Reducing Simulation Input-Model Risk via Input Model Averaging

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)672-684
Journal / PublicationINFORMS Journal on Computing
Volume33
Issue number2
Online published6 Oct 2020
Publication statusPublished - 2021

Abstract

Input uncertainty is an aspect of simulation model risk that arises when the driving input distributions are derived or "fit" to real-world, historical data. Although there has been significant progress on quantifying and hedging against input uncertainty, there has been no direct attempt to reduce it via better input modeling. The meaning of "better" depends on the context and the objective: Our context is when (a) there are one or more families of parametric distributions that are plausible choices; (b) the real-world historical data are not expected to perfectly conform to any of them; and (c) our primary goal is to obtain higher-fidelity simulation output rather than to discover the "true" distribution. In this paper, we show that frequentist model averaging can be an effective way to create input models that better represent the true, unknown input distribution, thereby reducing model risk. Input model averaging builds from standard input modeling practice, is not computationally burdensome, requires no change in how the simulation is executed nor any follow-up experiments, and is available on the Comprehensive R Archive Network (CRAN). We provide theoretical and empirical support for our approach.

Research Area(s)

  • input modeling, stochastic simulation, input uncertainty, MAXIMUM-LIKELIHOOD-ESTIMATION

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. Related Research Unit(s) information for this record is supplemented by the author(s) concerned.

Citation Format(s)

Reducing Simulation Input-Model Risk via Input Model Averaging. / Nelson, Barry L.; Wan, Alan T. K.; Zou, Guohua; Zhang, Xinyu; Jiang, Xi.

In: INFORMS Journal on Computing, Vol. 33, No. 2, 2021, p. 672-684.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review