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Speaker 1: Pushkin. Over the years, Cautionary Tales has warned you about

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Speaker 1: Ponzi schemes, dodgy Christmas savings clubs, and promotions that are

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Speaker 1: too good to be true. So it may come as

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Speaker 1: a surprise when I urge you to get into Risky Business.

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Speaker 2: Don't worry.

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Speaker 1: I'm not trying to pull a fast one. I just

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Speaker 1: want you to try out a podcast I think you'll enjoy.

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Speaker 1: Risky Business is a weekly show about making better decisions.

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Speaker 1: The hosts, Maria Konakova and Nate Silver, are both writers

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Speaker 1: and high stakes poker players. Maria and Nate cover everything

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Speaker 1: from politics to poker to personal decisions, so when they

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Speaker 1: asked me to join them for an episode, I was

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Speaker 1: eager to try. I even got the chance to talk

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Speaker 1: to Maria about one of the fraudsters featured in Cautionary Tales.

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Speaker 1: She has met him and he assured her yes, he

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Speaker 1: was a liar. You can listen to Risky Business wherever

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Speaker 1: you get your podcasts, But for now, here's the episode

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Speaker 1: featuring me. I hope you like it. There are duels,

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Speaker 1: prison breaks, banking bubbles, poetry, and Nate reveals he has

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Speaker 1: a relative who faked his own death.

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Speaker 2: Hi, everyone, Welcome back to Risky Business, our show about

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Speaker 2: making better decisions. I'm Maria Kanikova.

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Speaker 3: And I'm Nate Silver.

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Speaker 2: So today we have a slightly different and fun show

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Speaker 2: for you. We're going to be bringing on a very

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Speaker 2: special guest, Tim Harford, who hosts another Pushkin podcast, Cautionary Tales,

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Speaker 2: and we'll be talking about the intersection of risk and

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Speaker 2: cautionary tales from history. Tim, welcome to the show to

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Speaker 2: be on the show. Thank you so for listeners of Pushkin,

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Speaker 2: you're probably familiar with Tim as the host of Cautionary Tales.

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Speaker 2: I've been lucky enough to be on the show and

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Speaker 2: to have been a listener of the show. I think

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Speaker 2: it's wonderful. My episodes are obviously the best, but it's

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Speaker 2: all pretty good. Tim is an economist, a journalist. He

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Speaker 2: has a column in the Financial Times, He's written multiple

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Speaker 2: amazing books. You know, longtime friend of the two of us. Nate,

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Speaker 2: I don't know if you want to add anything else,

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Speaker 2: but we're so happy to be here and to do

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Speaker 2: kind of an episode where Cautionary Tales and Risky Business intersect,

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Speaker 2: where we talk about cautionary tales that are about risky business.

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Speaker 2: That are about risk, about taking risks, about how risk

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Speaker 2: taking can go wrong, and how sometimes you know, the

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Speaker 2: lines between legitimate risks and cons and deceptions might get

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Speaker 2: blurred a little bit and cross over into territory that

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Speaker 2: goes from legitimate to illegitimate very quickly.

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Speaker 3: Do you mean to say that campbling doesn't always work out, Maria?

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Speaker 2: It's weird, it's so weird.

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Speaker 1: The chance of loss is in fact one hundred percent,

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Speaker 1: which I guess we'll get we'll get to that, but yes, that.

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Speaker 2: Is absolutely true. So when we were kind of thinking

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Speaker 2: about ways to make this episode work, Tim, you thought

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Speaker 2: about one particular story where you and I have actually

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Speaker 2: intersected on this because we've both thought about this person

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Speaker 2: and he's someone who I actually had on my previous podcast,

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Speaker 2: The Grift, and he is a well, let's let's have

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Speaker 2: you lay him out. Let us meet Sam Israel, the

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Speaker 2: first kind of our first subject for today.

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Speaker 1: Sam Israel, the third I think is his full name. Third,

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Speaker 1: absolutely remarkable gentleman if gentlemen is the right term, and

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Speaker 1: it probably isn't. So we did a Cautionary Tales episode

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Speaker 1: live on stage about pyramid schemes and Ponzi schemes and

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Speaker 1: white people fall for them, but also white people set

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Speaker 1: them up. And this kind of strange snowball of disaster

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Speaker 1: where the Ponzi scheme becomes increasingly difficult to cover. And

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Speaker 1: the most amazing example I've ever come across is by

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Speaker 1: You Capital, which was set up by Sam Israel. I

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Speaker 1: saw one writer described Sam Israel and by You Capital.

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Speaker 1: It's like somebody took the Bernie Madoff story but was

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Speaker 1: told to write a Hollywood script and to punch it

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Speaker 1: up a bit, make it sing a bit more. And

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Speaker 1: everything that made made Off's Ponzi scheme notorious applies to

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Speaker 1: Sam Israel, but it just all gets crazier. So Sam

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Speaker 1: came from a wealthy family, I think of commodity traders

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Speaker 1: in Louisiana, but he wanted to show he could make

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Speaker 1: it himself, and he got into Wall Street at an

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Speaker 1: early age, absorbed that Wall Street culture, you know, all

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Speaker 1: that kind of broish culture of Wall Street in the

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Speaker 1: nineteen eighties. And so he takes all this in, he

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Speaker 1: keeps his mouth shut. He watches various dubious activities, and

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Speaker 1: not just the kind of the sex work and the excess,

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Speaker 1: but also illegality, financial illegality, observes people kind of insider trading,

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Speaker 1: for example, and then he sets up his own firm,

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Speaker 1: by You Capital, which is a hedge fund, and very quickly,

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Speaker 1: by You Capital turns from being a hedge fund into

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Speaker 1: being a ponzi scheme. And just to remind people what

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Speaker 1: a ponzi scheme is, it's very simple. Investors give you money,

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Speaker 1: and then you announce you've made massive profits, and then

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Speaker 1: more investors give you money, and you announce you made

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Speaker 1: even more massive profits, and then more investors give you money,

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Speaker 1: and you keep saying you've made massive profits. And if

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Speaker 1: anybody ever says, that's great, I'd like my money back, well,

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Speaker 1: that's easy. You can give them the money back with

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Speaker 1: the profits because more people keep giving you, keep giving

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Speaker 1: you their money. And the insight that Sam Israel had

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Speaker 1: was with a hedge fund, it's kind of open ended.

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Speaker 1: The money keeps growing, and why would anybody ever want

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Speaker 1: their money back if you keep telling them they made

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Speaker 1: another twenty percent this year, they made another twenty five

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Speaker 1: percent this year, Like no one ever asks for their

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Speaker 1: money back. They just leave the money with you. And

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Speaker 1: so the fraud went on for a very long time,

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Speaker 1: and then things started to unravel. But Maria, you met

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Speaker 1: Sam so so, and you met Sam in prison, so

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Speaker 1: a spoiler. So tell us what did you make for

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Speaker 1: him as a person? How did he get into this?

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Speaker 1: Why did he get into this?

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Speaker 2: Well, it's funny because my interview with him was from

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Speaker 2: a while back, you know, twenty seventeen something like that.

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Speaker 2: I honestly don't remember.

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Speaker 1: We should say he was arrested, I think in about

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Speaker 1: two thousand and seven, two thousand and eight something like that.

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Speaker 2: Wasn't yeah, exactly exactly, but so I was trying to

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Speaker 2: refresh my memory, and you know, just looked at some

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Speaker 2: of the transcripts and then also looked at an interview

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Speaker 2: that he did with Andrew Ross Sorkin, and he told

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Speaker 2: us the exact same thing at the beginning, which is like,

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Speaker 2: don't believe me, I'm a liar. And it's so funny

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Speaker 2: because I think that he thinks that that absolves him, right,

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Speaker 2: if he puts that disclaimer up top, then he can

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Speaker 2: sort of charm his way out and say, but actually,

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Speaker 2: I'm a good guy, right, I'm not like that bad

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Speaker 2: guy made off.

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Speaker 1: So I should say there's one amazing scene. Gee Lawson

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Speaker 1: wrote this book The Octopus, which is like the quintessential

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Speaker 1: account of Sammy's wail. But there's a scene in that

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Speaker 1: book where his wife walks in and catches him bent

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Speaker 1: over his desk snorting cocaine through a fifty dollar bill

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Speaker 1: and says, why are you taking cocaine? And he goes,

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Speaker 1: how dare you accuse me of taking drugs? So yeah, sorry,

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Speaker 1: I interrupted, but.

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Speaker 2: No, he is. It is the kind of guy he is.

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Speaker 2: And you know, he was incredibly charismatic, and he said,

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Speaker 2: I was doing really really well on Wall Street. Right,

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Speaker 2: he kind of got in. He didn't want to compete

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Speaker 2: with his siblings, wanted to do it on his own,

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Speaker 2: so he didn't want to go into the family business. Instead,

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Speaker 2: he had this opportunity to go into Wall Street, worked

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Speaker 2: at a very successful hedge fund, and was actually making money.

