Detecting bias due to input modelling in computer simulation

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)869-881
Number of pages13
JournalEuropean Journal of Operational Research
Volume279
Issue number3
DOIs
StatePublished - Dec 16 2019

Funding

We gratefully acknowledge the support of the EPSRC funded EP/L015692/1 STOR-i Centre for Doctoral Training, NSF Grant CMMI-1634982 and GOALI sponsor Simio LLC. We also thank Bruce Ankenman for insightful discussion and suggestions. An earlier version of this paper was published in the Proceedings of the 2017 Winter Simulation Conference as Morgan et al. (2017). We gratefully acknowledge the support of the EPSRC funded EP/L015692/1 STOR-i Centre for Doctoral Training, NSF Grant CMMI-1634982 and GOALI sponsor Simio LLC. We also thank Bruce Ankenman for insightful discussion and suggestions. An earlier version of this paper was published in the Proceedings of the 2017 Winter Simulation Conference as Morgan et al. (2017).

Keywords

  • Bias
  • Input modelling
  • Simulation
  • Uncertainty

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Detecting bias due to input modelling in computer simulation'. Together they form a unique fingerprint.

Cite this