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“We believe the campaign finance simulator is a game changer when it comes to predictive analysis,” shares 24cast.org founder, Asher Labovich. “Ultimately, where campaigns and voters choose to direct their donations depends on their specific objectives, but we hope that our campaign finance simulator helps to inform campaigns and donors so that they can better match their money to their goals.”
How Much Does Money Matter?
As an example of the impact of campaign funds on a close race, 24cast.org currently forecasts the Michigan District 8 House race with a margin favoring Democrats (D+3.3). However, the campaign finance simulator predicts this race could move significantly to a margin of D+7.3 with just an additional $2 million in campaign funds.
In another example, in a close contest like the Senate race in Montana, the campaign finance simulator predicts an influx of $20 million by Republicans would raise their margin by 6.48 points. Further, in a state like Pennsylvania, Democrats are already favored (D+5.8) but the machine model reveals that donations have a larger impact. Thus, the Campaign Finance Simulator predicts an allocation of $20 million into the Pennsylvania Senate race by the Democrats would raise their margin by over 4 points.
The Data-Driven Advantage
Developed by a student team of 13 dedicated math, computer and political science majors at Brown University, 24cast.org distinguishes itself from traditional election prediction models in that it consists of over 1,000 machine learning models and over 500,000 data inputs that allow the model to dynamically understand the interactions between various electoral influences. In addition, in back-tests on the 2020 general elections and the 2022 midterms, 24cast.org consistently outperformed the leading election prediction models.
With the addition of the campaign finance simulator, 24cast.org has swiftly become the leading data-driven, multi-tool election prediction source (and the only open-source model) available.
“We believe if voters are to trust election prediction and simulation models, they need to understand the assumptions, the data sources, and how factors are weighted. When we created our model, we made it open-source and we provided easy-to-understand graphs to open the “black box.” We want everyone to understand and also see how 24cast.org arrives at its predictions. Anyone can look at our code and even help us improve,” states 24cast.org project manager, Ariel Shifrin.
The Future of Election Prediction
The trailblazing 24cast.org student team attests that machine learning models have proven to produce highly accurate predictions and while novel, such models will undoubtedly become the standard. “Machine learning and AI are the future of election predictions. They are the least biased and most accurate,” affirms Labovich.
About 24cast.org
24cast.org by Brown Political Review aims to demystify the world of election predictions for politically engaged citizens and stakeholders. Developed by 13 college students from Brown University, 24cast.org consists of state-of-the-art machine learning models that predict key U.S. elections with minimal assumptions. The result is the most accurate election prediction technology available– along with its equally innovative companion tool, the Campaign Finance Simulator. Transparent and accessible to all, 24cast.org’s models are open source with graphical explanations for all predictions to help users understand how the predictions were reached. Working to achieve the “edge of predictability,” the team is on a mission to empower informed political engagement by educating the public on the power and usefulness of prediction technology. To learn more visit www.24cast.org.
MEDIA CONTACT: Anita S. Lane
Sensei Communications
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