Nonlinear Opinion Dynamics With Tunable Sensitivity

• Published in: IEEE transactions on automatic control Vol. 68; no. 3; pp. 1415 - 1430
• Main Authors: Bizyaeva, Anastasia, Franci, Alessio, Leonard, Naomi Ehrich
• Format: Journal Article Web Resource
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers Inc
• Subjects: Adaptation models; Aerodynamics; Agreement; Analytical models; bifurcation; bio-inspired engineering; Biological system modeling; Computer Science Applications; Continous time; Control and Systems Engineering; deadlock breaking; Decision making; disagreement; Dynamic scheduling; Electrical and Electronic Engineering; Engineering, computing & technology; Generalisation; Ingénierie, informatique & technologie; Linear weighted averages; multi-agent systems; network centrality; networked control systems; nonlinear dynamical systems; Nonlinear dynamics; Nonlinearity; opinion dynamics; Politics; Robustness; Sensitivity; State feedback; Tunables
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2022.3159527

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.