687. Are Prediction Markets the Best Forecasting Tool Ever — or Just Another Casino?

22:56You have a date when you're supposed to deliver something, and the question is, do you? And obviously, we often fail to make deadlines, so this is a live, realistic question. Will we make this deadline? Now, these markets we've created on deadlines, they have consistently been accurate. However, people who run projects don't want the markets. If you run a project, you want to know if you'll make the deadline, but you more want to have a good excuse if you fail. And everyone's favorite excuse if they fail is the following. We were going along just fine. Everybody thought we could make it. And then at the last minute, something weird came out of left field, knocked the project flat, and that's why we didn't make the deadline.

23:42Tariq Mansour, the CEO of Kalshi, calls Robin Hanson's work on prediction markets foundational. But he especially likes to cite the work of Philip Tetlock, a University of Pennsylvania psychologist who you may have heard on this show in the past talking about the folly of prediction. Tetlock argued in his book, Superforecasting, that when it comes to predicting the future, even an expert can be beat by a thoughtful amateur. Here's Mansoor again. He put a bunch of experts on a specific domain and a specific topic like geopolitics against a bunch of non-experts, kind of random people where the condition was sort of intellectually curious, read the news, they're sort of interested in different things.

24:22He had them predict a bunch of events. Then he measured how different the two groups performed against each other. The surprising outcome, which will not be surprising when I say it here, is that the second group outperformed. So in your view, how do you read that research? Why did the experts underperform? There's a variety of theories, but people that have domain expertise in something, over time, they become a little too dogmatic about that thing. Whereas the people that can take a step back and look at it more dispassionately tend to self-calibrate better and they have less bias. This is the key thing.

24:51It kind of means that domain expertise is not one of the most important things for being a good predictor of the future. That's a fairly heretical thing to say in some circles. Imagine saying that to politicians or researchers, financial firms, analysts, this notion that actually, if you want to get the right answer, you should get a very diverse set of participants who pride themselves in self-calibration, thinking critically about the word, reading beyond the headlines, having this idea of being able to have a clean filter on everything they read. That's more important than actually the expertise in a specific domain.

25:23That's foundational to what we do. Coming up after the break, how does a Calci contract get written? I'm Stephen Dubner. This is Freakonomics Radio. We will be right back.

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27:22Now I would like you to meet Nicole Kagan. We have the most robust data set that has ever existed on transaction-level data in the U.S. on a federally regulated exchange. What Kagan is talking about is at least the most robust publicly available set of transaction-level data. And how does she know this? I'm the head of research at Calci, and I also write the contracts and self-certify them with the CFTC. She joined Calci in April of 2025. At the time that I joined, the exchange was transacting, I think, about$300 million a volume a month. We're now doing about$14 billion a volume a month. Five years ago, the job that Kagan does today did not exist.

28:02So how did she get here? After I graduated from college, I worked for a hedge fund for a year. I went to graduate school at Oxford. Every person that I went to college with wanted to be a professor of economics, so I wanted to be a professor of economics. Are you serious? What were they thinking? I have no idea. But I decided after doing all of the PhD coursework and none of the dissertation that is actually probably the more interesting piece of doing a PhD that I wasn't so interested because I think that there's a lot of real world data that you don't have access to when you're an academic that you can actually just play around with when you're in industry.

28:31How do you see what you're doing now filling that gap? It's massively useful because we're making the data available to researchers for the first time. Drill down though, what kinds of data? So we have data on, for example, how people are trading, how users trade through time and move through different markets, how they enter the platform, where they exit the platform. We have micro data on individuals because we have KYC. KYC stands for Know Your Customer. That's an anti-fraud safeguard used by banks and other financial firms. So we have data on where people are based, for example, where their state of registration is.

