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Moving alcohol prevention research forward—Part II: new directions grounded in community‐based system dynamics modeling
Author(s) -
Apostolopoulos Yorghos,
Lemke Michael K.,
Barry Adam E.,
Lich Kristen Hassmiller
Publication year - 2018
Publication title -
addiction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.424
H-Index - 193
eISSN - 1360-0443
pISSN - 0965-2140
DOI - 10.1111/add.13953
Subject(s) - system dynamics , computer science , systems modeling , management science , process (computing) , stakeholder , social dynamics , risk analysis (engineering) , artificial intelligence , engineering , software engineering , medicine , public relations , political science , operating system
Background 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 non‐linearity, 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‐laboratory techniques. Conclusions Community‐based system dynamics modeling can provide powerful tools for policy and intervention decisions that can result ultimately in sustainable changes in research and action in alcohol misuse prevention.