Abstract

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.

中文翻译：

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基于代理的建模中的统计挑战

摘要

基于代理的模型 (ABM) 在许多研究社区中很受欢迎，但很少有统计学家对其理论发展做出贡献。它们是与我们研究的任何其他模型一样的模型，但总的来说，我们仍在学习如何将 ABM 与数据拟合，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。在接下来的内容中，我们布置了研究空间并鼓励统计学家解决 ABM 领域的许多研究问题。