In the case of complex dynamic systems analytical solution usually does not exist or may be very hard to find: that's the reason why it can be useful to apply simulation modelling. In static analytical modelling the results functionally depend simply on the input, that is, a number of parameters. Strategies, structures and decision rules used in the real world can be represented and tested in virtual world of the model: feedbacks alter our mental models and lead to the design of new strategies, new structures and new decision rules. Modelling is an iterative process, which includes mapping the problem from the real world to its model in the world of models (the abstraction process), model analysis and policy implementations, till mapping the solution back to the real system. Reproducing reality is fundamental in order to achieve a better comprehension of it, both from the side of the scientist who observes, and from that of the individual involved in the process, who is often not conscious of its role in the system nor of the consequences of his actions or of the reasons why he acts in a certain way. With simulation we directly create phenomena and forecasts concerning the behaviour of a system: simulation is real, since the dynamic of the system is happening taking place from time to time.
Although simplifications and ad hoc assumptions are the only way to treat a complex reality with refined mathematical formalizations, assumptions not consistent with reality will lead to solve wrongly defined questions. In non linear systems like economy, and society in a more broader sense, chaos theory shows that the minimal indefiniteness in the knowledge of initial conditions will inexorably grow, leading to meaningless forecasts. Human beings are neither perfectly rational nor perfectly predictable. Society can be observed, and can be described through verbal or formal models, the last supported by quantitative and statistical analysis: but it is impossible to recreate in a physical laboratory effective representations, and this makes more difficult to study and understand phenomena starting from the constituent elements.
Unfortunately in social sciences is not so easy to create experiments or functional models, and the lack of repeatability of phenomena and the extreme subjectivity of the elements that rise them make difficult to define in a unique and unambiguous way the cause and effect relationships. Functional models on the contrary are closer to real world, less abstracted and more able to be generalized: a model in conflict with physical laws can be described with words, and can also be formalized with mathematics, but it cannot be reproduced in reality. Both these representations share a high degree of abstraction and, in the formalization case, also of simplification. A mathematical model instead offers a well defined content and a larger degree of generalization: it's computable, and it can be used to calculate the values of the parameters that define the process we are analysing since it is nothing more than a system of equation that describes the relationships among variables. Verbal models are characterized by the highest level of flexibility, but at the same time are those less immediate in terms of generalization degree, and are not computable making larger the distance between ideas theorization and empirical verification. In doing so a process of abstraction is fundamental in order to get an idealization that at the same time is as much simple as possible, but sufficiently complex as to adequately represent the fundamental process we are interested in.įollowing Gilbert and Terna ( 2000) descriptions of real world can be of three types: verbal descriptions, logic or mathematical descriptions, and functional or simulation models.
Modelling plays an essential role in science: models give us the chance to analyse and understand real world and phenomena, and to gain some sort of control over it. Simulation of Dynamic Systems with Matlab and Simulink Klee, HaroldĬRC Press Inc, Taylor &Francis Group: Boca Raton London New York (2007) 784pp Simulation Modeling and Analysis with ARENA Altiok, T.
Review of Three Books on Simulation Modelling © Copyright