Modeling is a comparatively new area of activity involving the marriage of ideas from various disciplines, and is an essential and inseparable part of all scientific activity. Modeling techniques include statistical methods, computer simulation, system identification, and sensitivity analysis. None of these, however, is as important as the ability to understand the underlying dynamics of a complex system. These insights are needed to assess whether the assumptions of a model are correct and complete. The modeler must be able to recognize whether a model reflects reality, and to identify and deal with divergences between theory and data.

One of the main aims of scientific modeling, is to apply quantitative reasoning to observations about the world, in the hope of seeing aspects that may have escaped the notice of others. There are many specific techniques modelers use, which enable them to discover aspects of reality that may not be obvious to everyone. One of the essentials is the understanding of the role that assumptions play in the development of the model. The usual approach to model development is to characterize the system, make some assumptions about how it works and translate these into equations and a simulation program. After simulation, one of the final steps is the validation: whether we can trust the data presented by the model.

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