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Speaker 2: By the way, this is all according to sam Israel, Right.

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Speaker 2: I haven't actually looked at his returns. I did not

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Speaker 2: look at his balance sheets. I don't know how he

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Speaker 2: did as a trader. He assures me that he was

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Speaker 2: making millions for people and for himself in his prior

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Speaker 2: Wall Street days. So let's just we'll take that on faith.

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Speaker 2: But what I've learned working with con artists is you

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Speaker 2: can't take anything on faith, so asterisk. But he was

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Speaker 2: very successful on Wall Street, and I assume he must

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Speaker 2: have been to a certain degree because he started his

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Speaker 2: own hedge fund right and he was able to raise

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Speaker 2: I think about three hundred million to begin with of

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Speaker 2: outside money, which back then this was nineteen ninety six,

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Speaker 2: I want to say something around somewhere around there in

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Speaker 2: ninety so that was a lot of money. And he

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Speaker 2: went in with a partner who was a close friend

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Speaker 2: of his, who was a disgraced fund manager whose fund

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Speaker 2: had just gone under. But Sam believed in him and

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Speaker 2: thought that, you know, that he was a good guy

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Speaker 2: and that this would work. So the way Sam told

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Speaker 2: it to me was that when they started by you

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Speaker 2: in nineteen ninety six, he kind of relied on this guy,

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Speaker 2: you know, to be an equal partner, and that this

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Speaker 2: guy was started losing a lot of money, and Sam said, well,

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Speaker 2: I'm a good trader, I'm good at this. I'll be

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Speaker 2: able to kind of work my way out of the hole.

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Speaker 2: But he couldn't, and the hole kept getting bigger, and

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Speaker 2: so at some point he realized, you know, shit, we've

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Speaker 2: lost twelve percent in our first year. You know, it's

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Speaker 2: even worse in our second year. This is looking bad

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Speaker 2: and our ability to raise money is going down because

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Speaker 2: our returns are shit. They're absolutely horrible. And so that's

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Speaker 2: when it became a Ponzi scheme.

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Speaker 1: And his accountant, who was a guy called Dan Marino. Hell,

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Speaker 1: there is footnote for body. Well if anybody so think

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Speaker 1: about it, this is nineteen ninety six. If anybody googles

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Speaker 1: as pre google, right, if anybody who tries to search

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Speaker 1: on the Internet for Dan Marino, they get the football player.

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Speaker 1: So he's working with this accountant who is completely invisible

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Speaker 1: to Internet searches. And the accountants are smart. Yeah, it's

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Speaker 1: really smart. The accountant's completely crooked and basically sets up

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Speaker 1: his own fake Uh.

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Speaker 4: Sounds like a cricket I've read a stereotype based on

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Speaker 4: last but Deno sounds like a cricket account accountant or

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Speaker 4: an NFL quarterback.

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Speaker 1: So he sets up his own fake auditing firm. So

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Speaker 1: he's basically auditing his own accounts. And if anybody sort

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Speaker 1: of were to really go and check the go the

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Speaker 1: guy who says that Dan Marino's accounts are genuine is

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Speaker 1: an uditor called Dan Marino, and they're the same guy,

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Speaker 1: so that that was kind of an important part of

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Speaker 1: this fraud. But yeah, Dan Marinos told the author Gey

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Speaker 1: Lawson that one of the problems was, there's this idea, Oh,

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Speaker 1: we're going to have a really good year, We're going

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Speaker 1: to make a lot of money for real. And when

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Speaker 1: we make a lot of money for real, then it

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Speaker 1: will no longer be a palmzy scheme. You know, we'll

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Speaker 1: have you know, it's all genuine, it all come out

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Speaker 1: good in the end. Dan Marino said. The problem is

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Speaker 1: sometimes they did have good years, but whenever they had

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Speaker 1: a good year, they would claim it was an even

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Speaker 1: better year, and whenever they had a bad year, they

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Speaker 1: would never admit that they had a bad year. So

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Speaker 1: there was a you know, whatever it was, vanity, fear

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Speaker 1: of the consequences, whatever it was. He just made it

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Speaker 1: completely impossible for him ever to catch up with his

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Speaker 1: own life.

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Speaker 2: And I think that that's very typical of con artists,

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Speaker 2: right where they say I'll fix it, I'll make it better.

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Speaker 2: This isn't this is just temporary, but it never is.

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Speaker 2: But the most incredible thing about Sam Israel is that

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Speaker 2: once the scheme kind of comes undone, right, which happens

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Speaker 2: at some point, just like with SBF. Note right, at

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Speaker 2: some point people are going to ask for their money

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Speaker 2: and people are going to get spooked. And when they

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Speaker 2: get spooked, like there's going to be a day of reckoning.

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Speaker 2: And he had a moment where he said, oh shit,

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Speaker 2: you know I'm going to jail. And he thought he

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Speaker 2: would get seven or eight years, which was pretty typical

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Speaker 2: for white collar criminals. And then they switched judges and

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Speaker 2: the judge gave him two hundred and forty months, so

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Speaker 2: twenty years right instead. And he was like, oh shit,

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Speaker 2: you know I can't do twenty years. I'm going to

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Speaker 2: be an old man. This is this is not cool.

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Speaker 2: I was ready for seven I can't do twenty. And

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Speaker 2: so instead of you know, figuring out how do I

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Speaker 2: deal with this, he decides to fake his own death.

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Speaker 1: Yeah, which he's obviously going to work out really well.

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Speaker 2: Oh it's going to work out great, We'll be back

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Speaker 2: in a minute. He only has a few months to plan, right,

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Speaker 2: It's not like he has been thinking, oh, I'm going

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Speaker 2: to fake my own death. This is my exit strategy.

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Speaker 2: Because a lot of connor is they don't think that

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Speaker 2: far ahead, right, They don't think of the exit strategy.

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Speaker 2: They think it's always going to work out. So he says,

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Speaker 2: I'm gonna I'm gonna fake my own death. I've watched

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Speaker 2: this really cool movie it's called r V, and I'm

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Speaker 2: going to buy an RV and I'm going to, you know,

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Speaker 2: just go around the country and maybe make my way

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Speaker 2: to go by the way. One of the things he

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Speaker 2: told me was that as after his sentencing, as he

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Speaker 2: was walking out, an FBI agent, one of the ones

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Speaker 2: he'd gotten friendly with, looked at him and said, I

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Speaker 2: have two words for you, Costa Rica. I find it

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Speaker 2: very difficult to believe that that actually has.

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Speaker 1: Yes, I also doubt.

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Speaker 3: Marino Dan Marino two woods to you, Costa Rica. I

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Speaker 3: don't know what.

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Speaker 2: I think.

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Speaker 1: You've got a career this so but I'm curious. So, Marie,

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Speaker 1: you said that this is very this is short term thinking.

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Speaker 1: He didn't. He wasn't really thinking to the consequences of

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Speaker 1: his actions, and that that's true. Any Ponzi scheme inevitably

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Speaker 1: become completely unsustainable. You cannot possibly keep it going. It

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Speaker 1: will come to an end. So what is the way out?

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Speaker 1: And then the faking the suicide again like of course

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Speaker 1: he's gonna get caught. Of course that's not going to work.

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Speaker 2: So yeah, he decided he was going to jump off

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Speaker 2: a bridge.

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Speaker 1: Well not just he decided he was going to pretend

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Speaker 1: that he had jumped off.

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Speaker 3: A bridge, right, he actually jumped off a.

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Speaker 1: Bridge because he because he then he did actually actually claim.

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Speaker 2: Actually he actually jumped off a bridge. He did. So

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Speaker 2: this is the research he was doing. When you're trying

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Speaker 2: to fake your own death, and I interviewed this woman,

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Speaker 2: Elizabeth Greenwood, who wrote about faking your own death, you

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Speaker 2: have to think very far ahead. You have to figure

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Speaker 2: out money. You know, how am I going to be

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Speaker 2: off the grid right these days? Like how am I

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Speaker 2: going to survive? How am I going to get out

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Speaker 2: of the country, and you know where where's my cash

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Speaker 2: going to come from? All of these things. Instead, what

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Speaker 2: he spends his time thinking is researching all of the

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Speaker 2: bridges in New York to try to figure out which

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Speaker 2: one he could conceivably jump off of an die. So

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Speaker 2: he finds a bridge that's under construction, so there are

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Speaker 2: nets underneath, and he's like, oh, perfect, Like I'm gonna

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Speaker 2: jump and I'm going to land in the net and

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Speaker 2: then I'll use the net to scramble up and get out.

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Speaker 2: So he does this, not realizing that those nets are

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Speaker 2: pretty damn hard to climb out of, so he manages

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Speaker 2: to make the net. By the way, thinking about risk, right,

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Speaker 2: risk reward if you miss the net, the you know

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Speaker 2: the risk reward equation there is not great.

