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Risk Quantification in Stochastic Simulation under Input Uncertainty

Risk Quantification in Stochastic Simulation under Input Uncertainty When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean response at the true but unknown input parameter, including point estimation and confidence interval construction. Risk quantification of mean response under input uncertainty often plays an important role in system evaluation and control, because it provides inferences on extreme scenarios of mean response in all possible input models. To the best of our knowledge, it has rarely been systematically studied in the literature. In this article, first we introduce risk measures of mean response under input uncertainty and propose a nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. We further study the associated budget allocation problem for efficient nested risk simulation and finally use a sharing economy example to illustrate the importance of accessing and controlling risk due to input uncertainty. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Modeling and Computer Simulation (TOMACS) Association for Computing Machinery

Risk Quantification in Stochastic Simulation under Input Uncertainty

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
1049-3301
eISSN
1558-1195
DOI
10.1145/3329117
Publisher site
See Article on Publisher Site

Abstract

When simulating a complex stochastic system, the behavior of output response depends on input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty into the system. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean response at the true but unknown input parameter, including point estimation and confidence interval construction. Risk quantification of mean response under input uncertainty often plays an important role in system evaluation and control, because it provides inferences on extreme scenarios of mean response in all possible input models. To the best of our knowledge, it has rarely been systematically studied in the literature. In this article, first we introduce risk measures of mean response under input uncertainty and propose a nested Monte Carlo simulation approach to estimate them. Then we develop asymptotical properties such as consistency and asymptotic normality for the proposed nested risk estimators. We further study the associated budget allocation problem for efficient nested risk simulation and finally use a sharing economy example to illustrate the importance of accessing and controlling risk due to input uncertainty.

Journal

ACM Transactions on Modeling and Computer Simulation (TOMACS) – Association for Computing Machinery

Published: Feb 5, 2020

Keywords: Input uncertainty

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