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arXiv · 2009.04332

Nonlinear Opinion Dynamics with Tunable Sensitivity

Anastasia Bizyaeva·Alessio Franci·Naomi Ehrich Leonard
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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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BibTeXRIS

Anastasia Bizyaeva, Alessio Franci, Naomi Ehrich Leonard. 2020-09-09. Nonlinear Opinion Dynamics with Tunable Sensitivity. https://doi.org/10.1109/tac.2022.3159527

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