Modeling and Simulation

Modeling and Simulation


Introduction to Modeling and Simulation

Modeling and simulation constitute a powerful method for designing and evaluating complex systems and processes, and knowledge of modeling and simulation principles is essential to APL's many analysts and project managers as they engage in state-of-the-art research and development. This article presents an end-to-end description of these principles, taking the reader through a series of steps. The process begins with careful problem formulation and model construction and continues with simulation experiments, interpretation of results, validation of the model, documentation of the results, and final implementation of the study's conclusions. Each step must be approached systematically; none can be safely omitted. Critical issues are identified at each step, and guidelines are presented for the successful completion of modeling and simulation studies.



Constructing abstractions of systems (models) to facilitate experimentation and assessment (simulation) is both art and science. The technique is particularly useful in solving problems of complex systems where easier solutions do not present themselves. Modeling and simulation methods also allow experimentation that otherwise would be cumbersome or impossible. For example, computer simulations have been responsible for many advancements in such fields as biology, meteorology, cosmology, population dynamics, and military effectiveness. Without simulation, the study of these subjects can be inhibited by the lack of accessibility to the real system, the need to study the system over long time periods, the difficulty of recruiting human subjects for experiments, or all of these factors. Because the technique offers solutions to these problems, it has become a tremendously powerful tool for examining the intricacies of today's increasingly, ly complex world.

This article presents a structured set of guidelines to help the practitioner avoid the pit, falls and successfully apply modeling and simulation methodology. Guidelines are all that can be offered, however. Despite a firm foundation in mathematics, computer science, probability, and statistics, the discipline's mains intuitive. For example, the issues most relevant in a cardiology study may be quite different from those most significant in a military effectiveness study. Therefore, this article offers few strict rules; instead, it attempts to create awareness of critical issues and of the existing methods for resolving potential problems.



The following list summarizes this article's major suggestions for successful problem solving using modeling, and simulation:

  1. Define specific objectives.
  2. Start with a simple model, adding detail only as needed.
  3. Follow software engineering quality standards when developing the computerized model.
  4. Model random processes according to accepted criteria.
  5. Design experiments that support the study objectives.
  6. Provide a complete and accurate characterization of random output data.
  7. Validate all analyses and all, products of the problem, solving process.
  8. Document and implement conclusions.


Read it here 

by William A. Menner


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