Agent-based modeling of knowledge transfer within social networks

In this paper, we study both processes of direct and indirect knowledge transfer, from a modelling perspective, using agent-based models. In fact, there are several ways to model knowledge. We choose to study three different representations, and try to determine which one allows to better capture the dynamics of knowledge diffusion within a social network. Results show that when knowledge is modelled as a binary vector, and not cumulated, this enables us to observe some heterogeneity in agents' learning and interactions, in both types of knowledge transfer.