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Speaker 1: The designed to catch people or just like a span

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Speaker 1: of that somebody drops.

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Speaker 2: I mean that's the right, No, it's I think it

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Speaker 2: was gonna I think it was designed to catch bricks

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Speaker 2: and you know, falling debris from construction. So when a

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Speaker 2: person goes into it, it just like you know, it

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Speaker 2: goes all the way down. So then he's stuck in it.

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Speaker 2: And at this point he's like, I think I'm going

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Speaker 2: to die anyway, except it's going to be much worse

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Speaker 2: because I'm going to have spent my last minute trying

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Speaker 2: to scramble up this net. But he actually does manage

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Speaker 2: to get out, and he has a driver waiting for

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Speaker 2: him to take him to his RV and he thinks

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Speaker 2: he's going to drive off into the sunset. Oh, by

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Speaker 2: the way, another really really important thing, if you're trying

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Speaker 2: to fake your own death, do not tell your mother,

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Speaker 2: your girlfriend, your son, and the driver that you're going

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Speaker 2: to be faking your own death. Don't worry, mom, I'm

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Speaker 2: not actually going calling uber.

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Speaker 3: Do it in style.

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Speaker 2: But that's this is what happened. So you know, he's

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Speaker 2: fucked from the beginning because he has not thought any

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Speaker 2: of this through. But he does manage to make it

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Speaker 2: out of the net. He's met by a driver, makes

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Speaker 2: it to his RV, and for the first few weeks

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Speaker 2: it actually seems like everything is kind of okay because

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Speaker 2: even though he's living in an RV, living in RV parks,

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Speaker 2: you know, he's kind of getting away with it. And

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Speaker 2: then he walks into a bar one day and he

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Speaker 2: sees himself on TV on America's Most Wanted, and he

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Speaker 2: goes and reads about himself on the Internet, which is

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Speaker 2: a big, big no no if you're faking your own

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Speaker 2: death to start googling yourself, but he does that, and

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Speaker 2: he sees that his girlfriend has been arrested as an

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Speaker 2: accomplice and that they're looking to arrest his mother. He

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Speaker 2: doesn't realize that this is a trap, that the police

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Speaker 2: do this kind of thing to try to get people

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Speaker 2: kind of out of hiding. He thinks this is real,

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Speaker 2: and so he gets on a motorcycle, goes to the

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Speaker 2: police department to turn himself in, walks in. He says,

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Speaker 2: you know, hey, I'm here to turn myself in, and

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Speaker 2: the police officers like what you know? In for what?

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Speaker 2: You need to use the bathroom, like it's over there anyway,

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Speaker 2: there's all of this miscommunication. He says, no, you know,

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Speaker 2: I turning myself in. I'm wanted and I just don't

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Speaker 2: want any press here. And at this point the police

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Speaker 2: officer actually looks at him, realizes who he is, and

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Speaker 2: that is when he gets caught and gets an additional

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Speaker 2: two years added on to his sentence for faking his

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Speaker 2: own death and running away.

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Speaker 1: It's astonishing. I'm not one thing you said, Maria. You

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Speaker 1: said that the craziest thing about him, or the most

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Speaker 1: amazing thing about him. I'm not even sure that is

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Speaker 1: the craziest thing about him. But you know, we haven't

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Speaker 1: got all day, so that there are other stories you

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Speaker 1: could tell about Sam. But I wanted to ask Nate.

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Speaker 1: Given that, Nate, you've been thinking about the habits of

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Speaker 1: risk taking individuals, these people you call Riverians. Is short

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Speaker 1: termism part of the or kind of a side effect

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Speaker 1: or a glitch in the Riverian thinking system. So on

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Speaker 1: the one hand, you need that ability to think probabilistically,

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Speaker 1: you need that ability to take calculated risks. But I mean,

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Speaker 1: Sam as Weel just seems to have never been able

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Speaker 1: to see past the It had no problem taking risks,

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Speaker 1: but just couldn't see past the end of his nose.

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Speaker 4: Yeah. No, look, I think the better investors and gamblers

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Speaker 4: have a longer time horizon. That's kind of one of

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Speaker 4: one of Silicon Valley's secrets, despite their mini flaws, that

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Speaker 4: they do kind of think ten years ahead. But yeah,

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Speaker 4: some of this sounds very familiar, Marie, I know if

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Speaker 4: I'm talking to you, the notion of like a good

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Speaker 4: business gone bad. I mean, even FTX was a pretty

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Speaker 4: good business, right, It was like the leading brand for

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Speaker 4: crypto trading. They made legitimate profits, et cetera. But like

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Speaker 4: you know, it turns sour or I think you know,

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Speaker 4: Sam bacon Free couldn't resist the temptation to take all

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Speaker 4: this money staying on the sideline. He couldn't like resist

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Speaker 4: the temptation to go and gamble with it. But yeah,

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Speaker 4: the lack of advanced planning coupled with the kind of

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Speaker 4: miscalculating consequences. You know, if you're very charming, which I

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Speaker 4: think Sam Israel is more so than SBF, it's a

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Speaker 4: different story. You can kind of weasele your way out of.

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Speaker 3: Things, right.

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Speaker 4: You can think you can like dance your way out

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Speaker 4: of anything, and you can up the con a level

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Speaker 4: or two or three, and then you may on some level, no,

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Speaker 4: it's not going to work.

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Speaker 3: But I don't know. I mean, at some point there's

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Speaker 3: a point of no return, right.

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Speaker 2: There is a point of no return. Yeah, I think

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Speaker 2: I think that's absolutely right, Nate. I think one of

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Speaker 2: the this is a characteristic that you have both uh,

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Speaker 2: both with raverians and non reveriance and and Tim. I'm

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Speaker 2: sure you've come across this in other cautionary tales, but

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Speaker 2: I think a lot of it is this over confidence

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Speaker 2: in hubris, right, that comes with a certain level of

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Speaker 2: success and people. And I think to be an entrepreneur

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Speaker 2: and to be a risk taker, you need to be

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Speaker 2: over confident to a certain extent. You know, as as

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Speaker 2: we all know, if you actually know your odds of success,

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Speaker 2: you're not gonna You're not gonna start the damn company

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Speaker 2: you're not gonna You're not gonna try it because it's

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Speaker 2: the risk of failure and the chances of failure are

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Speaker 2: so high, So it's a it's this fine balance, and

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Speaker 2: I think over confidence so turns into delusion and turns

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Speaker 2: into this thinking that actually, you know, I can keep

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Speaker 2: doing this forever. And because you've gotten away with it

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Speaker 2: for so long, it seems like probabilistically speaking, you know,

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Speaker 2: your base rates change, I've gotten you know, okay, you

393
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Speaker 2: know one year, two years, three years, four years, everything's good,

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Speaker 2: This is all going good.

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Speaker 4: Yeah, if the coin comes up heads five times in

396
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Speaker 4: a row, I mean you see it.

397
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Speaker 3: In poker all the time, right, Yeah.

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Speaker 4: You know, winner's tilt is something which is maybe underdiscussed

399
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Speaker 4: loser tilt we all have experienced, maybe Maria, but Winter's

400
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Speaker 4: tilt where you're on a hot streak and you're like a,

401
00:21:45,730 --> 00:21:47,970
Speaker 4: maybe I have some gift from God to play poker

402
00:21:47,970 --> 00:21:51,170
Speaker 4: really well or something is also a big deal.

403
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Speaker 2: It absolutely is. One of My favorite psych studies is

404
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Speaker 2: from Ellen Langer, and I think the name of the

405
00:21:58,450 --> 00:22:02,770
Speaker 2: paper is something like heads, I win tails, It's chance

406
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Speaker 2: something like that. Yeah, And she had people bet not bet,

407
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Speaker 2: but guess the results of a coin toss, and it

408
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Speaker 2: was actually not a fair coin. It was rigged, and

409
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Speaker 2: there were different Basically, it came up heads and tails

410
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Speaker 2: the exact same number of times in all of the

411
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Speaker 2: different conditions, but in some of them it was pretty random.

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Speaker 2: In others it was clustered near the end, and in

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Speaker 2: the most important condition, the correct guesses were clustered near

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Speaker 2: the beginning, right, So basically you would say, you know, heads,

415
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Speaker 2: you'd guess, and then they'd make sure it landed on heads.

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Speaker 2: It was a rigged toss, so that you were right

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Speaker 2: it was yeah, And the people who were correct clustered

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Speaker 2: at the beginning would then and these were Harvard students,

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Speaker 2: by the way. Then they got all sorts of questions

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Speaker 2: like I'm good at predicting the outcomes of coin tosses,

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Speaker 2: and they would rate themselves as actually quite good at it.

