Input model uncertainty: why do we care and what should we do about it?

An input model is a collection of distributions together with any associated parameters that are used as primitive inputs in a simulation model. Input model uncertainty arises when one is not completely certain what distributions and/or parameters to use. This tutorial attempts to provide a sense of why one should consider input uncertainty and what methods can be used to deal with it.

[1]  James R. Wilson,et al.  Accounting for input model and parameter uncertainty in simulation , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[2]  Arkadi Nemirovski,et al.  Robust solutions of Linear Programming problems contaminated with uncertain data , 2000, Math. Program..

[3]  P. Glynn,et al.  Monte Carlo computation of conditional expectation quantiles , 1998 .

[4]  Jack P. C. Kleijnen,et al.  Sensitivity analysis versus uncertainty analysis: when to use what? in predictability and nonlinear , 1994 .

[5]  Jack P. C. Kleijnen Five-stage procedure for the evaluation of simulation models through statistical techniques , 1996, Winter Simulation Conference.

[6]  Russell C. H. Cheng,et al.  Calculation of confidence intervals for simulation output , 2004, TOMC.

[7]  Russell C. H. Cheng,et al.  Selecting input models , 1994, Proceedings of Winter Simulation Conference.

[8]  Jon C. Helton Computational structure of a performance assessment involving stochastic and subjective uncertainty , 1996, Winter Simulation Conference.

[9]  R. Cheng,et al.  Two-point methods for assessing variability in simulation output , 1998 .

[10]  David Draper,et al.  Assessment and Propagation of Model Uncertainty , 2011 .

[11]  Szu Hui Ng,et al.  Simulation input analysis: joint criterion for factor identification and parameter estimation , 2002, WSC '02.

[12]  Lee W. Schruben,et al.  Resampling methods for input modeling , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[13]  Stephen E. Chick Bayesian methods: bayesian methods for simulation , 2000, WSC '00.

[14]  R. Cheng,et al.  Sensitivity of computer simulation experiments to errors in input data , 1997 .

[15]  Peter W. Glynn,et al.  Computing the distribution function of a conditional expectation via Monte Carlo: discrete conditioning spaces , 1999, WSC '99.

[16]  Arkadi Nemirovski,et al.  Robust Convex Optimization , 1998, Math. Oper. Res..

[17]  S. Henderson,et al.  A kernel approach to estimating the density of a conditional expectation , 2003 .

[18]  Jon C. Helton,et al.  Challenge Problems : Uncertainty in System Response Given Uncertain Parameters ( DRAFT : November 29 , 2001 ) , 2001 .

[19]  Stephen E. Chick,et al.  Bayesian methods for simulation , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[20]  Szu Hui Ng,et al.  Reducing input parameter uncertainty for simulations , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[21]  James R. Wilson,et al.  Accounting for Parameter Uncertainty in Simulation Input Modeling , 2003 .

[22]  Michael Freimer,et al.  Collecting data and estimating parameters for input distributions , 2002, Proceedings of the Winter Simulation Conference.

[23]  S. Chick Bayesian Analysis For Simulation Input And Output , 1997, Winter Simulation Conference Proceedings,.

[24]  J. C. Helton,et al.  Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty , 1997 .

[25]  Russell C. H. Cheng Analysis of simulation experiments by bootstrap resampling , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[26]  Szu Hui Ng,et al.  Joint criterion for factor identification and parameter estimation , 2002, Proceedings of the Winter Simulation Conference.

[27]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[28]  Lee W. Schruben,et al.  Uniform and bootstrap resampling of empirical distributions , 1993, WSC '93.

[29]  Shane G. Henderson,et al.  Mathematics and hybrid modeling: mathematics for simulation , 2000, WSC '00.

[30]  Stephen E. Chick Steps to implement Bayesian input distribution selection , 1999, WSC '99.

[31]  Stephen E. Chick,et al.  Input Distribution Selection for Simulation Experiments: Accounting for Input Uncertainty , 2001, Oper. Res..

[32]  Shane G. Henderson,et al.  A kernel approach to estimating the density of a conditional expectation , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[33]  Lawrence Leemis,et al.  Panel on current issues in simulation input modeling , 2002, Proceedings of the Winter Simulation Conference.

[34]  Jack P. C. Kleijnen,et al.  Experimental Design for Sensitivity Analysis, Optimization and Validation of Simulation Models , 1997 .

[35]  Shane G. Henderson,et al.  Simulation mathematics and random number generation: mathematics for simulation , 2001, WSC '01.

[36]  Russell C. H. Cheng Analysis of simulation output by resampling , 2001 .

[37]  S. Henderson,et al.  Mathematics for simulation , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).