Computational Communication Science| Bridging the Gaps: Using Agent-Based Modeling to Reconcile Data and Theory in Computational Communication Science
In various branches of the social sciences, agent-based models (ABMs) have long been applied to enhance researchers’ understanding of complex systems and processes. However, in communication science, this approach is rarely used. In this article, we argue that ABMs have the potential to advance communication research in general, and computational communication science (CCS) in particular, by helping scholars address two major gaps. First, by generating emergent global phenomena from individual interactions, ABMs make it possible to explicitly link micro and macro perspectives in communication research. Second, by formalizing theories, ABMs offer mechanism-based explanations for observed empirical patterns in data. To familiarize more communication scholars with this approach, this article provides a systematic overview of the potentials, applications, and challenges of ABMs in communication science. Special attention is paid to the criteria of reliability and validity.