simulating

Simulating is the process of creating and running a digital representation of a real-world system to study its behavior under different conditions. A simulation rests on a model, an abstract description of structure and dynamics that captures essential features while omitting extraneous detail. The model is implemented as a computer program or protocol and executed to produce data that can be analyzed to inform decisions, design choices, or understanding of underlying mechanisms.

Simulations can be categorized by how they handle time, uncertainty, and interaction. Deterministic simulations yield the same results from the same inputs, while stochastic (or probabilistic) simulations incorporate randomness. Time can advance in discrete steps (discrete-event or discrete-time simulation) or continuously (continuous simulation). Main paradigms include discrete-event simulation, which models the system as a sequence of events; continuous simulation, which uses differential equations; agent-based simulation, which represents autonomous entities and their interactions; and Monte Carlo simulation, which uses random sampling to estimate outcomes.

Common practice involves defining objectives, building and verifying the model, implementing it in software, and running experiments to explore scenarios. Key activities include verification (checking that the model is implemented correctly), validation (assessing how well the model matches real data), calibration (adjusting parameters), and sensitivity or uncertainty analysis to understand how results depend on assumptions. Reproducibility is supported by controlling random seeds and providing sufficient documentation and data.

Simulating supports analysis and decision making across engineering, science, finance, epidemiology, climate research, manufacturing, and operations research. Historically, simulations emerged from Monte Carlo methods in the mid-20th century and gained prominence with the rise of digital computers, enabling increasingly realistic and large-scale models. Limitations include model bias, data limitations, and high computational costs, which require careful interpretation and robust methodology.