The Media Needs to Dig Deeper Into the Value Claims of Prediction Markets

Subramaniam Vincent is director of journalism and media ethics at the Markkula Center for Applied Ethics, Santa Clara University. Views are his own.

Prediction markets (PMs)—Kalshi, Polymarket, and more—are the latest ethical and regulatory mess to erupt in the digital media economy. But what should the media do about PMs?

The questions are many. What does it mean when news anchors and articles cite the odds numbers for various future events? What should the media call these odds? Probabilities? Chance, as CNN’s Harry Enten calls Kalshi’s odds on the “GOP Chance to Hold the House”? Should media organizations take money from PMs in return for offering ticker space to showcase the latest odds? Should reporters and editors just turn the whole PM space into a beat like some news outlets (e.g. Wired) are?

Before we lay out an approach for media organizations to answer these questions, a note on the context, since media policy cannot be developed in a vacuum. We need to first start with the legal and regulatory situation.

Are prediction markets legal? Yes. Are they a form of gambling? Yes, in Europe. Are they gambling in the U.S. Well, it’s complicated. In the U.S., there is a definitional dispute on their categorical classification sprawling out across federal/state power lines. Lawsuits are being filed left, right, and center. Over a dozen U.S. states have sued, primarily on grounds that the activity on PMs is gambling. Arizona even filed a criminal case against Kalshi. The federal futures regulator, Commodity Futures Trading Commission (CFTC), has stepped in on behalf of Kalshi and Polymarket trying to preempt the jurisdiction of the states and threatening to sue state regulators.

(On the regulatory stance from CFTC, one note: CFTC is supposed to have five commissioners, but it is led by one Trump appointee who is the chairman, and four seats are currently vacant. The U.S. president’s son, Donald Trump Jr., is connected with both Kalshi and Polymarket, and there are reports the Trump organization plans to launch its own PM.)

As the regulatory confusion sorts itself out, major episodes have emerged on either suspicion of or outright prosecution for insider trading (Maduro’s capture, Iran war, etc.), and betting actors trying to influence outcomes or pressure journalists to confirm or disconfirm events one way or another. The threats to journalist Emmauel Fabian are the most prominent example of PMs colliding with journalism and resulting in investigations.

It’s worth noting the main attraction of PMs is that they are secondary markets, designed to invite everyday people to take and trade bets on future political events. Such events are often ones where capable journalists and analysts may report on with deep sourcing, so the risks of manipulators or opportunists stepping in is real. And given the temptation on journalists themselves (to place bets) when they have private knowledge of political moves before their stories go public, it isn’t surprising that ProPublica took the lead recently and barred its journalists from participating in PMs.

PMs have also become a revenue opportunity for news organizations as “data sources” for “probabilities” of future events, particularly for election outcomes, political candidate prospects, and so forth. For broadcast and big new media organizations, polls and pollsters have long been a part of news investments. Horse-race stories—the routine reduction of election coverage to poll standings—have long been criticized by ethicists, and yet they remain a staple of the news cycle. PMs have now been added into the mix because of their “forecasting value.”

A whole range of mainstream news orgs have tied up with both Kalshi and Polymarket to either list their betting odds as data tickers on the same screens where they show poll results, or use their data in other ways. The PMs are paying the media companies in what can only be called “paid sourcing” or “brand placement”—all of which is a way for the PM players to legitimize their products through the media and expand betting user bases.

But beyond the surface-level ethical questions about the PM/media nexus, there are multiple and deeper reasons for everyone including media professionals to parse the claims PM promoters make about the utility of these markets themselves. There is a need to dig deeper into how theorists who have already studied these markets have described what they are and what they do. This is more important right now than simply accepting that prediction markets are cheap ways to get data about forecasts of future events, so the media should just join for the ride.

Prediction Markets Claim to Epistemic Value: Bring Privately Held Knowledge to Bear by Betting

Journalism and PMs do have something in common: the capacity to reduce information asymmetrically. When reporters pull in privately or semi-privately held knowledge and information from credible sources and make them public through stories, they are reducing information asymmetries to that extent. Stories carry their facts, claims, and beliefs of their sources and are generally accessible to everyone in discourse (setting aside paywalls for the sake of the principle that news is a supposed to be public good.)