29:06in the lead up to the next presidential election. It'll be really interesting to see if we're seeing different trading activity in swing states on swing state-related markets. Your main function is to write a contract, correct? Exactly right. We create contracts that are robust, legally robust, and then we offer a platform for the exchange of the capital on either side. It would be incredibly useful to hear how a contract gets written from idea to getting live on the Calci market. The first stage is having the idea. We then think about what the economic justification for listing a market on that idea is.

29:42Let's take a really simple contract on economic statistics. For instance, what will the Federal Reserve set interest rates at next month? That's a pretty important thing. At that point, we consider whether we already have a contract certification for it or whether we need to write a new contract. We have many submissions that we've already filed, in fact thousands, with the CFTC that allow us to list certain markets. They're written pretty generically. They allow us to switch out certain words and then list those markets. If we don't have that contract, we'll create a new contract. A new contract will take the form of something like, will econ stat be value in time period?

30:22Where we have these three variables that we're setting. Do you ever have good ideas for a contract but decide not to go forward because you think it'll get hung up for six months or something getting CFTC approval? No, generally speaking, when we're talking about the self-certification process, we get these submitted to the CFTC in the morning. The CFTC holds them until the afternoon and then we're allowed to list them unless they've intervened to tell us that we can't. What are some markets or contracts that you don't allow and why? Under the Commodities Exchange Act section, I think, 4011, there are six categories that are subject to restriction.

30:58These are markets that we do not offer. These are markets on terrorism, assassination, war, gaming, any activity that's unlawful under state or federal law, and anything that is contrary to the public interest as defined by and interpreted by the CFTC. If 4011 did not exist, would you offer all those contracts? No, I don't think we would. Because why not? We think a lot about the incentives surrounding the markets that we're creating. So we would never, for example, want to induce an actor to adversely impact some outcome or some other person or to cause harm because we have a market that is live.

31:35That's something actually we take very seriously. We have specific rulebook provisions that carve out what we do in cases of death. We might have markets on what somebody might say in a speech or whether somebody might attend an event or what somebody might say at that event. There are certain cases in which we might want to invoke rules around what we want to do if that person is subject to violence or death that prevent people from cashing out at a dollar or zero. And in that case, you just refund all money? As an exchange, it's difficult for us to void transactions. What we'll do is we might resolve to say the last fair price or refund if it's a possibility for us.

32:10The last time we were actually asked to delist a market could have been the elections when we were going back and forth in the regulatory process. This was prior to the 2024 authorization to list them. This is when you were asking forgiveness rather than permission phase. We were very much still asking permission, but I think the permission was a little bit piecemeal. That was a time that we did actively delist markets. Usually it is not the CFTC that will ask us to delist a market. It'll be us internally finding a market that we might not want to list. After the assassination of Charlie Kirk, any market related to Charlie Kirk had to be altered or taken down or settled.

32:48There are times where we might intervene to deal this markets where there are events that have happened that would compromise the integrity of those markets. Writing the rules is one of the most interesting jobs at Calci, and I don't just say that because that's my job. But there are a number of things that you need to think about. You need to think about what is the underlying that you want to refer to? What is the actual topic that you're going to resolve this on the basis of? For something like the federal funds rate or for something like inflation, for example, what is the actual number that you're going to resolve that market off of.

33:16Then you have to think of who is going to provide you with that number. The gold tier there is going to be the original statistical agency that produces that number, ideally a government or federal agency that produces that number. That's, I assume, changed a good bit during the second Trump administration. Yes, there's just been different reporting from government agencies. It depends on the market. By and large, our contracts haven't changed because they tend to have many source agencies listed. Then we have to think about what the variables are and how we define them. Let's say we're talking about the Fed funds rate or we're talking about inflation.

33:46We need to make it generic enough that we're able to use that variable to include either of those things. So we might say something like, it's an economic statistic as specified by the exchange. It can be as broad as that. And then we'll get to the actual payout criterion. The payout criterion defines when we would resolve a market to yes, when we would resolve a market to no, and when we might resolve a market to neither yes nor no. In these cases, we'll write something that says if the print is value or if econ statistic is value, then the market will resolve to yes. That's a crude simplification of the way that we might write the first line of these payout criterion.

