Technology adoption and herding behavior in complex social networks

Using a simple computational model, we study consequences of herding behavior in population of agents connected in networks with different topologies: random networks, small-world networks and scale-free networks. Agents sequentially choose between two technologies using very simple rules based on the previous choice of their immediate neighbors. We show that different seeding of technologies can lead to very different results in the choice of majority of agents. We mainly focus on the situation where one technology is seeded randomly while the other is directed to targeted (highly connected) agents. We show that even if the initial seeding is positively biased toward the first technology (more agents start with the choice of the first technology) the dynamic of the model can result in the majority choosing the second technology under the targeted hub approach. Even if the change to majority choice is highly improbable targeted seeding can lead to more favorable results. The explanation is that targeting hubs enhances the diffusion of the firm’s own technology and halts or slows-down the adoption of the concurrent one. Comparison of the results for different network topologies also leads to the conclusion that the overall results are affected by the distribution of number of connections (degree) of individual agents, mainly by its variance.