Integrating Systems Modelling and Data Science

2016 ◽  
Vol 5 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Erik Pruyt
Keyword(s):  
Big Data ◽  
System Dynamics ◽  
Data Science ◽  
Modelling And Simulation ◽  
System Dynamics Modelling ◽  
The Future ◽  
Systems Modelling ◽  
Dynamics Modelling

Although System Dynamics modelling is sometimes referred to as data-poor modelling, it often is –or could be– applied in a data-rich manner. However, more can be done in the era of ‘big data'. Big data refers here to situations with much more available data than was until recently manageable. The field of data science makes big(ger) data manageable. This paper provides a perspective on the future of System Dynamics with a prominent place for bigger data and data science. It discusses different approaches for dealing with bigger data. It reviews methods, techniques and tools for dealing with bigger data in System Dynamics, and sheds light on the modelling phases for which data science is most useful. Finally, it provides several examples of current applications in which big data, data science, and System Dynamics modelling and simulation are being merged.

scholarly journals Applications of system dynamics modelling to computer music

2000 ◽  
Vol 5 (3) ◽  
pp. 149-157 ◽  
Author(s):  
IAN WHALLEY
Keyword(s):  
System Dynamics ◽  
Computer Music ◽  
Music Composition ◽  
Modelling And Simulation ◽  
Simulation Tools ◽  
System Dynamics Modelling ◽  
Dynamics Modelling ◽  
Dynamic Shape

Based on a composer's psycho-acoustic imagination or response to music, system dynamics modelling and simulation tools can be used as a scoring device to map the structural dynamic shape of interest of computer music compositions. The tools can also be used as a generator of compositional ideas reflecting thematic juxtaposition and emotional flux in musical narratives. These techniques allow the modelling of everyday narratives to provide a structural/metaphorical means of music composition based on archetypes that are shared with wider audiences. The methods are outlined using two examples.

System dynamics models for the simulation of sustainable urban development

Kybernetes ◽  
2019 ◽  
Vol 49 (2) ◽  
pp. 460-504 ◽  
Author(s):  
Mirjana Pejic Bach ◽  
Emil Tustanovski ◽  
Andrew W.H. Ip ◽  
Kai-Leung Yung ◽  
Vasja Roblek
Keyword(s):  
System Dynamics ◽  
Urban Development ◽  
Environmental Problems ◽  
Sustainable Urban Development ◽  
System Modelling ◽  
Content Type ◽  
System Dynamics Modelling ◽  
Wide Range ◽  
The Future ◽  
Dynamics Modelling

Purpose System dynamics is a whole-system modelling and learning approach, useful for tackling non-linear problems, such as sustainable urban development. The purpose of this paper is to review system dynamics applications in the simulation of sustainable urban development over a period from 2005 to 2017. Design/methodology/approach The analysis reveals that the number of applications of system dynamics modelling in the area of urban sustainable development increased in the analysed period. Research has changed its focus from the modelling of environmental problems to more complex models, portraying the multidimensional socio-economic processes that have an impact on the sustainability of urban development. Analysed case studies most often use the behaviour reproduction test for model validation, but without a unified approach. In most cases, modelling has been done in China, Germany and the USA, while urban development in the Eastern European countries, Africa and Latin America has not often been investigated. This paper indicates the knowledge gaps and suggests future research directions. Findings Papers that report the use of system dynamics modelling reveal a wide range of applications in urban sustainability. The analysis shows significant emphasis on environmental problems, while the interest for modelling social problems has been increasing during the last several years. Most of the modelled problems examine the sustainability of resources (land, water) and waste management, which are used for insights into the reasons for the system behaviour, forecasting future behaviour and policy testing. Originality/value The presented models were developed in most cases for the purpose of understanding the phenomena examined, as well as the future use of the models in policy planning. This brings us back to the need for greater stakeholder involvement, not only in the initial phase, but also during the whole modelling process, which could increase understanding, use and ownership of the models in the future, and thus increase their practical application.

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