Simulation

Simulation is the imitation of the operation of a real-world process or system over time. It involves creating a model that represents the essential components, structures, and dynamics of the target system, and running that model to study its behavior under different inputs, conditions, and time horizons. Models may be physical, mathematical, or computational, with the latter two being the most common for complex systems.

Common types of simulation include discrete-event simulation, which models systems as a sequence of events occurring at discrete times; continuous simulation, which uses differential equations to represent changes continuously over time; and agent-based simulation, which models autonomous decision-makers and their interactions. Monte Carlo simulation uses random sampling to estimate the behavior of a system with uncertain inputs. Simulations can be deterministic or stochastic, static or dynamic, and may focus on process flows, performance, reliability, or behavior.

The typical workflow involves defining the purpose, constructing an appropriate model, obtaining data for parameters, verifying that the model is implemented correctly, validating that it accurately represents reality, and designing experiments to analyze outcomes. Results are analyzed to inform decisions, optimize performance, or predict future behavior.

Applications span many domains, including engineering, manufacturing, transportation and logistics, finance, healthcare, energy, environmental science, and education. The concept has evolved with advances in computing, giving rise to digital twins, high-fidelity simulations, and large-scale agent-based models.

Limitations include model risk, data quality and availability, assumptions that may not hold in practice, and computational costs. Careful validation, uncertainty quantification, and transparent reporting are essential to ensure useful and responsible simulation work.