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

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2:07and the whole time you stay in control. Put ChatGPT to work on your most ambitious ideas and projects. Get started at ChatGPT.com by selecting Work Mode, available on plus and pro plans. Hey there, it's Stephen Dupner. Before we start today's episode, I want to ask for your help with a future episode about psychotherapy. The episode includes what is called a misery index and we want to hear some of your stories. I realize this isn't for everyone, but if you are interested, use your phone to record a voice memo and send it to us at radioatfreakonomics.com. I'd like you to identify your most intense and persistent form of misery and include some specifics. What exactly was the emotion you were feeling? What's the worst it ever got? How did it affect you and how did you deal with it? If you only

3:09want to include your first name, that's fine. Again, the address is radioatfreakonomics.com. Many thanks and here now is today's episode. Most of us are not very comfortable with uncertainty. And that's a problem since so many things are uncertain like the future. Since the beginning of time, we have been trying to predict the future. The ancient Greeks were famous for their oracles. They would also cut open a sheep and read its liver for signs about whether it was a good time to start a war, for instance. For centuries, potato farmers in the Andes have looked skyward to the Pleiades to help determine when to plant their potatoes. 25 years ago, scientists were finally able to explain why this actually worked. Pleiades visibility is related to the climate pattern known as El Nino. Today, there are prediction markets on El Nino. And the markets indicate this year's El Nino may

4:12be the most extreme on record. So, are prediction markets an upgrade or maybe a disaster waiting to happen? Will they improve our collective decision making or are they just another casino in an economy full of casinos? In our prediction markets susceptible to insider trading, the biggest prediction market in the US, Calche, recently imposed its first ever lifetime ban against an individual trading on their site. This was over bets on whether George Santos, the disgraced New York congressman, would attend President Trump's state of the Union address. Santos had posted on social media that he was going to attend, but in the end, he didn't. On Calche, someone made a profit of $17,000 by first betting that Santos would attend and then betting that he wouldn't. Who was that someone? You guessed it. George Santos. So, how much credit should Calche get for figuring that out and banning Santos? Today, on Freakinomics Radio, we will hear from the CEO of Calche.

5:14Calche is the most accurate way to predict the future, as well as their chief contract writer. These are legal contracts that people are signing on to when they're trading those markets. We'll also hear from an intellectual godfather of prediction markets. The hope is that we could use betting markets as a general information institution all across society. And next week in part two of the series, we will hear from a regulator. What I think is the cost and a very real serious cost is trust in the markets. Or two part series, The Price of Prediction starts now. This is Freakinomics Radio, the podcast that explores the hidden side of everything, with your host, Stephen Dobner.

6:17Calche and Polymarket, the two big prediction markets at the moment, are together worth over $40 billion. Shane Copeland, who founded Polymarket in his early 20s, was for a time the world's youngest self-made billionaire. One Calche founder, Luana Lopez-Lara, is the youngest self-made female billionaire. The other founder and current CEO, Tarek Mansor, joined the billionaire list at age 29. But that is not what Mansor wants to talk about. This evaluation of the company or my network or others, now just don't think that's that important. It's kind of paper money. Okay, so what is important to talk about when it comes to Calche and other prediction markets? If you believe in markets and their ability to aggregate information and price something, in the case of prediction markets, they're essentially aggregating information about a question about the future. If you believe in the crowd wisdom and people kind of putting skin in the game on something, it tends to be more accurate than other places. For someone who doesn't know Calche, just describe what the firm is.

