Using Generative Adversarial Networks to Assist Synthetic Population Creation for Simulations
Using Generative Adversarial Networks to Assist Synthetic Population Creation for Simulations
复制标题
DOI：
10.23919/annsim55834.2022.9859422
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
Srihan Kotnana;Westfield;T. Anderson;Andreas Züfle
中科院分区：
文献类型：
--
作者：
Srihan Kotnana;Westfield;T. Anderson;Andreas Züfle
人工种群在基于代理的仿真和微观仿真中被大量使用，以创建真实世界种群的逼真表示。许多现有技术依赖于复制或选择通过调查获得的分类记录样本，以生成整个合成人口。这方面的挑战是分类记录样本中存在的潜在偏见。本文假设，这种分类记录可以通过训练生成对抗网络（GAN）来改进或替换。我们提出了一个案例研究的110万人口使用迭代比例拟合（IPF）。我们说明了IPF使用基于GAN的分类记录而不是基于原始人口普查的分类记录更适合。我们的研究结果显示了GAN在合成种群生成中的应用前景。
Synthetic populations are heavily used in agent-based simulations and microsimulations to create realistic representations of real-world populations. Many existing techniques rely on duplicating or selecting a sample of disaggregated records captured via surveys to generate the entire synthetic population. The challenge here is the potential bias present in the sample of disaggregated records. This paper posits that such disaggregated records can be improved or replaced by training a generative adversarial network (GAN). We present a case study of a 1.1 million population using iterative proportional fitting (IPF). We illustrate that IPF makes a better fit using GAN-based disaggregated records rather than original census-based disaggregated records. Our results show a promising use of GANs for synthetic population generation.