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Speaker 2: They would say, if I had more time to practice

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Speaker 2: and to guess, I'd get even better. So things that

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Speaker 2: made it very clear that they thought that this was

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Speaker 2: a skill and not actually completely random. And it was

426
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Speaker 2: so easy to get people to believe that they were

427
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Speaker 2: skilled at something where it was just complete randomness when

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Speaker 2: they had those those things happened at the beginning. So Tim,

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Speaker 2: I think this goes back to the beginning of your question.

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Speaker 2: This is how you get into Ponzi schemes. Think about

431
00:23:27,250 --> 00:23:29,890
Speaker 2: Bernie Madeoff right. He was successful for far longer than

432
00:23:29,930 --> 00:23:31,610
Speaker 2: Sam Israel, and Sam Israel, by the way, was the

433
00:23:31,650 --> 00:23:34,090
Speaker 2: single biggest Ponzi scheme before Bernie made Off.

434
00:23:34,530 --> 00:23:38,250
Speaker 1: Now I am I'm curious. We've been talking about people

435
00:23:38,330 --> 00:23:41,730
Speaker 1: who were have an appetite for risk and who it

436
00:23:41,810 --> 00:23:45,690
Speaker 1: all came apart for Maria, I know you've been doing

437
00:23:45,730 --> 00:23:48,210
Speaker 1: a little bit of research into one of the most

438
00:23:48,250 --> 00:23:52,890
Speaker 1: important gamblers in economic history.

439
00:23:53,530 --> 00:23:58,610
Speaker 2: Yeah, John Law. He was someone who I wrote about

440
00:23:58,650 --> 00:24:00,970
Speaker 2: for The Confidence Game and I've come back to so

441
00:24:01,090 --> 00:24:05,010
Speaker 2: my next book is about cheating. So I've kind of

442
00:24:05,050 --> 00:24:08,810
Speaker 2: been thinking about him. But one of the reasons I

443
00:24:08,850 --> 00:24:11,690
Speaker 2: am interested in John Law. So when we're talking about

444
00:24:11,690 --> 00:24:13,930
Speaker 2: someone like Sam Israel, right, it's pretty clear con artist,

445
00:24:14,050 --> 00:24:17,770
Speaker 2: right Ponzi scheme. When you're talking about someone like John Law,

446
00:24:18,450 --> 00:24:23,690
Speaker 2: it becomes much less clear because he's someone who was

447
00:24:23,770 --> 00:24:26,730
Speaker 2: a huge gambler and we know that sometimes he was successful,

448
00:24:26,730 --> 00:24:30,490
Speaker 2: but he also ran his father's business into the ground.

449
00:24:30,490 --> 00:24:33,850
Speaker 2: If I remember correctly through gambling. But I guess he

450
00:24:33,890 --> 00:24:36,570
Speaker 2: got better with time and killed.

451
00:24:36,450 --> 00:24:40,130
Speaker 4: Man and likely killed a man literal gambling or like

452
00:24:40,290 --> 00:24:41,090
Speaker 4: literal gambling.

453
00:24:41,370 --> 00:24:44,570
Speaker 2: So yeah, so he was someone who came from money.

454
00:24:45,010 --> 00:24:48,850
Speaker 2: Who's you know whose parents had a financial business.

455
00:24:48,970 --> 00:24:52,770
Speaker 1: We should say it was seventeen hundreds, just just a situation, right, Yes,

456
00:24:53,290 --> 00:24:55,330
Speaker 1: for those small number of listeners who don't know who

457
00:24:55,370 --> 00:24:57,130
Speaker 1: John Law is or everybody should.

458
00:24:57,370 --> 00:24:59,650
Speaker 2: We're in the seventeen hundreds and he's going to be

459
00:24:59,690 --> 00:25:03,010
Speaker 2: making friends with the Duke of Orleans or the Duke

460
00:25:03,050 --> 00:25:07,010
Speaker 2: of Orleans who was then Regent of France, and he's

461
00:25:07,050 --> 00:25:09,890
Speaker 2: going to be basically setting up France's banking system. So

462
00:25:09,930 --> 00:25:13,730
Speaker 2: the reason why I was fascinated by him is that

463
00:25:14,530 --> 00:25:17,730
Speaker 2: it's actually unclear if he was a con artist or not,

464
00:25:18,050 --> 00:25:21,050
Speaker 2: like did he believe that? Because it was the kind

465
00:25:21,090 --> 00:25:25,170
Speaker 2: of the end of his time at the height of

466
00:25:25,250 --> 00:25:28,210
Speaker 2: finance was with the establishment of this thing called the

467
00:25:28,210 --> 00:25:35,450
Speaker 2: Mississippi Company, which was a huge bubble and basically bankrupted

468
00:25:35,530 --> 00:25:38,570
Speaker 2: a ton of people. And the question is, you know,

469
00:25:39,370 --> 00:25:42,250
Speaker 2: did he know what he was doing and get unlucky

470
00:25:42,370 --> 00:25:44,490
Speaker 2: or did he like basically did he do this as

471
00:25:44,490 --> 00:25:46,650
Speaker 2: a kind of Ponzi scheme as a kind of con

472
00:25:46,850 --> 00:25:47,090
Speaker 2: or not.

473
00:25:47,530 --> 00:25:50,410
Speaker 1: And the fact that we still don't really agree on that,

474
00:25:50,450 --> 00:25:52,410
Speaker 1: I think is fascinating. I mean, we should say so.

475
00:25:53,690 --> 00:25:58,690
Speaker 1: He he was originally Scott, a Scott. He he killed

476
00:25:58,690 --> 00:26:01,290
Speaker 1: a guy and a duel, was sentenced to death for murder.

477
00:26:01,450 --> 00:26:06,770
Speaker 1: Escape from prison, traveled Europe, wound up in Paris, made

478
00:26:06,770 --> 00:26:09,730
Speaker 1: a few friends with some influential nobles, made a huge

479
00:26:09,730 --> 00:26:13,170
Speaker 1: amount of money gambling because he would set himself up

480
00:26:13,170 --> 00:26:16,490
Speaker 1: as the house and he understood the probability enough that

481
00:26:17,570 --> 00:26:20,210
Speaker 1: he knew he had an edge. So he's gambling with

482
00:26:20,250 --> 00:26:22,290
Speaker 1: all these nobles. He's making a huge amount of money.

483
00:26:22,970 --> 00:26:28,530
Speaker 1: And then he sets up his own bank and he

484
00:26:28,650 --> 00:26:32,410
Speaker 1: starts issuing paper money. This is not the first paper

485
00:26:32,450 --> 00:26:36,170
Speaker 1: money in the history of the world, it's not even

486
00:26:36,170 --> 00:26:38,170
Speaker 1: the first paper money in the history of France, but

487
00:26:38,250 --> 00:26:41,290
Speaker 1: it is. It's pretty new and people are still trying

488
00:26:41,290 --> 00:26:43,810
Speaker 1: to figure out kind of how it works. And of

489
00:26:43,810 --> 00:26:46,810
Speaker 1: course this is revolutionary. He's ahead of his time, like

490
00:26:46,850 --> 00:26:48,770
Speaker 1: paper money is how we do things right. It's kind

491
00:26:48,810 --> 00:26:51,530
Speaker 1: of amazing. And then the whole thing just gets wrapped

492
00:26:51,610 --> 00:26:53,450
Speaker 1: up with French government debt and gets wrapped up with

493
00:26:53,450 --> 00:26:56,850
Speaker 1: the Mississippi bubble, and the Mississippi bubble was it was

494
00:26:56,890 --> 00:27:00,090
Speaker 1: a stock market bubble. One stock was involved, the stock

495
00:27:00,130 --> 00:27:04,370
Speaker 1: of the Mississippi Company, and John Law controlled the Mississippi Company.

496
00:27:04,450 --> 00:27:07,290
Speaker 1: But it was clear that nobody really understood what was

497
00:27:07,330 --> 00:27:10,130
Speaker 1: going on except that go up, and if numb, but

498
00:27:10,250 --> 00:27:12,330
Speaker 1: go up, everyone everyone gets very excited.

499
00:27:12,450 --> 00:27:15,890
Speaker 4: Yeah, it's very intoxicating when the number goes up. Right,

500
00:27:17,250 --> 00:27:20,530
Speaker 4: I did wonder too, there is some survivorship bias in

501
00:27:20,610 --> 00:27:25,250
Speaker 4: which kind of scams and schemes we discover, you know,

502
00:27:25,410 --> 00:27:28,290
Speaker 4: the best frauds in history. Probably nobody knows about it. Yeah,

503
00:27:28,330 --> 00:27:31,650
Speaker 4: good point. Sbl was convinced that he could somehow navigate

504
00:27:32,130 --> 00:27:35,450
Speaker 4: his way through bankruptcy, or not through bankruptcy, now, his

505
00:27:35,450 --> 00:27:38,170
Speaker 4: way through this downfall and bitcoin and come out on

506
00:27:38,170 --> 00:27:38,810
Speaker 4: either side of it.