7:18The easiest, most compelling way to describe Calche, it's essentially a financial market that captures a much broader universe of things. Historically, financial markets have been sort of limited to an elite audience. Whether it's the stock market or commodities or interest rates. What we got excited about was broadening that universe to just no questions about whether an event is going to happen or not. It's about things that people care about or relate to, they understand whether it's Saint of Politics, the economy, climate, weather, culture, your name it. Mansor was born in Bakersfield, California, to Lebanese parents, but they moved back to Lebanon when he was young and they divorced when he was six. I grew up outside the system a bit. I mean, I was a math nerd, single mom. My dad is alive, but he was never really present. And then there was a lot of turbulence in Lebanon, a lot. Growing up, there's a mix of two things. There was one, my mom really had a high expectations of us. It's like you should do something big in life, make it count, make it worth it. And the second thing is we had a deep level of frustration,

8:19just the way that Lebanon worked, like the corruption. This is like a bad country. What drives me is I just have some sort of legacy. I want to build something that people point to and respect and have sort of validation. You were born in a good generation. One generation earlier would have been okay, but nerds won the war in a way. It's kind of amazing, honestly. The nerds are cool right now. It wasn't the case when we were growing up. It's like a 10, 15 year phenomenon now. But 20 years ago, I don't think that was consensus yet. In 2014, Mansor left Lebanon to attend MIT. He studied math and computer science. And he soon landed internships and jobs with elite firms, Goldman Sachs, Palantir, and Citadel. When I got the idea, you probably know the story, but when I was in 2016, I go and all the trades was like, hey, do you want to go long Trump or hedge against Trump, winning the election? But they're very sloppy trades, yeah. Very, there was very sloppy. We said, like, oh, sure, the S&P, that's the Trump trade. Bad trade. They were right about Trump winning, but then they lost money because the S&P actually rallied. So the use case is very clear. I just think

9:20it's opening up access for people with significantly broader or more diverse set of interests to have a shot that they don't have international financial market. It wasn't long before Mansor and his MIT classmate, Lopez Lara, started Calci. Where does the name come from? Calci means everything in Arabic. Back when we first started the company, towards the end of 2018, we got into the startup accelerator, Y-communator, and they needed like a name for the application. We were looking for like a cheap domain name and I kind of like names with K, it was like, what if we call it Calci? And then we're like, we'll change it later because it's like a bad name. It's hard to pronounce, but now it's a big brand, so it's hard to change. Many tech startups embrace the Facebook mantra, moving fast and breaking things. That's not Calci. What we did is the exact opposite. What Luana and I decided is we're going to abide by a core principle in the company's regulatory first. And we spent four years getting regulated before we launched a single market. We went to the federal government and said, hey,

10:20how do you regulate this? And what does that mean? Two pillars. How do you build a market that has market integrity? Where fraud is police, there's no insider trading or you police it. Number two is you enable the right set of customer protections. Calci opened to the public in 2021. Some of their first contracts were on mainstream current events, for instance, in a given week, how many Americans would get the COVID vaccine? Would the Tokyo Olympics be canceled? The hard part came when Calci wanted to offer election contracts. The CFTC, the commodity futures trading commission, said they couldn't. Calci sued and the case was decided by a federal appeals court in favor of Calci in the fall 2024, just in time for the presidential election. It had taken Calci a while to get there. Imagine the first four years of my career. I mean, it came out a great personal sacrifice. It really was very tough because regulation is not fun. It's not sexy. It's super boring. It's exhausting sometimes. Did you come close to quitting a few times? I mean, daily, pretty much. But the thing that I just really wanted to see exist in the world,

11:22I just felt like the cost of regret would be too great. Now, there's a little bit of sunk-cost fallacy when you're two years deep. I'll try another few months and then it keeps going. What were your advisors saying? People always trusted our approach because we were pretty dogmatic, but it's an anti-pattern. The pattern and Silicon Valley is you build and you move fast and you build product that customers love. Did you have potential funders tell you, like, Tariq, that's a really nice idea, but there are 50 people who are capable of doing something like this and they're not going to go the slow legit route. Yeah. I mean, Polly Market was launched at the time. That's why people know about the brand of Folly First. That's not because they started the company first. We started first. They started after us, but they took the idea and they were like, hey, we'll launch it off short. Why do we wait? That distinction is really a large part for why we're 90% market share today. Like why we've grown so much is because we stayed committed to that approach. And I think regulation has given us a huge edge because people trusted more and I think we can go mainstream. Institutions are onboarding at a higher rate because it's harder to do it from outside the system. You have to really change the system, which is harder. I do feel like many of us,

