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A framework for model integration and holistic modelling of socio-technical systems

Partners' Institution
Södertörn University
Wu, P.P.-Y., Fookes, C., Pitchforth, J., Mengersen, K., 2015. A framework for model integration and holistic modelling of socio-technical systems. Decision Support Systems 71, 14–27.
Thematic Area
Development studies
This paper presents a layered framework for the purposes of integrating different socio-technical systems (STS) models and perspectives into a whole-of-systems model. Holistic modelling plays a critical role in the engineering of STS due to the interplay between social and technical elements within these systems and resulting emergent behaviour. The framework decomposes STS models into components, where each component is either a static object, dynamic object or behavioural object. Based on existing literature, a classification of the different elements that make up STS, whether it be a social, technical or a natural environment element, is developed; each object can in turn be classified according to the STS elements it represents. Using the proposed framework, it is possible to systematically decompose models to an extent such that points of interface can be identified and the contextual factors required in transforming the component of one model to interface into another are obtained. Using an airport inbound passenger facilitation process as a case study socio-technical system, three different models are analysed: a Business Process Modelling Notation (BPMN) model, Hybrid Queue-based Bayesian Network (HQBN) model and an Agent Based Model (ABM). It is found that the framework enables the modeller to identify non-trivial interface points such as between the spatial interactions of an ABM and the causal reasoning of a HQBN, and between the process activity representation of a BPMN and simulated behavioural performance in a HQBN. Such a framework is a necessary enabler in order to integrate different modelling approaches in understanding and managing STS.
Relevance for Complex Systems Knowledge
Socio-technological systems (STS) are complex systems. First of all because the combine social and technological issues, where the linkages between elements of the two types are far from straightforward. Previous attempts from systems engineering miss out to provide a holistic approach to modelling of the socio- echnological system. Another previous failure is that few studies have considered natural environment as a part of the system. A challenge is that literature on socio-technological systems feature a number of overlapping and diverginoconpects, which makes it difiicut to reach a computational model , which is what the authors aspire to.

The study sets out to create a holistic system model by combining three different analytical framework This is deemed necessary to cover the complexity and multi-dimensionality of aspects. The approach choose to cover the different conceptiualizations of STS by setting up a hierarchy starting from the physical elements, which then are subdivided in social, technological and natural. Depending the study object, these categories can be further subdivided in two ore more subordinate layers, covering the main aspects and then the fractions these are constituted by. The hierarchy is an organized list of the STS  elements.

To cover the linkages between the elements, the authors work with three abstraction layyers. The higher layers are 1) static objects, 2) dynamic objects and 3) behavioural objects. Static objects are Object Definitions and Object relationships. Dynamic layers are Events, Activities and Processes.  Finally, the Behavioural objects are Metrics and Performance.

In the article, the elaborated framework is applied to a specific case, the traveller flow through an airport arrival system. This system is analysed by the various elements and stakeholders of all the hierarchial categories and in the three layers.
Point of Strength
The article give a good walkthrough on different approaches to describe and model socio-tehnological systems. It clearly states that reductionist models will not suffice, and argues for computational models. It shows the complexity and the risks of simplifying models. For educational purposes this could be bad news,as the level of research and programming skill must be considered as advanced. However, taking advantage of the arguments for modelling by hierarchies and layers, the article could be used as a basis to construct computerised models for STS-analysis that stepwise moves to an increasing complexity.