Networks Competition under Local Interaction and Behavioral Learning

Diffusion is modelled as a repeated coordination game between a large number of locally interacting heterogeneous agents. Agents are represented with stochastic learning algorithms that generate robust path-dependent patterns of behavior. Formal analyses of such locally interacting systems encounter many technical difficulties, hence we run numerical simulations. We find that lock-in is positively correlated to the interaction distance. Diversity, i.e. simultaneous coexistence of networks, appears for small interaction distances but vanishes as the size of neighborhoods increases. We also find an inverse relationship between the interaction distance and the speed of standardization.