.png){kind=icon}
.png){kind=icon}
Return
Detecting bias due to input modelling in computer simulation
.png){kind=icon}
OA
AI
DOI：10.1016/j.ejor.2019.06.003.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Simulation
Bias
Uncertainty
Input modelling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W