Simulation Methods
Simulation methods are computational techniques used to model and analyze complex real-world systems by creating digital representations that mimic their behavior over time. They involve running experiments on these models to predict outcomes, test hypotheses, or optimize processes without the cost or risk of physical trials. Common approaches include Monte Carlo simulation, discrete-event simulation, and agent-based modeling, applied across fields like engineering, finance, and healthcare.
Developers should learn simulation methods when building systems that require predictive analysis, risk assessment, or scenario testing in uncertain environments, such as financial forecasting, supply chain optimization, or epidemiological modeling. They are essential for decision-making in data-driven applications where real-world experimentation is impractical, enabling cost-effective validation and iterative improvement of designs.