What prediction markets actually know

The questions they answer that other markets can't, and what they still need to get right.

Prediction markets, which allow people to trade on event outcomes, entered the U.S. at scale last year and are now being used to track everything from geopolitics to entertainment award winners. But what are they?

As an economist who has long studied marketplaces and incentive mechanisms, my answer is simple: Prediction markets are simply markets. Markets are a fundamental tool for allocating resources — ensuring that goods and services get to those who value them. Along the way, markets also aggregate information: the market-clearing process takes everything participants know and distills it into signals like price.

Prediction market platforms and products directly harness that information‑aggregation power to try to forecast specific future events: They introduce an event-specific asset that pays off if a given outcome occurs, and people then trade that asset based on their beliefs about whether it will happen.

Companies have long embraced prediction markets to elicit tacit information from their employees for forecasting whether an important product will launch on time. We’ve also seen scientists use prediction markets to assess which experiments are likely to replicate. And we are now seeing multiple media outlets partner with prediction markets for “wisdom-of-the-crowds” information to complement reporting from their sources and traditional journalists.

By gathering information directly from market participants — their individual beliefs about the future — and aggregating that information into a marketplace, prediction markets seek to answer questions about the likelihood of various events. People can “bet” on these events in the same way they can “bet” on the future value of a company in the stock market, or on the future value of a commodity like oil. But instead of keying off an asset like oil, whose demand depends on many different factors at once, prediction markets introduce an asset that pays off if and only if a given event occurs.

If we see the price of oil go up, then we know demand has increased relative to supply, but we don’t necessarily know, for example, whether that’s because people expect escalating conflict in the Middle East, or because someone’s come up with a new use for petroleum. With a prediction market, by contrast, you can isolate predictions for each individual possibility. A prediction market for, say, “will the Strait of Hormuz be open at a particular date and time,” could center around a contract that pays one dollar per unit if that event happens. With people trading the asset back and forth, the market price can be interpreted as a probability sensor: an estimate of traders' aggregate beliefs about the likelihood that the event will occur.

Here’s how it works: Let’s say the market price per unit for the outcome is $0.50 — probability 50/50. If you think it’s more likely than a 50% chance that the Strait will be open — say, 67% — then you would buy. If you’re right, you’d gross $0.67 for a price of $0.50. That purchase will, in turn, push the market price and associated probability estimate upward, reflecting the idea that someone thought the market was underestimating. This works in reverse, too: when someone believes the market is overpriced, they would sell for less (or short), which lowers the market’s overall probability estimate.

When prediction markets work well, they can offer significant benefits over other forecasting methods. First of all, the simple fact that they provide a probability estimate is a superpower. Polls and surveys, by contrast, just give an opinion share — and to convert that into a probability, you have to reason statistically about how the share you measured relates to the overall population. Polls also typically reflect just a snapshot in time, whereas prediction markets can update in real time as new participants and/or new information arrive.

And crucially, prediction markets are incentivized: Buyers and sellers have “skin in the game” because they stand to lose if they bet wrong. This provides an incentive for prospective participants to think carefully about what information they have, and to bring their capital to the issues where they believe they are most informed. And quasi-conversely, the opportunity to leverage information and expertise in prediction markets can also create incentives for people to conduct their own research to learn more about the issue at hand. (Famously, leading up to the 2024 U.S. presidential election, one prediction market participant even conducted his own opinion surveys, using an atypical method to try to elicit information standard pollsters didn’t have.)

Finally, prediction markets have a big advantage in the breadth of coverage they offer. While someone with knowledge about events that may affect petroleum demand can, in principle, go short or long on oil, there are plenty of outcomes we might want to predict that aren’t well supported by large-scale commodities or equities. For these, prediction markets can be ideal. For example, prediction markets have recently sprung up to try and aggregate estimates of which AI models will perform best at various tasks — something too micro to be reflected in a traditional commodities market. Anyone can establish and fund prediction markets to answer these sorts of niche questions.

When big events dominate headlines, they can make prediction markets look narrower than they are. Sports may be a mass-market catalyst, but Kalshi has also seen growth across longer-tail markets.

These ideas aren’t new: They’ve been around in some form since at least 16th-century Europe, when they were used for predicting the next Pope. Contemporary prediction markets have their roots in economics, statistics, market design, and computer science: Charles Plott and Shyam Sunder introduced the first formal academic frameworks for them in the 1980s. The first modern prediction market — the Iowa Electronic Markets — launched soon afterward. Thanks to the internet, the model has grown to draw on dispersed, decentralized information worldwide.

At the same time, there’s still more required for prediction markets to fulfill their promise. There are infrastructure questions, like how to validate and reach consensus on whether a given event has occurred, as well as how to ensure the market’s operations are transparent and auditable. Or how to determine contract resolution, which may be disputed or manipulated, at scale.

On top of that, there are market design challenges: First, the participants with the relevant information have to show up. If everyone is uninformed, then the prediction market’s price signal doesn’t really tell us anything. Conversely, people with all different types of relevant information have to decide whether they want to participate, or else the prediction market estimate will be biased: I argued back in 2016 that prediction markets may have underestimated the likelihood of Brexit and the first Trump election because the people who were participating in prediction markets back then were insufficiently plugged into the rise of populism.

At the same time, if someone with “perfect” information shows up — like someone who knows in advance what the true outcome is going to be — that can be a problem, especially if they have the ability to affect what happens. Imagine, for instance, if someone from inside the papal conclave had bet in the “next Pope” prediction market, front-running the public announcement of Pope Leo — or even trying to tilt the papal election in support of the candidate they had bet on! If prospective participants expect insiders to be trading in the market, the rational decision would be to stay away, leading the market to unravel.

Finally, there’s the possibility that people might try to skew prediction market prices to impact public perception about the likelihood of a given outcome — turning prediction markets from tools for aggregating beliefs into tools for manipulating them. If an election candidate’s comms team wants the world to think they’re winning, they could use part of their war chest to try to sway the associated prediction market. That said, prediction markets are somewhat self-correcting in this regard because people can always take the other side of a contract that seems to push the probability estimate beyond what’s reasonable.

All of this speaks to a need for prediction markets to ensure greater transparency and clarity around how they manage participation, contract design, and operations. But if designers of prediction markets successfully solve these puzzles, they could become a core part of how we navigate the future.

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