With the U.S. 2024 presidential election just days away, technology is playing an increasingly significant role—not only in organizing and securing the electoral process but also in influencing voter opinions and promoting candidates. Beyond these applications, modern technology provides valuable predictive insights, allowing political experts worldwide to forecast election outcomes with high accuracy. But how reliable is this technology, and can it really predict the results with precision?
Why Predict Election Outcomes?
The U.S. President wields substantial influence across global sectors, most notably within the U.S. but also in international politics and economics. A precise forecasting system for U.S. elections enables political and economic experts worldwide to prepare strategies aligned with the most likely outcomes.
The president's economic influence is a key driver for companies to invest in election prediction systems. For instance, former President Donald Trump’s ban on Huawei profoundly impacted the tech giant’s smartphone production. Accurate election predictions allow companies to build strategies for adapting to policy changes introduced by each new administration, impacting financial markets and stock exchanges directly.
Election Prediction Before the Age of AI
Prior to AI, election predictions relied on human expertise, such as that of political historian Allan Lichtman, who has accurately predicted nine U.S. election outcomes over the last 20 years. Lichtman uses a system of indicators that reflect the political climate to forecast public voting decisions, relying on expert knowledge to analyze complex political factors.
While successful, this method isn’t flawless, as it depends on human expertise, which is subjective and varies by country. These limitations led tech companies to develop AI-based prediction systems that leverage unbiased data and computational accuracy.
Machine Learning in Election Prediction
Modern election prediction systems operate in two main phases: data collection and generating new data from that base, followed by analysis and final election predictions. This approach, detailed in a research paper from the open-access MDPI library, combines simulation techniques and machine learning to gather and analyze data more precisely. Previously tested in Brazil, Uruguay, and Peru, this model achieved a 100% accuracy rate for initial election rounds.
The process consists of four stages: identifying electoral factors influencing various demographics, distributing these factors based on population size and demographics, conducting simulations to generate voter-related data, and inputting this data into the machine learning system to obtain final predictions. This method yields high accuracy because it relies on real data, even within simulated models, and the precision of machine learning.
Another technique leverages Bayesian algorithms and Markov chains to analyze various factors, providing a similar approach to Lichtman’s but entirely automated. According to ScienceDirect, this method has demonstrated over 90% accuracy.
With advancements in machine learning and AI, the ability to predict election outcomes has become more refined. As we approach 2024, the question remains: can technology help us foresee the next occupant of the White House with complete certainty? Or will unforeseen factors continue to keep the final outcome uncertain until the last vote is counted?
Entering the Era of Artificial Intelligence
In June, two 19-year-olds developed an AI system capable of predicting election outcomes without a single opinion poll or interview. Cam Fink and Ned Koe, founders of the company "Aaru," utilized this system to forecast the results of the Democratic primary in New York. According to the political news site Semafor, Aaru's model demonstrated high accuracy in its predictions.
Aaru’s model operates by collecting demographic and electoral district data, generating numerous chatbot-like AI models that represent different demographic characters. These AI characters form unique personas by researching online in ways that mimic the thought patterns of people found in the demographic data—such as a conservative 40-year-old concerned with immigration issues or a businessman focused on expansion.
Once the model generates and emulates realistic characters, it initiates a simulated election process. Data gathered from this simulation is then analyzed to generate predictions close to actual outcomes.
Despite being a young company, Aaru has already attracted clients worldwide from various sectors. Among its prominent clients is the Walton family, owners of Walmart, who used Aaru's services for product opinion surveys, strengthening Aaru’s reputation and credibility.
While there are several methods to predict elections, Aaru’s AI model reduces error significantly compared to human-led efforts, especially when tested under various conditions over time.
