Original Title: The Super Bowl of prediction markets
Original Author: Scott Duke Kominers, a16z crypto
Original Translation: Saoirse, Foresight News
On February 8th, U.S. time (February 9th, 7:30 AM Beijing time), hundreds of millions of NFL fans were glued to their screens watching the Super Bowl, many also keeping an eye on another screen—monitoring the trading dynamics of prediction markets, where the betting categories are diverse, ranging from the championship winner and final score to the passing yards of each team's quarterback.
In the past year, the trading volume of U.S. prediction markets reached at least $27.9 billion, covering a wide array of trading targets, from sports event outcomes and economic policy-making to new product launches. However, the essence of these markets has always been controversial: do they belong to trading activities or gambling? Are they tools for aggregating public wisdom or means for 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 the core tools for allocating resources and integrating information. The operational logic of prediction markets is to issue assets linked to specific events—when the event occurs, the trader holding the asset can earn a profit, and people trade based on their judgments about the event's outcome, thus realizing the market's core value.
From a market design perspective, the information from prediction markets is far more reliable than trusting the opinion of a single sports commentator or even looking at Las Vegas betting odds. The primary goal of traditional sports betting institutions is not to predict the outcome of a game but to "balance betting funds" by adjusting odds, attracting funds to the side with less betting volume at any given moment. Las Vegas betting seeks to encourage players to bet on underdog outcomes, while prediction markets allow people to trade based on their genuine judgments.
Prediction markets also enable individuals to more easily extract effective signals from vast amounts of information. For example, if you want to predict the likelihood of new tariffs being introduced, deriving this from soybean futures prices would be quite indirect—because futures prices are influenced by multiple factors. However, if you directly pose this question in a prediction market, you can obtain a more straightforward answer.
The prototype of this model can be traced back to 16th century Europe, where people even placed bets on "the next pope." The development of modern prediction markets is rooted in contemporary economics, statistics, mechanism design, and computer science. In the 1980s, Charles Plott from Caltech and Shyam Sunder from Yale established a formal academic framework for it, and soon after, the first modern prediction market—the Iowa Electronic Markets—was officially launched.
The operational mechanism of prediction markets is actually quite simple. Taking the bet "Will Seattle Seahawks quarterback Sam Darnold pass within the opponent's one-yard line?" as an example, the market issues corresponding trading contracts. If the event occurs, each contract will pay the holder $1. As traders continuously buy and sell this contract, the market price of the contract can be interpreted as the probability of the event occurring, representing the overall judgment of traders about the outcome. For instance, if each contract is priced at $0.5, it means the market believes the probability of the event occurring is 50%.
If you judge the probability of the event occurring to be higher than 50% (for example, 67%), you can buy this contract. If the event ultimately occurs, the contract you bought at $0.5 can yield a profit of $1, resulting in a gross profit of $0.67. Your buying behavior will push up the market price of the contract, and the corresponding probability valuation will also rise, sending a signal to the market: someone believes the current market is underestimating the likelihood of the event occurring. Conversely, if someone believes the market is overestimating the probability, selling behavior will lower the price and probability valuation.
When prediction markets operate well, they can demonstrate significant advantages over other prediction methods. Polls and surveys can only yield opinion proportions; to convert them into probability valuations, statistical methods are needed to analyze the relationship between the survey sample and the overall population. Moreover, these survey results are often just static data at a certain moment, while the information in prediction markets continuously updates with the addition of new participants and new information.
More critically, prediction markets have a clear incentive mechanism, where traders are "personally invested." They must carefully sift through the information they possess and only invest funds and take risks in areas they understand best. In prediction markets, individuals can convert their information and expertise into profits, which also motivates everyone to actively deepen their understanding of relevant information.
Finally, the coverage of prediction markets far exceeds that of other tools. For example, someone who possesses information affecting oil demand can profit by going long or short on crude oil futures, but in reality, many outcomes we want to predict cannot be realized through commodity or stock markets. For instance, there have recently emerged specialized prediction markets attempting to integrate various judgments to predict the time it will take to solve specific mathematical problems—this information is crucial for scientific development and serves as an important benchmark for measuring the level of artificial intelligence development.
Despite their significant advantages, prediction markets still face many challenges to truly realize their value. First, at the level of market infrastructure, there are persistent issues that need clarification: how to verify whether a certain event has actually occurred and reach a consensus in the market? How to ensure the transparency and auditability of market operations?
Secondly, there are challenges in market design. For instance, there must be participants with relevant information entering the market to trade—if all participants are completely uninformed, the market price cannot convey any effective signals. Conversely, various participants with different relevant information need to be willing to engage in trading; otherwise, the valuation of the prediction market will be skewed, as seen in the prediction market before the Brexit referendum, which serves as a typical counterexample.
If participants with absolute insider information enter the market, it can also raise new issues. For example, if the Seahawks' offensive coordinator knows for certain whether Sam Darnold will pass within the one-yard line and can even directly influence this outcome, the participation of such individuals in trading would severely undermine market fairness. If potential participants believe there are insider traders in the market, they may rationally choose to exit, ultimately leading to the collapse of the entire market.
Additionally, prediction markets are also at risk of manipulation: some may turn this tool, originally intended for aggregating public judgment, into a means of manipulating public opinion. For instance, a candidate's campaign team might use campaign funds to influence the valuation of prediction markets to create an atmosphere of "victory is imminent." Fortunately, prediction markets possess a certain degree of self-correcting ability in this regard—if the probability valuation of a certain contract deviates from a reasonable range, there will always be traders choosing to take the opposite position, bringing the market back to rationality.
Based on the various risks mentioned above, prediction market platforms must focus on enhancing operational transparency, clearly disclosing the rules governing participant management, contract design, and market operations. If these issues can be successfully resolved, we can foresee that prediction markets will play an increasingly important role in the field of forecasting in the future.
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