arXiv subjects

David J. Eckman

Publications and source records attributed to David J. Eckman.

6 recordsLinked to original sources

arXiv2026

One-Shot Screening of Simulated Systems for Acceptability

We introduce a general-purpose framework for designing screening procedures for problems featuring a finite set of simulated systems, a.k.a. ranking-and-selection problems. The framework offers a novel perspective on screening in which decisions to retain or eliminate systems are based on confidence regions for the unknown problem instance rather than comparisons of estimated performances. Specifically, a system is retained if it has acceptable performance under some plausible configuration of response vectors contained in the confidence region. This perspective facilitates the design of procedures that guarantee to return either all acceptable systems, or each acceptable system, with high probability and accommodates many well-studied definitions of acceptability, including feasibility with respect to stochastic constraints and optimality with respect to one or more objectives. We further study a subclass of the framework that yields simple and computationally efficient screening procedures that often have lower-order time complexity than existing methods and naturally supports parallelization without loss of screening power. We demonstrate the effectiveness and efficiency of the procedures through numerical experiments.

stat.AP↗

arXiv2026

Subtrace-Conditional Validation of Simulation Models and Digital Twins

Validating simulation models against historical output data is essential for their successful deployment in digital-twin environments. We propose a statistical validation framework in which a simulation model is repeatedly initialized from observed system states, and conditional output distributions are obtained by fixing the random primitives from a subset of stochastic input models to their observed realizations while simulating the remaining primitives. These conditional output distributions are then used in goodness-of-fit tests to validate the simulation model with respect to combinations of input models. We also develop diagnostic tools to identify the input models that most contribute to any observed misalignment between a simulation model's outputs and reality. Numerical experiments on an M/M/1 queueing system and a digital-twin-enabled simulation of a tandem queueing system demonstrate that the proposed framework can detect misspecifications in input models that may be missed by existing approaches that validate only the marginal output distribution.

stat.CO↗

arXiv2026

Accelerating Reinforcement Learning Training Using Simulation Surrogate Models

High-fidelity simulation models are widely used to analyze complex stochastic systems, but their high computational cost motivates the development of cheaper surrogate models that approximate the simulation model's input-output relationship. In parallel, reinforcement learning (RL) has emerged as a powerful framework for making online decisions in stochastic environments, with increasing attention being given to the use of simulation models as training environments for RL models. We investigate a class of surrogate models suitable for accelerating RL training in settings where the reward structure, model parameters, or system dynamics change over time and explore their interactions with simulation models and RL models. Through numerical experiments on a stochastic service system modeled via discrete-event simulation, we demonstrate that leveraging surrogate models can substantially accelerate RL training and re-training.

stat.ML↗

arXiv2026

Quantifying and Attributing Submodel Uncertainty in Stochastic Simulation Models and Digital Twins

Stochastic simulation is widely used to study complex systems composed of various interconnected subprocesses, such as input processes, routing and control logic, optimization routines, and data-driven decision modules. In practice, these subprocesses may be inherently unknown or too computationally intensive to directly embed in the simulation model. Replacing these elements with estimated or learned approximations introduces a form of epistemic uncertainty that we refer to as submodel uncertainty. This paper investigates how submodel uncertainty affects the estimation of system performance metrics. We develop a framework for quantifying submodel uncertainty in stochastic simulation models and extend the framework to digital-twin settings, where simulation experiments are repeatedly conducted with the model initialized from observed system states. Building on approaches from input uncertainty analysis, we leverage bootstrapping and Bayesian model averaging to construct quantile-based confidence or credible intervals for key performance indicators. We propose a tree-based method that decomposes total output variability and attributes uncertainty to individual submodels in the form of importance scores. The proposed framework is model-agnostic and accommodates both parametric and nonparametric submodels under frequentist and Bayesian modeling paradigms. A synthetic numerical experiment and a more realistic digital-twin simulation of a contact center illustrate the importance of understanding how and how much individual submodels contribute to overall uncertainty.

stat.CO↗

arXiv2024

An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance

Using statistical learning methods to analyze stochastic simulation outputs can significantly enhance decision-making by uncovering relationships between different simulated systems and between a system's inputs and outputs. We focus on clustering multivariate empirical distributions of simulation outputs to identify patterns and trade-offs among performance measures. We present a novel agglomerative clustering algorithm that utilizes the regularized Wasserstein distance to cluster these multivariate empirical distributions. This framework has several important use cases, including anomaly detection, pre-optimization, and online monitoring. In numerical experiments involving a call-center model, we demonstrate how this methodology can identify staffing plans that yield similar performance outcomes and inform policies for intervening when queue lengths signal potentially worsening system performance.

stat.ME↗

arXiv2017

Comparing the Finite-Time Performance of Simulation-Optimization Algorithms

We empirically evaluate the finite-time performance of several simulation-optimization algorithms on a testbed of problems with the goal of motivating further development of algorithms with strong finite-time performance. We investigate if the observed performance of the algorithms can be explained by properties of the problems, e.g., the number of decision variables, the topology of the objective function, or the magnitude of the simulation error.

math.OC↗