Integrating machine learning and system dynamics

Hesham Mahmoud, Hubert Korzilius, Marcel Olde Rikkert, Susan Howick, Etiënne Rouwette and William Schoenberg

Chapter 5 in Methodology to Address Grand Challenges, 2026, pp 86-114 from Edward Elgar Publishing

Abstract: The exponential data growth across industry and academia has made machine learning (ML) a promising tool to support decision-making in public and private organizations. However, ML approaches still receive skepticism for both the opacity and complexity of the analysis process, which represent obstacles to the wider use of ML. System dynamics (SD) modeling, and particularly its participatory version, could complement ML. One of the grand challenges worldwide that could benefit from such a sophisticated methodological approach is the cost-effective management of a limited workforce in the elderly healthcare system to support a rapidly growing older population with care needs. The Netherlands, with its aging population, faces unprecedented pressure on its health and social care systems. We use this problem to demonstrate the combination of participatory SD and ML, and discuss strengths, limitations, and suggestions for implementation in practice and science.

Keywords: Machine Learning; System Dynamics; Elderly Healthcare System (search for similar items in EconPapers)
Date: 2026
ISBN: 9781035314300
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