To forecast political elections, popular pollsters  gather polls and combine information from them with fundamental data such as historical trends, the national economy, and incumbency. This process is complicated, and it includes many subjective choices (e.g., when identifying likely voters, estimating turnout, and quantifying other sources of uncertainty), leading to forecasts that differ between sources even when they use the same underlying polling data. With the goal of shedding light on election forecasts (using the United States as an example), we develop a framework for forecasting elections from the perspective of dynamical systems. Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model of disease spreading with public polling data 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. Our goals are to open up new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate a fascinating example of data-driven forecasting using dynamical systems. We conclude by forecasting the senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6  November.
To forecast political elections, popular pollsters and forecasters gather polls and combine information from them with fundamental data such as historical trends, the national economy, and incumbency. This process is complicated, and it includes many subjective choices (e.g., when identifying likely voters, estimating turnout, and quantifying other sources of uncertainty), leading to forecasts that differ between sources even when they use the same underlying polling data. With the goal of shedding light on election forecasts (using the United States as an example), we develop a theory for forecasting elections from the perspective of dynamical systems. Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model of disease spreading with public polling data 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 forecasters , and we use our new model to explore how subjective choices about uncertainty impact results. Our goals are to open new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate the value of data-driven forecasting using dynamical systems. We conclude with our forecast of the senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6 November, which we posted online as a preprint on 5 November.
A
and forecasters
R
framework
theory
R
pollsters
forecasters
D
up
R
a fascinating example
the value
R
by forecasting
with our forecast of
A
November, which we posted online as a preprint on 5