Assessing candidates for model abstraction

Today's modeling and simulation community is faced with the problem of developing and managing large complex system models comprised of a diverse set of subsystem component models. These component models may be described using varying amounts of detail and fidelity as well as differing modeling paradigms. Often, a complex simulation comprised of high fidelity subcomponent models may result in a more detailed system model than the simulation objective requires. Simulating such a system model results in a waste of simulation time with respect to addressing the simulation goals. One way to avoid wasting simulation cycles is to reduce the complexity of subcomponent models while not affecting the desired simulation objective. The process of reducing the complexity of these subcomponent models is known as abstract modeling. Abstract modeling reduces the subcomponent model complexity by eliminating, grouping, or estimating model parameters or variables at a less detailed level without grossly affecting the simulation results. One key issue concerning model abstracting is identifying the variables or parameters that can be abstracted away for a given simulation objective. This paper presents an approach to identifying candidate variables for model abstraction when considering typical C4ISR (Command, Control, Computers, Communications, Intelligence, Surveillance, and Reconnaissance) hardware systems.