Nonlinear Opinion Dynamics with Tunable Sensitivity

Abstract

We propose a continuous-time multi-option 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.

References in corpus (3)

• Analysis and control of agreement and disagreement opinion cascades
• Patterns of Nonlinear Opinion Formation on Networks
• Control of Agreement and Disagreement Cascades with Distributed Inputs

Cited by in corpus (11)

• Collective behavior from surprise minimization
• Multipolar social systems: Measuring polarization beyond dichotomous contexts
• Contraction and $k$-contraction in Lurie systems with applications to networked systems
• Switching transformations for decentralized control of opinion patterns in signed networks: application to dynamic task allocation
• Honeybee-like collective decision making in a kilobot swarm
• A Model for Multi-Agent Autonomy That Uses Opinion Dynamics and Multi-Objective Behavior Optimization
• Consensus formation and relative stimulus perception in quality-sensitive, interdependent agent systems
• Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians
• AVOCADO: Adaptive Optimal Collision Avoidance driven by Opinion
• Non-linear inhibitory responses enhance performance in collective decision-making
• Coevolution of Actions and Opinions in Networks of Coordinating and Anti-Coordinating Agents