Understanding nonlinear social contagion dynamics on dynamical networks, such
as opinion formation, is crucial for gaining new insights into consensus and
polarization. Similar to threshold-dependent complex contagions, the
nonlinearity in adoption rates poses challenges for mean-field approximations.
To address this theoretical gap, we focus on nonlinear binary-opinion dynamics
on dynamical networks and analytically derive local configurations,
specifically the distribution of opinions within any given focal individual’s
neighborhood. This exact local configuration of opinions, combined with network
degree distributions, allows us to obtain exact solutions for consensus times
and evolutionary trajectories. Our counterintuitive results reveal that neither
biased assimilation (i.e., nonlinear adoption rates) nor preferences in local
network rewiring — such as in-group bias (preferring like-minded individuals)
and the Matthew effect (preferring social hubs) — can significantly slow down
consensus. Among these three social factors, we find that biased assimilation
is the most influential in accelerating consensus. Furthermore, our analytical
method efficiently and precisely predicts the evolutionary trajectories of
adoption curves arising from nonlinear contagion dynamics. Our work paves the
way for enabling analytical predictions for general nonlinear contagion
dynamics beyond opinion formation.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: