Limited Imitation Contagion on Random Networks: Chaos, Universality, and Unpredictability

TL;DR

This paper explores a family of contagion models on random networks that incorporate limited imitation, revealing complex behaviors such as chaos, universality, and unpredictability influenced by network structure and initial conditions.

Contribution

It introduces a new class of contagion models with limited imitation, analyzing their dynamic behaviors and the impact of network structure on chaos and stability.

Findings

Period doubling occurs with increasing node degree.

Universality class depends on network interaction structure.

System fixedness stabilizes infected fraction but allows multiple equilibria.

Abstract

We study a family of binary state, socially-inspired contagion models which incorporate imitation limited by an aversion to complete conformity. We uncover rich behavior in our models whether operating with either probabilistic or deterministic individual response functions on both dynamic and fixed random networks. In particular, we find significant variation in the limiting behavior of a population's infected fraction, ranging from steady-state to chaotic. We show that period doubling arises as we increase the average node degree, and that the universality class of this well known route to chaos depends on the interaction structure of random networks rather than the microscopic behavior of individual nodes. We find that increasing the fixedness of the system tends to stabilize the infected fraction, yet disjoint, multiple equilibria are possible depending solely on the choice of the initially infected node.