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Review
. 2018 Feb;113(2):353-362.
doi: 10.1111/add.13955. Epub 2017 Aug 25.

Moving alcohol prevention research forward-Part I: introducing a complex systems paradigm

Affiliations
Review

Moving alcohol prevention research forward-Part I: introducing a complex systems paradigm

Yorghos Apostolopoulos et al. Addiction. 2018 Feb.

Abstract

Background and aims: The drinking environment is a complex system consisting of a number of heterogeneous, evolving and interacting components, which exhibit circular causality and emergent properties. These characteristics reduce the efficacy of commonly used research approaches, which typically do not account for the underlying dynamic complexity of alcohol consumption and the interdependent nature of diverse factors influencing misuse over time. We use alcohol misuse among college students in the United States as an example for framing our argument for a complex systems paradigm.

Methods: A complex systems paradigm, grounded in socio-ecological and complex systems theories and computational modeling and simulation, is introduced. Theoretical, conceptual, methodological and analytical underpinnings of this paradigm are described in the context of college drinking prevention research.

Results: The proposed complex systems paradigm can transcend limitations of traditional approaches, thereby fostering new directions in alcohol prevention research. By conceptualizing student alcohol misuse as a complex adaptive system, computational modeling and simulation methodologies and analytical techniques can be used. Moreover, use of participatory model-building approaches to generate simulation models can further increase stakeholder buy-in, understanding and policymaking.

Conclusions: A complex systems paradigm for research into alcohol misuse can provide a holistic understanding of the underlying drinking environment and its long-term trajectory, which can elucidate high-leverage preventive interventions.

Keywords: Agent-based modeling; college drinking prevention; complex systems science; computational modeling and simulation; policy; system dynamics modeling.

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