Forecasting Elections Using Compartmental Models of Infection
Forecasting Elections Using Compartmental Models of Infection
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DOI：
10.1137/19m1306658
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
A. Volkening;Daniel F. Linder;M. Porter;G. Rempała
中科院分区：
文献类型：
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作者：
A. Volkening;Daniel F. Linder;M. Porter;G. Rempała
预测选举是一个具有挑战性、高风险的问题，是一个充满不确定性、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系统角度预测选举的方法。我们的模型借鉴了流行病学的思想，并使用美国选举的民意调查数据来确定其参数。令人惊讶的是，我们的通用模型在 2012 年和 2016 年美国总统、参议员和州长竞选中的表现与流行预测者一样好。尽管传染和投票动态不同，但我们的工作提出了一种有价值的方法来阐明各州选举之间的关系。它还说明了以不同方式解释不确定性的影响，提供了使用动态系统进行数据驱动预测的示例，并为未来的政治选举研究提出了途径。最后，我们对 2018 年 11 月 6 日至 11 月的参议员和州长竞选进行了预测，并于 2018 年 11 月 5 日发布。
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 general model performs as well as popular forecasters for the 2012 and 2016 U.S. races for president, senators, and governors. Although contagion and voting dynamics differ, our work suggests a valuable approach to elucidate 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.