Simulation

Simulation executes a model under specified rules and inputs to explore behavior or hypothetical outcomes. Monte Carlo simulation samples modeled uncertainty. Results depend on the representation, implementation, and assumptions and do not automatically cover all real possibilities.

A simulation executes a representation of a system under specified rules, inputs, and conditions. It can explore time evolution, random outcomes, interactions, or hypothetical alternatives that are difficult to analyze directly. Monte Carlo methods sample modeled uncertainty; they do not require a particular number of runs and can address problems that are not time-based.

A simulation produces consequences of its assumptions, not independent observations of reality. Verification asks whether it implements the intended model, while validation asks whether it is adequate for a particular real-world use. More runs reduce some numerical sampling uncertainty without correcting wrong mechanisms, missing dependencies, or unrealistic inputs. Inspect the model and compare relevant outputs with external evidence.

When to use it

When a system's interactions or uncertainty are difficult to analyze directly and a checked model can inform a specific comparison.

How it can help

Use a simulation to compare scenarios while checking implementation, input plausibility, dependencies, and adequacy for the intended real-world decision.