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Speaker 1: Pushkin. Welcome back to Risky Business, a show about making

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Speaker 1: better decisions. I'm Maria Kannakova.

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Speaker 2: And I'm Nate Silk.

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Speaker 1: Today we're dropping into your feeds to share an episode

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Speaker 1: that Nate and I taped with The Odd Lots Podcast,

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Speaker 1: where we appeared to talk about prediction markets.

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Speaker 3: Yeah, this is a really fun interview. The host Joe Wisenthal,

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Speaker 3: I know d you a little bit. We've hung out

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Speaker 3: in Vegas together actually, and Tracy Alloway, Uh ask the

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Speaker 3: great questions. They're kind of nerdy in the same ways

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Speaker 3: that we are. So it was a fun, fun crossover

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Speaker 3: eighties sitcom crossover episode.

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Speaker 1: Basically, absolutely and if you enjoy the episode, you can

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Speaker 1: find more of Odd Lots wherever you're listening to this podcast.

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Speaker 4: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 5: Hello and welcome to another episode of The Odd Lots Podcast.

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Speaker 5: I'm Joe Wisenthal and.

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Speaker 6: I'm Tracy Alloway.

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Speaker 5: Tracy, I have to say prediction markets have actually become

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Speaker 5: a really significant part of what I would call my

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Speaker 5: news consumption.

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Speaker 6: I know you tweet about them a lot. In fact,

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Speaker 6: you have an ongoing joke that you keep repeating that

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Speaker 6: I would argue that novelty value has worn off. But

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Speaker 6: clearly I'm wrong because everyone on the platform finds it

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Speaker 6: very amusing.

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Speaker 5: Yeah, and other people have stolen my joke.

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Speaker 6: I've seen that.

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Speaker 4: Yeah.

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Speaker 2: So the joke is.

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Speaker 5: That, you know, when people are really confident about something

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Speaker 5: and something is priced at seventy five cents on the dollar,

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Speaker 5: they're selling a dollar for seventy five cents right now

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Speaker 5: on polymarket. My joke is that when something is fifty

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Speaker 5: to fifty, I say, oh, they're selling dollars for fifty cents,

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Speaker 5: which of course sounds confident but can mean in both directions.

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Speaker 5: It's worked really well. I'm getting a lot of mileage

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Speaker 5: out that one. And you may be bored of it now,

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Speaker 5: but I think we're in the valley of humor where

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Speaker 5: if I'm still making the joke a year from now

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Speaker 5: at Tracy, You're gonna find it funny. We shall see.

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Speaker 6: Okay. Prediction markets, Okay, prediction markets are interesting. I have

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Speaker 6: a lot of questions about them, And first of all,

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Speaker 6: I feel like I should caveat this entire conversation with

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Speaker 6: an admission that I'm pretty sure I have made on

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Speaker 6: this podcast before, but I intuitively do not understand probabilities.

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Speaker 6: I'm kind of serious, Like I understand rationally, why if

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Speaker 6: you flip a coin five times, the probability of having

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Speaker 6: five instances of heads is not fifty percent, but if

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Speaker 6: you flip it once, it's fifty percent. But like, on

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Speaker 6: an intuitive basis, I don't really feel that way, right,

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Speaker 6: And then the other thing I would say when I

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Speaker 6: see prediction markets. So one of the things that happened

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Speaker 6: recently was people were going like, oh, prediction markets are

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Speaker 6: so smart and so interesting because like on Predicted there

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Speaker 6: was a ten percent chance that Biden would drop out

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Speaker 6: of the race, and so people were going like, oh,

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Speaker 6: see the prediction market called it, and it was like, well,

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Speaker 6: a ten percent chance. That was higher maybe than some

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Speaker 6: other platforms or some pundits expected, but it happened, the

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Speaker 6: chance was one hundred percent.

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Speaker 3: Like I just.

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Speaker 6: Intuitively, I don't get it. I don't get I don't

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Speaker 6: get what the percentage actually means.

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Speaker 5: Right, So, Tree, the way you think about the lottery,

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Speaker 5: for example, is that if you buy a lottery ticket,

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Speaker 5: your odds should be fifty to fifty because either you

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Speaker 5: win or you done right.

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Speaker 6: Therefore it should be binary.

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Speaker 5: No, So the way I think about them, and we'll

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Speaker 5: be talking very shortly to people who know a lot

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Speaker 5: more than me. Is not that like I find conversations

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Speaker 5: about the rightness or the wrongness of prediction markets or

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Speaker 5: a given contract on a market to be sort of

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Speaker 5: the wrong question. It's more like, to me where I

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Speaker 5: find it valuable. It's like, Okay, a lot of people

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Speaker 5: think that Biden might drop out, right, right, But is

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Speaker 5: that mean there's an eighty percent chance? Does that mean

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Speaker 5: there's a fifty five percent chance? And so what I

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Speaker 5: find interesting about prediction markets is not so much whether

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Speaker 5: going to be right or wrong, but that it seems

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Speaker 5: like they can put some sort of number on the

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Speaker 5: emerging conventional wisdom. Because look, even before the debate, some

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Speaker 5: people thought Biden still had a chance of dropping out,

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Speaker 5: and so it's interesting that the traders in those markets

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Speaker 5: assigned non negligible odds to that happening, and then of

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Speaker 5: course it all spiked after the debate and stuff like that.

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Speaker 5: So to me, you know, in thinking about the election,

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Speaker 5: we have polls, we have models, we have prediction markets.

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Speaker 5: Now we have punditry that appears on opinion pages of

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Speaker 5: various times. It's yet another tool to sort of consume

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Speaker 5: information and figure out where people's heads are at. And

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Speaker 5: I like it for that reason.

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Speaker 6: Yeah, okay, but I still don't get the value of

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Speaker 6: putting a specific number on it. Like you could just

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Speaker 6: have an arrow that goes up or down that shows momentum.

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Speaker 6: And I'm being slightly facetious here, but like you could

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Speaker 6: look at Google trends or something, or you know, try

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Speaker 6: to gauge momentum that way. But okay, I have a

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Speaker 6: bunch of questions. I fully admit that this is something

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Speaker 6: that again on a sort of like instinctual level, I

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Speaker 6: do not quite understand. But we do, in fact have

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Speaker 6: the perfect guests to explain it all to us.

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Speaker 5: Yeah, yeah, I'm very excited. We are going to be

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Speaker 5: speaking with Nate Silver and Maria Kanakova. They're the co

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Speaker 5: hosts of the fairly brand new Risky Business podcast, which

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Speaker 5: is all about questions like this. They're both poker players.

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Speaker 3: They are both.

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Speaker 5: Recently played in the World Series of Poker and have

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Speaker 5: made a lot of money over the years playing poker.

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Speaker 5: They are people with probability in gambling and odds and

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Speaker 5: making decisions with money on the line. Both in their

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Speaker 5: blood is the author of the forthcoming book on the Edge,

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Speaker 5: The Art of Risking Everything, And he's also an advisor

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Speaker 5: to Polymarket, which is one of the big prediction markets

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Speaker 5: that everyone has been quoting these days. So a couple

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Speaker 5: of great people to talk to about things like betting

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Speaker 5: on elections and how they think about all this stuff.

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Speaker 5: So Nate and Maria, thank you guys both for coming

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Speaker 5: on odd Laws.

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Speaker 2: Thank you so much. Joe and Tracy, Yeah.

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Speaker 1: Thanks for having us.

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Speaker 5: Absolutely so. When we look at all these markets and

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Speaker 5: poly markets the hot one, but I forget the one

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Speaker 5: is predict It was also really big. Do I think

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Speaker 5: they're running into some they have some regulatory issues years

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Speaker 5: ago there used to be in trade. There's the Iowa

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Speaker 5: futures market, there's others. What is the value When people

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Speaker 5: look at all these numbers and say, like, what is

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Speaker 5: the value of markets like these?

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Speaker 3: I think it's the same value as any type of

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Speaker 3: market where you have price discovery from participants who have

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Speaker 3: a rational incentive to make smart trades and be closer

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Speaker 3: to whatever the value the market resolves.

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Speaker 2: To, you know.

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Speaker 3: I mean Tracy asks some existential questions about the notion

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Speaker 3: of probability for things that are kind of seen as

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Speaker 3: one off events. But whether things are intrinsically uncertain or

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Speaker 3: not is kind of above my paid grade, right. The

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Speaker 3: fact is that we have uncertain information. We're trying to

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Speaker 3: make judgement space and complete data and complete information. And

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Speaker 3: I think it's better to quantify things than to be

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Speaker 3: totally subjective and use weasel words and not have any

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Speaker 3: accountability for you know, a twenty percent chance versus a

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Speaker 3: forty percent chance for example.

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Speaker 1: Yeah, and I think that there is a huge difference

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Speaker 1: between the two and Tracy your existential questions make a

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Speaker 1: lot of sense to me as a psychologist, because you know,

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Speaker 1: the human mind really does not like probabilistic thinking. You're

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Speaker 1: not the only one, and we do want to think

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Speaker 1: in absolutes, right, We do like zero and one hundred

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Speaker 1: those are easy numbers for us, whereas twenty three percent

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Speaker 1: not so much. But this idea that if we actually

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Speaker 1: put our money where our mouth is, it will help

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Speaker 1: us calibrate those probabilities better, help us calibrate our intuitive

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Speaker 1: sense of how confident we are in something, how much

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Speaker 1: we actually believe in it. That's I think why prediction

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Speaker 1: markets in all sorts of literature and all sorts of

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Speaker 1: areas have gained momentum because you think differently. I bet

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Speaker 1: and I would put money on this tracy that if

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Speaker 1: I forced you to put money down on an opinion

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Speaker 1: or a thought, you would actually start homing in on

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Speaker 1: those instincts, on those decisions, and you would find that, Okay, well,

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Speaker 1: like you know, I'm probably less certain of this than

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Speaker 1: I thought I was, because I'm only willing to bet,

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Speaker 1: you know, ten dollars and not one hundred dollars. Asking

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Speaker 1: yourself questions like this, Actually putting down those bets really helps.

