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Forecasting Elections Using Compartmental Models of Infection
SIAM Review ( IF 10.8 ) Pub Date : 2020-11-03 , DOI: 10.1137/19m1306658
Alexandria Volkening , Daniel F. Linder , Mason A. Porter , Grzegorz A. Rempala

SIAM Review, Volume 62, Issue 4, Page 837-865, January 2020.
Forecasting elections---a challenging, high-stakes problem---is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process, we develop a method for forecasting elections from the perspective of dynamical systems. Our model borrows ideas from epidemiology, and we use polling data from United States elections to determine its parameters. Surprisingly, our model performs as well as popular forecasters for the 2012 and 2016 U.S. presidential, senatorial, and gubernatorial races. Although contagion and voting dynamics differ, our work suggests a valuable approach for elucidating how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an example of data-driven forecasting using dynamical systems, and suggests avenues for future research on political elections. We conclude with our forecasts for the senatorial and gubernatorial races on 6 November 2018 (which we posted on 5 November 2018).


中文翻译:

使用感染的区隔模型预测选举

SIAM评论,第62卷,第4期,第837-865页,2020年1月。
预测选举-一个具有挑战性,高风险的问题-是很多不确定性,主观性和媒体审查的主题。为了阐明这一过程,我们开发了一种从动力学系统的角度预测选举的方法。我们的模型借鉴了流行病学的思想,并且我们使用了来自美国大选的民意测验数据来确定其参数。令人惊讶的是,我们的模型在2012年和2016年美国总统,参议员和州长竞选中的表现与流行的预测指标一样好。尽管传染和投票方式有所不同,但我们的工作提出了一种有价值的方法,用于阐明各州之间选举之间的关系。它还说明了以不同方式考虑不确定性的影响,提供了使用动态系统进行数据驱动的预测的示例,并为今后有关政治选举的研究提供了途径。最后,我们对2018年11月6日的参议院和州长比赛做出了预测(我们于2018年11月5日发布了这些预测)。
更新日期:2020-12-05
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