PMs are incentivizing bettors with particular knowledge of the likelihood of a future event to weigh in with their money. These could be complex and consequential future real-world events that can impact the lives of thousands to millions of people. This likelihood numeric is the price and is posted on the platform for everyone to see. Therein lies the claim to bring in privately held beliefs about the likelihood of future events.

The problem is that PM odds factor in not just such knowledge—claimed to be held in dispersed crowds—but also the sentiments and beliefs of people who may not have specific knowledge. These people may simply be betting on their beliefs drawn from a narrative they subscribe to, intuition, expert sense, and so forth. Entire swathes of bettors on Kalshi and Polymarket are there for the ride (secondary markets) like short-term day traders, and hence there is credence to the claim by regulators around the world that this is gambling. A prime example is the Eric Swalwell prediction market odds for governor before the sexual assault allegations broke out.

For this and more reasons as the article will show, PMs are going to be a deeper challenge for the news media.

Chance or Wealth-Weighted Belief?

Let’s start with how the media is describing Kalshi or Polymarket’s numbers for future events. When Harry Enten, CNN’s chief data analyst, says, citing Kalshi, that the chance that the GOP will hold the House in the mid-terms is 14%, what is he asking the public to understand from the word “chance”? Is it a probability? It would appear so since in statistics 101, when chance is expressed as a percentage, it refers to probability.

But that is where the media is misleading people. Prediction markets do not measure opinions or voting intentions like polls. (Even polls have had a poor track record recently.) PMs measure the financially weighted probability beliefs of bettors. When a contract on Kalshi or Polymarket trades at $0.60, this means the market’s consensus is that there is approximately a 60% chance the specified event will occur. This is a probability belief, yes, but this number is not a simple average of what individual participants (bettors) believe. Wealthier traders can express stronger conviction by committing more capital. So, the chance that CNN’s Enten keeps talking about when referring to Kalshi’s numbers technically reflects what economists call a wealth-weighted average belief rather than an equally weighted mean.

Also, prediction markets are dynamic, secondary markets. A lot can happen between the opening of an event market and its actual resolution, that involves decisions by people, leaders, organizations, unexpected events, and so forth. PMs “track” what is going on through the belief lens of the betting crowds. So, PM estimates of the likelihood of some event happening (resolving to “true” in PM parlance) is not a straightforward probability estimate.

So, a prediction market’s contract price of $0.60 may look similar to polling results for candidates like 60% and on the broadcast ticker of a TV screen, both expressed as a percentage. But they are measuring categorically different things. The poll reflects what a sampled cross section of the relevant population says it will do. The PM price reflects what financially motivated bettors, weighted by their capital, believe will happen. When CNN or any other media treats them as interchangeable, it is a category error. This means the media is conflating meanings, impeding public understanding instead of fostering it. This is only one example of news media embracing prediction markets’ claims uncritically.

A related ethical issue with relaying Kalshi data as election results probability numbers is disclosure. CNN needs to say somewhere on the screen or in some accessible manner that Kalshi is paying CNN to be a data provider for this, not the other way around when media companies invest money in polling to generate data on public opinions. So, this is a kind of paid sourcing, but not being clearly disclosed as such. In contrast, the working class, issues-focused More Perfect Union (MPU) says it took a traditional journalistic route when Polymarket reached out to them asking for partnership to integrate their odds into MPU’s stories. MPU decided to do a critical 21-minute YouTube explainer on prediction markets.

A Short Note of History

PMs are not new. They have long been a research topic for information and market theorists, people studying information asymmetries, epistemics and the role markets can play in mitigating them. The University of Iowa has been operating a PM called the Iowa Electronic Markets (IEM) for over two decades now, specifically on political bets, election outcomes and so forth. While the goal has been both academic research (many universities are involved) and student education, the fundamental position limit of $500 for bets is a structural safeguard that is less well known. IEM also runs as a secondary market with traders’ bids and asks being matched by the platform. 

The research that emerged on the accuracy of PMs for some national election forecasting use cases (where PMs have outperformed polls) has made IEM the academic and real model, as well as forerunner to Kalshi and Polymarket. This broad sphere of studies and findings about PMs stretching back over two decades could be marshalled for norms and policy development in media organizations. One way to do this is to compare polls and prediction markets around both their core claims and ethical challenges in application for public discourse and prediction.

The media ethics program did this comparison recently to build a primer and a decision-making review matrix for media organizations on prediction markets.