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Speaker 1: And what Nate was saying, when you have lots of people, right,

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Speaker 1: when you have hundreds and thousands of people doing this,

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Speaker 1: that's when the calibration becomes better. Because if we have

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Speaker 1: a thousand Tracs who might you know, not believe in

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Speaker 1: probabilities that much or not understand them, and yet they're

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Speaker 1: all putting their money down. The idea is that in

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Speaker 1: this particular case, the crowd actually knows something right, there

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Speaker 1: is some wisdom to the amount of money you're putting down,

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Speaker 1: because once again, actually betting with your wallet so that

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Speaker 1: it hurts, that's when you start to actually question your beliefs.

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Speaker 6: Well, let me just say, off the bat, thank you

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Speaker 6: so much for trying to make me feel better. I

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Speaker 6: will just say, in my defense, I did do AP statistics,

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Speaker 6: and I did like I did okay, probably because back

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Speaker 6: then you had to draw charts by hand, and I

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Speaker 6: was really into drawing like neat charts. So I think

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Speaker 6: I got points for that.

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Speaker 2: Okay.

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Speaker 6: Let me ask a non existential question, which is, if

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Speaker 6: I go on to polymarket or predict it, and let's

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Speaker 6: just say I see an event and the probability is

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Speaker 6: something like forty percent, where is that forty percent coming from?

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Speaker 6: And what exactly is it telling me?

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Speaker 3: It's saying that in the long run, if you took

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Speaker 3: one hundred similar events some reference class, that the event

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Speaker 3: would occur about already out of every one hundred times

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Speaker 3: and wouldn't occur the.

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Speaker 2: Other sixty out of one hundred.

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Speaker 3: I think what bothers people is that they question whether

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Speaker 3: it's something like an election falls into an appropriate larger

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Speaker 3: subset of some random distribution of data.

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Speaker 2: Right. That bugs people.

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Speaker 3: If you're playing poker, then there's one to fifty two

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Speaker 3: cards that might come next. It's technically not random, right.

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Speaker 3: The hand is actually dealt out once it's shuffled but

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Speaker 3: de facto random, right, Or if you're watching a basketball game,

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Speaker 3: you have some sense that a free throw may or

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Speaker 3: may not go in, or may bounce off the back rim,

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Speaker 3: et cetera, et cetera. With elections, I mean, parties always

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Speaker 3: tell people that, oh, this is the most important election

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Speaker 3: of all time, but there are like a lot of

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Speaker 3: like fairly random contingencies in this election. Or example, to

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Speaker 3: use a somewhat pointed example, if Donald Trump had not

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Speaker 3: turned to look at a billboard of immigration data, then

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Speaker 3: the bullet that was fired at him might have done

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Speaker 3: much more damage than Grayze's ear, for example. Or in

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Speaker 3: two thousand, the butterfly ballot. This is going back a ways.

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Speaker 3: A ballot design issue in Palm Beach County, Florida probably

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Speaker 3: costs al Gore the election and cause old people to

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Speaker 3: confusingly vote for pap you can and instead. So there

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Speaker 3: are these kind of near random circumstances even in elections.

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Speaker 3: But even then, you know, not everything is knowable or predictable.

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Speaker 3: And if you think it's locked in that Harris is

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Speaker 3: going to win or Trump is going to win, then okay,

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Speaker 3: well great, maybe you're right, but you can make a

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Speaker 3: lot of money by betting on that at polymarket or

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Speaker 3: predicted or whatever else.

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Speaker 1: Yeah, I think one more time. You know, nothing in

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Speaker 1: life is certain ever, no matter how much we want

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Speaker 1: it to be, right, Like, I might die in the

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Speaker 1: next minute. I hope I don't, but I'm right.

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Speaker 6: There's a non.

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Speaker 5: Zero non zero jes exactly where my mind went when

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Speaker 5: you said nothing.

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Speaker 1: Right. But it's so true. And if you actually think

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Speaker 1: of the election like a poker hand, except you know,

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Speaker 1: think about it in like multiverse terms. I'm going to

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Speaker 1: go back to existential terms, Tracy. You try to get

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Speaker 1: us out of existential terms, I'm going to bring us

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Speaker 1: right back there. So imagine, you know, the multiverse experiment

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Speaker 1: where we have, you know, a million possible versions of

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Speaker 1: our reality, and in one of them, we had different ballots. Right,

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Speaker 1: they weren't butterfly ballots. Al Gore won the election. We

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Speaker 1: would probably not be here right now, because you know,

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Speaker 1: it's funny that it's a butterfly Yeah, butterfly ballot, butterfly effect. Right,

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Speaker 1: those tiny things do matter, but you do have to

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Speaker 1: realize that those probabilities still matter, right, like the fact

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Speaker 1: that Hillary Clinton lost and that Nate got a lot

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Speaker 1: of for it. I defended Nate very publicly, as did

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Speaker 1: a lot of people, because his forecast was the single

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Speaker 1: best one. It gave her a good chance of winning,

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Speaker 1: but it gave Donald Trump the highest chance of winning.

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Speaker 1: But people don't like you know, you know, they don't

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Speaker 1: understand that over twenty percent is a hell of a

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Speaker 1: lot of a percent. You know. In my last book,

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Speaker 1: I compared Nate's percentage just to poker, and I said,

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Speaker 1: the chances of Donald Trump winning according to Nate's prediction

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Speaker 1: was the same chance of you flopping a pair in Texas.

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Speaker 1: Hold them now, Think how often you flop a pair

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Speaker 1: right that you pair up one of the two cards

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Speaker 1: that you're holding. That happens all the time.

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Speaker 5: I really just like, sometimes I'm sitting there at the

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Speaker 5: poker table and I'm like, God, just give me a

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Speaker 5: set and then I could double up and walk away

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Speaker 5: for the night, and it never happens anyway. So I

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Speaker 5: have two quick thoughts. One is I always do find

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Speaker 5: it funny when people talk about, like our prediction markets

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Speaker 5: good or not, because a point that I've made many

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Speaker 5: times in writing, et cetera, is we actually do have

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Speaker 5: a very popular liquid prediction market that existed for a

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Speaker 5: long time, which is the short term treasury market, which

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Speaker 5: is explicitly a bet on what the you know, nine

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Speaker 5: members or whatever of the FMC are going to do

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Speaker 5: over the next And that's literally a prediction market. And

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Speaker 5: no one questions whether that market works. It does work,

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Speaker 5: and it exists, and there's a lot of money writing

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Speaker 5: on it, and it literally is not just metaphorically, it

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Speaker 5: is a prediction market.

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Speaker 6: You know.

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Speaker 5: It occurs to me though, when we talk about, okay,

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Speaker 5: what is a forty percent return? This must be easily testable, right,

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Speaker 5: Like if you take a thousand contracts that have existed

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Speaker 5: over the life of polymarket or the life of predicted

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Speaker 5: or intrade or whatever it was, and then say, like, okay,

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Speaker 5: one hundred days out from resolution, did the ones that

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Speaker 5: say forty percent? Did they resolve forty percent of the time?

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Speaker 2: Like? Has that been tested?

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Speaker 5: Like can we say that that is the case that

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Speaker 5: things with forty percent in the prediction market over some

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Speaker 5: timeframe do resolve forty percent towards the direction?

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Speaker 2: There's been somewhat mixed literature on this. I do know.

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Speaker 3: I guess this is just a not so humble brag right.

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Speaker 3: I do know historically that like the five point thirty

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Speaker 3: eight now Silver Bullet and election forecasts have been better

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Speaker 3: than prediction markets, which is unusual. Usually publicly available information

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Speaker 3: is not better than the market. However, they've been pretty

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Speaker 3: well calibrated according to most estimates.

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Speaker 2: And also I think the markets.

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Speaker 3: Are maturing a lot where Look, I've talked to a

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Speaker 3: lot of different types of businesses, investment banks and hedge

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Speaker 3: funds and things like that, and they understand that like

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Speaker 3: political risk is market risk when you have this bigure

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Speaker 3: difference between the two parties, that like whether Donald Trump

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Speaker 3: or Kamala Harris is the next president, has a lot

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Speaker 3: of implication for every sector of the economy or any

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Speaker 3: trades you want to make. And so you have seen

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Speaker 3: more institutional attempts to either bet directly through prediction markets

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Speaker 3: or bet on proxies. And again I'm an advisor to

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Speaker 3: poly market, but their volumes are meaningfully much higher than

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Speaker 3: in the past, their structures are good, so you don't

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Speaker 3: pay as much of a tax in essence, so I

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Speaker 3: kind of think they've turned a corner from being pretty

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Speaker 3: good to maybe verging on very good.

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Speaker 1: Yeah, I think, Nate, you just head on a really

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Speaker 1: important point, which is volume. Right, prediction markets are only

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Speaker 1: as good as the traffic that they get, and so

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Speaker 1: for a long time, you know, there was no legal

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Speaker 1: prediction market in the United States, and so it's very

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Speaker 1: difficult when you're talking about betting on political events within

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Speaker 1: the United States to have a well calibrated market if

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Speaker 1: people within the United States can't actually bet on it. Right,

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Speaker 1: So I think that a lot of things are changing,

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Speaker 1: and hopefully we'll change for the better because volume is

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Speaker 1: essential in calibrating correctly, and you need people with expertise

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Speaker 1: doing it as well. But you also you just need

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Speaker 1: bodies to you need both of these things.

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Speaker 3: The ratio of smart money to dumb money is really important. Yes,

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Speaker 3: in the Super Bowl, the literal actual chiefs versus forty

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Speaker 3: nine ers whatever, super Bowl, there is so much dumb

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Speaker 3: money being bet on the Super Bowl that the sharp

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Speaker 3: money can't consume all of it, and so therefore you

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Speaker 3: often have positive expective value bets by fading the public

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Speaker 3: in the Super Bowl. That's not true for a regular

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Speaker 3: season Hornet's Wizard's Game in the NBA or something, but

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Speaker 3: for very very big events like a big UFC fight

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Speaker 3: or the super Bowl, talk about some in the book

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Speaker 3: a little bit. Then those things potentially you have more

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Speaker 3: dumb money than sharp money. And so what you now

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Speaker 3: have happening, I think is like more of a professional

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Speaker 3: class of betters on politics, whether it is hedge funds

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Speaker 3: or whether it is just smart individuals who are betting

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Speaker 3: on the US election and elections around the world. You

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Speaker 3: see some sharp money coming into these markets.

