanks to the recent studies on noise-induced consensus in opinion dynamics, the noise-based intervention strategy has emerged as the only one meeting both of the above requirements, yet its underlying general theory is still lacking. In this paper, we perform rigorous theoretical analysis and simulations of a noise-based control strategy for opinion formation in which only a fraction of individuals is affected by randomly generated noise. We found that irrespective of the number of noise-driven individuals, including the case of only one single noise-affected individual, the system can attain a quasi-consensus in finite time, and the critical noise strength can be obtained. Our results highlight the efficiency of noise-driven mechanisms for the control of complex social dynamics., arXiv admin note: text overlap with arXiv:1711.01432
- Published
- 2022