Recent empirical evidence suggests that financial networks exhibit a core periphery network structure. This paper aims at giving an economic explanation for the emergence of such a structure using network formation theory. Focusing on intermediation benefits, we find that a core periphery...

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model,...

Agents involved in the formation of a social or economic network typically face uncertainty about the benefits of creating a link. However, the interplay of such uncertainty and risk attitudes has been neglected in the network formation literature. We propose a dynamic network formation model...

Policy makers spend large amounts of public resources on the foundation of science parks and other forms of geographically clustered business activities, in order to stimulate regional innovation. Underlying the relation between clusters and innovation is the assumption that co-located firms...

This paper develops a mathematical framework to study signal networks, in which nodes can be active or inactive, and their activation or deactivation is driven by external signals and the states of the nodes to which they are connected via links. The focus is on determining the optimal number of...

Modern banking systems are highly interconnected. Despite their various benefits, the linkages that exist between banks carry the risk of contagion. In this paper we investigate how banks decide on direct balance sheet linkages and the implications for contagion risk. In particular, we model a...

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...

We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of...