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Speaker 6: So just on this idea of like where the information

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Speaker 6: and the bets are actually coming from. Talk to us

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Speaker 6: a little bit about the difference between a book maker

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Speaker 6: versus a prediction market, because my impression of a bookie

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Speaker 6: is like they kind of set the odds and then

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Speaker 6: people decide whether or not they want to place money

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Speaker 6: on them, and then you know, they might calibrate those

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Speaker 6: odds depending on the response they are getting from people

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Speaker 6: placing the bets. But there's like a starting point that

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Speaker 6: the bookies have, and I guess it must be different

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Speaker 6: for prediction markets. Talk to us about like the difference

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Speaker 6: in establishing those probabilities.

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Speaker 3: I mean, yeah, the classic act of bookmaking is where

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Speaker 3: you have some in essence prior. I guess it's an

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Speaker 3: advanced show, right, So like you have. You start out

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Speaker 3: with some number that you kind of probably comes out

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Speaker 3: of a model or some conversation. You have the initial

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Speaker 3: line that a sportsbook post on an NFL game is

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Speaker 3: actually probably often quite beatable. Right, it's just some like

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Speaker 3: nerd in a back room somewhere who's doing his best.

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Speaker 3: But then you look for action from sharp betters. Maybe

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Speaker 3: the book maker thinks that Maria is really good at

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Speaker 3: sports bidding and I'm a total hack whale.

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Speaker 1: Definitely, definitely anyone listening that this is accurate.

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Speaker 3: So Maria bets on the Chiefs, she bets five K

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Speaker 3: on the Chiefs, and they'll move the line toward the

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Speaker 3: Chiefs in Maria's direction. Right, So now it's more extensive

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Speaker 3: for me to about on the Chiefs. If I bet

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Speaker 3: I think I'm a whale, they to do about it, right,

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Speaker 3: because they think that's just dumb money they're trying to

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Speaker 3: make a profit from. So that's traditional book making, whereas

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Speaker 3: the prediction markets, it's all happening more organically, right, there's

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Speaker 3: no human at the switch who was deciding and evaluating

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Speaker 3: where to move their numbers.

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Speaker 1: I think that the other important thing is that you,

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Speaker 1: I think suggested Nate but didn't say explicitly when you're

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Speaker 1: talking about sports books, when you're talking about bookmaking, this

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Speaker 1: all happens pretty much manually, where people actually look to

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Speaker 1: see who's placing the bets, and then they actually adjust

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Speaker 1: the lines, right like there is a human adjusting the

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Speaker 1: line and deciding, okay, this is what we need to do,

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Speaker 1: and that might happen hundreds of times a day, maybe

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Speaker 1: even more, and the line gets better and better. But

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Speaker 1: in prediction markets, those adjustments are just happening all the time.

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Speaker 3: It's the invisible hand, the same as like the stock market,

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Speaker 3: for example.

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Speaker 1: YEP.

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Speaker 5: I could imagine that there are various types of betters

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Speaker 5: on prediction markets, and you broke it down into sharps

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Speaker 5: versus whales or whatever it is, or done money versus

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Speaker 5: smart money, but they're also different. Even within the category

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Speaker 5: of smart money, there are probably many different approaches. So

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Speaker 5: I could imagine that there are some people who are

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Speaker 5: very sophisticated in their political analysis, and they could say,

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Speaker 5: you know, this person is a better chance of winning,

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Speaker 5: you know, getting the vice presidential tap than someone else.

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Speaker 5: Someone else might just say, you know what, anytime the

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Speaker 5: model or the price diverges from the silver bulletin model.

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Speaker 5: I'm going to go either short or long that I imagine

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Speaker 5: that there are arbitrage traders who are just sort of

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Speaker 5: maybe looking at the price on one exchange versus the

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Speaker 5: price of another exchange and attempting to take advantage of discrepancy,

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Speaker 5: just like it exists in stock market. I assume maybe

404
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Speaker 5: there are momentum people. It's like this is hot right now,

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Speaker 5: even if I don't believe it. But talk to us

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Speaker 5: about some of the different strategies that the good betters

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Speaker 5: on prediction markets take.

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Speaker 3: I mean, I think you identified most of them, Joe.

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Speaker 3: And you know, one thing you learned being in the

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Speaker 3: gambling world, playing poker and so forth, is that if

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Speaker 3: there is an arbitrage to have or an edge to

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Speaker 3: be had, someone's going to.

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Speaker 2: Hoover it up. Right.

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Speaker 3: There are actually people who go around and are professional

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Speaker 3: slot machine players, for example, because there are odd circumstances

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Speaker 3: where you might have a jackpod or a conditional probability

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Speaker 3: where playing a slot machine can don't do this at home,

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Speaker 3: do not do this at home, but there are cases

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Speaker 3: where race.

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Speaker 2: Is going to do it.

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Speaker 3: Yeah, Plus you need to play a slot machine. So

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Speaker 3: at the end of the day, something like the vice

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Speaker 3: presidential race. It's inherently somewhat subjective, but if you have practice,

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Speaker 3: I mean as a poker player. Poker players develop uncanny

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Speaker 3: intuitions for probabilities. They'll say, I need thirty one percent

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Speaker 3: to make this call profitable on the river, and it

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Speaker 3: was only getting thirty percent and so I folded, right.

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Speaker 3: I mean, they'll literally get to like one percent delta

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Speaker 3: versus the actual odds.

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Speaker 1: Are good poker players. You need an adjective in there.

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Speaker 3: Yeah, yeah, but you learn it kind of. I mean,

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Speaker 3: it's just it's muscle memory at some point.

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Speaker 2: Yeah.

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Speaker 1: Absolutely, And sometimes you don't even quite know what the

435
00:21:28,324 --> 00:21:30,964
Speaker 1: probabilities are. You just know that you have those odds

436
00:21:31,044 --> 00:21:33,204
Speaker 1: or you don't have those odds. You really do start

437
00:21:33,244 --> 00:21:36,444
Speaker 1: feeling them. So that is something that develops over time.

438
00:21:36,844 --> 00:21:39,684
Speaker 1: And I'm actually not sure though that it would develop

439
00:21:39,724 --> 00:21:44,124
Speaker 1: over time when you're doing something like prediction markets, right,

440
00:21:44,244 --> 00:21:47,524
Speaker 1: because slot machines are also something that is very experiential,

441
00:21:47,804 --> 00:21:50,084
Speaker 1: right where you actually kind of are doing this and

442
00:21:50,124 --> 00:21:52,324
Speaker 1: you're trying it over and over and over. And for

443
00:21:52,444 --> 00:21:55,284
Speaker 1: prediction markets, I think you often just place lots of

444
00:21:55,324 --> 00:21:58,804
Speaker 1: bets and it's not like you're sitting there actually watching

445
00:21:58,844 --> 00:22:02,084
Speaker 1: this and experiencing it and learning from it. So I

446
00:22:02,084 --> 00:22:04,764
Speaker 1: think you already. If you want to be a good better,

447
00:22:05,124 --> 00:22:08,644
Speaker 1: you need to learn those intuitions from something like poker

448
00:22:08,964 --> 00:22:11,124
Speaker 1: and then apply them to the prediction markets. I don't

449
00:22:11,124 --> 00:22:13,484
Speaker 1: think it works the other way around, because the way

450
00:22:13,524 --> 00:22:16,204
Speaker 1: that the brain works, you need that experience. You need

451
00:22:16,244 --> 00:22:19,964
Speaker 1: to sample those probabilities over and over and over. That's

452
00:22:20,004 --> 00:22:22,244
Speaker 1: why poker players know what one percent feels like, because

453
00:22:22,244 --> 00:22:25,924
Speaker 1: they've played tens of thousands, hundreds of thousands of hands

454
00:22:26,164 --> 00:22:29,084
Speaker 1: and they have seen the outcomes they've sampled correctly, right,

455
00:22:29,124 --> 00:22:31,604
Speaker 1: so they know exactly what that feels like. That doesn't

456
00:22:31,604 --> 00:22:34,124
Speaker 1: happen in prediction markets. So Nate can go into a

457
00:22:34,124 --> 00:22:36,724
Speaker 1: prediction market and he's going to crush, right, because he

458
00:22:36,884 --> 00:22:40,684
Speaker 1: has a good instinct for those probabilities. But someone who

459
00:22:40,924 --> 00:22:43,924
Speaker 1: doesn't already, I don't think that they're going to suddenly

460
00:22:44,084 --> 00:22:46,964
Speaker 1: develop it because they were betting in prediction markets.

461
00:22:47,444 --> 00:22:50,324
Speaker 6: Joe, I can say with one hundred percent confidence that

462
00:22:50,444 --> 00:22:53,524
Speaker 6: I have failed to apply my poker intuitions to my

463
00:22:53,684 --> 00:22:58,364
Speaker 6: slot machines playing strategy. That is definitely true. Okay, Wait,

464
00:22:58,524 --> 00:23:01,804
Speaker 6: so I'm looking at polymarket right now, and I should

465
00:23:01,804 --> 00:23:04,044
Speaker 6: look at this more because this is actually kind of fun.

466
00:23:04,844 --> 00:23:08,804
Speaker 6: How are the particular events that people are betting on?

467
00:23:09,484 --> 00:23:12,444
Speaker 6: Are those chosen or selected? Because right now I am

468
00:23:12,484 --> 00:23:15,804
Speaker 6: looking at will the US confirm that aliens exist in

469
00:23:15,844 --> 00:23:19,564
Speaker 6: twenty twenty four? What will Trump say in August? I

470
00:23:19,564 --> 00:23:21,684
Speaker 6: mean really, I feel like that could be anything? And

471
00:23:21,724 --> 00:23:25,404
Speaker 6: then will Taylor Swift get engaged in twenty twenty four?