34:22But then after that, we have to think about edge cases that might happen. What happens if you're talking about the Fed funds rate and the meeting is cancelled? Or it's delayed past a certain time period? Or something happens that interrupts or disrupts that meeting? Or what happens if in fact there are three numbers that are produced instead of one or two? This actually became really relevant last year because when the government shut down, there was no publication of inflation for the month of October. And that is effectively unheard of in the macro space. They sent hedge funds into a bit of a frenzy at the time.

34:55But we had a contract on what will the rate be in October? And there was no rate in October because it was never published. What happened in that case? We had our own formula that interpolated from past months what the figure would be. We made that clear. That was a clear part of the full rules. And then we added a green box to the page to clarify to users how we were going to be resolving those markets. I'm guessing some users disputed that reckoning. I'm sure that there were people that were more or less happy with that conceptually. But we do need to weigh what we do in these kinds of extreme situations because we can't just throw up our hands and say, well, we don't know what the rate is.

35:31We need to create clear paths to resolution in every edge case that we can imagine exists. This is true of this economic contract that we're discussing, but it's also true of things like elections, where you might have questions about what happens if it's challenged? What happens if it's overturned? What happens if there's a coup? What happens if they're never inaugurated? What happens if something happens to them? What happens if they change their name? Is it still the same person that we're talking about? What happens if both candidates have the same name? That's pretty confusing. How long does that process take for you and your team to create a contract that is able to include all those variables and potential edge cases.

36:02It sounds like it might take a year to write one contract, but plainly that's not the way you want your business to run. No, and in fact it's not the way the business runs. It depends on the complexity of the contract. If it's an area that we understand well, it could probably take as little as a few hours to a day because we're pretty used to it. What's the most complicated or longest contract you've published? We have markets on what people will say in a given speech. We recently rewrote the rules for those. Because why? There were lots of questions that came up about different grammatical features that might come up.

36:32What if there's an apostrophe in what they say? What if they misspeak? What if they pronounce a word incorrectly? Is that still the same word? Is that a different word? What happens if it's in a proper noun? What happens if it's in a live stream and then the live stream goes dark and then it's uploaded somewhere else later? It took us over a month to write this new set of rules for our mentions markets. They're seven or eight pages long. They are unbelievably detailed. There was an article that came out, I think Bloomberg published it. They were trying to get at why we wrote rules that were so detailed.

37:02The conclusion that they came to was it's for automated processing and it must be AI. It's actually got nothing to do with that at all. We wrote them for user clarity and didn't even consider the fact that if it's clear to a person, it's probably clear to a computer too. Do you at least run your contracts through an AI? As a stage of the process, we will usually run them through some kind of LLM to see if there's anything that we've missed explicitly or anything that is a logical contradiction that we haven't caught. But it's not the primary thing that we rely on when we're writing these contracts.

37:30Let me ask you about one more edge study. Let's say who will perform during a Super Bowl halftime show, especially if it's maybe Cardi B, who's not the headline performer, but maybe a performer, maybe not. I knew you were going to ask this question. The rules for this contract were not particularly unclear. These contracts basically said if the person is dancing and singing, then they're performing. What you saw in the video in the probably seven second clip in which she appeared is that she's definitely dancing. But you absolutely cannot tell whether or not she's singing. And she wasn't mic'd.

38:02Exactly. But it kind of looks like her lips are moving. From our team's perspective, we said, well, okay, she's clearly dancing. We can't tell if she's singing. We don't feel comfortable resolving this to yes or no because we can't tell. we're epistemically a little bit uncertain about this. What we're just going to say is we're going to resolve this to a last fair price. We do have the power to do that in cases where it is genuinely unclear whether or not an event met the resolution criteria that we had set. What does that mean to resolve it to the last fair price? Usually our contracts are binary.