12:24even in the political realm, or maybe especially in the political realm, but even in the financial realm, a lot of decisions are made with some insight and some information, but an awful lot of guesswork that we then convince ourselves is empirical somehow. Totally. I'm just curious whether you think that Kalshi ultimately or even now serves a bigger purpose of improving decision-making. It's not like we're making bad judgment calls based on the information we have. It's just that we have a very limited set of information. That's the key thing. During World War II, in a communist Frederick Hayek, dug by the knowledge problem. It was this very basic idea, which is that a lot of decisions is centralized. It's centralized authority figures, whether it's governments or heads of households or leaders and companies and so on and so forth. But the information that is relevant to that decision is actually pretty distributed. If you think about this as that tree, a lot of decisions are made at the root of the tree, but the information is lying in the notes of the tree. Distributed makes it sound to me like distributed among many people.

13:25The word I would think of is more like siloed or hidden. Are we talking about the same thing or no? It's all of them. It's actually distributed. It's siloed. It's hidden. It's fragmented even. So sometimes you may have a piece of information that standalone doesn't make much sense. You have to combine it with someone else's piece of information and all of a sudden it could click. And then it's also dynamic. These nodes in the network or the tree, the information is updating in real time. You could probe it. You could ask the node yesterday, but today it might have a different answer. At the time, Hayek didn't call them prediction markets, but he's like, well, the best way to solve that problem is probably some version of market prices. Because that's what they do. Market prices and traditional financial markets, this sort of aggregate information that could be distributed. This was this idea of this information market. Use market prices to disseminate and propagate information. What form did that take for Hayek? Did he try to do something like this? It was very theoretical. In my opinion, the first tests of that theory, it was in the 80s with the University of Iowa. Are you familiar with that experiment? Yes, these are the Iowa electronic markets. Exactly.

14:29And they ran a small scale prediction market. A few hundred people doing very limited sums. Real money? Real money, yes, yes. Real money is absolutely key. There needs to be skin in the game. You have to be punished if you lose and you have to be rewarded if you win. There's no better reward than making money and no better punishment than losing money. The Iowa electronic markets, originally called the Iowa political stock market, was focused on elections. The CFTC had allowed it on the condition that remained an academic experiment and traders were capped at $500 each. So how did it do when it came to predicting? One study compared its predictions to a large group of national polls and found that the Iowa markets beat the polls 74% of the time. And by now, there were other people starting to think hard about prediction markets. I had this idea of a much wider application of betting markets in the late 1980s. That is

15:31Robin Hansen. He is an economics professor at George Mason University. And it's always been in the background as something I was willing and eager to do if there were people to do it with, but there have been long stretches where there hasn't been that much interest. Do you feel you've been wandering in the wilderness and all of a sudden there is a city on the hill and you're invited and everybody cares? Well, there's a path to the city. We're not at the city yet. But maybe I can see a route that might go up the mountain and we're starting up throughout. What does the city look like? The hope is that we could use betting markets as a general information institution all across society. The main reason for that hope is when we do head to head pairwise comparisons of betting markets and some other institution at the same time, same topic, similar resources. The markets just do about as well or substantially better in terms of accuracy and similar cost. That's just a remarkable fact. We don't actually use speculative

16:31markets very much in our world, an academic or business or journalism or nonprofits that just seems like there's this huge opening to do much better. So many basic questions I have for you based on just that good statement. First of all, why do they do better? I'm an economics professor. So we have a lot of things we think we understand about this, but then most people aren't that inclined to believe economics professors about such things. So I'm mostly going to rely on the data and just say, look, the data says it does work better. But if you want reasons, I can give them to you. Yes, please. Let's start with the comparison of you, a reporter interviewing me, a non-reporter about something. I don't necessarily have the incentive to tell you the truth or to work hard to tell you the truth. I'm an incentive to be engaging, entertaining, dramatic, you know, tell you what you want to hear. Maybe embellish your own stature, not you, but others might. Right. So a prediction market in contrast just gives you a very clear sharp incentive to get it right. But secondly,

