Nonlinear Opinion Dynamics With Tunable Sensitivity

• 成果类型: Article
• 署名作者: Bizyaeva, Anastasia; Franci, Alessio; Leonard, Naomi Ehrich
• 署名单位: Princeton University; Universidad Nacional Autonoma de Mexico
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3159527
• 发表日期: 2023
• 页码: 1415-1430
• 关键词: sensitivity; Analytical models; Adaptation models; aerodynamics; Robustness; dynamic scheduling; Biological system modeling; agreement; bifurcation; bio-inspired engineering; deadlock breaking; decision making; DISAGREEMENT; Multi-agent systems; Network centrality; networked control systems; nonlinear dynamical systems; opinion dynamics

摘要

We propose a continuous-time multioption nonlinear generalization of classical linear weighted-average opinion dynamics. Nonlinearity is introduced by saturating opinion exchanges, and this is enough to enable a significantly greater range of opinion-forming behaviors with our model as compared to existing linear and nonlinear models. For a group of agents that communicate opinions over a network, these behaviors include multistable agreement and disagreement, tunable sensitivity to input, robustness to disturbance, flexible transition between patterns of opinions, and opinion cascades. We derive network-dependent tuning rules to robustly control the system behavior and we design state-feedback dynamics for the model parameters to make the behavior adaptive to changing external conditions. The model provides new means for systematic study of dynamics on natural and engineered networks, from information spread and political polarization to collective decision-making and dynamic task allocation.