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.
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 theory 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, Senate, and governor. Although contagion and voting dynamics differ, our work suggests a new approach to elucidate how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an illuminating example of data-driven forecasting using dynamical systems , and suggests avenues for future research on political elections . We conclude with our forecast of the senatorial and gubernatorial races on 6 ~November 2018 , which we posted on 5 November.
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To forecast political elections
Forecasting elections --- a challenging, high-stakes problem --- is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process
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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),
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Through a simple approach that
Our model
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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
and we use polling data from United States elections to determine its parameters. Surprisingly, our general model performs as well as popular forecasters
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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
for president, Senate, and governor. Although contagion and voting dynamics differ, our work suggests a new approach to elucidate how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an illuminating example
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, and suggests avenues for future research on political elections
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in the 2018 U.S. midterm elections of
on
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November
~November 2018
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online as a preprint