A typical modeling process begins with identification of a problem and analysis of the requirements of the situation. It is advisable to analyze the scope of the problem domain and the forces and dynamics of the environment. The next step is to identify the variables for the model. The identification of variables and their relationships is very important. One should always ask if using a model is appropriate? If a model is appropriate, then one asks what variables and relationships need to be specified using an appropriate modeling tool. A solution method or method needs to be chosen. Also, analysts need to specify assumptions and make any needed forecasts. Forecasting variables or parameters is sometimes part of the construction of an MDSS. Building a MDSS also involves integrating models and other DSS components like data files and data analysis procedures. Model-Driven DSS need to be validated, evaluated and managed. Model validation is the process of comparing a model's output with the actual behavior of the phenomenon that has been modeled. Validation attempts to answer the question "Have we built the right model?"
from Krishnan, R. and K. Chari "Model Management: Survey, Future Research Directions and a Bibliography," Interactive Transactions of ORMS, Vol. 3, No. 1, 2000. (URL http://www.coba.usf.edu/departments/isds/faculty/chari/model/doc.html)
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