On-off Threshold Models of Social Contagion

TL;DR

This paper introduces an off-threshold model for social contagion that captures imitation and non-conformity, analyzing its dynamics through simulations and mean field theory on various network structures.

Contribution

It extends threshold models by incorporating off-thresholds to better represent societal trends and provides a mean field framework for analyzing these dynamics.

Findings

Dynamics range from steady state to chaos

Large dense networks show convergence of stochastic and deterministic models

Mean field theory predicts smoothed response dynamics

Abstract

We study binary state contagion dynamics on a social network where nodes act in response to the average state of their neighborhood. We model the competing tendencies of imitation and non-conformity by incorporating an off-threshold into standard threshold models of behavior. In this way, we attempt to capture important aspects of fashions and general societal trends. Allowing varying amounts of stochasticity in both the network and node responses, we find different outcomes in the random and deterministic versions of the model. In the limit of a large, dense network, however, we show that these dynamics coincide. The dynamical behavior of the system ranges from steady state to chaotic depending on network connectivity and update synchronicity. We construct a mean field theory for general random networks. In the undirected case, the mean field theory predicts that the dynamics on the network are a smoothed version of the average node response dynamics. We compare our theory to extensive simulations on Poisson random graphs with node responses that average to the chaotic tent map.