Reducing Simulation Input-Model Risk via Input Model Averaging

Nelson, Barry and Wan, Alan T. K. and Zou, Guohua and Zhang, Xinyu and Jiang, Xi (2020) Reducing Simulation Input-Model Risk via Input Model Averaging. INFORMS Journal on Computing. ISSN 1091-9856

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
INFORMS Journal on Computing
Additional Information:
© 2020 INFORMS
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
152637
Deposited By:
Deposited On:
15 Mar 2021 15:15
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Jun 2021 04:23