Nonlinear Opinion Dynamics With Tunable Sensitivity

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 e...

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Bibliographic Details
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
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
Online Access:Get full text
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Description
Summary: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.
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scopus-id:2-s2.0-85126543006
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2022.3159527