Prediction Markets: Betting on the Future

In July 2024, a week before Joe Biden dropped out of the presidential race, Polymarket showed his chances of being the Democratic nominee at 30%. Pundits were still debating whether he'd stay in. Party officials were publicly supportive. But the money knew.

When real stakes are on the line, people get serious. They update on evidence. They don't just repeat what their tribe believes—they think about what's actually true. And when you aggregate those serious, stake-weighted judgments, you get something remarkable: a real-time probability estimate of the future.

Prediction markets aren't just gambling. They're an information aggregation technology. And they consistently outperform experts, polls, and pundits.

How Prediction Markets Work

A prediction market is a betting exchange for future events. Instead of betting on horses, you bet on whether things will happen.

The basic mechanism: You can buy a contract that pays $1 if an event occurs and $0 if it doesn't. The price of that contract represents the market's probability estimate.

If a contract trading at $0.65 pays $1 if Event X happens, the market is saying X has about a 65% chance of occurring. If you think the real probability is higher, you buy. If you think it's lower, you sell. Your money is your vote.

Prices aggregate beliefs. Someone with insider knowledge might buy heavily at $0.65, driving the price to $0.75. Someone who thinks the probability is overstated might sell, pushing it back down. The equilibrium price reflects the money-weighted consensus of everyone who cares enough to bet.

Confidence matters. Unlike a poll where every opinion counts equally, prediction markets weight opinions by confidence. If you're 90% sure, you bet big. If you're 50-50, you stay out. This means informed participants have more influence than uninformed ones.

Updates are continuous. New information gets incorporated immediately. When news breaks, prices move within seconds. There's no waiting for the next poll. The market is always live.

Incentives align with accuracy. This is the crucial difference from punditry. A pundit who's wrong faces no penalty—they just move on to the next confident prediction. A trader who's wrong loses money. The asymmetry matters enormously. Prediction markets create skin in the game, and skin in the game produces honesty.

The Evidence: Markets Beat Experts

The performance record of prediction markets is impressive:

The Iowa Electronic Markets have predicted U.S. presidential election outcomes more accurately than polls about 75% of the time. They don't just predict who wins—they predict vote shares with remarkable precision.

Intrade (before its regulatory troubles) correctly predicted the 2008 and 2012 elections, including most swing states. It also caught events like Saddam Hussein's capture before mainstream news.

Internal prediction markets at companies like Google, HP, and Microsoft have outperformed traditional forecasting for product launches, sales projections, and strategic questions. HP found that its internal market predicted printer sales more accurately than its official forecasts.

Polymarket in 2024 showed Biden's declining odds before the consensus shifted, correctly predicted various primary outcomes, and tracked real-time probability shifts during debates and news events.

Sports betting lines are the oldest and most liquid prediction markets. Vegas lines are remarkably accurate—so accurate that even sophisticated bettors struggle to beat them over time. The market knows.

The pattern is consistent across domains: when you combine diversity of opinion, financial stakes, and proper aggregation, you get predictions that beat the alternatives. The mechanism doesn't care whether you're forecasting elections, sports, or product sales. It just works.

Why Markets Work

Prediction markets satisfy Surowiecki's conditions almost by design:

Diversity. Anyone with money can participate. Traders include domain experts, quantitative analysts, political junkies, and random contrarians. No credentialing filters out useful perspectives.

Independence. Bets are placed individually. While traders can see current prices, they make their own decisions about whether prices are right. There's no committee meeting, no social pressure to conform.

Decentralization. Traders act on their own information—local knowledge, specialist expertise, privileged access, or just careful analysis that others overlooked. No central authority decides what information matters.

Aggregation. Prices continuously update to reflect the money-weighted balance of opinion. The mechanism is elegant: if you're right and others are wrong, you profit. This creates powerful incentives for accuracy.

Compare this to, say, a committee of experts. The committee has less diversity (same training, same blind spots). Independence is compromised (social dynamics, hierarchy, reputation management). Decentralization is absent (all knowledge must be verbalized in the meeting). Aggregation is ad hoc (often just the loudest voice wins). Markets beat committees because they satisfy the conditions for collective intelligence more completely.

The market also has a built-in error-correction mechanism. If the price is wrong—too high or too low relative to true probability—that's a profit opportunity. Someone will notice and bet against the crowd. Their trades move the price toward truth. Markets reward being right, not fitting in.

The Polymarket Era

Polymarket, launched in 2020, has become the most prominent prediction market for political and current events. During the 2024 election cycle, it attracted billions in trading volume and became a regular reference point in mainstream news coverage.

What Polymarket demonstrated:

Speed of information incorporation. During presidential debates, Polymarket prices moved in real-time as viewers assessed candidate performance. Traditional polls take days; markets take seconds.

Detection of insider knowledge. Sharp price movements sometimes preceded public announcements, suggesting that people with private information were trading. This isn't fraud—it's the market doing its job, incorporating all available information.

Calibration. Events that Polymarket priced at 70% happened about 70% of the time. Events priced at 30% happened about 30% of the time. The probabilities mean what they say.

