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.

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Cited by in corpus (11)

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• 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