KMS Of Academy of mathematics and systems sciences, CAS
| Reducing Simulation Input-Model Risk via Input Model Averaging | |
| Nelson, Barry L.1; Wan, Alan T. K.2; Zou, Guohua3; Zhang, Xinyu4,5; Jiang, Xi1 | |
| 2021-03-01 | |
| 发表期刊 | INFORMS JOURNAL ON COMPUTING
 |
| ISSN | 1091-9856 |
| 卷号 | 33期号:2页码:672-684 |
| 摘要 | Input uncertainty is an aspect of simulation model risk that arises when the driving input distributions are derived or "fit" to real-world, historical data. Although there has been significant progress on quantifying and hedging against input uncertainty, there has been no direct attempt to reduce it via better input modeling. The meaning of "better" depends on the context and the objective: Our context is when (a) there are one or more families of parametric distributions that are plausible choices; (b) the real-world historical data are not expected to perfectly conform to any of them; and (c) our primary goal is to obtain higher-fidelity simulation output rather than to discover the "true" distribution. In this paper, we show that frequentist model averaging can be an effective way to create input models that better represent the true, unknown input distribution, thereby reducing model risk. Input model averaging builds from standard input modeling practice, is not computationally burdensome, requires no change in how the simulation is executed nor any follow-up experiments, and is available on the Comprehensive R Archive Network (CRAN). We provide theoretical and empirical support for our approach. |
| 关键词 | input modeling stochastic simulation input uncertainty |
| DOI | 10.1287/ijoc.2020.0994 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Science Foundation[CMMI-1634982] ; City University of Hong Kong[7004985] ; Hong Kong Research Grants Council[11500419] ; National Natural Science Foundation of China[71973116] ; National Natural Science Foundation of China[11971323] ; National Natural Science Foundation of China[11529101] ; National Natural Science Foundation of China[71925007] ; National Natural Science Foundation of China[71522004] ; National Natural Science Foundation of China[71631008] ; Ministry of Science and Technology of China[2016YFB0502301] ; Youth Innovation Promotion Association of the Chinese Academy of Sciences |
| WOS研究方向 | Computer Science ; Operations Research & Management Science |
| WOS类目 | Computer Science, Interdisciplinary Applications ; Operations Research & Management Science |
| WOS记录号 | WOS:000656875100016 |
| 出版者 | INFORMS |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.amss.ac.cn/handle/2S8OKBNM/58813 |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Zhang, Xinyu |
| 作者单位 | 1.Northwestern Univ, Evanston, IL 60208 USA 2.City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China 3.Capital Normal Univ, Beijing 100048, Peoples R China 4.Univ Sci & Technol China, Hefei 230052, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Nelson, Barry L.,Wan, Alan T. K.,Zou, Guohua,et al. Reducing Simulation Input-Model Risk via Input Model Averaging[J]. INFORMS JOURNAL ON COMPUTING,2021,33(2):672-684. |
| APA | Nelson, Barry L.,Wan, Alan T. K.,Zou, Guohua,Zhang, Xinyu,&Jiang, Xi.(2021).Reducing Simulation Input-Model Risk via Input Model Averaging.INFORMS JOURNAL ON COMPUTING,33(2),672-684. |
| MLA | Nelson, Barry L.,et al."Reducing Simulation Input-Model Risk via Input Model Averaging".INFORMS JOURNAL ON COMPUTING 33.2(2021):672-684. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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