Election Modeling Talk through TDI

This talk may be of interest to some COPS members. https://tdai.osu.edu/events/2018/11/27/11021/

Title: “Forecasting U.S. elections using compartmental models of infection”

Authors: Alexandria Volkening (MBI, Ohio State University), Daniel F. Linder (Augusta University), Mason A. Porter (University of California, Los Angeles), and Grzegorz A. Rempala (Ohio State University)

Abstract: U.S. election prediction involves polling likely voters, making assumptions about voter turnout, and accounting for various features such as state demographics and voting history. While political elections in the United States are decided at the state level, errors in forecasting are correlated between states. With the goal of shedding light on the forecasting process and exploring how states influence each other, we develop a framework for forecasting elections in the U.S. from the perspective of dynamical systems. Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model with public polling data from HuffPost and RealClearPolitics to forecast gubernatorial, senatorial, and presidential elections at the state level. Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular pollsters, and we use our new model to explore how subjective choices about uncertainty impact results. We conclude by comparing our forecasts for the senatorial and gubernatorial races in the U.S. midterm elections of November 6, 2018, with those of popular pollsters.