38:32The way that the prediction market works is that there's a question that's asked and at the end if the event happened you get paid out$1 and if it didn't happen$0. Every position is what we would call fully collateralized which means that if there's somebody that puts up 30 cents on one side in order for that actual exchange transaction to go through, there needs to be somebody who puts up 70 cents on the other side to make a dollar. Sometimes there are cases where we need to settle to a value that isn't zero or one. And this is what we might invoke to be like a last fair price, or it could be at times 50-50.

39:00A good example of 50-50 is if multiple people win an award. We might say that because no singular person won outright, but two people won, they'll each resolve to 50 cents. Will you write the Super Bowl performance contract differently next year? We've actually already amended that contract. Basically, we wrote a longer contract that was a little bit clearer about what we would define as a performance and not. Okay. So under the new contract, would Cardi B have performed or not performed? The answer is no. You now have to be singing, including audible lead or backing vocals. I feel like there is a fundamental difference between people who believe in prediction markets And then a lot of the rest of the world who see primarily a betting market and don't see it as a useful tool for actually surfacing good information or for judging the value of information.

39:48So I'm asking you, I mean, this is as softball as it gets. I'm asking you to give the best evidence or argument you can for why what you're doing is actually valuable in a pro-social way. There was research that came out of the Federal Reserve a few months ago with some researchers that looked at our macroeconomics forecasts and they compared them to consensus estimates. Consensus estimates, the gold standard in financial markets. And they basically found that Calci markets actually outperformed consensus in their predictive ability, which is a finding that Calci Research had had three months prior.

40:19But of course, we're not researchers at the Fed, so we don't have the level of credibility that is assigned to them. it's not just valuable to know how people are thinking and get a temperature check in a market based estimate kind of way there's power to that of course when you're giving people skin in the game and you're asking them to put their money where their mouth is they're more likely to represent to you what they think is going to happen in the future in an accurate way because their incentive of course is to make money and i think that we see that across all markets to the extent that you think that market-based pricing is good it also applies to pricing of the future The difference with prediction markets is that you're able to get direct exposure to an event that's going to happen.

40:56If you're talking about polling as a comparison and you ask who's going to win the next election, there are certain biases that might affect how people respond to that question. Potentially, they don't want their neighbors to know that they're going to be voting for the less savory candidate. Maybe it is a case that if you're an expert forecaster, you don't want to step out of line with the consensus estimate for the next inflation print. But if you're taking market-based estimates that people are putting money behind, those kinds of biases just don't apply because the incentive construction is so different.

41:23It's not even just the pure play probability that is useful of the thing that's going to happen in the future, but there are a number of derivatives of that that are also useful. In the paper that was put out by the researchers at the Fed, they basically said, well, not just do we have an understanding of what's going to happen with inflation or interest rates next month, but what's also interesting is we can understand the distribution of opinions for the first time. We can see whether they're tightly compressed around one outcome or whether they're more dispersed. And that, of course, carries its own layer of information.

41:55Earlier this year, Kalshi put out a research paper on New York's mayoral race, which Zoran Mamdani won after starting out as a very long shot. The paper is called Slowly, Then All at Once. We actually looked at Mamdani's rise to power and we mapped it. We then did a component analysis to try and figure out what were the events that moved the market. What that gives you is access to a credibility layer on all of the media that you're consuming. It can tell you what is it that people care about and are paying attention to. Are there certain news outlets that they might price in as more valuable than others?

42:28Is it the case that they're responding to polling in a certain way? The prediction markets, they're reflecting information that polling can't pick up on, like Momentum, and they're doing it in a continuously updating way that is distributionally rich and it is not able to be offered by any parallel mechanism. Even if prediction markets are more reliable than political polling, and they have been for a long time, they still aren't close to foolproof. Both Kalshi and its big rival, Polymarket, had Francesca Hong at 95 % to win Wisconsin's Democratic primary for governor. She lost. A week later, the markets gave Angie Nixon only a single-digit probability of winning her Florida Democratic primary for Senate, and she won easily.