472
00:23:25,444 --> 00:23:28,324
Speaker 6: So the big questions of our time? But how are

473
00:23:28,364 --> 00:23:31,484
Speaker 6: those sort of like why those particular questions? Is it

474
00:23:31,564 --> 00:23:33,684
Speaker 6: just if anyone wants to start a market in a

475
00:23:33,724 --> 00:23:34,564
Speaker 6: particular event.

476
00:23:35,164 --> 00:23:37,884
Speaker 3: Yeah, I'd say the polymarket guys who I know a

477
00:23:37,884 --> 00:23:41,484
Speaker 3: little bit, are very attuned to finance, Twitter and politics,

478
00:23:41,484 --> 00:23:44,724
Speaker 3: Twitter and crypto Twitter. Sometimes they're kind of jokes and

479
00:23:44,804 --> 00:23:46,324
Speaker 3: kind of troll is. I mean, they're trying to have

480
00:23:46,484 --> 00:23:48,964
Speaker 3: some fun, right, and so you'll see some questions that

481
00:23:49,004 --> 00:23:50,324
Speaker 3: are a little bit memified.

482
00:23:50,364 --> 00:23:50,724
Speaker 2: For sure.

483
00:23:50,964 --> 00:23:53,044
Speaker 1: I actually saw Nate and I'm not sure if this

484
00:23:53,124 --> 00:23:55,044
Speaker 1: was I don't remember if it was on polymarket or

485
00:23:55,084 --> 00:23:57,604
Speaker 1: one of the other prediction markets, but there was a

486
00:23:57,604 --> 00:24:01,164
Speaker 1: prediction market on our podcast, including you know, number of

487
00:24:01,404 --> 00:24:04,244
Speaker 1: f bombs that Nate will drop within the first ten minutes.

488
00:24:04,564 --> 00:24:06,004
Speaker 1: You know, things like.

489
00:24:05,964 --> 00:24:08,364
Speaker 6: Wait, do you guys bet on that? Presumably you can

490
00:24:08,364 --> 00:24:09,204
Speaker 6: make a lot of money.

491
00:24:09,684 --> 00:24:12,124
Speaker 3: I mean some sites say that that's okay, right if

492
00:24:12,164 --> 00:24:15,204
Speaker 3: you talked so. The other the big free money prediction

493
00:24:15,324 --> 00:24:18,404
Speaker 3: market site is Manifold, which has a s very dedicated

494
00:24:18,404 --> 00:24:21,484
Speaker 3: community traders. So even though as a polymarket advisor, now

495
00:24:21,524 --> 00:24:23,404
Speaker 3: I'd say I think real money is a way to go,

496
00:24:23,604 --> 00:24:25,204
Speaker 3: it's pretty good. I certainly have a lot of respect

497
00:24:25,204 --> 00:24:27,964
Speaker 3: for Manifold, But yeah, a Menifold is kind of radical

498
00:24:28,004 --> 00:24:31,044
Speaker 3: transparency where anyone can put up a market about anything

499
00:24:31,084 --> 00:24:33,404
Speaker 3: and they will even say yeah, as long as there's

500
00:24:33,444 --> 00:24:36,924
Speaker 3: no complict of interest, then inside information only makes the

501
00:24:37,004 --> 00:24:37,764
Speaker 3: market smarter.

502
00:24:38,004 --> 00:24:39,204
Speaker 2: I'm a little bit worried about that.

503
00:24:39,244 --> 00:24:41,564
Speaker 3: I mean, this is an issue in sports markets too,

504
00:24:41,644 --> 00:24:45,524
Speaker 3: where now you have ESPN bets might offer a market

505
00:24:45,524 --> 00:24:47,844
Speaker 3: on like the NFL draft or something. Right, well, if

506
00:24:47,844 --> 00:24:50,644
Speaker 3: you're an ESPN NFL beat reporter, you might have a

507
00:24:50,684 --> 00:24:52,484
Speaker 3: lot of knowledge about what the Atlanta Falcons are going

508
00:24:52,564 --> 00:24:55,924
Speaker 3: to do. And for politics markets, I mean something moved

509
00:24:55,924 --> 00:25:00,244
Speaker 3: the markets toward Tim Walls, Kamala Harris's VP pick aggressively

510
00:25:00,684 --> 00:25:03,084
Speaker 3: about fifteen minutes before that pick was confirmed.

511
00:25:04,164 --> 00:25:05,324
Speaker 2: What is that? I don't know.

512
00:25:05,644 --> 00:25:07,844
Speaker 3: Maybe it's that there were SUVs as there seemed to

513
00:25:07,884 --> 00:25:10,524
Speaker 3: be outside of Tim Wallas's resident Minnesota where we lives

514
00:25:10,564 --> 00:25:14,924
Speaker 3: in Minnesota. But inside information is I mean from a

515
00:25:15,044 --> 00:25:17,884
Speaker 3: user standpoint, it's all incorporated, so it's useful. But like

516
00:25:17,964 --> 00:25:20,004
Speaker 3: you do wonder a little bit about like are there

517
00:25:20,324 --> 00:25:23,324
Speaker 3: ulterior motives for people who have, you know, inside knowledge

518
00:25:23,324 --> 00:25:26,204
Speaker 3: about sports, politicst cetera, financial events to bet?

519
00:25:26,484 --> 00:25:29,084
Speaker 1: Yeah, and then you know there's a thin line. I'm

520
00:25:29,084 --> 00:25:31,284
Speaker 1: working right now on my next book, which is about cheating,

521
00:25:31,364 --> 00:25:33,724
Speaker 1: and there's a thin line when you can use inside

522
00:25:33,764 --> 00:25:36,724
Speaker 1: information where you know everything is fair game between that

523
00:25:36,884 --> 00:25:39,404
Speaker 1: and cheating right and starting to throw games and starting

524
00:25:39,404 --> 00:25:43,084
Speaker 1: to actually do things where how you bet affects how

525
00:25:43,124 --> 00:25:46,484
Speaker 1: you act, and that's incredibly difficult to police. It's always

526
00:25:46,524 --> 00:25:48,764
Speaker 1: been difficult to police, and I think it's going to

527
00:25:48,804 --> 00:25:51,844
Speaker 1: become even more so. So I personally, and I'm not

528
00:25:51,884 --> 00:25:54,844
Speaker 1: involved with any prediction markets, I personally would like to

529
00:25:54,884 --> 00:25:57,844
Speaker 1: see there be some sort of penalties in it. Not

530
00:25:57,924 --> 00:26:00,484
Speaker 1: being quite as open to that sort of thing, just

531
00:26:00,524 --> 00:26:03,364
Speaker 1: because I think that there is a huge possibility for

532
00:26:03,524 --> 00:26:08,324
Speaker 1: abuse and for things that actually end up sabotaging the

533
00:26:08,364 --> 00:26:11,724
Speaker 1: integrity of especially things like sports games, but politics, you know,

534
00:26:11,764 --> 00:26:13,684
Speaker 1: all sorts of contests.

535
00:26:13,204 --> 00:26:15,044
Speaker 5: Right, Tracy, and I I think it was last year

536
00:26:15,084 --> 00:26:19,244
Speaker 5: maybe twenty twenty two, we interviewed Roustin benham Oh, the

537
00:26:19,284 --> 00:26:22,124
Speaker 5: head of the CFTC, and they have not been friendly

538
00:26:22,284 --> 00:26:26,164
Speaker 5: towards prediction markets for some of these specific reasons which

539
00:26:26,164 --> 00:26:28,844
Speaker 5: you there are probably good reasons why you don't want

540
00:26:28,884 --> 00:26:32,764
Speaker 5: manipulation of actual political events to have money on the line.

541
00:26:32,884 --> 00:26:37,164
Speaker 5: And actually, while we're on this topic specifically, since you're here, Nate,

542
00:26:37,204 --> 00:26:42,404
Speaker 5: my understanding is that polymarket is not legally available to

543
00:26:42,444 --> 00:26:47,004
Speaker 5: people in the US, but as many people know, VPNs exist,

544
00:26:47,044 --> 00:26:49,924
Speaker 5: and because it uses stable coins to fund it, there

545
00:26:49,964 --> 00:26:52,404
Speaker 5: are ways around it. Is that an accurate characterization of

546
00:26:52,444 --> 00:26:53,484
Speaker 5: its state we look?

547
00:26:53,524 --> 00:26:55,204
Speaker 3: Since I am in an especial capacity for them, I

548
00:26:55,284 --> 00:26:57,044
Speaker 3: las say is that you are not allowed to bet

549
00:26:57,084 --> 00:26:58,004
Speaker 3: on polymoticket if you're in.

550
00:26:57,964 --> 00:26:58,844
Speaker 2: The United States. Got it?

551
00:26:58,884 --> 00:27:01,244
Speaker 5: Okay, we know what it's going on here. You know,

552
00:27:01,324 --> 00:27:05,244
Speaker 5: you mentioned prediction markets on podcasts actually there and you

553
00:27:05,284 --> 00:27:07,524
Speaker 5: mentioned manifold at one point, Tracy, I don't know if

554
00:27:07,564 --> 00:27:10,164
Speaker 5: you saw it. This fellow who's like it used to

555
00:27:10,204 --> 00:27:13,244
Speaker 5: be a pro magic the gathering player and gambling's Vmashevitz.

556
00:27:13,684 --> 00:27:16,924
Speaker 5: He put up a contract and it said, well's Vmashevitz

557
00:27:17,164 --> 00:27:19,924
Speaker 5: get invited onto the Odd Lots podcast this year. And

558
00:27:20,004 --> 00:27:21,404
Speaker 5: I thought about going, Oh, I was.

559
00:27:21,364 --> 00:27:22,404
Speaker 6: Like, I just got money on there.