507
00:27:38,850 --> 00:27:40,210
Speaker 3: And maybe people wouldn't really notice.

508
00:27:40,250 --> 00:27:43,290
Speaker 4: Right, Maybe it's like a page a sixteen story, not

509
00:27:43,330 --> 00:27:45,530
Speaker 4: in a one story. If he like, if there's a

510
00:27:45,530 --> 00:27:49,130
Speaker 4: spontaneous rise in bitcoin prices and they recover these losses

511
00:27:49,210 --> 00:27:51,170
Speaker 4: that they have, although they were ten million and ten

512
00:27:51,210 --> 00:27:53,610
Speaker 4: billion in a hole, which is pretty hard to overcome.

513
00:27:53,930 --> 00:27:56,570
Speaker 2: It's funny, Nate. I think it's that's a really important

514
00:27:56,570 --> 00:27:59,690
Speaker 2: point that the best con artists are never caught. When

515
00:28:00,170 --> 00:28:02,570
Speaker 2: people when we talk about connartists and people ask me,

516
00:28:02,610 --> 00:28:04,970
Speaker 2: you know, why aren't there as many female con artists,

517
00:28:05,970 --> 00:28:09,410
Speaker 2: I say a few things, but one of the it's

518
00:28:09,730 --> 00:28:12,450
Speaker 2: kind of a joke but kind of not, which is

519
00:28:12,890 --> 00:28:15,130
Speaker 2: that they're just better at it. So we don't know

520
00:28:15,170 --> 00:28:17,090
Speaker 2: them because they haven't been caught. They don't have as

521
00:28:17,170 --> 00:28:19,850
Speaker 2: much ego, and they know when to disappear and how

522
00:28:19,890 --> 00:28:25,210
Speaker 2: to disappear much better than the sam Israel's of the world.

523
00:28:25,570 --> 00:28:27,730
Speaker 4: Or like cheating in poker, a lot of these famous

524
00:28:28,250 --> 00:28:31,810
Speaker 4: cheating scandals, like online cheating scandals, people are are very

525
00:28:31,850 --> 00:28:35,130
Speaker 4: greedy where they win at like, you know, thirteen centered

526
00:28:35,170 --> 00:28:38,490
Speaker 4: deviations above some random rate, whereas if we won at

527
00:28:38,490 --> 00:28:41,010
Speaker 4: two center deviations above random, it would be almost impossible

528
00:28:41,050 --> 00:28:43,890
Speaker 4: to detect and you'd have a great life. Although we

529
00:28:43,890 --> 00:28:45,610
Speaker 4: don't find those people though, right, we don't find the

530
00:28:45,610 --> 00:28:47,770
Speaker 4: people that are that are actually good at cheating a.

531
00:28:47,770 --> 00:28:48,210
Speaker 3: Lot of time.

532
00:28:49,450 --> 00:28:52,250
Speaker 2: Yeah, but so so as we you know, wrap up

533
00:28:52,610 --> 00:28:55,610
Speaker 2: the story of John law I do think that it's

534
00:28:55,650 --> 00:28:59,050
Speaker 2: interesting that, I mean, economists don't agree, historians don't agree

535
00:28:59,890 --> 00:29:03,050
Speaker 2: whether whether or not he was, you know, greedy cheat.

536
00:29:03,210 --> 00:29:05,450
Speaker 2: He was obviously greedy. I think everyone agrees on that,

537
00:29:06,090 --> 00:29:08,490
Speaker 2: but whether he thought that this could actually all worked out.

538
00:29:09,170 --> 00:29:12,090
Speaker 2: I found a rhyme that I would love to share

539
00:29:12,090 --> 00:29:15,570
Speaker 2: if you guys are in poetry mode, that come from

540
00:29:15,610 --> 00:29:19,650
Speaker 2: the time about what happened with the Mississippi bubble. And

541
00:29:19,690 --> 00:29:23,130
Speaker 2: it goes like this. My shares, which on Monday I bought,

542
00:29:23,250 --> 00:29:26,970
Speaker 2: were worth millions. On Tuesday, I thought so. On Wednesday,

543
00:29:27,010 --> 00:29:30,130
Speaker 2: I chose my abode in my carriage. On Thursday I

544
00:29:30,290 --> 00:29:34,090
Speaker 2: rode to the ballroom. On Friday I went to the workhouse.

545
00:29:34,210 --> 00:29:35,690
Speaker 2: Next day I was sent.

546
00:29:36,650 --> 00:29:39,370
Speaker 3: First poetry reading on the Risky Business podcast.

547
00:29:39,410 --> 00:29:40,450
Speaker 2: I believe it is.

548
00:29:40,530 --> 00:29:43,010
Speaker 1: It is I think that should stop tradition. That's very good.

549
00:29:45,770 --> 00:29:48,330
Speaker 2: And one nobleman of the time said, thus sends the

550
00:29:48,370 --> 00:29:51,690
Speaker 2: system of paper money, which has enriched a thousand beggars

551
00:29:51,730 --> 00:29:55,370
Speaker 2: and impoverished one hundred thousand men. And obviously that's not true,

552
00:29:55,650 --> 00:29:57,810
Speaker 2: which is why we You know, that was not the

553
00:29:57,930 --> 00:29:59,930
Speaker 2: end of the system of paper money. It was just

554
00:30:00,370 --> 00:30:02,930
Speaker 2: it just happened to be the end of John Law.

555
00:30:03,410 --> 00:30:05,810
Speaker 2: He had to escape France, by the way, because he

556
00:30:05,970 --> 00:30:08,130
Speaker 2: was convicted and was going to be sent to prison there.

557
00:30:08,170 --> 00:30:10,690
Speaker 2: So he dressed up as a beggar, ran away to

558
00:30:10,730 --> 00:30:15,090
Speaker 2: Italy and died in Venice, totally impoverished. And I guess

559
00:30:15,170 --> 00:30:17,010
Speaker 2: the cautionary tales.

560
00:30:17,970 --> 00:30:21,930
Speaker 1: It is a cautionary tale. And I guess the lesson,

561
00:30:22,050 --> 00:30:24,610
Speaker 1: or maybe the lesson is that the economics lesson is,

562
00:30:24,650 --> 00:30:26,050
Speaker 1: if you're going to have paper money, if you're going

563
00:30:26,050 --> 00:30:29,330
Speaker 1: to have somebody who has the right to just create

564
00:30:29,370 --> 00:30:32,290
Speaker 1: money with the printing press, you've got to make sure

565
00:30:32,330 --> 00:30:34,490
Speaker 1: you have the right controls over that person or over

566
00:30:34,530 --> 00:30:37,170
Speaker 1: that institution. It can't just be some guy who killed

567
00:30:37,170 --> 00:30:39,770
Speaker 1: a guy in a duel and came over one a

568
00:30:39,770 --> 00:30:42,090
Speaker 1: lot of money and gambling and then he's the guy

569
00:30:42,130 --> 00:30:42,770
Speaker 1: who can do it.

570
00:30:42,770 --> 00:30:43,490
Speaker 3: It's you.

571
00:30:43,490 --> 00:30:47,410
Speaker 1: You need this institutional scaffolding, which you know, when we

572
00:30:47,450 --> 00:30:48,810
Speaker 1: have it, it seems to work just fine.

573
00:30:50,530 --> 00:30:52,130
Speaker 3: We'll be back right after this.

574
00:31:06,370 --> 00:31:11,210
Speaker 4: I mean, are there like two or three on big takeaways,

575
00:31:11,290 --> 00:31:14,490
Speaker 4: like common patterns and when you know something is becoming

576
00:31:14,530 --> 00:31:16,010
Speaker 4: a cautionary tale or a can.

577
00:31:18,530 --> 00:31:23,490
Speaker 1: At the risk of quoting that classic opening line of

578
00:31:23,530 --> 00:31:27,810
Speaker 1: Ana Karenina that all happy families are alike and every

579
00:31:27,970 --> 00:31:31,890
Speaker 1: unhappy family is unhappy in its own way, I think

580
00:31:31,970 --> 00:31:35,250
Speaker 1: one of the striking things about cautionary tales is that

581
00:31:35,970 --> 00:31:38,010
Speaker 1: there are a lot of different ways for things to

582
00:31:38,050 --> 00:31:47,650
Speaker 1: go wrong. The organizational problems, informational problems, engineering problems, hubris

583
00:31:47,850 --> 00:31:53,050
Speaker 1: and arrogance, short sightedness, lots and lots of self delusion,

584
00:31:53,210 --> 00:31:57,210
Speaker 1: lots of wishful thinking. I mean, it's a miracle that

585
00:31:58,090 --> 00:32:00,810
Speaker 1: human civilization survives, actually, given how many different ways there

586
00:32:00,850 --> 00:32:03,730
Speaker 1: are to go wrong. But I mean that is part

587
00:32:03,770 --> 00:32:08,210
Speaker 1: of the slightly perverse joy of researching and writing these

588
00:32:08,250 --> 00:32:13,410
Speaker 1: cautionary tales. There is always a new disaster, and always

589
00:32:13,410 --> 00:32:15,050
Speaker 1: a new way for disaster to happen.

