Polymarket and Kalshi, two prediction market platforms, have expressed a strong willingness to cooperate with the U.S. Commodity Futures Trading Commission (CFTC) to crack down on “insider trading”; however, Robin Hanson, an economist regarded as one of the foundational figures of prediction market theory, has put forward a completely opposite view. He believes that so-called “insiders participating in trades” is not a flaw of prediction markets—instead, it is the core mechanism that enables prediction markets to quickly reflect real information and generate public value.
But if the function of prediction markets is built on insiders profiting, then are uninformed retail traders destined to be the ones who get taken? Hanson’s answer is rather cold. He cites a poker-table analogy he often uses in his finance classes for students: when you sit down at a table, you should first look to see who the fool at the table is; if you can’t tell who the fool is, then that fool is very likely you.
Kalshi and Polymarket try to cut out insider trading
The spark for this controversy came from an earlier indictment by the U.S. Department of Justice against a U.S. military servicemember involved in planning an action to arrest Venezuelan President Nicolás Maduro. Prosecutors allege that the servicemember allegedly used confidential information to place a $33,000 bet on Polymarket that the related raid operation would occur, and then profited by about $400,000 after the event came to pass. The day before, Kalshi also announced that it would fine three federal candidates who bet on their own election outcomes and suspend their accounts.
With regulatory pressure increasing, Kalshi and Polymarket have already rolled out new rules prohibiting political figures from trading contracts related to their own elections, and also limiting athletes’ betting on events of the leagues they belong to themselves, as well as restricting employees from trading prediction contracts related to their employers. Platforms are trying to sever the sensitive link between “prediction markets” and “insider bets.”
Foundational figure of prediction market theory: insider information is one of the values of prediction markets
But Robin Hanson does not agree with this direction. Hanson is a professor at George Mason University, who has long studied information aggregation and prediction markets, and helped develop many market scoring rules adopted by prediction markets. He bluntly says that if the market’s purpose is to provide more accurate price signals, then the people who know the answer best entering the market is precisely what is key to the market performing its function.
Within Hanson’s theoretical framework, prediction markets are not simply speculative games, but a kind of institutional design of “paying people to tell the truth faster.” When some people have information closer to the truth, they buy or sell contracts, causing market prices to move closer to the actual outcome more quickly. If these people are entirely excluded, prediction markets may devolve into another kind of poll, news tracking, or crowd speculation, losing the speed and accuracy it has relative to traditional information channels.
This is also where prediction markets are most controversial yet most valuable. For ordinary users, platforms like Polymarket and Kalshi may be a venue for arbitrage, betting, or trading; for regulators and some policy figures, they are often seen as gambling, or even dangerous tools that financialize politics, war, disasters, and public events.
But for market-oriented economists like Hanson, the true value of prediction markets is that they can convert information scattered throughout society—and even information that has not yet been made public—into observable price signals.
In the article, an example is given: in the final few hours of the Biden administration’s term, there were anonymous Polymarket traders who correctly bet on four specific individuals being pardoned before Biden left office, and they profited by about $300,000. From a traditional legal and ethical perspective, such trades inevitably raise questions about whether someone got the information in advance; but from the standpoint of prediction market theory, this is precisely why market prices reflect the direction of events earlier than news reports do.
Hanson: Prediction markets are a more democratic institution
However, Hanson does not completely deny the risks of trading on confidential information. He acknowledges that trade-offs do exist in society. On one hand, some organizations want to keep secrets; on the other hand, the larger public world often wants to know those secrets. He believes the issue should not swing to either extreme.
For example, U.S. military servicemembers betting and profiting $400,000 before a military operation actually occurs could indeed constitute “operational risk.” U.S. Senator Elissa Slotkin has also used this as an example to push legislation that would prohibit government employees from participating in prediction market trading. Hanson does not deny the dangers of cases like this, but he believes that if society is going to discuss insider trading in prediction markets, it cannot ignore the large flows of insider information that already exist in traditional financial markets.
Hanson points out that in typical financial markets, before major company news is released, stock prices often already reflect some portion of the information in advance. He believes that this comes to a significant degree from insider trading itself, or from market participants who follow up after noticing abnormal trading; and the insider trading cases that the U.S. Securities and Exchange Commission (SEC) actually prosecutes are only a very small part of the whole.
In Hanson’s view, the concept of insider trading was originally fairly narrow, mainly targeting corporate insiders trading using information from within the company. But as the scope of regulation expands—especially when the CFTC extends the concept to “anyone who makes a commitment to keep information secret”—insider trading has gradually shifted from corporate governance rules to broad obligations that require everyone to help organizations keep secrets. He believes this transition has gone too far.
Hanson proposes that if the government wants to prohibit civil servants from trading on prediction markets to avoid exposing confidential or nonpublic information, then based on the same logic, civil servants should also be prohibited from leaking information to reporters. Because journalism is also built on uncovering and exposing information that organizations do not want made public, and society generally recognizes that journalistic disclosure has public value.
He criticizes that if only reporters, experts, or a small group of elites are allowed to handle information aggregation, but ordinary people are barred from revealing information in the market through trading, then behind it there is in fact an attitude of elitism. Hanson believes prediction markets are a more democratic institution, because anyone can participate in information disclosure and price formation.
Focus on insider trading: are retail traders destined to be the ones who get taken?
But this viewpoint also raises another sharp question: if the function of prediction markets is built on insiders profiting, then are uninformed retail traders destined to be the ones who get taken? Hanson’s answer is quite cold. He cites a poker-table analogy he often tells his students in his finance class: when you sit down at a table, you should first look to see who the fool at the table is; if you can’t tell who the fool is, then that fool is very likely you.
In other words, he believes individuals should recognize their position in an information-asymmetry market. If an investor doesn’t know whether they have an information advantage, they should get out, not demand that the market eliminate all risks. For Hanson, prediction markets indeed involve risk and are not suitable for everyone to participate in; but as long as they can generate faster, more accurate public information, this system has value.
In the end, Hanson describes prediction markets as a “great democratic institution”: everyone is allowed to participate, but that doesn’t mean everyone is advised to participate.
This line also points to the deepest contradiction in today’s prediction markets. To obtain regulatory legitimacy, Polymarket and Kalshi are actively excluding the traders with the greatest information advantage; but in prediction markets’ original theory, it is precisely those people who make prices move closer to the truth. Regulators want a prediction market without insider trading, but Hanson reminds that if insiders are completely removed, what remains may be nothing more than a riddle game packaged as a financial product.
This article The foundational figure of prediction market theory Robin Hanson: Insider information is one of the values of prediction markets was first published on Chain News ABMedia.
