Technology Contagion in Networks

We represent a social system as a network of agents and model the process of technology diffusion as a contagion propagating in such a network. By setting the necessary conditions for an agent to switch (ie. to adopt the technology), we address the question of how to maximize the contagion of a technology subject to a Moore’s law (eg. solar modules) in a network of agents. We focus the analysis on the effects of the network structure and technological learning on diffusion. To this end, we study three classes of networks, namely lattice, small-world and random networks. Our numerical results show that both the lattice and the small-world networks facilitate the contagion. These networks exhibit high levels of clustering, and additional contacts increase the probability of contagion through social reinforcement. Conversely, networks exhibiting short path length and a low level of clustering (ie. random networks) guarantee an equivalent speed of diffusion with smaller ranges (ie. variance) in terms of aggregate adoption. Whatever the structure, learning effects are critical for contagion to spread in agents networks.