590
00:32:16,250 --> 00:32:17,730
Speaker 2: You make that sound almost gleeful.

591
00:32:18,490 --> 00:32:20,970
Speaker 1: Yeah, I mean sometimes I have to remind myself that, like,

592
00:32:21,290 --> 00:32:23,130
Speaker 1: I'm not supposed to be enjoying this, because some of

593
00:32:23,170 --> 00:32:26,770
Speaker 1: them are very, very sad. Some of them are straightforwardly

594
00:32:26,810 --> 00:32:30,290
Speaker 1: hilarious and no great harm is done, but a lot

595
00:32:30,330 --> 00:32:32,410
Speaker 1: of them are pretty painful.

596
00:32:33,410 --> 00:32:36,330
Speaker 4: Yeah, Maria, I remember you telling me that, like, cons

597
00:32:36,410 --> 00:32:39,770
Speaker 4: are most likely to occur when you're in an upcycle

598
00:32:39,850 --> 00:32:41,610
Speaker 4: or a down cycle, right, not in the steady state,

599
00:32:41,690 --> 00:32:44,370
Speaker 4: but when there's something new and novel and people are panicking.

600
00:32:44,770 --> 00:32:49,410
Speaker 2: Yeah, moments of transition. So whether those are societal transitions

601
00:32:50,290 --> 00:32:54,090
Speaker 2: or whether they're personal transitions, is when you're most vulnerable

602
00:32:54,170 --> 00:32:56,490
Speaker 2: to get cons So it's not a personality trait. It's

603
00:32:56,530 --> 00:33:00,090
Speaker 2: not intelligence, it's not it's not anything like that. It

604
00:33:00,250 --> 00:33:03,370
Speaker 2: is this kind of moment of either up or down

605
00:33:03,410 --> 00:33:10,290
Speaker 2: euphoria or you know, despondency. But when things are uncertain

606
00:33:10,370 --> 00:33:15,690
Speaker 2: and your worldview gets challenged, gets shattered, gets displaced, you

607
00:33:15,730 --> 00:33:19,930
Speaker 2: look for certainty and that's when con artists swoop in

608
00:33:20,170 --> 00:33:21,730
Speaker 2: and give that certainty to you.

609
00:33:21,850 --> 00:33:24,210
Speaker 1: So as long as nothing ever changes, we will say

610
00:33:24,250 --> 00:33:25,730
Speaker 1: from Collins, we're good.

611
00:33:26,330 --> 00:33:30,010
Speaker 4: You know my uh my great grandfather faked his death

612
00:33:30,690 --> 00:33:31,090
Speaker 4: and got a.

613
00:33:31,050 --> 00:33:34,130
Speaker 2: Wait what okay, Nate, can we just can we we

614
00:33:34,170 --> 00:33:38,170
Speaker 2: need this story. I'm sorry we're pausing everything, Nate, please.

615
00:33:38,370 --> 00:33:40,930
Speaker 3: I mean his name was Ferdinand Thrunn, which is a

616
00:33:40,930 --> 00:33:41,490
Speaker 3: great name.

617
00:33:42,090 --> 00:33:42,890
Speaker 2: It's an amazing name.

618
00:33:42,930 --> 00:33:44,650
Speaker 4: He was written up in the Chicago Tribune and the

619
00:33:44,650 --> 00:33:46,410
Speaker 4: New York Times and places like that. Yeah, he was

620
00:33:46,450 --> 00:33:49,530
Speaker 4: like an insurance frauds ster. We should do this as

621
00:33:49,570 --> 00:33:54,170
Speaker 4: a separate segment sometime, yeah, But basically his technique was

622
00:33:54,210 --> 00:33:57,050
Speaker 4: to commit crimes so devious that there was no law

623
00:33:57,090 --> 00:33:58,970
Speaker 4: to charge them with. Because I hadn't figured out like

624
00:33:59,210 --> 00:34:01,650
Speaker 4: this particular type of fraud. But we should do proper

625
00:34:01,690 --> 00:34:02,810
Speaker 4: research and do an episode on this.

626
00:34:02,890 --> 00:34:08,450
Speaker 1: Maria, Yeah, absolutely so, so Maria Knight. I now we've

627
00:34:08,490 --> 00:34:11,210
Speaker 1: discussed some as well. We discussed John Law. I actually

628
00:34:11,290 --> 00:34:14,010
Speaker 1: had a couple of questions, given that you guys are

629
00:34:14,010 --> 00:34:17,050
Speaker 1: absolutely experts on this sort of thing. I had a

630
00:34:17,050 --> 00:34:19,010
Speaker 1: couple of questions for you that I hope you won't

631
00:34:19,010 --> 00:34:22,890
Speaker 1: mind me asking, and that The first one is, I

632
00:34:23,970 --> 00:34:28,050
Speaker 1: have you your two most recent books in front of me.

633
00:34:28,210 --> 00:34:31,170
Speaker 1: I have Nates on the Edge, I have Maria's The

634
00:34:31,170 --> 00:34:35,610
Speaker 1: Biggest Bluff Stone Cold Classic Amazing. I looked in the index.

635
00:34:37,290 --> 00:34:40,930
Speaker 1: The word experiments does not appear in the index of

636
00:34:40,970 --> 00:34:44,650
Speaker 1: either of your books. Expected value, of course does appear,

637
00:34:44,650 --> 00:34:48,890
Speaker 1: but experiments does not. And I just wondered whether you

638
00:34:49,010 --> 00:34:54,490
Speaker 1: to brilliant risk takers, analysts, poker players. But I thought

639
00:34:54,930 --> 00:34:58,250
Speaker 1: a poker player can't really experiment, Like if you want to,

640
00:34:58,370 --> 00:35:01,250
Speaker 1: if you want to find out, you need to, you

641
00:35:01,290 --> 00:35:02,730
Speaker 1: need to make the bet. You know, you need to

642
00:35:02,730 --> 00:35:05,530
Speaker 1: put down the money. There's no cheap way to find

643
00:35:05,570 --> 00:35:10,370
Speaker 1: out what the other person's cards are. And maybe that

644
00:35:10,770 --> 00:35:15,290
Speaker 1: is a blind spot in poker playing relative to decision

645
00:35:15,290 --> 00:35:17,570
Speaker 1: making advice in everyday life, because what I've what I've

646
00:35:17,610 --> 00:35:20,650
Speaker 1: always been saying to people is Okay, if you're facing

647
00:35:20,690 --> 00:35:23,050
Speaker 1: an uncertain situation, you know, you don't know what the

648
00:35:23,090 --> 00:35:25,770
Speaker 1: right thing to do is. Maybe there's an experiment, maybe

649
00:35:25,770 --> 00:35:27,610
Speaker 1: that you can run a little pilot, Maybe you can

650
00:35:27,650 --> 00:35:29,730
Speaker 1: run a little you know, a b test. Maybe there's

651
00:35:29,770 --> 00:35:33,530
Speaker 1: a cheap way to find out without betting all your

652
00:35:33,650 --> 00:35:36,930
Speaker 1: chips metaphorically speaking. So I just wanted to ask why

653
00:35:37,130 --> 00:35:39,370
Speaker 1: why did neither of you talk about experiments? And is

654
00:35:39,810 --> 00:35:43,130
Speaker 1: this in fact a blind spot in the poker player's

655
00:35:43,210 --> 00:35:44,290
Speaker 1: view of the world.

656
00:35:45,050 --> 00:35:48,610
Speaker 4: It's a great point to I have a home game

657
00:35:48,650 --> 00:35:51,210
Speaker 4: I play maybe every third week. That's a one dollar

658
00:35:51,250 --> 00:35:53,970
Speaker 4: two dollar game, which for me is you know, on

659
00:35:54,010 --> 00:35:55,690
Speaker 4: the lower side of the stakes I play, and I

660
00:35:55,770 --> 00:35:59,290
Speaker 4: probably am doing some experimenting in that game, right.

661
00:36:00,370 --> 00:36:00,570
Speaker 3: You know?

