Forecasting elections using compartmental models of infection

Alexandria Volkening, Daniel F. Linder, Mason A. Porter, Grzegorz A. Rempala

Research output: Contribution to journalArticlepeer-review

Abstract

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).

Original languageEnglish (US)
Pages (from-to)837-865
Number of pages29
JournalSIAM Review
Volume62
Issue number4
DOIs
StatePublished - 2020

Keywords

  • Compartmental modeling
  • Complex systems
  • Elections
  • Forecasting
  • Polling data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computational Mathematics
  • Applied Mathematics

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