When we use simulation to evaluate the performance of a stochastic system, the simulation often contains input distributions estimated from real-world data; therefore, there is both simulation and input uncertainty in the performance estimates. Ignoring either source of uncertainty underestimates the overall statistical error. Simulation uncertainty can be reduced by additional computation (e.g., more replications). Input uncertainty can be reduced by collecting more real-world data, when feasible. This paper proposes an approach to quantify overall statistical uncertainty when the simulation is driven by independent parametric input distributions; specifically, we produce a confidence interval that accounts for both simulation and input uncertainty by using a metamodel-assisted bootstrapping approach. The input uncertainty is measured via bootstrapping, an equation-based stochastic kriging metamodel propagates the input uncertainty to the output mean, and both simulation and metamodel uncertainty are derived using properties of the metamodel. A variance decomposition is proposed to estimate the relative contribution of input to overall uncertainty; this information indicates whether the overall uncertainty can be significantly reduced through additional simulation alone. Asymptotic analysis provides theoretical support for our approach, while an empirical study demonstrates that it has good finite-sample performance.
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