An agent-based random-utility-maximization model to generate social networks with transitivity in geographic space

• We develop a stochastic agent-based model to generate social networks in geographic space.
• The model is consistent with random-utility-maximizing (RUM) behavior of agents.
• The model can be estimated using likelihood estimation and is scalable to large populations.
• An application demonstrates how the model can be estimated and used to generate a social network.
• The model is able to reproduce homophily, spatial proximity and transitivity tendencies.

Stochastic actor-based approaches receive increasing interest in the generation of social networks for simulation in time and space. Existing models however cannot be readily integrated in agent-based models that assume random-utility-maximizing behavior of agents. We propose an agent-based model to generate social networks explicitly in geographic space which is formulated in the random-utility-maximizing (RUM) framework. The proposed model consists of a friendship formation mechanism and a component to simulate social encounters in a population. We show how transitivity can be incorporated in both components and how the model can be estimated based on data of personal networks using likelihood estimation. In an application to the Swiss context, we demonstrate the estimation and ability of the model to reproduce relevant characteristics of networks, such as geographic proximity, attribute similarity (homophily), size of personal networks (degree distribution) and clustering (transitivity). We conclude that the proposed social-network model fits seamlessly in existing large-scale micro-simulation systems which assume RUM behavior of agents.