17:34if you ask me about something I don't actually know that much about, I'll still give you answers because I want to talk to you the reporter and get in your piece. Whereas the speculative markets give you an incentive to just shut up and don't speak about things you don't know very much about. You're enticed to go look at all the markets and ask which of these markets do you know more about and only speak up about those. There's this old saying in poker, when you sit down to a poker table, look around and find the fool. That's who you'll be making your money off of. If you don't see the fool, it's you walk away and most markets you'd be the fool. So you should not trade most markets. You should just leave them alone. That incentive to select is a second powerful thing. In addition, having an incentive, you just only have the feedback from people who think their world class about something. Number three is that if you ask me a question, I would just give you the direct answer of what I think on the subject. But with the markets, there's always an existing set of market

18:34prices on all these different topics. You're invited to not have a direct opinion on these topics, but go look for biases, look for patterns, look for errors in these prices. And if you can find any pattern that looks like it's a mistake, you are paid to fix that. Robyn Hansen sees the true potential of prediction markets in their ability to inform decisions at scale. When most people get into this topic, the thing they think they want the markets to be about are the usual topics in the media and public conversations. And that's what I thought initially, too. And then after a while, I learned decision theory as a grad student and realized that according to our standard decision theory, information is valuable because it advises decisions. There's a world of people making decisions out there and that's an enormous potential demand for information. That's where I have since put my hopes. Hansen laid out this vision in a

19:341999 paper. He proposed a new form of governance for organizations and the broader political system using a form of prediction markets he calls decision markets. So this is my grand vision. Advice all around for what to do, for example, a firm could have a market on the stock price of the firm conditional on the CEO leaving or the CEO staying by the end of the quarter, which would be advice about whether the CEO should leave or stay. Those prices would say which scenarios worth more to the company. You could also do other major decisions of a firm, restructuring mergers, acquisitions, introduction of new products, non-profits could do this, political parties, governments, and you're even personal, like a student deciding which college to go to or what major could have a market in the consequences for their life. You could have a market in if you dated someone, how long would that relationship last? In a case like dating, where is

20:35the information coming from though? Who's betting in that and how do they know what they're talking about? The wonderful thing about these markets is you don't have to decide who knows best. You just have to make sure that whoever might know is invited to participate. There's a lot of people around you who have seen you date for a bit and they have some opinions. Friends, family, maybe friends of the person you're dating and so on, yeah, or people you've dated before. Well, I've got advice there. Right. Okay. But if you're talking about a group of people with perhaps warped incentives, I would think the people you formerly dated, maybe your own family, etc. On what dimensions would those predictions be valuable? Well, those people do have information and the question is, can we elicit their information without it being distorted by their other interests involved? And the answer is actually yes. All information institutions, including journalism, have this problem that people who have access to grind might try to distort your output. If you interview someone who wants the world to think a certain way about a topic, they may give you

21:38distorted testimony, right? But betting markets are remarkably resistant to that sort of influence. That's one of their strengths. In fact, on average, when you add traders to a market who are trying to manipulate it, who are trying to distort the price, if other people expect those traders to show up there, overall the price gets more accurate. That's a remarkable fact about these. When I've heard you speak in the past about the value of, let's say, an internal prediction market in a firm, this was, I don't know, 10, 15, 20 years ago, I thought, oh my goodness, a great idea, b, there's no way this idea will not take over the world. Eli Lilly ran an internal prediction market in the early 2000s that correctly identified drug compounds that would survive phase three. There was a Google project called Google Profit, PROPH IT. From what I could see, they were both successful, but they didn't last and indeed firms around the world didn't rush to create internal prediction markets. Can you explain why?

22:44That city on the hill we were talking about. That's past the jungle or the forest on the hill where we try to get past corporate politics. If we think about the example of a deadline, deadlines have been one of the earliest applications and corporations because they're just really simple. You have a date when you're supposed to deliver something and 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 a feel, knock the project flat, and that's why we didn't make the deadline.

