Name
Abstract | ||
|---|---|---|
We introduce a new paradigm in simulation experiment design and analysis, called “green simulation,” for the setting in which experiments are performed repeatedly with the same simulation model. Green simulation means reusing outputs from previous experiments to answer the question currently being asked of the simulation model. As one method for green simulation, we propose estimators that reuse outputs from previous experiments by weighting them with likelihood ratios, when parameters of distributions in the simulation model differ across experiments. We analyze convergence of these estimators as more experiments are repeated, while a stochastic process changes the parameters used in each experiment. As another method for green simulation, we propose an estimator based on stochastic kriging. We find that green simulation can reduce mean squared error by more than an order of magnitude in examples involving catastrophe bond pricing and credit risk evaluation.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3129130 | ACM Trans. Model. Comput. Simul. |
Keywords | Field | DocType |
Likelihood ratio method, multiple importance sampling, score function method, simulation metamodeling | Kriging,Convergence (routing),Mathematical optimization,Weighting,Reuse,Computer science,Stochastic process,Mean squared error,Statistics,Estimator,Design of experiments | Journal |
Volume | Issue | ISSN |
27 | 4 | 1049-3301 |
Citations | PageRank | References |
2 | 0.66 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingbin Feng | 1 | 2 | 0.66 |
| Jeremy Staum | 2 | 76 | 13.25 |