560
00:27:22,804 --> 00:27:24,484
Speaker 5: I'm gonna take all my I'm gonna take all my

561
00:27:24,524 --> 00:27:28,204
Speaker 5: fake manifold currency go along and then invite them on

562
00:27:28,204 --> 00:27:31,124
Speaker 5: the I didn't do that, but it is very amusing

563
00:27:31,164 --> 00:27:33,364
Speaker 5: to think of the various ways you could very easily

564
00:27:33,364 --> 00:27:34,364
Speaker 5: get Thank you.

565
00:27:34,284 --> 00:27:36,524
Speaker 6: For protecting the integrity of the podcast and.

566
00:27:36,564 --> 00:27:38,604
Speaker 2: The market, yes, and the market the most.

567
00:27:38,684 --> 00:27:41,524
Speaker 6: The price discovery mechanism of prediction markets. Thank you.

568
00:27:41,724 --> 00:27:44,524
Speaker 5: But I want to get back to actually get deeper

569
00:27:44,644 --> 00:27:48,124
Speaker 5: into this question of like you know, on polymarket, there's

570
00:27:48,124 --> 00:27:50,524
Speaker 5: a leader board or there's a top volume this week,

571
00:27:50,564 --> 00:27:52,244
Speaker 5: and I think you can see leaderboards just kind of

572
00:27:52,244 --> 00:27:54,564
Speaker 5: open so you can see talk to us more about

573
00:27:54,564 --> 00:27:58,364
Speaker 5: like what good traders are doing and why some people

574
00:27:58,564 --> 00:28:00,044
Speaker 5: seem to be better at this than others.

575
00:28:00,804 --> 00:28:02,644
Speaker 3: I think good traders are good at kind of quick

576
00:28:02,684 --> 00:28:06,524
Speaker 3: slice intuition and working with limited information, and you know,

577
00:28:06,564 --> 00:28:09,124
Speaker 3: where's that skill come from. I mean, it's partly the

578
00:28:09,164 --> 00:28:13,124
Speaker 3: common nation of being both analytical and really competitive.

579
00:28:13,324 --> 00:28:13,484
Speaker 2: Right.

580
00:28:13,564 --> 00:28:16,324
Speaker 3: These are not people who are just in the abstract

581
00:28:16,924 --> 00:28:20,124
Speaker 3: building academic models, but they're people who have to use

582
00:28:20,204 --> 00:28:22,604
Speaker 3: the cliche and other author They have skin in the game,

583
00:28:22,884 --> 00:28:24,644
Speaker 3: They have a lot of practice, and they really want

584
00:28:24,684 --> 00:28:28,204
Speaker 3: to win, which is why gamifying these things and having

585
00:28:28,244 --> 00:28:31,924
Speaker 3: leaderboards and having discussion forums for people to explain the

586
00:28:32,004 --> 00:28:33,284
Speaker 3: rationales behind their bets.

587
00:28:33,324 --> 00:28:35,084
Speaker 2: I mean, these are really.

588
00:28:34,884 --> 00:28:37,484
Speaker 3: Really competitive people, which is true for the other fields

589
00:28:37,484 --> 00:28:39,844
Speaker 3: that Marie and I indulge in, and the cream rises

590
00:28:39,884 --> 00:28:41,444
Speaker 3: to the top over the long run, I suppose.

591
00:28:59,204 --> 00:29:02,244
Speaker 6: So one thing I wanted to ask is we've obviously

592
00:29:02,484 --> 00:29:06,284
Speaker 6: been focused on prediction markets, but if I want to

593
00:29:06,364 --> 00:29:10,844
Speaker 6: express an opinion or bet money on something like who's

594
00:29:10,924 --> 00:29:13,804
Speaker 6: going to win the US election, putting a bet on

595
00:29:13,884 --> 00:29:17,964
Speaker 6: poly market or something like that is not my only avenue.

596
00:29:18,204 --> 00:29:22,324
Speaker 6: So we know that Trump has that spack DJT. I'm

597
00:29:22,364 --> 00:29:27,044
Speaker 6: looking at the share price right now. It's spiked, you know,

598
00:29:27,204 --> 00:29:31,644
Speaker 6: in sort of early July, I guess, post the assassination attempt,

599
00:29:31,964 --> 00:29:34,924
Speaker 6: but it's come back down dramatically. And there are also

600
00:29:35,004 --> 00:29:36,724
Speaker 6: things like I mean, I think at this point there

601
00:29:36,724 --> 00:29:39,444
Speaker 6: are a number of them, but Trump related cs and

602
00:29:39,604 --> 00:29:41,844
Speaker 6: tokens and things like that. So I could just buy

603
00:29:41,884 --> 00:29:44,004
Speaker 6: one of those if I think that Trump is going

604
00:29:44,084 --> 00:29:47,124
Speaker 6: to win in November. How are you seeing people sort

605
00:29:47,124 --> 00:29:52,684
Speaker 6: of evaluate the opportunity from prediction markets versus things like,

606
00:29:53,404 --> 00:29:55,644
Speaker 6: you know, a Trump spack or Trump token.

607
00:29:56,204 --> 00:29:58,084
Speaker 3: I mean, look, if I were talking to the CFTC,

608
00:29:58,284 --> 00:30:00,764
Speaker 3: then one thing I'd say is that because there are

609
00:30:00,804 --> 00:30:04,724
Speaker 3: proxies for all these things anyway, that certain sectors of

610
00:30:04,724 --> 00:30:07,204
Speaker 3: the economy might be better off with the Harris versus

611
00:30:07,244 --> 00:30:10,804
Speaker 3: Trump administration, or interest trades treatments might be different.

612
00:30:11,004 --> 00:30:13,644
Speaker 2: Or Bitcoin itself, yeah, which has sort of.

613
00:30:13,604 --> 00:30:16,604
Speaker 6: Become yeah, which has become like a true administration.

614
00:30:16,324 --> 00:30:19,444
Speaker 5: Become kind of a Trump proxy, given how suddenly Bitcoin

615
00:30:19,484 --> 00:30:20,844
Speaker 5: friendly has become yeah.

616
00:30:20,924 --> 00:30:21,244
Speaker 2: Yeah.

617
00:30:21,284 --> 00:30:23,964
Speaker 3: So you know, why have people bet on these noisy

618
00:30:23,964 --> 00:30:27,604
Speaker 3: proxies when they're trying to hedge political risk, which is

619
00:30:27,644 --> 00:30:29,644
Speaker 3: also economic risk. So that's me as I guess now,

620
00:30:29,644 --> 00:30:32,724
Speaker 3: as a paid spokes and for prediction market company talking

621
00:30:32,724 --> 00:30:34,924
Speaker 3: to the CFTC and saying you and we're all making

622
00:30:34,924 --> 00:30:36,644
Speaker 3: you all have to figure out this risk anyways, So

623
00:30:36,644 --> 00:30:38,844
Speaker 3: why not have prediction markets instead of having to bet

624
00:30:38,884 --> 00:30:41,244
Speaker 3: on DJT coins or whatever.

625
00:30:41,484 --> 00:30:43,804
Speaker 1: Yeah. I think a really important point is that this

626
00:30:43,844 --> 00:30:46,124
Speaker 1: is what traders do all the time, right, This is

627
00:30:46,244 --> 00:30:49,804
Speaker 1: the job of a professional trader is to buy and

628
00:30:49,924 --> 00:30:52,404
Speaker 1: sell things and trade things based on what they think

629
00:30:52,484 --> 00:30:55,484
Speaker 1: is going to happen, especially if you're a macro trader. Right,

630
00:30:55,524 --> 00:30:58,164
Speaker 1: there are people so Nate and I this week on

631
00:30:58,204 --> 00:31:01,604
Speaker 1: the podcast had Bill Perkins, who is a very famous

632
00:31:01,724 --> 00:31:05,564
Speaker 1: energy trader who made millions of dollars trading on predicting

633
00:31:05,644 --> 00:31:08,924
Speaker 1: correctly what's going to happen to oil future prices too,

634
00:31:09,604 --> 00:31:14,164
Speaker 1: energy prices depending on certain global events. What was he doing.

635
00:31:14,204 --> 00:31:16,244
Speaker 1: He was betting on the likelihood that those events were

636
00:31:16,284 --> 00:31:18,484
Speaker 1: going to happen, right, and he thought that the market

637
00:31:18,524 --> 00:31:20,644
Speaker 1: was off, and then he was able to make those

638
00:31:20,724 --> 00:31:24,364
Speaker 1: trades accordingly. And so if you think about it in

639
00:31:24,404 --> 00:31:27,364
Speaker 1: those terms, I mean, we have prediction markets. That's what

640
00:31:27,364 --> 00:31:30,204
Speaker 1: the stock market is, that's what all of these different

641
00:31:30,204 --> 00:31:34,724
Speaker 1: commodities markets are. You're constantly, constantly betting on the future

642
00:31:35,044 --> 00:31:37,404
Speaker 1: and betting on these things that will happen. And the

643
00:31:37,404 --> 00:31:40,524
Speaker 1: best macro traders out there, the people who trade events,

644
00:31:40,564 --> 00:31:43,204
Speaker 1: are the ones who have those intuitions and who are

645
00:31:43,324 --> 00:31:46,284
Speaker 1: able to figure out, Okay, what do I do and

646
00:31:46,324 --> 00:31:48,524
Speaker 1: how do I pull the trigger? Right? These are also

647
00:31:48,564 --> 00:31:50,364
Speaker 1: people who have to have a high risk tolerance and

648
00:31:50,404 --> 00:31:53,404
Speaker 1: who need to understand risk taking. For someone like Bill Perkins,

649
00:31:53,444 --> 00:31:55,924
Speaker 1: he's also a poker player, right, So a lot of times,

650
00:31:55,924 --> 00:31:57,644
Speaker 1: once again, these things go hand in hand.