23:47Tariq Mansoor, the CEO of Kalshi, calls Robin Hansen'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, Super Forecasting, that when it comes to predicting the future, even an expert can be beat by a thoughtful amateur. Here's Mansoor again. He played 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. He 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 outperform. 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

24:49and look at it more dispassionately tend to self-calibrate better, and they have less bias. This is the key thing. It 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 specific domain. That's foundational to what we do. Coming up after the break, how does a Calhchee contract get written? I'm Stephen Dubner. This is Freakinomics Radio. We will be right back. Freakinomics Radio is sponsored by Mint Mobile. For kids, summer means freedom, and as adults, summer still means freedom thanks to Mint Mobile's summer sale. Right now, all of Mint's plans

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28:04Now I would like you to meet Nicole Keegan. We have the most robust data set that has ever existed on transaction level data in the US on a federally regulated exchange. What Keegan is talking about is at least the most robust, publicly available set of transaction level data. And how does she know this? I'm ahead of research at Calci and I also write the contracts and self-sertify 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 of volume a month, we're now doing about $14 billion of volume a month. Five years ago, the job that Keegan does today did not exist. So 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

29:07probably 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. How 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 dot 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 the state of registration is, in 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,

30:08and 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 Calchi market. The first stage is having the idea. We then think about what the economic justification for listing a market on that idea is. Let'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? Where we have these three variables that we're setting.

31:11Do you ever have good ideas for a contract but to side 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 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 40-11, there are six categories that are subject to restriction. These 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 an interpreted by the CFTC. If 40-11 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.

32:12So 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. That'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 catching out at a dollar or a 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. The last time we were actually asked to deal list, 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're very much still asking permission,

33:14but I think the permission was a little bit piecemeal. That was a time that we did actively deal list markets. Usually it is not the CFTC that will ask us to deal list 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. There are times where we might intervene to deal list 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 Cal State. 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? Then 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,

34:17there'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. We need to make it generic enough that we're able to use that variable to include either of those things. We might say something like it's an economic statistic as specified by the exchange. It can be as broad as that. 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. 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 e-con 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. 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

35:22meeting? 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. That is effectively unheard of in the macro space. This sent edge funds into a bit of a frenzy at the time, but we had a contract on what will the rate be in October? 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. 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. We do need to weigh what we do in these extreme situations because we can't just throw up our hands and say, well, we don't know what the rate is. We need to create clear paths to resolution in every edge case that we can imagine exists.

36:22This 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 in potential edge cases? It 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. What if there's an apostrophe in what they say? What if they misspeak? What if they

37:23pronounce a word incorrectly? Is that still the same word? Is that a different word? What happens if it's an appropanound? 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 it why we wrote rules that were so detailed. The 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. Let me ask you about one more edge study. Let's say who will perform during a Super Bowl halftime show, especially if it's

38:24maybe Cardi B who's not the headline performer, but maybe a performer, maybe not. And you're 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 mics. Exactly. 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 where epistemically a little bit uncertain about this. But 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. The way that the prediction market works is that there's a question that's asked in at the end if the event happened, you get paid out $1. And if it didn't happen, $0.

39:25Every 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 0 or 1. And this is what we might invoke to be like a last fair price. Or it could be at times 50-50. A 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 rate 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. A lot of the rest of the world who see primarily a betting market and don't see it as a useful

40:28tool for actually surfacing good information or for judging the value of information. So I'm asking you, I mean, this is a 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 can pair them to consensus estimates, consensus estimates, the gold standard in financial markets. And they basically found that Calcity markets actually outperformed consensus in their predictive ability, which is a finding that Calcity research had had three months prior, but 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 they're 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

41:31that 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. If 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. It's not even just the pureplay 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

42:32they're tightly compressed around one outcome or whether they're more dispersed. That of course carries its own layer of information. Earlier this year, Calche put out a research paper on New York's Mayoral Race, which is a Ron Mamdani one after starting out as a very bong shop. 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? Is 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

43:36time, they still aren't close to foolproof. Both Calche and its big rival, Polly Market, 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. Here's what Calche's CEO, Tarek Mansor, 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 Mansor again. One of the things that we're rolling out is 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. If something has a 5%

