Moving alcohol prevention research forward-Part II: New directions grounded in community-based system dynamics modeling
BACKGROUNDS AND AIMS: Given the complexity of factors contributing to alcohol misuse, appropriate epistemologies and methodologies are needed to understand and intervene meaningfully. We aimed to (1) provide an overview of computational modeling methodologies, with an emphasis on system dynamics modeling; (2) explain how community-based system dynamics modeling can forge new directions in alcohol prevention research; and (3) present a primer on how to build alcohol misuse simulation models using system dynamics modeling, with an emphasis on stakeholder involvement, data sources, and model validation. Throughout, we use alcohol misuse among college students in the United States as a heuristic example for demonstrating these methodologies.
METHODS: System dynamics modeling employs a top-down aggregate approach to understanding dynamically complex problems. Its three foundational properties-stocks, flows, and feedbacks-capture nonlinearity, time-delayed effects, and other system characteristics. As a methodological choice, system dynamics modeling is amenable to participatory approaches; in particular, community-based system dynamics modeling has been used to build impactful models for addressing dynamically complex problems.
RESULTS: The process of community-based system dynamics modeling consists of numerous stages: (1) creating model boundary charts, behavior-over-time-graphs, and preliminary system dynamics models using group model-building techniques; (2) model formulation; (3) model calibration; (4) model testing and validation; and (5) model simulation using learning-lab techniques.
CONCLUSIONS: Community-based system dynamics modeling can provide powerful tools for policy and intervention decisions that can ultimately result in sustainable changes in research and action in alcohol misuse prevention.
Y. Apostolopoulos and M. K. Lemke. (2017). Moving alcohol prevention research forward-Part II: New directions grounded in community-based system dynamics modeling.