651
00:31:58,124 --> 00:32:01,124
Speaker 5: I remember Tracy when I really was like into poker,

652
00:32:01,124 --> 00:32:03,124
Speaker 5: and I used to read these like biographies of poker

653
00:32:03,164 --> 00:32:06,404
Speaker 5: players and reading about like how much some of them

654
00:32:06,484 --> 00:32:09,404
Speaker 5: just like love to bet on literally everything was really fascinating,

655
00:32:09,484 --> 00:32:11,924
Speaker 5: Like they would like two guys would see like a

656
00:32:11,964 --> 00:32:13,924
Speaker 5: fly on the table and they're like, oh, I'll bet that.

657
00:32:14,044 --> 00:32:16,484
Speaker 5: I'll go that's right, one thousand dollars that that fly

658
00:32:16,644 --> 00:32:18,644
Speaker 5: is going to take off in the next ten seconds

659
00:32:18,644 --> 00:32:20,404
Speaker 5: from the felt or something like that. And I get

660
00:32:20,444 --> 00:32:23,364
Speaker 5: the impression, and it sort of speaks to what Maria

661
00:32:23,484 --> 00:32:27,804
Speaker 5: was saying about building up then intuition, like over thousands

662
00:32:27,844 --> 00:32:30,724
Speaker 5: and thousands of events. It almost read to me like

663
00:32:30,804 --> 00:32:34,524
Speaker 5: the great poker players see probability on something like the

664
00:32:34,564 --> 00:32:36,964
Speaker 5: way we see a color of something. It's like that

665
00:32:37,044 --> 00:32:40,244
Speaker 5: I see your sunglasses on the table, they're green. Someone

666
00:32:40,284 --> 00:32:43,924
Speaker 5: else looks at that sunglass and say, I see there's

667
00:32:43,924 --> 00:32:46,964
Speaker 5: some chance that Tracy's gonna pick up the sunglasses.

668
00:32:46,604 --> 00:32:48,604
Speaker 6: But probability that they're gonna break or the.

669
00:32:48,524 --> 00:32:50,884
Speaker 5: Products here they're gonna self combust. And it's just like

670
00:32:51,004 --> 00:32:53,524
Speaker 5: becomes the way they see the world. We have a

671
00:32:53,564 --> 00:32:56,484
Speaker 5: few minutes left, Nate, what's going to happen in the election?

672
00:32:57,044 --> 00:32:59,244
Speaker 3: So we are actually pretty close to prediction markets. The

673
00:32:59,284 --> 00:33:01,844
Speaker 3: Silver Bullets and forecast as of this morning has Harris

674
00:33:01,884 --> 00:33:03,764
Speaker 3: with a fifty three percent chance and Trump at forty

675
00:33:03,764 --> 00:33:06,204
Speaker 3: seven percent. If you want to round that down to

676
00:33:06,204 --> 00:33:08,524
Speaker 3: fifty to fifty, a poker player would say it's not

677
00:33:08,604 --> 00:33:10,924
Speaker 3: quite the same, but fifty fifty more or less. I

678
00:33:10,964 --> 00:33:13,084
Speaker 3: think the question is like whether she so she clearly

679
00:33:13,124 --> 00:33:16,244
Speaker 3: has momentum now in the polls. We can debate what

680
00:33:16,284 --> 00:33:19,284
Speaker 3: momentum means exactly, whether that implies momentum will continue. But

681
00:33:19,364 --> 00:33:21,444
Speaker 3: there are a couple of downsides for Harris. One is

682
00:33:21,444 --> 00:33:24,764
Speaker 3: that the electoral college likely still favors Trump. It's a

683
00:33:24,844 --> 00:33:26,844
Speaker 3: very very close popular vote, and to us that she

684
00:33:26,884 --> 00:33:27,884
Speaker 3: could be at something of.

685
00:33:27,844 --> 00:33:29,244
Speaker 2: A high water mark.

686
00:33:29,404 --> 00:33:33,204
Speaker 3: There is enthusiasm among Democrats right now that verge is

687
00:33:33,244 --> 00:33:36,364
Speaker 3: almost up to being like rapidly excited for Kamala Harris,

688
00:33:36,604 --> 00:33:38,084
Speaker 3: but we still have three months to go.

689
00:33:38,444 --> 00:33:39,404
Speaker 2: Cannot be sustained.

690
00:33:39,764 --> 00:33:43,044
Speaker 5: We shall see and then, you know, looking across the

691
00:33:43,084 --> 00:33:46,404
Speaker 5: different markets, you know, as you mentioned, I'm looking at

692
00:33:46,404 --> 00:33:50,524
Speaker 5: poly Market right now. It's forty nine forty nine percent kamalas,

693
00:33:50,644 --> 00:33:53,244
Speaker 5: so they're a little bit tighter than what you haven't

694
00:33:53,484 --> 00:33:55,884
Speaker 5: like so far. I don't know, are there things that

695
00:33:55,924 --> 00:33:58,364
Speaker 5: you're watching for in the markets themselves. You know, we

696
00:33:58,404 --> 00:34:01,324
Speaker 5: did an episode on sports gambling with The Sports Better

697
00:34:01,524 --> 00:34:04,004
Speaker 5: and he was talking about, you know, sometimes there's edge

698
00:34:04,124 --> 00:34:06,964
Speaker 5: in very niche markets. So he was a tennis he

699
00:34:07,124 --> 00:34:09,164
Speaker 5: liked to bet on tennis, but he was saying, you know,

700
00:34:09,204 --> 00:34:11,804
Speaker 5: there's there's often opportunities where you can like bet like,

701
00:34:11,844 --> 00:34:14,164
Speaker 5: well so and so will win in two sets or

702
00:34:14,444 --> 00:34:17,204
Speaker 5: three sets instead of four or whatever it is. Are

703
00:34:17,244 --> 00:34:20,444
Speaker 5: there interesting things that you're seeing on the markets, whether

704
00:34:20,484 --> 00:34:23,604
Speaker 5: it's Polymarket or others that seem off or on or

705
00:34:23,644 --> 00:34:27,524
Speaker 5: interesting or things that we should watch perhaps besides that

706
00:34:27,644 --> 00:34:29,844
Speaker 5: headline Harris versus Trump contract.

707
00:34:30,204 --> 00:34:31,924
Speaker 3: I mean you can bet on individual states. You can

708
00:34:32,004 --> 00:34:34,564
Speaker 3: bet on things like margins of victory and individual states.

709
00:34:34,564 --> 00:34:36,164
Speaker 3: And you know, if you have a model like mine,

710
00:34:36,204 --> 00:34:39,124
Speaker 3: then you have lots of opinions about that that you

711
00:34:39,164 --> 00:34:42,004
Speaker 3: can bet on potentially, However, the volumes tend to be

712
00:34:42,444 --> 00:34:44,724
Speaker 3: a lot smaller. There's also kind of a more macro question,

713
00:34:44,764 --> 00:34:48,404
Speaker 3: which is in recent elections, the markets have been a

714
00:34:48,404 --> 00:34:51,604
Speaker 3: little bit Republican leaning relative to what the models say.

715
00:34:51,684 --> 00:34:55,124
Speaker 3: Hum Partly that's because I think the demographics of who

716
00:34:55,164 --> 00:34:57,204
Speaker 3: trade on these markets, right, they are a little bit

717
00:34:57,244 --> 00:35:01,044
Speaker 3: crypto pilled, very literally, so in cas it's looking like polymarket,

718
00:35:01,244 --> 00:35:02,884
Speaker 3: So that affects things a little bit.

719
00:35:02,924 --> 00:35:05,324
Speaker 2: But also, you know, poles have been off.

720
00:35:05,724 --> 00:35:08,204
Speaker 3: By a little bit in favor, you know, the low

721
00:35:08,244 --> 00:35:11,564
Speaker 3: bulld Trump in twenty sixteen, twenty twenty, not in twenty

722
00:35:11,644 --> 00:35:12,004
Speaker 3: twenty two.

723
00:35:12,044 --> 00:35:13,164
Speaker 2: But Trump wasn't on the ballot.

724
00:35:13,244 --> 00:35:16,244
Speaker 3: So one way to read the delta between my forecast

725
00:35:16,284 --> 00:35:19,244
Speaker 3: in the markets is that the market is pricing in

726
00:35:19,924 --> 00:35:23,164
Speaker 3: just a little tiny bit of Trump beating his poles,

727
00:35:23,204 --> 00:35:25,644
Speaker 3: which you know, might not be entirely irrational.

728
00:35:26,364 --> 00:35:29,364
Speaker 6: I'm glad you brought this up because the wisdom that

729
00:35:29,524 --> 00:35:33,564
Speaker 6: is being derived from this particular crowd, Like the crowd

730
00:35:33,724 --> 00:35:40,164
Speaker 6: is technically non American, but definitely you know, mostly male

731
00:35:40,324 --> 00:35:43,604
Speaker 6: from what we know, and maybe a little Republican leaning,

732
00:35:43,804 --> 00:35:48,404
Speaker 6: as you just mentioned. Are there like efforts underway or

733
00:35:48,484 --> 00:35:53,044
Speaker 6: is there a desirability to broaden out the number of

734
00:35:53,084 --> 00:35:56,284
Speaker 6: people who are using this in order to maybe like

735
00:35:56,644 --> 00:35:59,244
Speaker 6: up the size of the crowd and make sure that

736
00:35:59,284 --> 00:36:02,924
Speaker 6: you're getting the best sample that you could possibly get.

737
00:36:03,044 --> 00:36:05,844
Speaker 6: And again, if you look at the official polls, the

738
00:36:05,924 --> 00:36:10,204
Speaker 6: poles always attempt to get a slice of the American

739
00:36:10,244 --> 00:36:13,884
Speaker 6: population that is reflective of its actual makeup. That's not

740
00:36:13,964 --> 00:36:17,404
Speaker 6: really happening on these prediction markets. So is there anything

741
00:36:17,404 --> 00:36:19,764
Speaker 6: that you could do about that or is it a concern?

