a16z: The "Super Bowl Moment" of Predicting Markets

Original Title: The Super Bowl of prediction markets

Original Author: Scott Duke Kominers, a16z crypto

Original Translation: Saoirse, Foresight News

On February 8, millions of National Football League (NFL) fans in the United States gathered in front of their screens to watch the Super Bowl, while many also kept an eye on another screen — closely monitoring the transaction dynamics of the prediction market. The range of betting categories here is vast, covering everything from the event's champion, final score, to each team's quarterback's passing yards.

Over the past year, the trading volume of the US prediction market has reached at least $27.9 billion, with trading targets ranging from sports event results, economic policy-making, to new product releases, among others. However, the nature of such markets has always been controversial: is it a trading behavior or gambling? Is it a news tool that aggregates collective wisdom 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 starts with a simple premise: a prediction market is fundamentally a market. And a market is the core tool for resource allocation and information integration. The operational logic of a prediction market is to introduce assets linked to specific events — when the event occurs, traders holding the asset can profit, and people trade based on their judgment of the event's direction, thus realizing the core value of the market.

From a market design perspective, referencing prediction market information is far more informative than trusting the opinion of a single sports commentator or even looking at Las Vegas betting odds. The core purpose of traditional sports betting institutions is not to predict the outcome of matches but to adjust the odds to "balance betting funds" and attract funds to the side with less betting volume at any given time. Las Vegas betting aims to make players willing to bet on the underdog result, while the prediction market allows people to trade based on their true judgment.

Prediction markets also allow people to more easily extract effective signals from a large amount of information. For example, if you want to predict the likelihood of new tariffs being imposed and deduce it from soybean futures prices, the process would be very indirect because futures prices are influenced by multiple factors. However, if this question is directly posed in the prediction market, a more intuitive answer can be obtained.

The prototype of this model can be traced back to 16th-century Europe when people would even bet on the "next papal election." The development of modern prediction markets is rooted in the theoretical systems of contemporary economics, statistics, mechanism design, and computer science. In the 1980s, Charles Plott of the California Institute of Technology and Shyam Sunder of Yale University established a formal academic framework for it, and shortly thereafter, the first modern prediction market — the Iowa Electronic Markets — was officially launched.

The operation mechanism of a prediction market is actually quite simple. Taking the bet on "Whether Seattle Seahawks quarterback Sam Darnold will pass the ball within the opponent's one-yard line" as an example, the market will issue the corresponding trading contract. If the event occurs, each contract will pay out 1 USD to the holder. As traders continuously buy and sell this contract, the market price of the contract can be interpreted as the probability of the event happening, representing the overall judgment of traders on the outcome. For instance, if each contract is priced at 0.5 USD, it means the market believes the probability of the event happening is 50%.

If you believe the probability of the event happening is higher than 50% (e.g., 67%), you can buy this contract. If the event eventually comes true, the contract you bought for 0.5 USD will yield a profit of 1 USD, with a gross profit of 0.67 USD. Your buying behavior will drive up the market price of the contract, and the corresponding probability valuation will also increase. This sends a signal to the market: Someone believes the current market is undervaluing the likelihood of the event. Conversely, if someone thinks the market is overestimating the probability, selling behavior will push down the price and probability valuation.

When a prediction market operates well, it demonstrates significant advantages compared to other forecasting methods. Opinion polls and surveys can only provide a view percentage, and to convert it into a probability valuation, statistical methods need to be used to analyze the relationship between the survey sample and the overall population. Moreover, such survey results often represent static data at a specific moment, while prediction market information continues to update with the entrance of new participants and the emergence of new information.

More importantly, a prediction market has a clear incentive mechanism, and traders are directly involved. They need to carefully review the information they have and only invest in areas they understand best, bearing the associated risks. In a prediction market, individuals can translate their information and expertise into profits, incentivizing them to delve deeper into relevant information.

Finally, the coverage of a prediction market far exceeds other tools. For example, someone holding information that impacts oil demand can profit by longing or shorting oil futures. However, in reality, many results we want to predict cannot be achieved through commodity or stock markets. For instance, recently, specialized prediction markets have emerged, attempting to integrate various judgments to predict the time it takes to solve specific mathematical problems—an essential piece of information for scientific development and a key benchmark for measuring the level of artificial intelligence.

Despite its significant advantages, for a prediction market to truly realize its value, it still needs to address many issues. First is at the market infrastructure level, where there are persistent issues that need clarification: How to validate if a specific event has indeed occurred and reach a consensus in the market? How to ensure transparency and auditability in market operations?

Second is the challenge of market design. For instance, there must be participants with relevant information entering the market — if all participants are clueless, the market price cannot convey any useful signals. Conversely, various participants with different relevant information need to be willing to engage in trading; otherwise, the estimation of the prediction market will deviate. The prediction market before the UK's Brexit referendum is a typical negative example.

However, if a participant with access to absolute insider information enters, it will also trigger new problems. For example, if the Seahawks' offensive coordinator knows for sure whether Sam Darnold will pass within the one-yard line and can even directly influence this outcome, if such individuals participate in trading, market fairness will be severely compromised. If potential participants believe there are insiders in the market, they may rationally choose to exit, ultimately causing a market collapse.

Furthermore, prediction markets also face the risk of manipulation: someone may turn this tool, originally used to aggregate public judgment, into a means of manipulating public opinion. For example, a candidate's campaign team, in order to create an atmosphere of "inevitable victory," may use campaign funds to influence the valuation of the prediction market. However, fortunately, prediction markets have a certain self-correcting ability in this regard—if the probability valuation of a particular contract deviates from a reasonable range, there will always be traders choosing to operate in the opposite direction to bring 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, market operations, and other aspects. If these issues can be successfully addressed, we can foresee that prediction markets will play an increasingly important role in the future of the forecasting field.

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