Author: Scott Kominers
Compiled by: Jiahuan, ChainCatcher
Prediction markets allow people to trade on the outcomes of events. Last year, they entered the United States on a large scale and are now used to track everything from geopolitical events to entertainment awards results. But what exactly are they?
As an economist who has long studied markets and incentive mechanisms, my answer is simple: Prediction markets are, at their core, markets. Markets are a fundamental tool for allocating resources, ensuring goods and services flow to those who value them most.
In this process, markets also aggregate information: market clearing (i.e., reaching supply-demand equilibrium) is essentially a mechanism for gathering all participants' knowledge and distilling it into a price signal.
Prediction market platforms and products leverage this information-aggregation power directly to forecast specific future events: they design an asset tied to an event that pays off only if a specific outcome occurs, and people trade this asset based on their judgment of whether that outcome will happen.
This usage has existed for a long time.
Companies have long used prediction markets to extract implicit information from employees, such as forecasting whether a key product can be launched on time; scientists use them to assess which experiments are likely to be successfully replicated; and today, several media outlets partner with prediction markets, using 'the wisdom of the crowd' to supplement their own sources and journalists' reporting.
Prediction markets directly collect information from participants—that is, each person's judgment about the future—and then converge this information into a market to answer how likely a particular event is to happen.
People can place bets on these events, similar to 'betting' on a company's future value in the stock market or 'betting' on the future price of oil or other commodities. The difference is that the demand for assets like oil is influenced by many factors simultaneously, whereas the assets designed by prediction markets pay off only when a specific event occurs.
If oil prices rise, we know demand has increased relative to supply, but we may not know the reason: it could be due to expectations of escalating conflict in the Middle East, or someone might have found a new use for oil.
With prediction markets, you can isolate and predict each possibility individually.
For example, a prediction market on 'whether the Strait of Hormuz will remain open at a specific point in time' could revolve around a contract that pays one dollar per unit if the event occurs.
As people repeatedly buy and sell this asset, the market price becomes a 'probability barometer,' reflecting traders' collective judgment of the likelihood of that event.
How exactly does it work? Suppose the unit market price for a particular outcome is $0.50, equivalent to a 50% probability. If you believe the strait has a higher chance of staying open, say 67%, you would buy; if correct, you would receive a total payout of $0.67 for a cost of $0.50.
This buying pressure then pushes up the market price and the corresponding probability estimate, effectively saying, 'someone thinks the market has undervalued it.' The reverse is also true: when someone thinks the price is too high, they sell (or short) at a lower price, thereby pulling the market's overall probability estimate down.
When prediction markets function well, they have several clear advantages over other forecasting methods.
First, they provide a direct probability estimate, which in itself is a 'superpower.'
Opinion polls and surveys give only a 'proportion of opinions'; converting this to a probability requires statistical inference to determine the relationship between the sampled proportion and the population. Moreover, polls are often a snapshot at a single point in time, whereas prediction markets can update in real time as new participants and new information join.
More crucially, prediction markets have built-in incentives: buyers and sellers put real money on the line and lose if they are wrong. This motivates participants to seriously weigh their information and invest their money on issues they are most confident about.
Conversely, the ability to profit from information and expert judgment in a prediction market also incentivizes people to proactively conduct research and clarify issues.
(A well-known example is that before the 2024 U.S. presidential election, a prediction market participant even conducted his own poll, using unconventional methods to uncover information that standard pollsters could not reach.)
Finally, prediction markets also have a huge advantage in coverage. A person who understands which events might affect oil demand can, in principle, go long or short on oil; but many outcomes we want to predict do not have corresponding commodity or stock markets to bet on. In such cases, prediction markets become an ideal choice.
For example, a host of prediction markets have recently emerged specifically to aggregate judgments on questions like 'which AI model performs best on various tasks'—these issues are too granular to be reflected in traditional commodity markets. And anyone can create and fund a prediction market for such niche questions.
These ideas are not new. Similar practices existed in Europe as early as the 16th century, when people used them to predict the next Pope.
The foundations of modern prediction markets lie in economics, statistics, market design, and computer science. Charles Plott and Shyam Sunder proposed the first formal academic framework in the 1980s, and soon after, the first modern prediction market, the Iowa Electronic Markets, was born.
With the internet, this model can now aggregate dispersed information from around the globe. For prediction markets to truly realize their potential, several prerequisites must be met.
One category is infrastructure issues: how to verify and reach consensus on 'whether an event has occurred,' how to ensure market operation is transparent and auditable, and how to handle, at scale, the settlement of contracts that may be controversial or even subject to manipulation.
Another category involves market design challenges. First, people who truly possess relevant information must be willing to participate. If participants are uninformed, the price signal doesn't actually indicate anything; conversely, only by having participants with various types of information involved can the prediction market's estimates avoid distortion.
I pointed out as early as 2016 that prediction markets likely underestimated the probability of Brexit and Trump's first election win because participants at the time were not sufficiently aware of the rise of populism.
Another problem is that if someone has 'perfect' information, such as knowing the true outcome in advance, this is also troublesome, especially if they can influence the event's outcome.
Imagine: what if an insider from the papal conclave went to place bets in a 'next Pope' prediction market, trading before Leo's election was officially announced, or even tried to sway the election to ensure their chosen candidate won?
For this reason, if potential participants anticipate that insiders are present trading, the rational choice is simply to stay away, and the market collapses.
Finally, there is also the possibility that someone might deliberately distort prediction market prices to influence public perception of the probability of a certain outcome, turning it from a tool for 'aggregating beliefs' into a tool for 'manipulating beliefs.'
For example, a candidate's public relations team, wanting to make the outside world believe they are a sure winner, might allocate part of their campaign funds to influence the relevant market.
However, in this regard, prediction markets have some self-correcting ability: once a contract's probability is pushed to an unreasonable level, someone will always be willing to take the other side of the trade.
All of this indicates that prediction markets require higher transparency and clarity in participant management, contract design, and operational aspects. But as long as designers can solve these puzzles, prediction markets have the potential to become one of our core tools for anticipating the future.
