Editor's Note: Prediction markets are transitioning from a niche trading tool to a more widespread public information arena.
The logic is not complicated: design a future event as a tradable contract, allow participants to express their judgment with real money, and then use the price to derive an approximate probability. Compared to polls, expert predictions, or traditional asset prices, the advantage of prediction markets is that they can aggregate dispersed information in real-time and incentivize those who truly possess information to participate through a mechanism where being wrong results in a financial loss.
This is also the most noteworthy aspect of this article. The author does not mythicize prediction markets as an "oracle machine," but instead returns to the understanding of market mechanisms itself: markets not only allocate resources but also aggregate information; prediction markets take this aggregative capability and directly apply it to judging whether an event will occur. From geopolitics and election results to AI model performance and scientific experiment reproducibility, many finely segmented issues that were previously difficult to express through traditional financial assets can now be transformed into a set of tradable probabilities.
However, the effectiveness of prediction markets is not automatic. It depends on who is trading, how contracts are designed, how outcomes are adjudicated, and whether the market is easily manipulated by insiders or vested interests. If those with genuine information do not participate, prices may be merely noise; if insiders bet in advance, the market may lose its fairness; if political teams or project parties use funds to inflate the probability of a certain outcome, the prediction market may also shift from being an "information aggregation tool" to a "public opinion manipulation tool."
Therefore, the next step for prediction markets is not just to increase trading volume but to establish a more trustworthy market infrastructure: transparent participation rules, clear contract designs, auditable settlement mechanisms, and constraints on manipulative behavior. Its true value is not in having people "bet on the future" but in providing a new public signal of probability in a highly uncertain environment.
The following is the original text:
Prediction markets allow people to trade around event outcomes. Last year, such markets began to enter the public eye on a large scale in the United States and are now being used to track events ranging from geopolitics to entertainment award winners. But what exactly are prediction markets?
As an economist who has long studied markets and incentive mechanisms, my answer is simple: prediction markets are fundamentally just markets. Markets are the basic tool for resource allocation, ensuring that goods and services flow to those who value them most. In this process, markets also aggregate information: the market clearing mechanism absorbs various information held by participants and condenses it into signals such as prices.
The prediction market platform and product leverage this information aggregation directly to attempt to forecast specific future events. They introduce an asset tied to a particular event: if a certain outcome occurs, the asset pays out; subsequently, people trade this asset based on their judgment of the event's likelihood. Enterprises have long used prediction markets, such as to extract tacit knowledge from employees to predict the timely release of a significant product. The scientific community has also employed prediction markets to assess which experiments are more likely to be reproducible. Today, we also see numerous media outlets collaborating with prediction markets, using this "collective wisdom" information to complement reporting from sources and traditional journalists.
The prediction market directly collects individual participants' information, their personal judgments about the future, and aggregates this information into a market, attempting to address questions about the probability of different events occurring. People can "bet" on a company's future value like in the stock market or "bet" on the future value of oil in the commodities market, treating these events as bets. However, unlike traditional markets, prediction markets do not rely on assets influenced by multiple factors like oil; instead, they introduce an asset that only pays out when a specific event happens.
If we see an increase in oil prices, we know that demand has increased relative to supply. However, we may not always know the underlying reason: whether it's due to an anticipated escalation of a conflict in the Middle East or because someone has invented a new use for oil. In contrast, prediction markets can break down each possibility individually. For example, a prediction market about "whether the Strait of Hormuz will remain open on a specific date and time" can be centered around a contract: if this event occurs, each unit of the contract pays out $1. As people continue to buy and sell this asset, the market price can be understood as a "probability sensor": it estimates the overall traders' judgment of the likelihood of the event happening.
The specific mechanics are as follows: suppose the market price for a certain outcome is $0.50 per unit, representing a 50% probability. If you believe the probability of the Strait of Hormuz opening is higher than 50%, say 67%, you would buy. If your prediction is correct, you would receive an expected return of $0.67 at the price of $0.50. Your buying action then drives up the market price and its corresponding probability estimate, reflecting that someone believed the market had previously underestimated the event's likelihood of occurring. The reverse is also true: if someone thinks the market price is too high, they would sell at a lower price or short, thus reducing the market's overall probability estimate.
