Original Author: Scott Duke Kominers, a16z 加密
Original Compilation: Saoirse, Foresight News
On February 8th, US time (7:30 AM Beijing time, February 9th), hundreds of millions of National Football League (NFL) fans were glued to their screens watching the Super Bowl. Many were also watching another screen closely—monitoring the trading activity on prediction markets, where the betting options are incredibly diverse, ranging from the championship winner and final score to the passing yards of each team’s quarterback.
Over the past year, the trading volume in US prediction markets has reached at least $27.9 billion, covering a vast array of subjects from sports event outcomes and economic policy decisions to new product launches. However, the nature of these markets has always been controversial: Are they a form of trading or gambling? Are they a tool for aggregating collective wisdom for news, or a means of scientific validation? And is the current development model the optimal solution?
As an economist who has long studied markets and incentive mechanisms, my answer begins with a simple premise: Prediction markets are, at their core, markets. And markets are fundamental tools for allocating resources and aggregating information. The logic of prediction markets is to create assets tied to specific events—traders holding these assets receive payouts when the event occurs. People trade based on their judgments about the event’s outcome, and this is where the core value of the market is realized.
From a market design perspective, referencing information from prediction markets is far more valuable than trusting the opinion of a single sports commentator, or even looking at Las Vegas betting odds. The primary goal of traditional sportsbooks is not to predict the outcome of a game, but to “balance the betting pool” by adjusting odds to attract money to the side with less betting volume at any given moment. Las Vegas betting aims to make players willing to bet on underdog outcomes, whereas prediction markets allow people to trade based on their genuine judgments.
Prediction markets also make it easier to extract meaningful signals from vast amounts of information. For example, if you want to gauge the likelihood of new tariffs being imposed, deriving this from soybean futures prices is an indirect process—as futures prices are influenced by multiple factors. But if you pose this question directly in a prediction market, you get a more straightforward answer.
The prototype of this model can be traced back to 16th-century Europe, where people even placed bets on “who the next Pope would be.” The development of modern prediction markets is rooted in contemporary theories of economics, statistics, mechanism design, and computer science. In the 1980s, Charles Plott of Caltech and Shyam Sunder of Yale University established its formal academic framework. Soon after, the first modern prediction market—the Iowa Electronic 市场s—was launched.
The mechanism of prediction markets is actually quite simple. Take a wager on “whether Seattle Seahawks quarterback Sam Darnold will throw a pass within one yard of the opponent’s end zone.” The market issues corresponding trading contracts; if the event occurs, each contract pays the holder $1. As traders continuously buy and sell this contract, its market price can be interpreted as the probability of the event occurring, representing the collective judgment of traders. For instance, a contract priced at $0.50 implies the market believes there’s a 50% chance the event happens.
If you judge the probability to be higher than 50% (say, 67%), you can buy the contract. If the event ultimately occurs, the contract you bought for $0.50 yields $1, resulting in a gross profit of $0.67. Your buying action pushes up the contract’s market price, and the corresponding probability estimate rises, signaling to the market that someone believes the current market underestimates the likelihood. Conversely, if someone believes the market overestimates the probability, selling activity will lower the price and probability estimate.
When prediction markets function well, they demonstrate significant advantages over other forecasting methods. Opinion polls and surveys only yield percentages of viewpoints; converting these into probability estimates requires statistical methods to analyze the relationship between the survey sample and the overall population. Furthermore, such survey results are often static snapshots in time, whereas information in prediction markets continuously updates with the entry of new participants and the emergence of new information.
More crucially, prediction markets have clear incentive mechanisms; traders are “skin in the game.” They must carefully process the information they possess and only invest capital and assume risk in areas they understand best. In prediction markets, people can convert their information and expertise into profits, which also incentivizes them to actively seek deeper understanding of relevant fields.
Finally, the coverage scope of prediction markets far surpasses other tools. For instance, someone with information affecting oil demand can profit by going long or short on crude oil futures. However, in reality, many outcomes we wish to predict cannot be realized through commodity or stock markets. Recently, specialized prediction markets have emerged attempting to aggregate various judgments to predict the timeline for solving specific mathematical problems—information crucial for scientific development and an important benchmark for measuring the progress of artificial intelligence.
Despite their significant advantages, for prediction markets to truly realize their value, numerous issues must be addressed. First, at the market infrastructure level, there are persistent questions to clarify: How to verify whether a specific event has truly occurred and achieve market consensus? How to ensure the transparency and auditability of market operations?
Second are the challenges of market design. For example, there must be participants with relevant information entering to trade—if all participants are uninformed, market prices cannot convey any meaningful signal. Conversely, various participants holding different relevant information need to be willing to trade; otherwise, the valuation in prediction markets can become biased. The prediction markets before the UK Brexit referendum serve as a classic counterexample.
Furthermore, the entry of participants with absolute insider information creates new problems. For instance, if the Seahawks’ offensive coordinator knows 定义nitively whether Sam Darnold will pass within the one-yard line, or can even directly influence the outcome, participation by such individuals severely undermines market fairness. If potential participants believe there are insider traders in the market, they may rationally choose to exit, ultimately leading to market collapse.
Additionally, prediction markets face the risk of manipulation: someone might turn this tool, originally meant for aggregating collective judgment, into a means of manipulating public opinion. For example, a candidate’s campaign team, to create an atmosphere of “impending victory,” might use campaign funds to influence valuations in prediction markets. Fortunately, prediction markets possess a degree of self-correcting ability in this regard—if the probability estimate for a contract deviates from a reasonable range, there will always be traders choosing to take the opposite position, bringing the market back to rationality.
Given these various risks, prediction market platforms must focus on enhancing operational transparency and clearly disclosing rules regarding participant management, contract design, market operations, and other aspects. If these issues can be successfully resolved, we can foresee prediction markets playing an increasingly important role in the future of forecasting.
本文来源于互联网： a16z: The “Super Bowl Moment” for Prediction Markets
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