Tools for Modeling and Simulation

Definitions

There are several ways to understand the distinction between models and simulations. Models simplify the way the world works by hiding information that is confounding, otherwise unimportant, or hard to see, while bringing to the foreground those elements that are central to the system. They help us visualize processes that could not otherwise be seen with the naked eye as a result of a very large or small scale, or speeds too fast or too slow to be noticed by the human eye. Allan Collins and John R. Ferguson classified theoretical models in science as structural, causal, and dynamic models. Structural models focus on the physical relationships between the model features (e.g., molecules and atoms model). Causal models, which are more conceptual, focus on the processes within a system that relate to the causal mechanism; for example, in an ecosystem food-web model, it is possible to see how an increase in a predator population might result in a decrease in its prey population. Dynamic models enable the users to see and test different assumption by changing the settings and observing the effects of these changes on how the model runs. Like dynamic models, simulations involve imitation of the behavior of a system or process over time; however, a simulation may also allow a person to play an active role within it.

What, then, is the relationship between modeling and simulations? While the main purpose of modeling is usually to expose and explicitly explain the underlying mechanism, the main purpose of simulations is to describe and reproduce the experience, often deliberately including the complexity presented in reality (e.g., a combat flight simulation), where the underlying mechanism is often only implicit. The act of modeling may result in a simulation, and the act of using a simulation may result in an understanding of the underlying model.

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