During the process of model building, the modeler must be constantly concerned with how closely the model reflects the system definition. The process of determining the degree to which the model corresponds to the real system, or at least accurately represents the model specification document, is referred to as model validation. Proving absolute validity is a non attainable goal. As Neelamkavil (1987) explains, "True validation is a philosophical impossibility and all we can do is either invalidate or fail to invalidate." For this reason, what we actually seek to establish is a high degree of face validity. Face validity means that, from all outward indications, the model appears to be an accurate representation of the system. From this standpoint, validating a model is the process of substantiating that the model, within its domain of applicability, is sufficiently accurate for the intended application (Schlesinger, 1979).
There is no simple test to establish the validity of a model. Validation is an inductive process through which the modeler draws conclusions about the accuracy of the model based on the evidence available. Gathering evidence to determine model validity is largely accomplished by examining the model structure (i.e., the algorithms and relationships) to see how closely it corresponds to the actual system definition. For models having complex control logic, graphic animation can be used effectively as a validation tool. Finally, the output results should be analyzed to see if the results appear reasonable. If circumstances permit, the model may even be compared to that actual system to see how they correspond. If these procedures are performed without encountering a discrepancy between the real system and the model, the model is said to have face validity.