Weeks before the U.S. election, polls painted a tight race between the top two candidates—Harris leading by just 1%. But prediction markets told a different story. According to Polymarket, Trump had the odds in his favor, with close to 60% of the capital invested in the presidential wager. Why the discrepancy?
It likely boils down to incentives.
Prediction markets let people wager on the outcomes of real-world events — anything from elections to sports games. Participants buy shares of an outcome based on its likelihood. For example, if Trump’s chance of winning was 60%, his shares traded at $0.60 each. If you bought 1,000 shares for $600, you’d have ended up with $1,000 once the result was confirmed, as winning shares are worth $1 each.
These markets are powerful because they aggregate opinions in a way that’s incentivised. Unlike a survey, where participants are passively polled, prediction market participants have something to lose: their money. This “skin in the game” encourages better research, deeper insights, and more informed decisions.
Furthermore, prediction markets are decentralised. They’re not controlled by a central authority like traditional polls or betting agencies. This allows for freer, less biased predictions—an antidote to the noise from pundits and experts who often miss the mark.
Here’s how you can use prediction markets to make better decisions.
Ditch the noise: Mainstream media thrives on attention, not accuracy. News outlets are incentivised to chase clicks, align with political agendas, or please corporate sponsors. By contrast, prediction markets prioritise truth — participants profit only when their insights align with reality.
Be sceptical about ‘reputable’ sources: Even respected institutions like academia and scientific journals aren’t immune to bias. Researchers face pressures to publish or perish, which results in studies designed to please funders or fit editorial agendas. Prediction markets, on the other hand, are self-correcting because they reflect the collective wisdom of independent participants.
Make them your default source of information: if many people bet money on one prediction outcome, it’s reasonable to think they’ve done extensive research on the subject or have insider information. They are, therefore, a data-driven approach to forecasting.
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