  The vast majority of stochastic simulation models are imperfect in that they
fail to exactly emulate real system dynamics. The inexactness of the simulation
model, or model discrepancy, can impact the predictive accuracy and usefulness
of the simulation for decision-making. This paper proposes a systematic
framework to integrate data from both the simulation responses and the real
system responses to learn this discrepancy and quantify the resulting
uncertainty. Our framework addresses the theoretical and computational
requirements for stochastic estimation in a Bayesian setting. It involves an
optimization-based procedure to compute confidence bounds on the target outputs
that elicit desirable large-sample statistical properties. We illustrate the
practical value of our framework with a call center example and a manufacturing
line case study.
