In 1907, the English polymath Francis Galton attended a livestock exhibition where 787 people guessed the weight of an ox. No individual guess was correct. The median of all guesses was 1,207 pounds. The actual weight was 1,198 pounds. The crowd's aggregate estimate was within 1 percent of the truth, a result so striking that Galton, who had set out to demonstrate the unreliability of democratic judgment, was forced to revise his own beliefs.¹

Prediction markets formalize this phenomenon. They are exchanges where participants buy and sell contracts that pay out based on the outcome of future events. A contract that pays one dollar if a candidate wins an election and zero dollars if she loses will trade at a price that represents the market's aggregate probability estimate. If the contract trades at 0.65, the market believes there is a 65 percent probability of that candidate winning.

The Iowa Electronic Markets, operated by the University of Iowa since 1988, consistently outperformed polls in predicting U.S. election outcomes, typically by several percentage points. Polymarket, a blockchain-based prediction market, processed over 10 billion dollars in trading volume in 2025. Kalshi, the first CFTC-regulated prediction market in the United States, demonstrated approximately 20 percent better accuracy than polling aggregates on the 2024 presidential election.²³

Why prediction markets work. The aggregation mechanism has three properties that make it effective. First, participants have skin in the game: they lose real money when they are wrong, which suppresses the overconfidence that plagues expert panels. Second, the information sources are diverse: a contract price reflects the collective knowledge of everyone who trades it, from political insiders to statistical modelers to casual observers. Third, the updating is continuous: as new information arrives, traders adjust their positions and the price adjusts in real time.

Why prediction markets fail. The limitations are equally important. Markets can only exist for events that someone defines and creates a contract for. They are subject to manipulation when liquidity is thin. They suffer from correlated blind spots when participants share the same information diet. And they cannot model causal chains. A prediction market can tell you that there is a 35 percent probability of an oil supply disruption in the next twelve months. It cannot tell you that the disruption, if it occurs, will reduce global supply by 4 million barrels per day because the Strait of Hormuz handles 21 percent of global petroleum trade and there is no alternative route for Qatari LNG exports.

The opportunity is to use prediction market prices not as the prediction itself but as an input signal. When Crystal Ball's constraint graph produces a probability estimate that diverges significantly from the market price, one of them is wrong. If the divergence is greater than 20 percentage points, either the graph has a miscalibrated edge or the market has a blind spot. Both are actionable. The divergence becomes a research signal: investigate why the model and the market disagree, and the investigation will either improve the model or identify a mispricing.