662
00:36:00,690 --> 00:36:03,050
Speaker 4: Last night I made I had a nut flushtra which

663
00:36:03,170 --> 00:36:04,330
Speaker 4: some of our artists will know what that means. I

664
00:36:04,370 --> 00:36:07,210
Speaker 4: had the Asi flustra on a boarder that was King

665
00:36:07,330 --> 00:36:12,490
Speaker 4: Queen x X. I made like a three x overbent

666
00:36:12,690 --> 00:36:14,770
Speaker 4: shove on the turn. I'm just getting an experiment. I

667
00:36:14,810 --> 00:36:16,970
Speaker 4: bet probably there's some frequency which you're supposed to do

668
00:36:17,050 --> 00:36:18,890
Speaker 4: this in game theory. But like, but if I lose

669
00:36:18,930 --> 00:36:21,810
Speaker 4: this pot, then you know, I'll win sometimes to catch

670
00:36:21,850 --> 00:36:23,610
Speaker 4: my flush and like it'll be a fun hand to

671
00:36:23,610 --> 00:36:26,530
Speaker 4: show down and whatever. But I did feel I think

672
00:36:26,530 --> 00:36:29,530
Speaker 4: we do probably experiment a little bit, and that kind

673
00:36:29,530 --> 00:36:32,010
Speaker 4: of like naughty feeling you have of like having an

674
00:36:32,010 --> 00:36:34,090
Speaker 4: experiment and getting away with it, like you're kind of

675
00:36:34,130 --> 00:36:36,650
Speaker 4: taking the piss to use. Is that the British term. Yeah,

676
00:36:36,690 --> 00:36:38,730
Speaker 4: you're taking the piss. You kind of get away with it.

677
00:36:38,930 --> 00:36:41,250
Speaker 4: Like that's a very satisfying feeling, and I think encourages

678
00:36:41,730 --> 00:36:42,890
Speaker 4: kind artistry sometimes.

679
00:36:42,970 --> 00:36:47,290
Speaker 2: Yeah, yeah, I think that there are so two things. One, yes,

680
00:36:47,330 --> 00:36:49,770
Speaker 2: I think that you can experiment this way, and to me,

681
00:36:50,170 --> 00:36:52,290
Speaker 2: I do it when I move down in stakes as well,

682
00:36:52,530 --> 00:36:57,250
Speaker 2: right when the money is not not as meaningful and

683
00:36:57,290 --> 00:36:59,930
Speaker 2: you get to test certain theories out Right, So if

684
00:36:59,970 --> 00:37:04,610
Speaker 2: I'm studying and I'm kind of figuring out different possibilities

685
00:37:04,650 --> 00:37:07,530
Speaker 2: for playing similar spots, I might test those out and

686
00:37:07,530 --> 00:37:10,130
Speaker 2: feel comfortable testing them out when I don't care as

687
00:37:10,210 --> 00:37:13,090
Speaker 2: much about the stakes, when it's not kind of as important.

688
00:37:13,290 --> 00:37:17,290
Speaker 2: Now that said, we can't experiment as in the traditional

689
00:37:17,410 --> 00:37:20,010
Speaker 2: So I'm you know, I'm a trained psychologist, right, So

690
00:37:20,130 --> 00:37:22,810
Speaker 2: when I was doing studies for my PhD, you have

691
00:37:22,890 --> 00:37:25,610
Speaker 2: to have a very strict experimental design where you have

692
00:37:25,730 --> 00:37:27,890
Speaker 2: your control group, and you have your test groups, and

693
00:37:28,210 --> 00:37:31,690
Speaker 2: you know, you have all of these things where you're

694
00:37:32,330 --> 00:37:36,210
Speaker 2: trying to subtly change the conditions and see if the

695
00:37:36,250 --> 00:37:39,930
Speaker 2: outcome changes. That obviously you can't do because in some ways,

696
00:37:40,050 --> 00:37:42,450
Speaker 2: you know, my whole book was an experiment. So experiments

697
00:37:42,490 --> 00:37:44,450
Speaker 2: not in the index, but the biggest bluff, all of

698
00:37:44,490 --> 00:37:47,650
Speaker 2: it was was experiment. And I saw poker as kind

699
00:37:47,650 --> 00:37:50,570
Speaker 2: of a psychology laboratory of testing out a lot of

700
00:37:50,570 --> 00:37:54,530
Speaker 2: the psychological theories that I had kind of known in

701
00:37:54,690 --> 00:37:57,050
Speaker 2: theory and putting them into practice at the table and

702
00:37:57,090 --> 00:37:58,970
Speaker 2: being able to see, oh, you know, this is how

703
00:37:59,010 --> 00:38:02,010
Speaker 2: this plays out, this is how that plays out. And

704
00:38:02,090 --> 00:38:04,090
Speaker 2: I do think that poker is great for that. But

705
00:38:04,170 --> 00:38:08,050
Speaker 2: of course you can't when you're playing in a tournament,

706
00:38:08,090 --> 00:38:11,570
Speaker 2: when you're playing in a game, you can't have the

707
00:38:11,650 --> 00:38:16,210
Speaker 2: exact same conditions where you say, Okay, on this exact

708
00:38:16,250 --> 00:38:18,610
Speaker 2: same board, I'm going to do this, let's see what happens.

709
00:38:18,770 --> 00:38:19,130
Speaker 1: All right.

710
00:38:19,170 --> 00:38:21,690
Speaker 2: Pretend I didn't do that. Let's go back. Now, I'm

711
00:38:21,730 --> 00:38:24,050
Speaker 2: going to do something else and we'll see what happens there.

712
00:38:24,290 --> 00:38:27,850
Speaker 2: Because even if Nate and I were thinking, oh, okay,

713
00:38:28,010 --> 00:38:30,690
Speaker 2: let's see what it feels like to overbt three times

714
00:38:30,970 --> 00:38:33,610
Speaker 2: the pot in this particular spot, and Nay says, okay,

715
00:38:33,650 --> 00:38:35,810
Speaker 2: I'm going to do this, and then Maria you do

716
00:38:36,170 --> 00:38:41,210
Speaker 2: why people that's not in poker, that experiment is actually

717
00:38:41,250 --> 00:38:44,890
Speaker 2: not a valid control group because Nate and I are

718
00:38:44,890 --> 00:38:47,490
Speaker 2: so different. People respond to us differently, and all of

719
00:38:47,530 --> 00:38:49,730
Speaker 2: a sudden, you can't control the environment because it's a

720
00:38:49,770 --> 00:38:52,410
Speaker 2: different environment. The moment you switch out the players, right,

721
00:38:52,490 --> 00:38:55,330
Speaker 2: all of a sudden, the experimental conditions change, even if

722
00:38:55,370 --> 00:38:58,250
Speaker 2: you've changed literally nothing except who's sitting in that chair.

723
00:38:58,930 --> 00:39:03,090
Speaker 2: And I think that's actually fascinating and a really that's

724
00:39:03,130 --> 00:39:05,730
Speaker 2: how life works. That's why sometimes psych studies from the

725
00:39:05,770 --> 00:39:09,530
Speaker 2: laboratory don't generalize well to the real world, because the

726
00:39:09,570 --> 00:39:12,730
Speaker 2: real world does get messier and it's much more difficult

727
00:39:12,770 --> 00:39:16,010
Speaker 2: to control. No. Look, I think of your variables.

728
00:39:15,610 --> 00:39:19,610
Speaker 4: As someone who's self reflectively kind of seen his social

729
00:39:19,610 --> 00:39:24,610
Speaker 4: status rise and fall different times. If you're charming and

730
00:39:24,690 --> 00:39:27,130
Speaker 4: privileged and are seen as being on a winning streak,

731
00:39:27,250 --> 00:39:28,050
Speaker 4: you can get away.

732
00:39:27,850 --> 00:39:31,090
Speaker 3: With a lot. Yeah. People really are afraid to call

733
00:39:31,130 --> 00:39:32,170
Speaker 3: you on your bullshit.

734
00:39:32,770 --> 00:39:36,930
Speaker 1: Yeah, okay, yes, second quick question. Second quick question, because

735
00:39:36,610 --> 00:39:41,770
Speaker 1: I have to take advantage of the opportunity to tap

736
00:39:41,810 --> 00:39:45,170
Speaker 1: into your wisdom. Okay, So you I think I think

737
00:39:45,170 --> 00:39:47,130
Speaker 1: it's fair to say that both of you would advocate

738
00:39:48,330 --> 00:39:50,690
Speaker 1: you're putting a probability on something. If you're going to

739
00:39:50,690 --> 00:39:52,690
Speaker 1: make a risky decision, you have to have an idea.

