Simulation is the execution of a sequence of model operations to produce output based on these model assumptions (Robinson, 2004 Gilbert and Troitzsch, 2005). This virtual experimentation is enabled by implementation of a model using a computer program or software platform to specify model structure and parameters that govern the boundary conditions of the model. In simulation modeling, computers provide a virtual world with which to experiment and thus gain insight into real-world problems and questions. By attempting to mimic the factual, simulation models may be used to explore the counterfactual (Morrison, 2015). Simulation modeling involves the use of computers to conduct virtual experimentation with alternative assumptions arising from “what if” questions about a dynamic problem of interest. Validation of a model through correspondence of simulated results with observed behavior facilitates its use as an analytical tool for evaluating strategies and policies that would alter system behavior. Upon implementation, model analysis is performed through rigorous experimentation to test how model structure produces simulated patterns of behavior over time and space. The model development process involves a shift from qualitative design to quantitative analysis upon implementation of a model in a computer program or software platform. Simulation modeling approaches include system dynamics, discrete event simulation, agent-based modeling, and multi-method modeling. Numerous design choices are made in model development that involve continuous or discrete representations of time and space. As opposed to conceptual or physical models, simulation models enable numerical experimentation with alternative parametric assumptions for a given model design.

Advances in computational capacity have enabled dynamic simulation modeling to become increasingly widespread in scientific research.

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