A next-generation modular and compositional framework for System Dynamics modeling and beyond
System Dynamics (SD) is a powerful methodology for understanding and managing complex systems over time, with applications in various fields such as public health, business, and environmental management. Despite its strengths, traditional System Dynamics modeling methods face several critical limitations, including a lack of modularity, inadequate representation of complex relationships, stratification that obscures model transparency, rigid coupling of model syntax and semantics, and limited support for model composition and reuse. These challenges restrict the scalability, adaptability, and accuracy of models, particularly when applied to large-scale or interdisciplinary systems.
To address these limitations, this presentation introduces a novel framework grounded in Applied Category Theory (ACT), which provides a rigorous mathematical foundation for representing, relating, composing, and stratifying complex systems. By leveraging ACT, this research establishes a next-generation SD modeling approach that integrates mathematical rigor with practical utility, significantly enhancing the flexibility, expressiveness, and applicability of System Dynamics for researchers and practitioners across various domains.
To contextualize System Dynamics modeling in the realm of infectious disease simulation, the presentation will begin with an overview of a COVID-19 theory-based machine learning model, developed incorporating the Bayesian methodology of Particle Filtering algorithm to a Covid-19 System Dynamics model. This model played a pivotal role in supporting decision-making across 17 Canadian jurisdictions during the COVID-19 pandemic by providing daily transmission monitoring, short-term projections, and counterfactual intervention analysis over a year.