Agent-Based Modelling and Social Network Analysis

Abstract

Abstract Social network analysis offers a powerful method for comprehending intricate systems created through agent-based computational models. Scholars contend that intricate agent networks have the capacity to grasp both the dynamics at an individual level and the overarching characteristics at a global level within a complex system. Consequently, they can contribute to a deeper comprehension of the underlying principles governing such systems. Within this endeavour, we undertake a comprehensive examination of the existing body of literature that establishes a connection between agent-based models and social network analysis. The focal aim of this exploration is to cater to the domain of management studies. We define a baseline for a network topology for a taxonomy of ‘macro’ and ‘micro’ characteristics of social interaction networks. We apply and extend this taxonomy for agent-based models and classify existing models in macro structures, macro patterns of interaction, micro interactions and multilayers, and micro level (clustering and local interaction). We also emphasize the learning methods for agent-based models of social networks.