44:40chance of succeeding that's being marketed as 70% chance of succeeding, you won 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 a 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 Calche market because you have MNPI on how that trial is running. MNPI stands for material non-public 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 Calche likes to talk it

45:44up. But here is an uncomfortable fact. Roughly 90% of Calche's trading volume is in one category we've barely mentioned yet. Can you guess which one? I'll give you a hint. It's the kind of trading that is making draft kings and fan duel nervous. That's coming up after the break. This is Freakin'omics Radio and I'm Stephen Dubner. Freakin'omics Radio is sponsored by Southern New Hampshire University. The best investments are the ones you make in yourself and no one knows that better than Southern New Hampshire University. With more than 200 online degree programs, no set-class times and low online tuition, Southern New Hampshire University is built to fit your life today and set you up for success tomorrow. Education is an investment. Why not make it a smart one? Apply for free at snhu.edu slash Freakin'omics. Freakin'omics Radio is sponsored by

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47:51alongside the active noise cancellation creates a new level of quality comfort and control. Terrain Response 2 offers seven terrain modes to choose from, fine-tuning the vehicle for any challenging roads ahead. A force inside and out, the Range Rover Sport is available with a choice of powerful engines, including a plug-in hybrid with an estimated range of 53 miles. Build your Range Rover Sport at RangeRover.com slash U.S. slash Sport. The Range Rover Sport is a great vehicle for any type of driving. Pretty 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, we're 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

48:53for truth seeking, you get rewarded for being right, and you get punished for being wrong. That again is Tarrick Mansor, CEO of the prediction market, Calci. We'll succeed when trust in those prediction markets becomes mainstream. I think it's starting. One 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 Calci and you are worth, but this plainly is a lucrative enterprise. Can you explain how Calci makes money? So we are a neutral platform, like the New York Stock Exchange or NASDAQ. We are training 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. My job is to provide a fair, neutral competitive grounds for people to compete. Then they do their

49:54thing. 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 signal ratio is going up. The reason is because most ways that we ingest information is especially true for social media. The algorithm essentially incentivized clickbait. If you write a well-new ones well-measured multi-paragraph take about something, you get four likes on X. If you write something extreme and off the rails, you get tens of thousands of likes. It's incentivized clickbait. The incentive structure in picture market is the exact opposite. That multi-paragraph, new ones boring, well-calibrated take, that's the one that makes money. You're doing about 4 billion overall a week and I think it might be a little bit more now, right? I mean there is 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? Is that more of an entertainment? I think figuring out whether albums are going to succeed or not, people are like

50:55fanatics and passionate about it. But you ask a lot of these people like, hey, do you trade financial markets like S&P and they're like, no, because I don't gamble. It's just like, what do you mean by that? When we launch weather, people are 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. There is no shot for us to beat the Wall Street Hatch Funds, etc. 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. So sports actually, the share is going down over time pretty fast. So the other categories are actually growing

51:59faster 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 to disengagement first. And I think it's very, very important. There is speculation in all financial markets. You know, when you look at the percentage of, you know, hedging for speculation in any dritter's market, like grain futures or commodity futures or sock options, it tends to be very tilted towards speculation. And the reason you want speculations, 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 put in 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, there 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 for your Supreme Court decision, which was like, is this gambling or is this a financial

52:59instrument? People used to call grain futures gambling. But the key thing is it's less about whether their 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 the 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 calcium model or financial market model has no risks? The answer is no, right? They have risks. In my view, it's like, there's a responsible gambling and there's also a responsible trading. You know, how many times have you heard of people losing their house over options trading, especially with zero data expires 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. So how far does that responsibility go? Wherever the sheep accumulate in the world of

54:00markets, 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 calcium may be on shaky ground. This will end up debated and discussed amongst nine individuals in a small conference room in Washington, DC. 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. You 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 Theo Jacobs, who was edited by Ellen Frankman and mixed by Jake Lumus, with help from Jeremy Johnston. The Freakonomics Radio Network staff also includes Dalvin Abouwaji, Ellen Or Osborne, Elsa Hernandez, Gabriel Roth, Eloria Modtenacourt, and Pete Madden.

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