An Uncertainty Quantification Method for Inexact Simulation Models

TL;DR

This paper introduces a Bayesian framework to quantify uncertainty in inexact stochastic simulation models by integrating data from both simulation and real systems, improving predictive accuracy.

Contribution

It develops a systematic, optimization-based method to estimate model discrepancy and compute confidence bounds, addressing theoretical and computational challenges.

Findings

Framework effectively quantifies uncertainty in simulation models.

Application to call center and manufacturing case studies demonstrates practical utility.

Provides confidence bounds with desirable statistical properties.

Abstract

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.