Statistical Challenges in Agent-Based Modeling
Statistical Challenges in Agent-Based Modeling
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DOI：
10.1080/00031305.2021.1900914
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发表时间：
2021-04-21
影响因子：
1.8
通讯作者：
Hooten, Mevin B.
中科院分区：
文献类型：
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作者：
Banks, David L.;Hooten, Mevin B.
基于主体的模型（ABM）在许多研究领域很受欢迎，但很少有统计学家对其理论发展做出贡献。它们和我们研究的任何其他模型一样都是模型，但总的来说，我们仍然在学习如何将ABM与数据相适应，以及如何对ABM输出的不确定性进行量化陈述。ABM验证也是一个欠发达的领域，新的统计发展已经成熟。在下文中，我们列出了研究空间，并鼓励统计学家解决ABM范围内的许多研究问题。
Agent-based models (ABMs) are popular in many research communities, but few statisticians have contributed to their theoretical development. They are models like any other models we study, but in general, we are still learning how to fit ABMs to data and how to make quantified statements of uncertainty about the outputs of an ABM. ABM validation is also an underdeveloped area that is ripe for new statistical developments. In what follows, we lay out the research space and encourage statisticians to address the many research issues in the ABM ambit.