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Reducing and Calibrating for Input Model Bias in Computer Simulation

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Reducing and Calibrating for Input Model Bias in Computer Simulation. / Morgan, Lucy; Rhodes-Leader, Luke; Barton, Russell.
In: INFORMS Journal on Computing, Vol. 34, No. 4, 30.08.2022, p. 2368-2382.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Morgan, L, Rhodes-Leader, L & Barton, R 2022, 'Reducing and Calibrating for Input Model Bias in Computer Simulation', INFORMS Journal on Computing, vol. 34, no. 4, pp. 2368-2382. https://doi.org/10.1287/ijoc.2022.1183

APA

Morgan, L., Rhodes-Leader, L., & Barton, R. (2022). Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing, 34(4), 2368-2382. https://doi.org/10.1287/ijoc.2022.1183

Vancouver

Morgan L, Rhodes-Leader L, Barton R. Reducing and Calibrating for Input Model Bias in Computer Simulation. INFORMS Journal on Computing. 2022 Aug 30;34(4):2368-2382. Epub 2022 Mar 31. doi: 10.1287/ijoc.2022.1183

Author

Morgan, Lucy ; Rhodes-Leader, Luke ; Barton, Russell. / Reducing and Calibrating for Input Model Bias in Computer Simulation. In: INFORMS Journal on Computing. 2022 ; Vol. 34, No. 4. pp. 2368-2382.

Bibtex

@article{a6d3791c502e4384b18d3781f8714f3d,
title = "Reducing and Calibrating for Input Model Bias in Computer Simulation",
abstract = "Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/ processes used to drive it. To be specific, when input models are estimated from a finite amount of real-world data they contain error and this error propagates through the simulation to the outputs under study. When the simulation response is a non-linear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is one of the errors to arise. In this paper we introduce a method that re-calibrates the input parameters of parametric input models to reduce the bias in the simulation output. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling. ",
keywords = "Simulation, Input Modelling Error, Bias Reduction",
author = "Lucy Morgan and Luke Rhodes-Leader and Russell Barton",
year = "2022",
month = aug,
day = "30",
doi = "10.1287/ijoc.2022.1183",
language = "English",
volume = "34",
pages = "2368--2382",
journal = "INFORMS Journal on Computing",
issn = "1091-9856",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "4",

}

RIS

TY - JOUR

T1 - Reducing and Calibrating for Input Model Bias in Computer Simulation

AU - Morgan, Lucy

AU - Rhodes-Leader, Luke

AU - Barton, Russell

PY - 2022/8/30

Y1 - 2022/8/30

N2 - Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/ processes used to drive it. To be specific, when input models are estimated from a finite amount of real-world data they contain error and this error propagates through the simulation to the outputs under study. When the simulation response is a non-linear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is one of the errors to arise. In this paper we introduce a method that re-calibrates the input parameters of parametric input models to reduce the bias in the simulation output. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling.

AB - Input model bias is the bias found in the output performance measures of a simulation model caused by estimating the input distributions/ processes used to drive it. To be specific, when input models are estimated from a finite amount of real-world data they contain error and this error propagates through the simulation to the outputs under study. When the simulation response is a non-linear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is one of the errors to arise. In this paper we introduce a method that re-calibrates the input parameters of parametric input models to reduce the bias in the simulation output. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling.

KW - Simulation

KW - Input Modelling Error

KW - Bias Reduction

U2 - 10.1287/ijoc.2022.1183

DO - 10.1287/ijoc.2022.1183

M3 - Journal article

VL - 34

SP - 2368

EP - 2382

JO - INFORMS Journal on Computing

JF - INFORMS Journal on Computing

SN - 1091-9856

IS - 4

ER -