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Article

A System Dynamics Modeling Support System Based on Computational Intelligence

School of Engineering and Information Technology, University of New South Wales at Australian Defense Force Academy, Northcott Drive, Campbell, Canberra, ACT 2600, Australia
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Received: 12 August 2019 / Revised: 1 September 2019 / Accepted: 4 September 2019 / Published: 25 September 2019
(This article belongs to the Collection System Dynamics)
System dynamics (SD) is a complex systems modeling and simulation approach with wide ranging applications in various science and engineering disciplines. While subject matter experts lead most of the model building, recent advances have attempted to bring system dynamics closer to fast growing fields such as data sciences. This may prove promising for the development of novel support methods that augment human cognition and improve efficiencies in the model building process. A few different directions have been explored recently to support individual modeling stages, such as the generation of model structure, model calibration and policy optimization. However, an integrated approach that supports across the board modeling process is still missing. In this paper, a prototype integrated modeling support system is presented for the purpose of supporting the modelers at each stage of the process. The proposed support system facilitates data-driven inferring of causal loop diagrams (CLDs), stock-flow diagrams (SFDs), model equations and the estimation of model parameters using computational intelligence (CI) techniques. The ultimate goal of the proposed system is to support the construction of complex models, where the human power is not enough. With this goal in mind, we demonstrate the working and utility of the proposed support system. We have used two well-known synthetic reality case studies with small models from the system dynamics literature, in order to verify the support system performance. The experimental results showed the effectiveness of the proposed support system to infer close model structures to target models directly from system time-series observations. Future work will focus on improving the support system so that it can generate complex models on a large scale. View Full-Text
Keywords: modeling support system; system dynamics modeling; computational intelligence; genetic programming ensemble; simulated annealing modeling support system; system dynamics modeling; computational intelligence; genetic programming ensemble; simulated annealing
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MDPI and ACS Style

Abdelbari, H.; Shafi, K. A System Dynamics Modeling Support System Based on Computational Intelligence. Systems 2019, 7, 47. https://0-doi-org.brum.beds.ac.uk/10.3390/systems7040047

AMA Style

Abdelbari H, Shafi K. A System Dynamics Modeling Support System Based on Computational Intelligence. Systems. 2019; 7(4):47. https://0-doi-org.brum.beds.ac.uk/10.3390/systems7040047

Chicago/Turabian Style

Abdelbari, Hassan, and Kamran Shafi. 2019. "A System Dynamics Modeling Support System Based on Computational Intelligence" Systems 7, no. 4: 47. https://0-doi-org.brum.beds.ac.uk/10.3390/systems7040047

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