Abstract for: Hybridizing Agent-Based with System Dynamics Models: Principles for Theory Development in Public Policy and Management Research
Developing theory that credibly characterizes the nonlinear relationships inherent to wicked and complex problems is a formidable task. This paper highlights how computational models that hybridize agent-based modeling and system dynamics approaches can facilitate theory-building. Although there are texts on how to construct agent-based models and system dynamics models individually, there is a relative dearth of methodological guidance on how to combine and subsequently leverage these simulation types for public policy and management applications such as theorizing. In Part I, background information for agent-based and system dynamics models is presented along with examples of theory-building from recent peer-reviewed literature. Next, drawing on established guidelines for both types of computational modeling methods, a three-part framework is presented for hybridizing agent-based with system dynamics modeling methods in theory building. In Part II, these principles are applied to an example of theorizing how cooptation in strategic planning may work to influence public organization performance following a crisis.