43:12Here's what Kalshi CEO Tarek Mansour posted on X. Before the prediction markets got it wrong headlines roll in, a 5 % probability doesn't mean it won't happen. it means it should happen one in 20 times. If 5 % candidates never won, the markets would be broken. Markets on elections are always going to make a lot of noise, but there are other important markets that are much quieter. Here's Mansoor again. One of the things that we're rolling out is like markets on FDA approvals. Figuring out where to place capital on different drugs is one of the main drivers of drug discovery over time. That's incredibly important.

43:52Like if something has a 5 % chance of succeeding that's being marketed as 70 % chance of succeeding, you want the prediction market there. If you have a significantly more accurate gauge in what's going to go through and what's not, it's going to enable significantly better allocation of capital across the board. And here's Nicole Kagan again. One thing that we're looking into now is listing more granular biotech biopharma markets. One thing that we are wrestling with on our end is if you're Pfizer and you've got some clinical trials in motion, you as Pfizer are prohibited from trading on the Calci market because you have MNPI on how that trial is running.

44:25MNPI stands for material non-public information. You as an institution shouldn't be able to take a position on that market, at the very least not directly. So we need to think about also what the incentive is for other companies in the space to potentially want to hedge out risk for approvals for, say, their competitors' new drug. FDA approvals, corporate decision making, election forecasting. Even if you are a prediction market skeptic, you can probably see some value there and you can see why Calci likes to talk it up. But here is an uncomfortable fact. Roughly 90 percent of Calci's trading volume is in one category we've barely mentioned yet.

45:06Can you guess which one? I'll give you a hint. It's the kind of trading that is making DraftKings and FanDuel nervous. That's coming up. After the break, this is Freakonomics Radio, and I'm Stephen Dubner.

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46:47Pretty much every single human, you, me, anyone else you can think of, is making predictions all the time about everything. It's just that we're not very systematic or accurate, and we are rarely held to account if our predictions turn out to be wrong. The whole point of a prediction market, meanwhile, is this. The whole point is you get rewarded for truth-seeking. You get rewarded for being right. And you get punished for being wrong. That, again, is Tarek Mansour, CEO of the prediction market Kalshi. Will succeed when trust in those prediction markets becomes mainstream. I think it's starting.

47:27One of the statistics that I like the most is 70 to 80 % of our active users don't trade. They're just logging in next to X or they read a few newspapers in the morning. It helps them calibrate or fact check. I understand you don't want to talk about how much CalShe and you are worth, but this plainly is a lucrative enterprise. Can you explain how CalShe makes money? So we are a neutral platform. We're like the New York Stock Exchange or NASDAQ. People are trading against each other and I take 1 % on average. That is independent, agnostic on who's going to win or lose. That fee is the fee that I get for running the business and offering the regulated infrastructure and all the things that come with trading on Calci.

48:06My job is to provide a fair, neutral, competitive grounds for people to compete. And they do their thing. They do their research. They go out and seek information. They do whatever it is they do to get more accurate predictions and then go and trade them in the open market against each other. Like I think the word is getting more bifurcated, more polarized. I think the noise to signal ratio is going up. The reason is because most ways that we ingest information, this is especially true for social media, the algorithm essentially incentivizes clickbait. If you write a well-nuanced, well-measured, multi-paragraph take about something, you get four likes on X.

48:41If you write something extreme and off the rails, you get tens of thousands of likes. It's incentivized clickbait. The incentive structure in prediction markets is the exact opposite. That multi-paragraph, nuanced, boring, well-calibrated take, that's the one that makes money. You're doing about$4 billion overall a week. I think it might be a little bit more now, right? I mean, there's crypto politics. I think culture is the fastest growing. And then like financials. Can you make an argument that culture betting is good for society as well? Or is that more of an entertainment? I think figuring out whether albums are going to succeed or not.

