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Green simulation designs for repeated experiments

Published: 06 December 2015 Publication History

Abstract

In this article we present the concept of green simulation, which views simulation outputs as scarce resources that should be recycled and reused. Output recycling, if implemented properly, can turn the computational costs in an experiment into computation investments for future ones. Green simulation designs are particularly useful for experiments that are repeated periodically. In this article we focus on repeated experiments whose inputs are observations from some underlying stochastic processes. Importance sampling and multiple importance sampling are two particular output recycling implementations considered in this article. A periodic credit risk evaluation problem in the KMV model is considered. Results from our numerical experiments show significant accuracy improvements, measured by mean squared errors, as more and more outputs are recycled and reused.

References

[1]
Alrefaei, M. H., and S. Andradóttir. 2001. "A Modification of the Stochastic Ruler Method for Discrete Stochastic Optimization". European Journal of Operational Research 133 (1): 160--182.
[2]
Ankenman, B., B. L. Nelson, and J. Staum. 2010. "Stochastic Kriging for Simulation Metamodeling". Operations Research 58 (2): 371--382.
[3]
Cherubini, U., E. Luciano, and W. Vecchiato. 2004. Copula Methods in Finance. John Wiley & Sons.
[4]
Craig, P. S., M. Goldstein, A. H. Seheult, and J. A. Smith. 1997. "Pressure Matching for Hydrocarbon Reservoirs: A Case Study in the Use of Bayes Linear Strategies for Large Computer Experiments". In Case studies in Bayesian statistics, 37--93. Springer.
[5]
Feng, M. B. 2016. "Green Simulation". doctoral dissertation in preparation, Northwestern University.
[6]
Frey, R., A. J. McNeil, and M. Nyfeler. 2001. "Copulas and Credit Models". Risk 10:111--114.
[7]
Fu, M. C. 1994. "Optimization via Simulation: A Review". Annals of Operations Research 53 (1): 199--247.
[8]
Gerstner, T., M. Griebel, M. Holtz, R. Goschnick, and M. Haep. 2008. "A General Asset-Liability Management Model for the Efficient Simulation of Portfolios of Life Insurance Policies". Insurance: Mathematics and Economics 42 (2): 704--716.
[9]
Hesterberg, T. C. 1988. Advances in Importance Sampling. Ph. D. thesis, Stanford University.
[10]
Hong, L. J., and B. L. Nelson. 2009. "A Brief Introduction to Optimization via Simulation". In Proceedings of the 2009 Winter Simulation Conference, edited by M. D. Rossetti, R. R. Hill, A. D. B. Johansson, and R. G. Ingalls, 75--85. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[11]
Liu, M., B. L. Nelson, and J. Staum. 2010. "An Efficient Simulation Procedure for Point Estimation of Expected Shortfall". In Proceedings of the 2010 Winter Simulation Conference, edited by B. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, and E. Yucesan, 2821--2831. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[12]
Liu, M., and J. Staum. 2010. "Stochastic Kriging for Efficient Nested Simulation of Expected Shortfall". Journal of Risk 12 (3): 3.
[13]
Maggiar, A., A. Wächter, I. S. Dolinskaya, and J. Staum. 2015. "A Derivative-Free Algorithm for the Optimization of Functions Smoothed via Gaussian Convolution Using Multiple Importance Sampling". Working paper, Northwestern University.
[14]
Merton, R. C. 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates". The Journal of Finance 29 (2): 449--470.
[15]
Owen, A., and Y. Zhou. 2000. "Safe and Effective Importance Sampling". Journal of the American Statistical Association 95 (449): 135--143.
[16]
Owen, A. B. 2013. Monte Carlo Theory, Methods and Examples. Accessed Mar 25th, 2015. http://statweb.stanford.edu/~owen/mc/.
[17]
Sanchez, S. M. 2005. "Work Smarter, Not Harder: Guidelines for Designing Simulation Experiments". In Proceedings of the 2005 Winter Simulation Conference, edited by M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, 69--82. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers, Inc.
[18]
Tokdar, S. T., and R. E. Kass. 2010. "Importance Sampling: A Review". Wiley Interdisciplinary Reviews: Computational Statistics 2 (1): 54--60.
[19]
Veach, E., and L. J. Guibas. 1995. "Optimally Combining Sampling Techniques for Monte Carlo Rendering". In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, 419--428. ACM.
[20]
Yan, D., and H. Mukai. 1992. "Stochastic Discrete Optimization". SIAM Journal on Control and Optimization 30 (3): 594--612.

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  1. Green simulation designs for repeated experiments

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    cover image ACM Conferences
    WSC '15: Proceedings of the 2015 Winter Simulation Conference
    December 2015
    4051 pages
    ISBN:9781467397414

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    IEEE Press

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    Published: 06 December 2015

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    WSC '15
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    WSC '15: Winter Simulation Conference
    December 6 - 9, 2015
    California, Huntington Beach

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    WSC '15 Paper Acceptance Rate 202 of 296 submissions, 68%;
    Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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    • (2023)Generalized Importance Sampling for Nested SimulationProceedings of the Winter Simulation Conference10.5555/3643142.3643176(409-420)Online publication date: 10-Dec-2023
    • (2021)Green Simulation with Database Monte CarloACM Transactions on Modeling and Computer Simulation10.1145/342933631:1(1-26)Online publication date: 21-Jan-2021
    • (2020)Simulation optimization by reusing past replicationsProceedings of the Winter Simulation Conference10.5555/3466184.3466521(2923-2934)Online publication date: 14-Dec-2020
    • (2020)Identifying the best system in the presense of stochastic constraints with varying thresholdsProceedings of the Winter Simulation Conference10.5555/3466184.3466507(2812-2820)Online publication date: 14-Dec-2020
    • (2020)Reusing simulation outputs of repeated experiments via likelihood ratio regressionProceedings of the Winter Simulation Conference10.5555/3466184.3466220(325-336)Online publication date: 14-Dec-2020
    • (2018)Uniform convergence of sample average approximation with adaptive multiple importance samplingProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320717(1646-1657)Online publication date: 9-Dec-2018
    • (2018)Online quantification of input uncertainty for parametric modelsProceedings of the 2018 Winter Simulation Conference10.5555/3320516.3320711(1587-1598)Online publication date: 9-Dec-2018
    • (2016)Green simulation with database Monte CarloProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042241(1108-1118)Online publication date: 11-Dec-2016
    • (2016)A simulation analytics approach to dynamic risk monitoringProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042162(437-447)Online publication date: 11-Dec-2016

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