Resistance to narrative. While pundits told stories about momentum and vibes, Polymarket prices reflected cold probability assessments. The market didn't care about narratives—it cared about what was likely to actually happen.

The 2024 election also revealed limitations. Polymarket heavily favored Trump in the final weeks—more than most polling aggregates suggested. Some argued this reflected superior information aggregation; others argued it reflected a participant base skewed toward crypto-native Trump supporters. The question of who trades on prediction markets affects what they reveal.

The Limits of Prediction Markets

Markets aren't magic. They have real constraints:

Liquidity. Thin markets don't aggregate well. If only a few people are betting on a question, prices can be moved by idiosyncratic trades. You need enough trading volume for the wisdom of crowds to emerge.

Manipulation. Large players can move prices, at least temporarily. If a billionaire wants to make a candidate look more or less likely to win, they can bet accordingly. The market might eventually correct, but the distortion exists.

Self-reference. For some events, the market's prediction can affect the outcome. If a prediction market says a candidate will lose, donors might abandon them, causing them to actually lose. The prediction becomes self-fulfilling or self-defeating.

Moral hazards. If you can profit from an event, you might be tempted to cause it. This was the concern with DARPA's terrorism market—would creating a financial interest in terrorist attacks incentivize terrorism? Probably not (the amounts were tiny), but the perception killed the program.

Participation bias. Who trades on prediction markets? Mostly young, male, crypto-native, politically engaged—not a representative sample of the population. Their collective judgment might systematically miss what other groups see.

Legal uncertainty. In the United States, prediction markets operate in regulatory gray zones. The CFTC has restricted real-money markets on elections. This limits liquidity and participation, reducing the markets' accuracy.

Long-term questions. Markets work best for events with clear resolution dates. Questions like "Will this technology change society?" don't have obvious settlement criteria. The further out you go, the harder it is to maintain liquidity and accuracy.

The Case for Wider Adoption

Despite limitations, prediction markets offer something no other forecasting method provides: continuous, stake-weighted, publicly visible probability estimates.

Imagine if every major policy question had a liquid prediction market. What's the probability that this drug will pass FDA approval? That this infrastructure project will be completed on time? That this diplomatic initiative will succeed?

Decision-makers would have real-time information about likely outcomes. Voters could assess politicians against market-based benchmarks. Researchers could track how beliefs evolve over time. The public conversation would be anchored to probability rather than spin.

Some governments are experimenting. The UK's Office for Science and Technology commissioned prediction markets for pandemic preparedness questions. Various organizations run internal markets for strategic forecasting. The infrastructure exists.

The barriers are mostly cultural and political. Markets feel cold. Betting on serious outcomes feels disrespectful. Authorities don't like being outperformed by anonymous traders. The DARPA terrorism market was probably a good idea that was politically impossible.

But the alternative is what we have now: pundits confidently wrong, polls that take weeks to update, and decisions made on vibes and narratives rather than probability. Prediction markets aren't perfect, but they're better than the status quo.

Markets as Epistemic Technology

The deeper point about prediction markets isn't that they're accurate—though they are. It's that they represent a different way of knowing.

Traditional expertise is centralized. We identify experts, grant them authority, and listen to their pronouncements. This works when expertise is legible and experts are trustworthy. It fails when expertise is dispersed, when experts have blind spots, when institutions protect consensus over accuracy.

Markets distribute epistemic authority. No one is in charge. Authority flows to whoever is willing to bet and be right. The homeless person with a nose for politics can move prices just like the professor with a model. What matters is accuracy, not credentials.

This is uncomfortable for established institutions. Universities, think tanks, consultancies—their business model depends on the assumption that certified experts know more than the crowd. Prediction markets challenge that assumption. Sometimes they confirm expert consensus. Sometimes they don't. And when they don't, the market is usually right.

The information aggregation problem is fundamental. Every organization, every government, every individual faces the challenge of making decisions under uncertainty. Prediction markets are one solution—not the only one, but a powerful one. Ignoring them because betting feels unseemly is like ignoring antibiotics because needles are unpleasant.

The future will be predicted. The question is whether we use the best tools available.

The Takeaway

Prediction markets aggregate dispersed information through money-weighted betting. They consistently outperform traditional forecasting methods because they satisfy the conditions for collective intelligence: diversity, independence, decentralization, and proper aggregation.

Markets aren't perfect. They can be manipulated, suffer from participation bias, and struggle with thin liquidity. But they're better than the alternatives—and they're getting better as more people use them.

Polymarket and its predecessors have demonstrated what's possible. The question now is whether we'll expand prediction markets to questions that matter—or keep pretending that pundits and polls are good enough.

Further Reading

- Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives. - Arrow, K. J., et al. (2008). "The Promise of Prediction Markets." Science. - Hanson, R. (2013). "Shall We Vote on Values, But Bet on Beliefs?" Journal of Political Philosophy.

This is Part 3 of the Collective Intelligence series. Next: "Superforecasting"