Abstract
System dynamics, as a methodology for analyzing and understanding various types of systems, has been applied in research for several decades. We undertook a review to identify the latest application domains and map the realm of system dynamics. The systematic review was conducted according to the PRISMA methodology. We analyzed and categorized 212 articles and found that the vast majority of studies belong to the fields of business administration, health, and environmental research. Altogether, 20 groups of modeling and simulation topics can be recognized. System dynamics is occasionally supported by other modeling methodologies such as the agent-based modeling approach. There are issues related to published studies mostly associated with testing of validity and reasonability of models, leading to the development of predictions that are not grounded in verified models. This study contributes to the development of system dynamics as a methodology that can offer new ideas, highlight limitations, or provide analogies for further research in various research disciplines.
Highlights
System dynamics represents a specific and original methodological approach to the modeling and simulation of various types of systems
While causal-loop diagrams are used for qualitative modeling, stock-and-flow diagrams are applied in quantitative modeling that leads to the development of models that can be simulated and analyzed
Its multidisciplinary nature has created a wide variety of topics that are rich enough to offer new ideas, highlight limitations, or provide analogies for further research
Summary
System dynamics represents a specific and original methodological approach to the modeling and simulation of various types of systems. As to date the literature does not include a comprehensive analysis of domains in which SD has been applied as a methodological approach, the main objective of this systematic review is to provide insight into the overall context of SD as an approach for modeling and simulation of various issues. This can help to understand when SD can be chosen as a modeling alternative, identify gaps in application areas, and suggest further steps to make progress in this manner. It is shown that possible applications lie in a wide selection of domains
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