This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Can crowds of people trading on what they believe will happen collectively predict the future? A new Tel Aviv University study of more than 1 million forecasts suggests that they often come remarkably close.
But when it comes to certain events, from international conflicts to elections, the odds may tell a different story. A research team from Tel Aviv University's School of Psychological Sciences, led by professor Michael Gilead and including Justin Dickerson, Hilla Shinitzky, Maayan Peer and Mattan S. Ben-Shachar, analyzed 1,014,122 forecasts across four major prediction platforms: Polymarket, Kalshi, Manifold and Metaculus.
Covering 12 domains over a decade, the study, published in the scientific journal Proceedings of the National Academy of Sciences (PNAS), examines how well collective predictions perform across topics ranging from elections and geopolitics to technology and security. Prediction markets are increasingly used to estimate the likelihood of political, economic and technological events. Rather than examining a handful of high-profile predictions, the researchers looked at more than a million forecasts to understand how these systems perform on a much larger scale.
Their findings reveal an important distinction: While prediction markets are generally good at estimating probabilities, their reliability varies depending on the type of event being predicted. The findings do not suggest that prediction markets can predict the future with certainty. Instead, they show that collective forecasts can provide valuable information about what might happen, provided we understand their limitations.
By identifying the areas in which prediction markets systematically overestimate or underestimate the likelihood of events, the study offers a clearer picture of how to interpret these forecasts, especially when the outcomes could have significant political, economic or technological consequences. Justin Dickerson et al., Calibration, accuracy, and domain variation across one million collective forecasts from prediction platforms, Proceedings of the National Academy of Sciences (2026). DOI: 10.1073/pnas.2615627123 Journal information: Proceedings of the National Academy of Sciences Swati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019.
She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile → Master's in physics with research experience. Long-time science news enthusiast.
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