742
00:36:20,284 --> 00:36:22,644
Speaker 1: I think that in an ideal world, of course we

743
00:36:22,684 --> 00:36:25,764
Speaker 1: would take all of these things into consideration and you

744
00:36:25,804 --> 00:36:29,964
Speaker 1: would have a more representative sample of betters. But we're

745
00:36:30,004 --> 00:36:32,204
Speaker 1: not living in an ideal world, and like we're not,

746
00:36:33,324 --> 00:36:36,644
Speaker 1: I can't affect the regulation, but I do think that

747
00:36:37,404 --> 00:36:42,444
Speaker 1: it's important to get a sort of sample that is

748
00:36:42,564 --> 00:36:45,484
Speaker 1: representative of the people who are going to be voting, right,

749
00:36:45,524 --> 00:36:49,124
Speaker 1: So it's not just representative of the population, but who's

750
00:36:49,164 --> 00:36:51,684
Speaker 1: actually going to be going to the polls, who's going

751
00:36:51,684 --> 00:36:55,884
Speaker 1: to be casting the ballots. Those are important questions to ask,

752
00:36:56,324 --> 00:36:59,444
Speaker 1: and sometimes the fact that things are skewed also reflects

753
00:36:59,484 --> 00:37:02,404
Speaker 1: like who's actually going to be walking? Who are we

754
00:37:02,524 --> 00:37:04,884
Speaker 1: actually going to be asking? So I think there's so

755
00:37:04,964 --> 00:37:07,844
Speaker 1: many different parts of this question. And yes, Nate, as

756
00:37:07,884 --> 00:37:09,964
Speaker 1: you correctly point out, I am worried about, yes said,

757
00:37:10,004 --> 00:37:12,724
Speaker 1: I am worried about all of these different things. But

758
00:37:12,804 --> 00:37:15,244
Speaker 1: some of them are just like a fact of existence,

759
00:37:15,444 --> 00:37:17,804
Speaker 1: and we need to account for them. But we need

760
00:37:17,844 --> 00:37:20,484
Speaker 1: to realize that, Okay, like if we try to make

761
00:37:20,484 --> 00:37:23,324
Speaker 1: it representative, we might actually be skewing things if the

762
00:37:23,404 --> 00:37:25,844
Speaker 1: vote isn't going to be representative. If that makes sense.

763
00:37:26,284 --> 00:37:28,964
Speaker 5: Yeah, Actually that leads me to one more question, which

764
00:37:28,964 --> 00:37:31,204
Speaker 5: is that just thinking about the election itself, setting aside

765
00:37:31,324 --> 00:37:34,764
Speaker 5: betting markets. So there is reason to think that maybe

766
00:37:34,844 --> 00:37:39,204
Speaker 5: in twenty sixteen, perhaps the polls weren't capturing all of

767
00:37:39,244 --> 00:37:41,644
Speaker 5: Trump's support, and I guess it's sort of played out

768
00:37:41,644 --> 00:37:43,884
Speaker 5: like that, but right now it actually seems to cut

769
00:37:43,924 --> 00:37:47,484
Speaker 5: in both directions. So there is a view that, okay,

770
00:37:47,524 --> 00:37:50,644
Speaker 5: polls aren't good anymore, and that people don't pick up

771
00:37:50,684 --> 00:37:54,084
Speaker 5: the phone and they become much more expensive and less efficient,

772
00:37:54,364 --> 00:37:55,924
Speaker 5: and that there could be a skew in the type

773
00:37:55,924 --> 00:37:57,924
Speaker 5: of person who answers the phone On the other hand,

774
00:37:58,444 --> 00:38:02,524
Speaker 5: one thing that we've seen particularly emerging in midyear elections

775
00:38:03,004 --> 00:38:05,524
Speaker 5: is that Democrats just like they love to turn out

776
00:38:05,524 --> 00:38:08,044
Speaker 5: to vote, They loved the vote in any random election,

777
00:38:08,484 --> 00:38:10,844
Speaker 5: And there is the PERCEP shouldn' probably accurate that many

778
00:38:10,884 --> 00:38:14,124
Speaker 5: Trump voters are you know, less engaged and they might

779
00:38:14,164 --> 00:38:19,004
Speaker 5: support Trump, but whether that support actually translates into showing up,

780
00:38:19,204 --> 00:38:22,924
Speaker 5: registering to vote, finding the polling location, going there, and

781
00:38:23,004 --> 00:38:26,084
Speaker 5: voting is another question. How are you thinking about like

782
00:38:26,124 --> 00:38:29,604
Speaker 5: some of these sort of risks right now in twenty

783
00:38:29,684 --> 00:38:32,564
Speaker 5: twenty four, with some of the quality of data, and

784
00:38:33,004 --> 00:38:34,844
Speaker 5: you know, ways it could be biased right now.

785
00:38:35,124 --> 00:38:35,324
Speaker 2: Yeah.

786
00:38:35,404 --> 00:38:38,124
Speaker 3: Look, the model more or less assumes that although polls

787
00:38:38,404 --> 00:38:40,724
Speaker 3: can and probably will be biased some degree, that the

788
00:38:40,724 --> 00:38:44,364
Speaker 3: direction of the bias is unpredictable, which I Joe kind

789
00:38:44,364 --> 00:38:48,164
Speaker 3: of see as a spinoff from like efficient market hypothesis.

790
00:38:48,724 --> 00:38:50,724
Speaker 2: The markets aren't always right. They do silly things.

791
00:38:50,764 --> 00:38:52,324
Speaker 3: But if you think you know better than the market

792
00:38:52,364 --> 00:38:54,884
Speaker 3: what Nvidia should be priced at or something, then then

793
00:38:54,884 --> 00:38:57,964
Speaker 3: you can make a killing trading options or whatever else. Look,

794
00:38:58,164 --> 00:39:01,204
Speaker 3: pollsters have an incentive to get the right answer, And

795
00:39:01,244 --> 00:39:03,564
Speaker 3: we've also moved away from kind of like a purist

796
00:39:03,644 --> 00:39:06,484
Speaker 3: world where you call everybody in the phone book at

797
00:39:06,564 --> 00:39:08,564
Speaker 3: random and say can I speak to the person with

798
00:39:08,604 --> 00:39:11,364
Speaker 3: the next birthday? And they always answer. We moved away

799
00:39:11,404 --> 00:39:14,884
Speaker 3: from that to where like poles are basically models, so

800
00:39:14,924 --> 00:39:17,924
Speaker 3: my model is kind of like aggregating models into one

801
00:39:17,964 --> 00:39:21,284
Speaker 3: big meta model. And so therefore I think the market

802
00:39:21,324 --> 00:39:24,564
Speaker 3: incentives are pretty good, right, And when poles are biased

803
00:39:24,764 --> 00:39:28,404
Speaker 3: toward Democrats, the Republican leading polsters tend to get more

804
00:39:28,444 --> 00:39:30,644
Speaker 3: work the next cycle, so it's a little bit it's

805
00:39:30,684 --> 00:39:32,044
Speaker 3: fairly self correcting.

806
00:39:32,324 --> 00:39:33,644
Speaker 2: I think, actually that's a.

807
00:39:33,684 --> 00:39:35,644
Speaker 1: Very good way of looking at it. So I hope

808
00:39:35,684 --> 00:39:36,164
Speaker 1: you're right. Night.

809
00:39:37,164 --> 00:39:39,724
Speaker 5: Nate and Maria, thank you so much both of you

810
00:39:39,764 --> 00:39:42,884
Speaker 5: for coming on the podcast. Good luck and congratulations on

811
00:39:42,924 --> 00:39:45,804
Speaker 5: the launch of the new podcast. Maybe a little competition,

812
00:39:45,844 --> 00:39:48,204
Speaker 5: who knows, but no really appreciate having you both on.

813
00:39:48,284 --> 00:39:51,684
Speaker 5: ODD loves to talk about something that's very timely and exciting.

814
00:39:51,804 --> 00:39:54,084
Speaker 1: Thank you so much. It's been a pleasure.

815
00:40:06,684 --> 00:40:09,084
Speaker 5: Tracy, that was a lot of fun. I'm a terrible gambler,

816
00:40:09,484 --> 00:40:12,484
Speaker 5: really bad, but I still like it. I don't like

817
00:40:12,524 --> 00:40:15,164
Speaker 5: losing money, but I still like in my mind, I

818
00:40:15,204 --> 00:40:18,284
Speaker 5: wish I were good, and I like talking about it, well, I.

819
00:40:18,244 --> 00:40:21,684
Speaker 6: Thought Maria's point about when you actually have to put

820
00:40:21,724 --> 00:40:25,004
Speaker 6: money behind like your thought process about who is going

821
00:40:25,004 --> 00:40:28,524
Speaker 6: to win the election, it sort of sharpens your reasoning

822
00:40:28,764 --> 00:40:31,644
Speaker 6: and your thinking. I thought that was interesting. By the way, Joe,

823
00:40:31,684 --> 00:40:35,164
Speaker 6: I have good news for you and I have bad news.

824
00:40:35,284 --> 00:40:39,804
Speaker 6: Oh okay, So the bad news is there's a question

825
00:40:40,004 --> 00:40:44,084
Speaker 6: on poly market that is, will America bans in in

826
00:40:44,164 --> 00:40:47,604
Speaker 6: twenty twenty four. The good news is that the current

827
00:40:47,644 --> 00:40:51,244
Speaker 6: probability is seven percent. So I think you're okay, that's

828
00:40:51,284 --> 00:40:51,564
Speaker 6: all right.

