Detecting bias due to input modelling in computer simulation

Lucy E. Morgan, Barry L. Nelson, Andrew C. Titman, David J. Worthington

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2017 Winter Simulation Conference, WSC 2017
EditorsVictor Chan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781538634288
StatePublished - Jun 28 2017
Event2017 Winter Simulation Conference, WSC 2017 - Las Vegas, United States
Duration: Dec 3 2017Dec 6 2017

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Other2017 Winter Simulation Conference, WSC 2017
CountryUnited States
CityLas Vegas

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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