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  • Detecting Bias due to Input Modelling in Computer Simulation

    Rights statement: This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 279, 3, 2019 DOI: 10.1016/j.ejor.2019.06.003

    Accepted author manuscript, 840 KB, PDF document

    Embargo ends: 8/06/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Detecting bias due to input modelling in computer simulation

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>16/12/2019
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Volume279
Number of pages13
Pages (from-to)869-881
Publication statusE-pub ahead of print
Early online date8/06/19
Original languageEnglish

Abstract

This is the first paper to approach the problem of bias in the output of a stochastic simulation due to using input distributions whose parameters were estimated from real-world data. We consider, in particular, the bias in simulation-based estimators of the expected value (long-run average) of the real-world system performance; this bias will be present even if one employs unbiased estimators of the input distribution parameters due to the (typically) nonlinear relationship between these parameters and the output response. To date this bias has been assumed to be negligible because it decreases rapidly as the quantity of real-world input data increases. While true asymptotically, this property does not imply that the bias is actually small when, as is always the case, data are finite. We present a delta-method approach to bias estimation that evaluates the nonlinearity of the expected-value performance surface as a function of the input-model parameters. Since this response surface is unknown, we propose an innovative experimental design to fit a response-surface model that facilitates a test for detecting a bias of a relevant size with specified power. We evaluate the method using controlled experiments, and demonstrate it through a realistic case study concerning a healthcare call centre.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 279, 3, 2019 DOI: 10.1016/j.ejor.2019.06.003