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Model Validity. Please note: 1) A model is never perfect. 2) An absolute measure of validity does not exist. On what basis can we compare the validity of models? Four phases are suggested: 1. An evaluation of model structure. 2. An evaluation of model logic.
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Please note: 1) A model is never perfect. 2) An absolute measure of validity does not exist. On what basis can we compare the validity of models? Four phases are suggested: 1. An evaluation of model structure. 2. An evaluation of model logic. 3. An evaluation of design and/or input data. 4. An evaluation of model response. Validating a Model
Keep track of the model structure at all times. Best way is to start with a preliminary simple model and slowly build on it in a systematic and logical way. KISS (Keep It Simple, Stupid!) An Evaluation of Model Structure
If the model logic truly reflects the system model, then the model will react to a stimulus (or change) in the same way as the actual system would. Often, therelative difference in the outputs are all that we need to be concerned with (trends), rather than whether the model response and the actual system values are identical. An Evaluation of Model Logic
Two types of data in model development: 1) the design data or information used to construct the model 2) the input data of data used to stimulate the system Data collection and verification may well be the most overlooked portion of model construction. Textbooks contribute to the problem because the data is often presented directly. The process of data collection often consumes the major amount of time and resources when dealing with actual problems. Good practice: First decide on the basic form of the model, then identify its specific data needs. An Evaluation of Design and/or Input Data
True validation is often said to be reflected solely in its ability to predict the behavior of the system that has been modeled. For example: Consider a model which predicts the national economy based on the height of the hemlines on women's dresses (some obsrvers have noted a correlation). The fact that this model is accurate does not mean it is valid! Look at the response, the trends, and compare with actual data or theoretical information from other sources at hand to validate model response. An Evaluation of Model Response