A System Dynamics Modeling Support System Based on Computational Intelligence

Hassan Abdelbari,Kamran Shafi

doi:10.3390/systems7040047

Abstract

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.

Highlights

System dynamics (SD) is a modeling and simulation approach with wide ranging applications in various disciplines such as management [1], constructions [2] and agriculture [3]
The observation was made that each stock variable’s generated equation contained the correct variables. This was because the knowledge of causalities among the variables was known from the predefined causal loop diagrams (CLDs)
Our ability to store and process increasing amounts of data and to utilize computational intelligence (CI) methods to turn this data into useful information is driving a revolution across the SD field

Summary

Introduction

System dynamics (SD) is a modeling and simulation approach with wide ranging applications in various disciplines such as management [1], constructions [2] and agriculture [3]. Several opportunities exist for recent advances in computational intelligence (CI) and machine learning techniques to be leveraged to support and improve the model building process. Such techniques do not replace human cognition, they do alleviate some of the tedious tasks, such as data analysis, knowledge extraction, model calibration and model validation. Several studies have reported on the application of CI methods to SD modeling Salient examples of such works include: the use of fuzzy logic (FL) for knowledge acquisition and representation [7] and identification

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Conclusion