When prediction markets function effectively, they offer significant advantages over other forecasting methods. Firstly, the ability to provide a probability estimate alone is a powerful capability. In contrast, polls and surveys often only provide opinion proportions; to convert these proportions into probabilities, inferences are required from a statistical perspective on the relationship between the proportion measured and the general population. Polls are usually snapshots in time, while prediction markets can be continuously updated in real-time with new participants and information entering the market.
More critically, prediction markets have an incentive mechanism. Both buyers and sellers have a “skin in the game,” facing losses if their judgment is wrong. This motivates potential participants to carefully consider what information they possess and allocate funds to issues where they believe they have the information edge. In turn, prediction markets provide an opportunity for individuals to leverage information and expertise, encouraging them to actively conduct research and gain a deeper understanding of relevant issues. A notable case is that prior to the 2024 U.S. presidential election, a prediction market participant even conducted their own poll using an unconventional method in an attempt to uncover information that traditional polling firms did not have.
Lastly, prediction markets also hold a key advantage: broad coverage. Someone knowledgeable about an event that may impact oil demand can, in principle, express their view by taking a long or short position on oil. However, many outcomes we wish to predict do not have a large commodity or stock market to efficiently carry them. For these issues, prediction markets may be the ideal tool. For instance, there have recently been prediction markets established to aggregate people's judgments on the performance of different AI models across various tasks. These issues are too niche and complex to be reflected in traditional commodity markets. Anyone can create and fund a prediction market to address such specialized issues.
These ideas are not new. They have existed in some form at least since the 16th century in Europe when a similar mechanism was used to predict the next pope. Modern prediction markets are rooted in economics, statistics, market design, and computer science. In the 1980s, Charles Plott and Shyam Sunder proposed the first formal academic frameworks. Subsequently, the first modern prediction market—the Iowa Electronic Markets—was launched. Thanks to the internet, this model absorbed decentralized and global information from around the world.
Simultaneously, to fully realize the potential of prediction markets, many challenges still need to be addressed. These include infrastructure issues, such as how to verify and achieve consensus on whether an event has occurred, and how to ensure market operations are transparent and auditable; as well as how to handle contract settlement on a large scale, as contract outcomes may be disputed or manipulated.
Furthermore, there are market design challenges. First, participants with relevant information must truly enter the market. If not everyone has an information advantage, then the price signal of the prediction market may not actually tell us much. On the contrary, various individuals with relevant information must also be willing to participate; otherwise, the estimation results of the prediction market may be biased. I pointed out in 2016 that prediction markets may have underestimated the likelihood of Brexit and Trump's initial election because the individuals participating in the prediction markets at that time lacked sufficient awareness of the rise of populism.
Meanwhile, if someone with "perfect information" enters the market, such as someone who knows in advance what the true outcome will be, this could also be a problem, especially if this person can also influence the course of events. Imagine if someone were inside a secret conclave for the election of a pope, but placed a bet in the "next pope" prediction market before Pope Leo publicly announced, even attempting to sway the papal election towards the candidate they bet on. What would happen? If potential participants anticipate insider trading in the market, the rational choice is to stay away from this market, ultimately leading to the breakdown of the market mechanism.
Finally, there is another possibility: someone trying to distort the prediction market price to influence the public's perception of the probability of a certain outcome. This would transform the prediction market from a tool for aggregating beliefs to a tool for manipulating beliefs. If a candidate's campaign team wants the public to believe they are winning, they could easily use some campaign funds to try to influence the relevant prediction markets. However, prediction markets have a self-correcting mechanism to some extent, as when the estimated probability of a contract is pushed to an obviously unreasonable position, there will always be someone willing to take the other side of the trade.
All of these issues point to the same need: prediction markets must establish higher transparency and clearer rules in participant management, contract design, and market operation. But if the designers of prediction markets can successfully address these challenges, they may become a key tool for our understanding and addressing the future.
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