A Monte Carlo simulation can hide a lot of assumptions about the the actual system being modelled and the distribution of random variables (both on a univariate

A Monte Carlo simulation can hide a lot of assumptions about the the actual system being modelled and the distribution of random variables (both on a univariate basis and as a dependency). Its flexibility and power of estimating (otherwise maybe untractable) metrics comes at the expense of transparency, potential simulation noise and difficulty in estimating accurate "what if" scenarios. The risk is that one might get out simply what one assumes. Here a list of questions you can ask to minimize the associated risks and steer your development: * Do I have a good, self contained, description of the system that I want to simulate? Is it even possible to define it in practical terms? * Do I have historical data that can pin down its stochastic behavior? Can I estimate a statistical model reliably? * Does the uncertainty around model estimation justify retaining the full model or could I possibly simplify it and obtain semi-analytic results? * Can I validate my estimates out-of-sample