A critical realist approach to agent-based modeling: Unlocking prediction in non-positivist paradigms

Information systems (IS) scholarship and practice aim to predict phenomena and outcomes of IS use. These phenomena of IS use are typically set in multi-leveled, dynamic, and complex contexts that lend explanation to the non-positivist tradition in IS research. However, limited methodological options exist to make predictions. In this research, we propose stratified agent-based modeling, a step-by-step approach that enables prediction in non-positivist paradigms. Drawing upon the critical realist philosophy of science, which suggests ontological stratification and assumes open systems, we adopt a retroduction-based explanation formation and agent-based modeling to simulate different potential states of a complex system. The critical step in combining critical realism with agent-based modeling involves identifying and codifying the underlying generative mechanisms (i.e., causal powers) into various components of the agent-based model. We propose four steps toward prediction under the critical realist paradigm: (1) capturing the phenomenon, (2) identifying the generative mechanism, (3) building the agent-based model, and (4) simulating states of the system. We present an exemplar of our proposed approach that investigates the effectiveness of strategies to combat malicious content propagation in social networks.