Author: Long Yue
Source: Wall Street News
When a decentralized crowd can predict wars, policies, and market trends more accurately than U.S. federal agencies, American regulators can no longer sit still.
Prediction markets are experiencing rapid expansion. The Mises Institute recently published a lengthy article by Angelo Monaco, which outlines the operational logic of prediction markets, their explosive growth trajectory, and the reasons behind the U.S. government's urgency to impose controls on them.
The article argues that the underlying motive for U.S. regulators to suppress prediction markets is ostensibly to 'protect the public' but essentially to 'protect themselves.' What regulatory agencies truly fear is not that these markets will fail, but that they function too well—so well that they publicly expose the limitations of the regulators' own predictive capabilities.
The logic of prediction markets is not complex. Platforms like Polymarket and Kalshi are essentially financial exchanges: users buy and sell contracts based on the outcomes of real-world events. Contract prices fluctuate between 1 cent and 99 cents, directly reflecting the market's collective judgment on the probability of an event occurring. If the event happens, the contract settles at $1; accurate predictors profit, while those who are wrong incur losses. This mechanism forces every participant to back their judgment with real money.
Currently, the monthly trading volume of prediction markets has exceeded $24 billion. Analysts predict the overall market size will surpass $240 billion and is poised to achieve an annual trading volume exceeding $1 trillion before 2030. This growth rate is exceptional within the financial industry.
The Iran War: Prediction Markets Were Several Hours Ahead of the Pentagon Press Conference
The article uses the Iran conflict in early 2026 as a core case study to illustrate the practical value of prediction markets.
From late 2025 to January 2026, when local unrest in Iran first began, mainstream analysis institutions and media generally predicted that energy markets would remain stable, with the forecast range for the annual average price of Brent crude oil being $55 to $60 per barrel. However, during the same period, a clear divergence signal emerged in crude oil options markets and decentralized geopolitical event contracts—while analysts on television were telling the public 'not to panic,' traders betting with real money were already significantly increasing the probability priced in for 'worst-case scenarios.'
The market began pricing in the structural vulnerability of the Strait of Hormuz weeks before the U.S.-led coalition launched airstrikes in February.
In March, when Iran blockaded the Strait of Hormuz, disrupting about 20% of global oil supply, prediction markets on platforms like Polymarket and IMF PortWatch had already provided clear judgments hours before the Pentagon held its press conference. They did so by integrating satellite tracking data, signals from surging insurance rates, and data from regional shipping companies.
The article points out that if you relied solely on traditional energy forecasts in January, you would have been told that a sharp rise in oil prices was a 'low-probability event.'
The Court Has Already Said: The U.S. CFTC's Concerns 'Lack Specific Evidence'
Does the regulators' logic hold water? The article suggests the answer is no.
The most representative legal case is Kalshi v. CFTC. The U.S. Commodity Futures Trading Commission (CFTC) had sought to ban contracts related to congressional elections in federal court, but the U.S. Court of Appeals for the District of Columbia Circuit explicitly rejected the government's motion for a stay. The court's wording was direct: the CFTC's concerns about market manipulation and threats to election integrity were 'speculative and lacking in specific evidentiary support.'
The court further found that the CFTC had exceeded its statutory authority and failed to demonstrate that political outcome trading would cause immediate harm to the public interest. This ruling directly paved the way for the legalization of commercial election event contracts in the United States.
The most significant 'national security threat' case cited by the CFTC involved a U.S. Army soldier who, in April 2026, used classified information about operations in Venezuela to profit over $404,000 on a prediction market. This case was heavily promoted by the federal government. However, the article notes that this remains the only major case involving national security to date. Using an isolated incident to argue for systemic harm is logically untenable.
The Real Motive of U.S. States: Not Protecting the Public, But Protecting Tax Revenue
If the federal-level crackdown is more about 'narrative control,' the motives at the state level are more straightforward—money.
The article cites data from the American Gaming Association's commercial gaming revenue tracker: since early 2025, prediction market platforms have cost state governments approximately $950 million in potential gaming tax revenue.
The reason lies in a regulatory arbitrage loophole: traditional sports betting operators must pay high Gross Gaming Revenue (GGR) taxes to state gaming commissions, while prediction market platforms define themselves as 'financial instruments,' thus only paying standard corporate income tax and completely bypassing the state-level gaming tax system.
Taking Minnesota as an example, when the state passed a prediction market ban, the core argument in the legislative debate was not 'social harm' but market share and tax revenue loss. The article's assessment is that the 'harm' pointed to by states is often projected tax losses and threats to traditional gaming monopolies, rather than documented social problems.
Hayek Already Talked About This
In arguing for the informational value of prediction markets, the article cites the classic argument by economist Friedrich Hayek.
Hayek pointed out that the decentralized price mechanism is the only tool capable of coordinating the 'local knowledge' scattered across the globe. No single expert, federal agency, or algorithm can master the fragmented information dispersed worldwide. Prediction markets essentially do one thing: crowdsource global wisdom.
In contrast, public opinion polls and regulatory reports are static snapshots—often outdated by the time they are released. Prediction markets are dynamic and continuous. When a geopolitical event occurs or economic data is leaked, the instantaneous fluctuation in contract prices tells you how important that piece of information is faster than any editor rushing to publish.
The article also mentions an everyday scenario: if a cable TV host is shouting that a certain piece of legislation 'will definitely pass,' but the corresponding prediction market contract price is only 12 cents, you immediately know the gap between rhetoric and reality. It's a real-time 'reality check.'
