What are the reasons for using a simulation instead of taking real samples?

team, against a team of enemy systems; or from the performance of individual subsystem components to performance of the full system. The first type of vertical extrapolation involves an empirical question: whether the operational performance estimated for a single system can be used in a simulation to provide information about multiple system engagements. Experiments should be carried out in situations in which one can test the multiple system engagement to see whether this type of extrapolation is warranted. This kind of extrapolation should often be successful, and given the safety, cost, and environmental issues raised by multisystem engagements, it is often necessary. The second type of vertical extrapolation depends on whether information about the performance of components is sufficient for understanding performance of the full system. There are systems for which a good deal of operational understanding can be gained by testing portions of the system, for example, by using hardware-in-the-loop simulations. This is, again, an empirical question, and tests can be carried out to help identify when this type of extrapolation is warranted. This question is one for experts in the system under test rather than a statistical question.

The ATACMS/BAT calibration described in represents both horizontal and vertical extrapolation. First, extrapolation is made to different types of weather, terrain, and tactics. Second, the extrapolation is made from several tanks to a larger number of tanks. The first extrapolation requires more justification than the second. In such situations, it might be helpful to keep the degree of true extrapolation to a minimum through choice of the test scenarios.

A third possible type of extrapolation is time extrapolation; the best example is reliability growth testing (see ).

In conferences devoted to modeling and simulation for testing of military systems, most of the presentations have been concerned with potential and future uses of simulations for operational test evaluation. We have found few examples of constructive simulations that have been clearly useful for identifying operational deficiencies of a defense system. This lack may be due to the limitations of modeling and simulation for this purpose, to its lack of application, or to the lack of feedback to demonstrate the utility of such a simulation. To make a strong case for the increased use of modeling and simulation in operational testing and evaluation, examples of simulation models that have successfully identified operational deficiencies that were missed in developmental test need to be collected, and the simulations analyzed to understand the reasons for their success.

We are reluctant to make general pronouncements about which type of simulations would be effective or ineffective for operational assessment of a military system. Everything else being equal, the order of preference from most preferred to least preferred should be live, virtual, and then constructive simulation. For constructive simulations and the software aspects of virtual simulation, the more "physics-based" the better: the actions of the system (and any enemy systems) should be based on well-understood and well-validated physical representations of the process. The use of computer-aided design and computer-aided manufac

Models and simulations are computer representations of complex phenomena in the real world. They are used to explain or predict real life occurrences. There are many reasons to use computer simulations rather than real world experiments:

• Experimenting in the real world may be expensive. For example, a new design for an airplane might fall apart in strong winds. Using a computer to model the shape of the airplane and to simulate the wind behavior can eliminate some bad designs before building a real airplane.
• Experimenting in the real world may be time-consuming, such as testing the effects of a genetic mutation in a species across generations.
• Experimenting in the real world may be dangerous, such as testing whether a nuclear reactor will survive an earthquake.
• Experimenting in the real world may be unethical, such as giving a population a disease to test how fast it spreads.

Computer models rarely capture the full complexity of real situations. For example, models that scientists use to predict the impact of global climate change have to account for hundreds of interconnected factors such as wind patterns, the course of rivers, geological fault lines that cause earthquakes, and interactions of local plants and animals. It would be impossible to include all real interdependent factors in a model. So, researchers make simplifying assumptions in their models.

Researchers may use an iterative design process, starting with a very simple model and refining that model based on their past experiences to make it more realistic for the next simulation. Highly detailed models may push the limits of current computer speeds. So, researchers may have to limit the complexity of the model. Complex models and simulations depend on abstractions (simplifications) to avoid the many details of real world phenomena.

Why is it important to use simulation data instead of real?

What are the reasons for using simulations?

Why are simulations better than experiments?

What are the benefits of using a simulation to collect data?