The novelty of our model is to combine models of collective action on networks with models of social learning. Agents are connected according to an undirected graph, the social network, and have the choice between two actions: either to adopt a new behavior or technology or stay with the default behavior. The individual believed return depends on how many neighbors an agent has, how many of those neighbors already adopted the new behavior and some agent-specic cost-benefit parameter. There are four main insights of our model: (1) A variety of collective adoption behaviors is determined by the network. (2) Average inclination governs collective adoption behavior. (3) Initial inclinations determine the critical mass of adoption which ensures the new behavior to prevail. (4) Equilibria and dynamic be- havior changes as we change the underlying network and other parameters. Given the complexity of the system we use a standard technique for estimating the solution.
Diffusion Social Networks Social Learning Tipping Technology Adoption