Reducing and Calibrating for Input Model Bias in Computer Simulation

Lucy E. Morgan, Luke Rhodes-Leader, Russell R. Barton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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. When the simulation response is a nonlinear function of its inputs, as is usually the case when simulating complex systems, input modelling bias is amongst the errors that arise. In this paper, we introduce a method that recalibrates the input parameters of parametric input models to reduce the bias in the simulation output. The proposed method is based on sequential quadratic programming with a closed form analytical solution at each step. An algorithm with guidance on how to practically implement the method is presented. The method is shown to be successful in reducing input modelling bias and the total mean squared error caused by input modelling error.

Original languageEnglish (US)
Pages (from-to)2368-2382
Number of pages15
JournalINFORMS Journal on Computing
Issue number4
StatePublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


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