When input distributions to a simulation model are estimated from real-world
data, they naturally have estimation error causing input uncertainty in the simulation output.
If an optimization via simulation (OvS) method is applied that treats the input distributions as “correct,” then there is a risk of making a suboptimal decision for the real world, which we call input model risk. This paper addresses a discrete OvS (DOvS) problem of selecting the realworld optimal from among a finite number of systems when all of them share the same input distributions estimated from common input data. Because input uncertainty cannot be reduced without collecting additional real-world data—which may be expensive or impossible—a DOvS procedure should reflect the limited resolution provided by the simulation model in distinguishing the real-world optimal solution from the others. In light of this, our input–output uncertainty comparisons (IOU-C) procedure focuses on comparisons rather than selection: it provides simultaneous confidence intervals for the difference between each system’s real-world mean and the best mean of the rest with any desired probability, while accounting for both stochastic and input uncertainty. To make the resolution as high as possible (intervals as short as possible) we exploit the common input data effect to reduce uncertainty in the estimated differences. Under mild conditions we prove that the IOU-C procedure provides the desired statistical guarantee asymptotically as the real-world sample size and simulation effort increase, but it is designed to be effective in finite samples.