Nonlinear Opinion Dynamics with Tunable Sensitivity

Anastasia Bizyaeva, Alessio Franci, Naomi Ehrich Leonard

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We propose a continuous-time multi-option nonlin-ear 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.

Original languageEnglish (US)
JournalIEEE Transactions on Automatic Control
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Adaptation models
  • Aerodynamics
  • Analytical models
  • Biological system modeling
  • Dynamic scheduling
  • Robustness
  • Sensitivity


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