829
00:40:51,644 --> 00:40:54,404
Speaker 5: I have Zalt instead. There's one hundred other ones that,

830
00:40:54,524 --> 00:40:56,924
Speaker 5: as we learned in a recent episode, will probably get

831
00:40:56,964 --> 00:41:00,884
Speaker 5: through border security regardless of the regulatory environment. But no,

832
00:41:01,124 --> 00:41:02,844
Speaker 5: I thought it was fun. And look, it gets back

833
00:41:02,844 --> 00:41:06,684
Speaker 5: to two things that I still basically think, which is

834
00:41:06,684 --> 00:41:09,324
Speaker 5: that A, in the form of regular markets prediction market

835
00:41:09,524 --> 00:41:12,964
Speaker 5: have worked for a long time, and B they're just

836
00:41:13,084 --> 00:41:16,964
Speaker 5: from my perspective, I don't participate in betting markets, but

837
00:41:17,364 --> 00:41:20,084
Speaker 5: I find it interesting, is like, what is the conventional

838
00:41:20,124 --> 00:41:22,804
Speaker 5: wisdom right now on what's going to happen where people's

839
00:41:22,844 --> 00:41:26,204
Speaker 5: heads at And as a news consumer, I find them

840
00:41:26,324 --> 00:41:29,524
Speaker 5: additive in a way that just say tweets like I

841
00:41:29,524 --> 00:41:31,444
Speaker 5: think Kamala is going to win or has the momentum.

842
00:41:31,964 --> 00:41:34,284
Speaker 5: It's it's something helpful. On top of that, it's.

843
00:41:34,164 --> 00:41:38,604
Speaker 6: Definitely something to talk about in the media and aout

844
00:41:38,684 --> 00:41:41,004
Speaker 6: that is true. I do think not to go all

845
00:41:41,044 --> 00:41:44,084
Speaker 6: existential again, but I do think it opens up really

846
00:41:44,164 --> 00:41:48,044
Speaker 6: interesting questions about the nature and the purpose of market.

847
00:41:48,244 --> 00:41:51,244
Speaker 6: So Nate talked in the beginning about, well, this is

848
00:41:51,244 --> 00:41:54,724
Speaker 6: a price discovery vehicle, and you know, I can see that.

849
00:41:55,324 --> 00:41:59,244
Speaker 6: But I guess if you were a market traditionalist, you know,

850
00:41:59,284 --> 00:42:01,804
Speaker 6: there might be people out there who think that markets

851
00:42:01,844 --> 00:42:06,204
Speaker 6: are about efficiently allocating capital, and so the question is like,

852
00:42:06,244 --> 00:42:11,284
Speaker 6: what are you really allocating capital too in this particular instance.

853
00:42:11,364 --> 00:42:13,924
Speaker 6: But then I guess the reverse argument would be like, well,

854
00:42:13,964 --> 00:42:17,364
Speaker 6: there is value in making a correct prediction, and so

855
00:42:17,524 --> 00:42:19,244
Speaker 6: you are rewarding that behavior.

856
00:42:19,484 --> 00:42:21,004
Speaker 5: So I think that's a great point, And I think

857
00:42:21,044 --> 00:42:23,604
Speaker 5: I would say two things from my perspective, which is

858
00:42:23,724 --> 00:42:27,804
Speaker 5: Ny pointed out. For some of these bets, there is real.

859
00:42:27,804 --> 00:42:29,084
Speaker 2: Economic issue at stake.

860
00:42:29,204 --> 00:42:31,804
Speaker 5: So if you are interested in the future of electric

861
00:42:31,884 --> 00:42:34,724
Speaker 5: vehicles or oil or whatever it is, then it might

862
00:42:34,764 --> 00:42:37,364
Speaker 5: make a very big difference who wins the election. In

863
00:42:37,404 --> 00:42:40,364
Speaker 5: other words, like you like you already have skin in

864
00:42:40,404 --> 00:42:43,084
Speaker 5: the game and this becomes an instrument to hedging, or

865
00:42:43,124 --> 00:42:45,404
Speaker 5: I'm like a little bit more skeptical as like, okay,

866
00:42:45,444 --> 00:42:47,964
Speaker 5: so here is one of the things I see. What

867
00:42:48,084 --> 00:42:50,924
Speaker 5: will Trump say during his interview with Elon Musk. There's

868
00:42:50,924 --> 00:42:53,404
Speaker 5: a seventy two percent he says make America great again.

869
00:42:53,444 --> 00:42:56,084
Speaker 5: There's a forty nine percent chance he mentions crypto. There's

870
00:42:56,084 --> 00:42:59,284
Speaker 5: a fifty seven percent chance you mentioned censorship, et cetera.

871
00:42:59,684 --> 00:43:02,764
Speaker 5: Like for a market like that, that seems like pure

872
00:43:02,924 --> 00:43:08,404
Speaker 5: speculation because there are very few actual outside exogenists.

873
00:43:08,404 --> 00:43:11,164
Speaker 6: Well crypto, but yeah, no, that's I take the point.

874
00:43:11,284 --> 00:43:13,804
Speaker 5: Well, like so okay, like okay, so for maybe a

875
00:43:13,804 --> 00:43:15,724
Speaker 5: better one, will Taylor Swift get engaged?

876
00:43:15,804 --> 00:43:16,004
Speaker 2: Right?

877
00:43:16,524 --> 00:43:18,484
Speaker 5: Or you know the number here.

878
00:43:18,844 --> 00:43:21,964
Speaker 6: That might that might actually mean something for inflation.

879
00:43:21,884 --> 00:43:24,164
Speaker 5: Or the marriage market, right, because a lot of people

880
00:43:24,204 --> 00:43:26,764
Speaker 5: think that if Taylor Swift gets engaged, then a bunch

881
00:43:26,804 --> 00:43:28,684
Speaker 5: of people are like, it's time for us to tie

882
00:43:28,724 --> 00:43:30,004
Speaker 5: the knot and sort of do the same thing.

883
00:43:30,044 --> 00:43:31,964
Speaker 6: Maybe we should just do this for a while and

884
00:43:32,164 --> 00:43:34,244
Speaker 6: just play a game where we go through all of

885
00:43:34,284 --> 00:43:37,644
Speaker 6: these particular events and questions come up with the real

886
00:43:37,644 --> 00:43:38,964
Speaker 6: world economic it come.

887
00:43:38,924 --> 00:43:41,724
Speaker 5: Up with a j there's one. The number of Elon

888
00:43:41,844 --> 00:43:46,124
Speaker 5: Musk tweets between August second August nine, currently between eighty

889
00:43:46,204 --> 00:43:48,084
Speaker 5: and eighty nine is at one hundred percent, So people,

890
00:43:48,404 --> 00:43:49,844
Speaker 5: well to keep tweeting.

891
00:43:50,044 --> 00:43:53,364
Speaker 6: Maybe Elon must tweets have an impact on the presidential election.

892
00:43:53,484 --> 00:43:55,844
Speaker 6: There has an impact on a bunch of real world stuff.

893
00:43:56,084 --> 00:43:59,164
Speaker 5: Five percent that there's a doge coin etf in twenty

894
00:43:59,204 --> 00:44:01,604
Speaker 5: twenty four. That seems a little high to me, because

895
00:44:01,844 --> 00:44:04,204
Speaker 5: but you know, we don't even have anyway. We could

896
00:44:04,244 --> 00:44:06,564
Speaker 5: go on and on. It's very fun. It's just another

897
00:44:06,604 --> 00:44:07,924
Speaker 5: way for me to consume the news.

898
00:44:08,964 --> 00:44:12,004
Speaker 6: All right, Well, I'm sure we'll probably end up doing

899
00:44:12,044 --> 00:44:15,324
Speaker 6: more prediction market episodes in the future, but for now,

900
00:44:15,324 --> 00:44:16,124
Speaker 6: shall we leave it there?

901
00:44:16,204 --> 00:44:16,964
Speaker 2: Let's leave it there.

902
00:44:17,164 --> 00:44:20,164
Speaker 6: This has been another episode of the Athlots podcast. I'm

903
00:44:20,204 --> 00:44:23,284
Speaker 6: Tracy Alloway. You can follow me at Tracy Alloway.

904
00:44:23,084 --> 00:44:25,884
Speaker 5: And I'm joll Wisenthal. You can follow me at the Stalwart.

905
00:44:26,044 --> 00:44:28,764
Speaker 5: Follow our guest Nate Silver, He's at Nate Silver five

906
00:44:28,884 --> 00:44:33,284
Speaker 5: thirty eight. Maria Kanakova, She's at Mconnakova. Follow our producers

907
00:44:33,364 --> 00:44:37,124
Speaker 5: Carmen Rodriguez at Kerman armand Dashill Bennett at deshbot and

908
00:44:37,244 --> 00:44:40,884
Speaker 5: Kilbrooks at Kilbrooks. And thank you to our producer Moses Onam.

909
00:44:41,124 --> 00:44:43,604
Speaker 5: And for more Oddlots content, go to Bloomberg dot com

910
00:44:43,644 --> 00:44:46,124
Speaker 5: slash odd lots, where of transcripts, a blog and a

911
00:44:46,164 --> 00:44:49,324
Speaker 5: newsletter and you can chat about all of these topics

912
00:44:49,364 --> 00:44:52,204
Speaker 5: in our discord. In fact, there's a very active channel

913
00:44:52,204 --> 00:44:54,164
Speaker 5: where people are talking about this stuff all the time

914
00:44:54,164 --> 00:44:57,124
Speaker 5: in the elections channel and the pundit dunk take channel

915
00:44:57,164 --> 00:45:00,044
Speaker 5: where they people drop bad predictions or make fun of people.

916
00:45:00,324 --> 00:45:03,804
Speaker 5: Very entertaining subchannel, and there might be my favorite. Check

917
00:45:03,844 --> 00:45:07,284
Speaker 5: out our discord, Discord dot gg slash odlot.

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00:45:07,244 --> 00:45:09,684
Speaker 6: And if you enjoy all thoughts, if you like when

919
00:45:09,724 --> 00:45:13,204
Speaker 6: we talk about prediction markets and the nature of markets themselves,

920
00:45:13,244 --> 00:45:16,004
Speaker 6: then please leave us a positive review on your favorite

921
00:45:16,004 --> 00:45:19,644
Speaker 6: podcast platform. And remember, if you are a Bloomberg subscriber,

922
00:45:19,724 --> 00:45:22,804
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923
00:45:23,124 --> 00:45:25,564
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924
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925
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Speaker 6: the Bloomberg channel on Apple Podcasts and follow the instructions there.

926
00:45:32,644 --> 00:45:49,044
Speaker 6: Thanks for listening.