Reducing Simulation Input-Model Risk via Input Model Averaging
文献类型：期刊论文
| 作者 | Nelson, Barry L.5; Wan, Alan T. K.4; Zou, Guohua3; Zhang, Xinyu1,2; Jiang, Xi5 |
| 刊名 | INFORMS JOURNAL ON COMPUTING
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 33期号:2页码:672-684 |
| 关键词 | input modeling stochastic simulation input uncertainty |
| ISSN号 | 1091-9856 |
| DOI | 10.1287/ijoc.2020.0994 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000656875100016 |
| 出版者 | INFORMS |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/58812]  |
| 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.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Sci & Technol China, Hefei 230052, Peoples R China 3.Capital Normal Univ, Beijing 100048, Peoples R China 4.City Univ Hong Kong, Kowloon, Hong Kong, Peoples R China 5.Northwestern Univ, Evanston, IL 60208 USA |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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