Simulation modeling

A longitudinal analysis of the variables’ effect on each other. In simulation modeling, the equations are constructed to duplicate, to a greater or lesser degree, the actual functioning of the system that is being studied. Simulation modeling is used extensively today in fields ranging from astrophysics to social interaction. This method starts with an idea about how the system functions and gives meaning to the coefficients. An attempt is made to duplicate the system being modeled in the form of equations, not solely by drawing on statistical relationships among variables, but rather by logic and inference about how the system works. The validity of the model is tested by comparing its output with actual historical data. The purpose of simulation is the structure of variables based on their co-variation as an input to the real purpose, which is the estimation of future values and the behavior of those values over time. Simulation modeling is complicated but has the advantage of forcing us to pay attention to how things really work. No prepackaged programs for simulations modeling are available; each model entails a custom job. However, some specialized modeling languages, such as Dynamo, were developed for these applications. In futures research the role of these models is indicative of a series of future options, whether they are desirable or undesirable. Its procedure has 3 main stages to it: 1) Qualitative analysis of the problem. 2) Formalization and modelling of the issue. 3) Treatment according to the defined rules. Once the objectives have been defined, the scenarios built, the focal system and environment delineated and the level of aggregates determined the following steps are followed: 1) Structural analysis, identification of the significant variables, a diagram of the relationships between variables and the introduction of intermediary and auxiliary variables. 2) Selection of the technique that the modeling and the simulation will use according to the goals and the nature of the variables. 3) Modelling, formulating the structure and the programming of the relationships that are described in the model. 4) Quantifying the parameters. 5) Trying the model out. This implies formulating, programming and correcting the program in the computer and running the tests on validity, consistency, convergence, sensibility, etc. 6) Validating the model with a basis on theoretical, practical and mathematical theories. This step also includes establishing the controls for the model.