Table of figures. Table of tables. Preface. 1. Introduction to simulation modeling and metamodeling. References. 2. The simulation model and metamodel. References. 3. The metamodel in perspective: statistical considerations in simulation experiments. References. 4. Metamodeling. References. 5. Survey of current research. References. 6. Metamodeling: some additional examples. References. Appendix: The linear regression model. Bibliography. Index.
Researchers develop simulation models that emulate real-world situations. While these simulation models are simpler than the real situation, they are still quite complex and time consuming to develop. It is at this point that metamodeling can be used to help build a simulation study based on a complex model. A metamodel is a simpler, analytical model, auxiliary to the simulation model, which is used to better understand the more complex model, to test hypotheses about it, and provide a framework for improving the simulation study.
The use of metamodels allows the researcher to work with a set of mathematical functions and analytical techniques to test simulations without the costly running and re-running of complex computer programs. In addition, metamodels have other advantages, and as a result they are being used in a variety of ways: model simplification, optimization, model interpretation, generalization to other models of similar systems, efficient sensitivity analysis, and the use of the metamodel's mathematical functions to answer questions about different variables within a simulation study.
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