Author: Scott Kominers

Compiled by: Jiahua, ChainCatcher

Prediction markets allow people to trade on the outcomes of events. Last year, it entered the U.S. on a large scale and is now used to track various events from geopolitics to entertainment award results. But what exactly is it?

As an economist who has long studied markets and incentive mechanisms, my answer is simple: prediction markets are essentially markets. Markets are the fundamental tools for allocating resources, ensuring that goods and services flow to those who value them the most.

In this process, markets are also aggregating information: market clearing (i.e., reaching equilibrium between supply and demand) is essentially a mechanism to summarize the perceptions of all participants and distill them into price signals.

Prediction market platforms and products directly utilize this information aggregation capability to predict specific future events: they design assets linked to the events, which generate returns once specific outcomes occur, allowing people to trade these assets based on their judgments about whether the outcomes will happen.

Such uses have existed for a long time.

Companies have long used prediction markets to obtain implicit information from employees, such as predicting whether a key product will be released on time; scientists use them to assess which experiments are likely to be successfully replicated; and nowadays, several media collaborate with prediction markets to use "the wisdom of the crowd" to complement their own information sources and reporters' coverage.

Prediction markets collect information directly from participants, that is, everyone’s judgments about the future, and aggregate this information into a market to answer how likely a certain event is to happen.

People can "bet" on the future value of a company in the stock market or "bet" on the future prices of commodities like oil. The difference is that the demand for assets like oil is influenced by many factors at once, while the assets designed in prediction markets only yield returns when a specific event occurs.

If oil prices rise, we know it’s due to demand rising relative to supply, but we may not know the underlying reasons: it could be that people expect an escalation of conflicts in the Middle East, or someone has found a new use for oil.

With prediction markets, you can isolate each possibility and predict it individually.

For instance, a prediction market targeting "Will the Strait of Hormuz remain open at a specific time?" can revolve around a contract like this: once the event occurs, each unit of the contract pays one dollar.

As people repeatedly buy and sell this asset, the market price becomes a "probability indicator," reflecting traders' overall judgment about the likelihood of the event occurring.

How does this work in detail? Suppose the unit market price for a certain outcome is $0.50, equivalent to a 50% probability. If you believe that the likelihood of the strait remaining open is higher than 50%, say 67%, you would buy in; once your judgment is correct, you would gain $0.67 for an investment of $0.50.

This purchase will push up the market price and the corresponding probability estimate, which amounts to saying "someone thinks the market has underestimated it." Conversely, when someone thinks the price is too high, they will sell (or short) at a lower price, pulling down the overall market probability estimate.

When prediction markets are functioning well, they have several obvious advantages compared to other prediction methods.

First, it can directly provide a probability estimate, which in itself is a "superpower."

Public opinion polls and questionnaires only provide "proportions of opinions," and to convert them into probabilities, you need to perform statistical inference to determine the relationship between the measured proportion and the overall population. Moreover, polls often represent merely a snapshot at a given time, while prediction markets can update in real-time as new participants and information are introduced.

More critically, prediction markets come with built-in incentives: both buyers and sellers stake real money, and making a wrong bet incurs losses. This compels participants to carefully weigh the information they have, investing in the issues they feel most confident about.

Conversely, being able to profit in prediction markets through information and professional judgment will encourage people to actively conduct research and clarify issues.

(A well-known example is: before the 2024 U.S. presidential election, a prediction market participant even conducted a poll himself, using an unconventional approach to unearth information that standard polling organizations could not obtain.)

Finally, prediction markets also have significant advantages in coverage. Someone who understands which events may affect oil demand can, in principle, go long or short on oil; however, many outcomes we want to predict do not have corresponding commodity or stock markets available for betting. In this case, prediction markets become an ideal choice.

For example, recently, there has been a surge of prediction markets specifically designed to aggregate judgments like "which AI model performs best on various tasks," a question that is too niche for traditional commodity markets to reflect. And anyone can create and fund a prediction market for such niche questions.

These ideas are not new. As early as the 16th century in Europe, similar practices existed, where people used them to predict the next pope.

The foundation of modern prediction markets lies in economics, statistics, market design, and computer science. Charles Plott and Shyam Sunder proposed the earliest formal academic framework in the 1980s, shortly after which the first modern prediction market, the Iowa Electronic Markets, was born.

With the help of the internet, this model can now aggregate dispersed information from all over the world. However, for prediction markets to truly fulfill their potential, there are still many prerequisites.

One class of issues is infrastructure problems: how to verify and reach consensus on "whether a certain event has occurred," how to ensure market operations are transparent and auditable, and how to manage at scale those contracts that may spark controversy or be subject to manipulation.

Another class includes challenges in market design. First, those who truly possess relevant information must be willing to participate. If participants are uninformed, price signals essentially do not convey anything; conversely, only by ensuring that those holding various information participate can the estimations of prediction markets remain accurate.

I pointed out in 2016 that prediction markets may have underestimated the probability of Brexit and Trump's first election because the participants at the time did not understand the rise of populism well enough.

Another issue is if someone possesses "perfect" information, such as knowing the true outcome in advance, this can also be problematic, especially if they can influence the direction of the event.

Imagine if an insider from the secret meetings of the papal election went to bet in the prediction market for "the next pope" before the news of Leo’s election was officially announced, or even tried to influence the election to ensure the candidate they bet on wins—what would happen?

Because of this, once potential participants anticipate that insiders will be trading, the rational choice is to simply stay away, and the market collapses as a result.

Finally, some may deliberately distort the prices in prediction markets to influence public perception of the probabilities of certain outcomes, turning it from a "belief aggregation" tool into a "belief manipulation" tool.

For example, a candidate's public relations team might spend part of their campaign budget to influence related markets to make the public believe they are certain to win.

However, in this regard, prediction markets have a certain self-correcting ability: once the probability of a contract is pushed to an unreasonable height, there will always be someone willing to take the opposite side of the trade.

All of this indicates that prediction markets need to achieve greater transparency and clarity in participation management, contract design, and operational levels. But as long as designers can solve these problems, prediction markets may become one of our core tools for forecasting the future.

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