Diffusion of Innovation over Social Networks under Limited-trust Equilibrium

We consider the diffusion of innovation in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors and decides what alternative product to adopt to maximize its payoff. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trustworthiness to others to maximize their long-term payoffs. To capture such trustworthy behavior, we deviate from the expected utility theory and use a new notion of rationality based on limited-trust equilibrium (LTE). By incorporating such notion into the diffusion model, we analyze the convergence of emerging dynamics to their equilibrium points using a mean-field approximation. We study the equilibrium state and the convergence rate of the diffusion process using the absorption probability and the expected absorption time of a reduced-size absorbing Markov chain. We also show that the LTE diffusion model under the best-response strategy can be converted to the well-known linear threshold model. Simulations show that when agents behave trustworthily, their long-term payoffs will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimation of the convergence property over random networks.