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

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

Manuscript Submission Information

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