An agent-based model of teams under external shocks

There is increasing interest in studies examining whether agent-based simulation models can reproduce the interactions between actors of an organization or group in an artificial environment where the agents make decisions with one another. In particular scholars are increasingly interested in whether an actor’s social network matter in the decision making process. In this paper, we review the mechanisms for creating artificial social networks in a group setting. We use an agent-based model to generate dynamic Preferential Attachment networks, and expose these networks to exogenous shocks. This causes these networks to cleave under their weakest point, using Newman’s concept of mod-ularity. We examine the resultant giant component and repeat the process to examine whether exposing a social network to repeated shocks creates more ro-bust or less robust networks. We compare these results with empirical data from a longitudinal network study. We provide findings which may be of use not only to the GDN and behavioral operational research community but also to the wider human resources management community.