49:13People are like fanatics and passionate about it. But you ask a lot of these people like, hey, do you trade financial markets like SNP? And they're like, no, because I don't gamble. They're just like, what do you mean by that? When we launched weather, people were like, oh my God, people are weather gambling and stuff like that. You ask those traders, they're like the most sophisticated, hardcore researchers. They're like scraping satellite data. They tell you we don't trade in financial markets because it's gambling. And it's like, what do you mean by that? It's like, well, the game is rigged against us.

49:37There is no shot for us to be the Wall Street hedge funds, et cetera. Whereas here I can do research. I've spent already copious amounts of time reading and getting informed and I can like make money on it. They're participating in a financial market where they can have an edge. When I look at your numbers, your trades, it looks like roughly 90 % of the trades are in sports right now. So you've kind of become a sports betting site, at least for now. So talk about the benefits and risks of being that sports heavy, whether gambling addiction is a problem you think about much, whether you want to diminish your share of sports betting, etc.

50:13So sports, actually, the share is going down over time pretty fast. So the other categories are actually growing faster than sports. But that's natural because if sports is 90 and the others are small, growing fast, I mean, you can double from one to two in a month. They're still pretty big. Don't forget, we're very big, right? So the 10 % is still very big. I want to kind of draw a distinction between this and gambling first. And I think that's very, very important. There is speculation in all financial markets. So, you know, when you look at the percentage of, you know, hedging versus speculation in any derivatives markets like grain futures or commodity futures or stock options, it tends to be very tilted towards speculation.

50:44And the reason you want speculation is you want liquidity. Without speculation, you don't get liquidity in any of these markets. Now, the thing about speculation is that speculation has similarities to gambling, right? Like you're putting money to make more money on something you don't control, right? That's basically the definition of gambling in a lot of states. And that's why historically, you know, every time there's a new financial instrument or more means of access, like when Robin Hood came around, it was like, oh, you know, gambling in the stock market. Or when grain futures got legalized in the US, I don't know if you know this, but they got legalized via a Supreme Court decision, which was like, is this gambling or is this a financial instrument?

51:18People used to call grain futures gambling. But the key thing is it's less about whether there's speculation or not in the market and more so about the business model. One business model is a business model that seeks out losers and then blocks winners. They do not want price discovery to happen naturally. The other one is a model that's geared towards price discovery. You want the smart people. Now, does that mean that second model, like the cash model or financial markets model has no risks? The answer is no, right? They have risks. In my view, it's like there's irresponsible gambling and there's also irresponsible trading.

51:50How many times have you heard of people losing their house over options trading, especially with zero data expiry options or futures or active day trading of stocks and prediction markets present similar risks? And I think we have a responsibility as a platform, which is part of why we have this regulatory first approach to monitor those risks and make sure that we don't let people kind of fall off the cliff.

52:13So, how far does that responsibility go? Wherever the sheep accumulate in the world of markets, the wolves go there. Nearly 20 states have already taken legal or regulatory action against prediction markets. Coming up next time in part two of the series, we will hear from a former CFTC chairman who thinks Kalshi may be on shaky ground. This will end up debated and discussed amongst nine individuals in a small conference room in Washington, D.C. and that's called the Supreme Court. That's next time on the show. Until then, take care of yourself. And if you can, someone else too. Freakonomics Radio is produced by Renbud Radio.

52:54You can find our entire archive on any podcast app. It's also at freakonomics.com where we publish transcripts and show notes. This episode was produced by Tao Jacobs. It was edited by Ellen Frankman and mixed by Jake Loomis with help from Jeremy Johnston. The Freakonomics Radio Network staff also includes Dalvin Abouaji, Eleanor Osborne, Elsa Hernandez, Gabriel Roth, Ilaria Montenacourt, and Pete Madden. Our theme song is Mr. Fortune by The Hitchhikers, and our composer is Luis Guerra. As always, thank you for listening.

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