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  • Detecting Bias due to Input Modelling in Computer Simulation

    Rights statement: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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Detecting bias due to input modelling in computer simulation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Detecting bias due to input modelling in computer simulation. / Morgan, Lucy; Nelson, Barry Lee; Titman, Andrew Charles et al.
2017 Winter Simulation Conference (WSC). Piscataway, NJ, USA: IEEE Press, 2017. p. 1974-1985.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Morgan, L, Nelson, BL, Titman, AC & Worthington, DJ 2017, Detecting bias due to input modelling in computer simulation. in 2017 Winter Simulation Conference (WSC). IEEE Press, Piscataway, NJ, USA, pp. 1974-1985, Winter Simulation Conference 2017, Las Vegas, United States, 3/12/17. https://doi.org/10.1109/WSC.2017.8247932

APA

Morgan, L., Nelson, B. L., Titman, A. C., & Worthington, D. J. (2017). Detecting bias due to input modelling in computer simulation. In 2017 Winter Simulation Conference (WSC) (pp. 1974-1985). IEEE Press. https://doi.org/10.1109/WSC.2017.8247932

Vancouver

Morgan L, Nelson BL, Titman AC, Worthington DJ. Detecting bias due to input modelling in computer simulation. In 2017 Winter Simulation Conference (WSC). Piscataway, NJ, USA: IEEE Press. 2017. p. 1974-1985 doi: 10.1109/WSC.2017.8247932

Author

Morgan, Lucy ; Nelson, Barry Lee ; Titman, Andrew Charles et al. / Detecting bias due to input modelling in computer simulation. 2017 Winter Simulation Conference (WSC). Piscataway, NJ, USA : IEEE Press, 2017. pp. 1974-1985

Bibtex

@inproceedings{4333582ed9b94e06bfa399e9cfb06d8a,
title = "Detecting bias due to input modelling in computer simulation",
abstract = "Bias due to input modelling is almost always assumed negligible and ignored. It is known that increasing the amount of real-world data available for modelling input processes causes this form of bias to decrease faster than the variance due to input uncertainty. However, this does not mean bias is irrelevant when considering the error in a simulation performance measure caused by input modelling. In this paper we present a response surface approach to bias estimation in simulation models along with a diagnostic test for identifying, with controlled power, bias due to input modelling of a size that would be concerning to a practitioner. ",
keywords = "Response surface methodology, Computational modeling, Charge coupled devices, Uncertainty, Estimation, Context modeling, Covariance matrices",
author = "Lucy Morgan and Nelson, {Barry Lee} and Titman, {Andrew Charles} and Worthington, {David John}",
note = "{\textcopyright}2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; Winter Simulation Conference 2017 ; Conference date: 03-12-2017 Through 06-12-2017",
year = "2017",
month = dec,
day = "3",
doi = "10.1109/WSC.2017.8247932",
language = "English",
isbn = "9781538634301",
pages = "1974--1985",
booktitle = "2017 Winter Simulation Conference (WSC)",
publisher = "IEEE Press",
url = "http://meetings2.informs.org/wordpress/wsc2017/",

}

RIS

TY - GEN

T1 - Detecting bias due to input modelling in computer simulation

AU - Morgan, Lucy

AU - Nelson, Barry Lee

AU - Titman, Andrew Charles

AU - Worthington, David John

N1 - Conference code: 50th

PY - 2017/12/3

Y1 - 2017/12/3

N2 - Bias due to input modelling is almost always assumed negligible and ignored. It is known that increasing the amount of real-world data available for modelling input processes causes this form of bias to decrease faster than the variance due to input uncertainty. However, this does not mean bias is irrelevant when considering the error in a simulation performance measure caused by input modelling. In this paper we present a response surface approach to bias estimation in simulation models along with a diagnostic test for identifying, with controlled power, bias due to input modelling of a size that would be concerning to a practitioner.

AB - Bias due to input modelling is almost always assumed negligible and ignored. It is known that increasing the amount of real-world data available for modelling input processes causes this form of bias to decrease faster than the variance due to input uncertainty. However, this does not mean bias is irrelevant when considering the error in a simulation performance measure caused by input modelling. In this paper we present a response surface approach to bias estimation in simulation models along with a diagnostic test for identifying, with controlled power, bias due to input modelling of a size that would be concerning to a practitioner.

KW - Response surface methodology

KW - Computational modeling

KW - Charge coupled devices

KW - Uncertainty

KW - Estimation

KW - Context modeling

KW - Covariance matrices

U2 - 10.1109/WSC.2017.8247932

DO - 10.1109/WSC.2017.8247932

M3 - Conference contribution/Paper

SN - 9781538634301

SP - 1974

EP - 1985

BT - 2017 Winter Simulation Conference (WSC)

PB - IEEE Press

CY - Piscataway, NJ, USA

T2 - Winter Simulation Conference 2017

Y2 - 3 December 2017 through 6 December 2017

ER -