The Current State-of-the-Art of System Dynamics Modelling and Simulation

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (1 June 2016) | Viewed by 35826

Special Issue Editor


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Guest Editor
Associate Professor, Faculty of Technology, Policy and Management, Delft University of Technology, PO Box 5015, 2600 GA Delft, The Netherlands
Interests: system dynamics; modelling and simulation; dynamic complexity; model-based decision support; endogenous dynamics

Special Issue Information

Dear Colleagues,

System dynamics is a modelling and simulation approach for dealing with dynamically complex issues that are characterized by feedback and accumulation effects. System dynamics has been around since the late 1950s. For several decades, the system dynamics field has been a mature field characterized by a clear and constant state-of-the-art. Framed differently, some may argue that the system dynamics field has been characterized for some time—in spite of the many topics that featured on the system dynamics research agenda—by stagnation. The founding father of system dynamics, Jay W. Forrester, even argued that the field was on an aimless plateau. Many long-awaited innovations and advances have recently been realized though. While leaving the core system dynamics approach intact, these innovations have significantly transformed the state-of-the-art of system dynamics modelling and simulation. Hence, it is time to provide an updated picture.

This Special Issue aims at providing an overview of the current state-of-the-art of system dynamics modelling and simulation. Contributions to this Special Issue should therefore focus on recent innovations that, building on the core system dynamics approach, make up the current (or likely future) state-of-the-art of system dynamics modelling and simulation. Methodological issues to be covered in this issue include, but are not limited to:

  • Recent innovations in group model building or joint model building, model conceptualization, and system dynamics model construction.
  • New multi-method and hybrid modelling and simulation approaches for dealing with feedback loop and accumulation effects, as well as with other important characteristics and effects such as agent-based characteristics, geo-spatial characteristics, uncertainty, discrete events, etc.
  • Innovations in sampling of uncertainties, approaches for including real-world (big) data in system dynamics models, and approaches for integrating system dynamics models in real decision-support systems.
  • Innovations in model testing, model analysis, model calibration of system dynamics-like models, and model selection.
  • Innovations in approaches to cluster and analyse the outcomes of system dynamics models, including analytic approaches, statistical approaches, data science approaches, and machine learning approaches adapted to system dynamics model outcomes.
  • Innovations in approaches to test policy robustness and design robust policies.
  • New ways to: visualize outcomes of simulation models, to share insights with decision-makers, to involve decision makers and stakeholders, and even new system dynamics model uses.
  • New system dynamics approaches, such as multi-model system dynamics for dealing with issues characterized by deep uncertainty, stochastic system dynamics for dealing with issues characterized by statistical uncertainty, or new systems dynamics platforms and (open source) software.

This Special Issue has two aims and reaches out to two different audiences. First, it aims at informing systems thinkers and modellers from other traditions about the current state-of-the-art of system dynamics. Second, it aims at enabling practitioners of traditional system dynamics who want to update their skills and require the most modern skills to achieve this. Each article should therefore start from the traditional system dynamics approach, discuss recent innovations related to a core aspect of system dynamics modelling and simulation, and illustrate the resulting state-of-the-art with a real-world application. Moreover, authors are strongly encouraged to provide models and (links to) algorithms in order to enable readers to reproduce similar results.

Dr. Erik Pruyt
Guest Editor

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Keywords

  • System dynamics
  • Modelling and simulation
  • Dynamic complexity
  • Model-based policy support
  • Updated state-of-the-art

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Published Papers (3 papers)

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Research

5321 KiB  
Article
The Climate Change-Road Safety-Economy Nexus: A System Dynamics Approach to Understanding Complex Interdependencies
by Mehdi Alirezaei, Nuri C. Onat, Omer Tatari and Mohamed Abdel-Aty
Systems 2017, 5(1), 6; https://doi.org/10.3390/systems5010006 - 23 Jan 2017
Cited by 37 | Viewed by 11575
Abstract
Road accidents have the highest externality costs to society and to the economy, even when compared to the externality damages associated with air emissions and oil dependency. Road safety is one of the most complicated topics, which involves many interdependencies, and so, a [...] Read more.
Road accidents have the highest externality costs to society and to the economy, even when compared to the externality damages associated with air emissions and oil dependency. Road safety is one of the most complicated topics, which involves many interdependencies, and so, a sufficiently thorough analysis of roadway safety will require a novel system-based approach in which the associated feedback relationships and causal effects are given appropriate consideration. The factors affecting accident frequency and severity are highly dependent on economic parameters, environmental factors and weather conditions. In this study, we try to use a system dynamics modeling approach to model the climate change-road safety-economy nexus, thereby investigating the complex interactions among these important areas by tracking how they affect each other over time. For this purpose, five sub-models are developed to model each aspect of the overall nexus and to interact with each other to simulate the overall system. As a result, this comprehensive model can provide a platform for policy makers to test the effectiveness of different policy scenarios to reduce the negative consequences of traffic accidents and improve road safety. Full article
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3819 KiB  
Article
Model of the Russian Federation Construction Innovation System: An Integrated Participatory Systems Approach
by Emiliya Suprun, Oz Sahin, Rodney A. Stewart and Kriengsak Panuwatwanich
Systems 2016, 4(3), 29; https://doi.org/10.3390/systems4030029 - 16 Aug 2016
Cited by 18 | Viewed by 10437
Abstract
This research integrates systemic and participatory techniques to model the Russian Federation construction innovation system. Understanding this complex construction innovation system and determining the best levers for enhancing it require the dynamic modelling of a number of factors, such as flows of resources [...] Read more.
This research integrates systemic and participatory techniques to model the Russian Federation construction innovation system. Understanding this complex construction innovation system and determining the best levers for enhancing it require the dynamic modelling of a number of factors, such as flows of resources and activities, policies, uncertainty and time. To build the foundations for such a dynamic model, the employed study method utilised an integrated stakeholder-based participatory approach coupled with structural analysis (MICMAC—Matrice d'Impacts Croisés Multiplication Appliquée à un Classement Cross-Impact Matrix). This method identified the key factors of the Russian Federation construction innovation system, their causal relationship (i.e., influence/dependence map) and, ultimately, a causal loop diagram. The generated model reveals pathways to improving construction innovation in the Russian Federation and underpins the future development of an operationalised system dynamics model. Full article
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3093 KiB  
Article
Using Textual Data in System Dynamics Model Conceptualization
by Sibel Eker and Nici Zimmermann
Systems 2016, 4(3), 28; https://doi.org/10.3390/systems4030028 - 4 Aug 2016
Cited by 53 | Viewed by 11043
Abstract
Qualitative data is an important source of information for system dynamics modeling. It can potentially support any stage of the modeling process, yet it is mainly used in the early steps such as problem identification and model conceptualization. Existing approaches that outline a [...] Read more.
Qualitative data is an important source of information for system dynamics modeling. It can potentially support any stage of the modeling process, yet it is mainly used in the early steps such as problem identification and model conceptualization. Existing approaches that outline a systematic use of qualitative data in model conceptualization are often not adopted for reasons of time constraints resulting from an abundance of data. In this paper, we introduce an approach that synthesizes the strengths of existing methods. This alternative approach (i) is focused on causal relationships starting from the initial steps of coding; (ii) generates a generalized and simplified causal map without recording individual relationships so that time consumption can be reduced; and (iii) maintains the links from the final causal map to the data sources by using software. We demonstrate an application of this approach in a study about integrated decision making in the housing sector of the UK. Full article
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