"Forecasting Elections Using Compartmental Models of Infection"

One of my particularly exciting papers just came out in final form. Here are some details (and a convenient tweet for you to spread it on social media).

Title: Forecasting Elections Using Compartmental Models of Infection

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

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).

The published version of our paper on forecasting elections using dynamical systems (with compartmental models of infection) is now live: https://t.co/t2IXPBiXSG

On my webpage: https://t.co/YVfya7jbjy

2020 election forecasts: https://t.co/UQ9MXqAJLh

[cc @al_volkening] pic.twitter.com/CKBbrzt8Vj

— Mason Porter (@masonporter) October 28, 2020