Effects of mixing in threshold models of social behavior
Andrei R. Akhmetzhanov, Lee Worden, Jonathan Dushoff

TL;DR
This paper studies how mixing and network effects influence the dynamics of social behavior models, revealing how information flow impacts the persistence or change of social norms.
Contribution
It extends the Granovetter model by analyzing the effects of mixing and network structure on social behavior dynamics, combining analytical and numerical methods.
Findings
Finite mixing increases the likelihood of shifting to the ground state.
Network effects can be approximated by finite-neighborhood effects.
Dynamics tend to converge to a manifold determining possible equilibria.
Abstract
We consider the dynamics of an extension of the influential Granovetter model of social behavior, where individuals are affected by their personal preferences and observation of the neighbors' behavior. Individuals are arranged in a network (usually, the square lattice) and each has a state and a fixed threshold for behavior changes. We simulate the system asynchronously either by picking a random individual and either update its state or exchange it with another randomly chosen individual (mixing). We describe the dynamics analytically in the fast-mixing limit by using the mean-field approximation and investigate it mainly numerically in case of a finite mixing. We show that the dynamics converge to a manifold in state space, which determines the possible equilibria, and show how to estimate the projection of manifold by using simulated trajectories, emitted from different initial…
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