An Introduction to Multi-paradigm Modelling and Simulation

Modelling and simulation are becoming increasingly important enablers in the analysis and design of complex systems. To tackle problems of ever increasing complexity, modelling and simulation research is shifting from simulation techniques to modelling methodology and technology. In this article, the emerging field of Computer Automated Multi-Paradigm Modelling is presented. Multi-paradigm modelling adresses and integrates three orthogonal directions of research: 1. multi-formalism modelling, concerned with the coupling of and transformation between models described in different formalisms, 2. model abstraction, concerned with the relationship between models at different levels of abstraction, and 3. meta-modelling, concerned with the description (models of models) of classes of models, which allows formalism specification. The article first introduces the general concepts of Modelling and Simulation theory, and explains how rigourous application thereof provides a sound basis for the meaningful exchange and re-use of knowledge about the behaviour of complex systems. The representation of models in diverse formalisms, at different levels of abstraction, and the (behaviour-conserving) transformation between the formalisms is demonstrated. 1 MODELLING AND SIMULATION At a first glance, it is not easy to characterize modelling and simulation. Certainly, a variety of application domains such as fluid dynamics, energy systems, and logistics management make use of it in one form or another. Depending on the context, modelling and simulation is often seen as a sub-set of Systems Theory, Control Theory, Numerical Analysis, Computer Science, Artificial Intelligence, or Operations Research. Increasingly, modelling and simulation integrates all of the above disciplines. As a paradigm, it is a way of representing problems and thinking about them as Real-World entity Base Model System S only study behaviour in experimental context experiment within context Model M Simulation Results Experiment Observed Data within context simulate = virtual experiment Model Base a-priori knowledge validation REALITY MODEL