MAE Seminar (ZOOM): Nonlinear Opinion Dynamics - Consensus and Dissensus with Tunable Sensitivity to Input

Zoom Link: https://uci.zoom.us/j/95443940985?pwd=anhVNnpsWjRxM3FoZWp6Qy9HRG5SUT09 

Abstract: I will present an analytically tractable model of continuous-time opinion dynamics for an arbitrary number of agents that communicate over a network and form real-valued opinions about an arbitrary number of options. Drawing from biology, physics and social sciences, the model saturates opinion exchanges and modulates them with an attention parameter. The resulting nonlinear dynamics exhibit the range of opinion formation behaviors predicted by model-independent bifurcation theory but not exhibited by models in the literature. Consensus and dissensus opinions form reliably, even in homogeneous networks and without strong evidence about options. We design dynamics for the attention parameter that depend on the opinion state, and we show how these feedback dynamics tune thresholds that govern sensitivity and robustness of the opinion formation process to input. The model provides new means for systematic study of dynamics on natural and engineered networks, from collective decision making and dynamic task allocation to information spread and political polarization.

This is joint work with Anastasia Bizyaeva (Princeton) and Alessio Franci (UNAM, Mexico) and based on the paper: https://arxiv.org/pdf/2009.04332.pdf

Bio: Naomi Ehrich Leonard is Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty in applied and computational mathematics at Princeton University. She received her bachelor's degree in mechanical engineering from Princeton University and her doctorate in electrical engineering from the University of Maryland. She is a fellow of the American Academy of Arts and Sciences, SIAM, IEEE, IFAC and ASME and a MacArthur Fellow. Her current research focuses on dynamics and control of multi-agent systems on networks with application to distributed decision making, spreading processes, multi-robot teams and collective behavior.