740
00:39:52,930 --> 00:39:54,970
Speaker 1: Is this like a five to one? Is is it

741
00:39:55,050 --> 00:39:57,330
Speaker 1: a three to one? Is there twenty percent chances is

742
00:39:57,370 --> 00:39:59,410
Speaker 1: going to happen? Is there are seventy percent chances is

743
00:39:59,450 --> 00:40:02,730
Speaker 1: going to happen? And you know, and there's a difference

744
00:40:02,770 --> 00:40:05,250
Speaker 1: between say a fifty three percent chance and a forty

745
00:40:05,250 --> 00:40:07,890
Speaker 1: eight percent chance, even though they're both close to fifty

746
00:40:07,890 --> 00:40:11,650
Speaker 1: to fifty. So it's important to quantify as much as

747
00:40:11,690 --> 00:40:14,410
Speaker 1: you can, even though you don't always have the data. Okay,

748
00:40:15,490 --> 00:40:18,850
Speaker 1: So two very little stories to get you to reflect

749
00:40:18,890 --> 00:40:25,170
Speaker 1: on the case in favor of quantification. Apparently, when the

750
00:40:25,290 --> 00:40:29,450
Speaker 1: US government was pondering the Bay of Pigs invasion, the

751
00:40:29,530 --> 00:40:32,610
Speaker 1: Joint chiefs of Staff thought that the chance of success

752
00:40:32,690 --> 00:40:36,050
Speaker 1: was thirty percent, and a report was prepared for President

753
00:40:36,170 --> 00:40:39,890
Speaker 1: Kennedy and Kennedy and thirty percent was presumably they thought,

754
00:40:40,010 --> 00:40:42,610
Speaker 1: are the president working to understand percentages? So he was

755
00:40:42,650 --> 00:40:46,410
Speaker 1: told there is a fair chance of success, and by

756
00:40:46,490 --> 00:40:50,610
Speaker 1: fair chance of success they meant well, thirty percent. Now

757
00:40:50,690 --> 00:40:53,650
Speaker 1: we don't know what Kennedy understood by a fair chance

758
00:40:53,690 --> 00:40:55,370
Speaker 1: of success, but he seems to have thought it was

759
00:40:55,410 --> 00:40:58,930
Speaker 1: a good chance of success, so and he approved this

760
00:40:59,090 --> 00:41:03,890
Speaker 1: total fiasco. So it might seem more user friendly to

761
00:41:04,050 --> 00:41:07,930
Speaker 1: express things as well, this is common or uncommon, or

762
00:41:08,450 --> 00:41:10,930
Speaker 1: likely or un likely, But actually none of those words

763
00:41:11,010 --> 00:41:14,330
Speaker 1: really mean the same thing to the person who's uttering

764
00:41:14,610 --> 00:41:17,090
Speaker 1: the word as to the person who's receiving it. So

765
00:41:17,130 --> 00:41:22,450
Speaker 1: you should always put probabilities on things. Here's the counterexample. Counterexample.

766
00:41:22,770 --> 00:41:25,130
Speaker 1: Think back to the financial crisis two thousand and seven,

767
00:41:25,130 --> 00:41:30,450
Speaker 1: two thousand and eight. You had quants putting probabilities on things.

768
00:41:30,890 --> 00:41:32,690
Speaker 1: This is the probability that such and such a thing

769
00:41:32,770 --> 00:41:35,890
Speaker 1: will default based on what we know about history, based

770
00:41:35,930 --> 00:41:38,850
Speaker 1: on what we know about other things that are correlated

771
00:41:38,850 --> 00:41:44,890
Speaker 1: with it. But actually all of those probabilities were spuriously precise.

772
00:41:44,930 --> 00:41:47,810
Speaker 1: People had too much confidence in the probabilities, and it's

773
00:41:47,850 --> 00:41:49,570
Speaker 1: fine to say, oh, we think there's like a zero

774
00:41:49,610 --> 00:41:52,210
Speaker 1: point five percent chance that this will default. That's fine.

775
00:41:52,770 --> 00:41:54,650
Speaker 1: Then the problem is you feed the zero point five

776
00:41:54,650 --> 00:41:57,090
Speaker 1: percent into a model, which gets fed into another model,

777
00:41:57,330 --> 00:41:59,570
Speaker 1: which gets fed into another model, and in the end

778
00:41:59,610 --> 00:42:01,810
Speaker 1: you'd be like, oh, well, we've repackaged this thing, and

779
00:42:01,850 --> 00:42:04,370
Speaker 1: now there's like only a one in a trillion chance

780
00:42:04,410 --> 00:42:06,770
Speaker 1: that this will default. And it turns out that that's

781
00:42:06,810 --> 00:42:10,330
Speaker 1: all dependent on the quality of your original sumption, and

782
00:42:10,370 --> 00:42:13,290
Speaker 1: you shouldn't be betting the existence of Western civilization on

783
00:42:13,410 --> 00:42:17,450
Speaker 1: that calculation. And people got it wrong. So the case

784
00:42:17,450 --> 00:42:20,770
Speaker 1: against quantification is once you have a number, then you

785
00:42:20,810 --> 00:42:23,610
Speaker 1: are tempted to rely on that number too much and

786
00:42:23,650 --> 00:42:27,210
Speaker 1: to analyze or to manipulate or to remodel or to

787
00:42:27,330 --> 00:42:31,130
Speaker 1: reanalyze that number too much, and to forget that actually

788
00:42:31,210 --> 00:42:35,330
Speaker 1: the number was always basically just an educated guess.

789
00:42:36,810 --> 00:42:40,290
Speaker 2: Well, I think that when you're talking about quantification and

790
00:42:40,330 --> 00:42:45,210
Speaker 2: when you're talking about probabilities, it's the exact same logic

791
00:42:45,250 --> 00:42:48,370
Speaker 2: that you have to apply to algorithms and to kind

792
00:42:48,370 --> 00:42:50,490
Speaker 2: of building algorithms, which is something that Nate and I

793
00:42:50,490 --> 00:42:53,090
Speaker 2: have talked about on the pod with AI, which is

794
00:42:53,410 --> 00:42:57,850
Speaker 2: garbage in garbage out right. If your assumptions are garbage,

795
00:42:57,890 --> 00:43:01,370
Speaker 2: then your probabilistic assessment is going to be garbage. So

796
00:43:01,610 --> 00:43:04,850
Speaker 2: I actually think that both of these things they're not

797
00:43:04,970 --> 00:43:08,970
Speaker 2: counter examples. The thirty percent chance of the Bay of pigs.

798
00:43:09,450 --> 00:43:13,290
Speaker 2: I just think that these were people who had a

799
00:43:13,290 --> 00:43:16,330
Speaker 2: lot of qualifications, had done the research, had done regulus

800
00:43:16,570 --> 00:43:20,170
Speaker 2: rigorous analysis, and that was not garbage. And right there

801
00:43:20,370 --> 00:43:22,610
Speaker 2: they had assumptions that were there for a good reason.

802
00:43:22,850 --> 00:43:25,970
Speaker 2: They had good historical data. I have no idea how

803
00:43:26,010 --> 00:43:31,130
Speaker 2: they came upon the thirty percent. I'm just making yeah,

804
00:43:31,290 --> 00:43:35,490
Speaker 2: hindsight by us exactly, And so you get thirty percent

805
00:43:35,770 --> 00:43:38,610
Speaker 2: and by the way, you should absolutely have told Kennedy

806
00:43:38,650 --> 00:43:41,930
Speaker 2: thirty percent and not fair chance. There are so many

807
00:43:41,970 --> 00:43:45,290
Speaker 2: psych studies about this that trying to put words with

808
00:43:45,410 --> 00:43:50,890
Speaker 2: percentages backfires because people do not understand. It's like if

809
00:43:50,930 --> 00:43:53,370
Speaker 2: you have a weather man and says a fair chance

810
00:43:53,370 --> 00:43:56,090
Speaker 2: of rain, Right, let's talk about not a fair chance

811
00:43:56,290 --> 00:43:58,890
Speaker 2: that the Bay of pigs is a success fair chance

812
00:43:58,890 --> 00:44:02,010
Speaker 2: of rain? Do you bring an umbrella? You want to

813
00:44:02,050 --> 00:44:04,130
Speaker 2: know what the percentage chances. You want to know that

814
00:44:04,210 --> 00:44:08,090
Speaker 2: actual number because otherwise a quantification gets all out of whack.

815
00:44:08,450 --> 00:44:11,650
Speaker 2: Financial cis when you have those numbers in the models.

816
00:44:12,450 --> 00:44:16,970
Speaker 2: Those were people whose incentives were not aligned with giving

817
00:44:17,050 --> 00:44:21,810
Speaker 2: you a correct probabilistic assessment. Their incentives were to make money.

818
00:44:22,090 --> 00:44:24,570
Speaker 2: That's also a question that you have to make. You

819
00:44:24,650 --> 00:44:27,170
Speaker 2: always have to ask, and this is something that Nate

820
00:44:27,210 --> 00:44:31,650
Speaker 2: and I talk about as well. When you're making these assumptions,

821
00:44:31,770 --> 00:44:36,730
Speaker 2: do the incentives align, Where are the assumptions coming from,

822
00:44:36,810 --> 00:44:40,210
Speaker 2: and do you have an incentive to be correct? Right?

823
00:44:40,650 --> 00:44:44,330
Speaker 2: And in this particular case, their incentive was to make

824
00:44:44,410 --> 00:44:47,410
Speaker 2: money for them today and to make their company think

825
00:44:47,450 --> 00:44:49,850
Speaker 2: that this was going to be a great bet and okay,

826
00:44:49,890 --> 00:44:53,130
Speaker 2: that it was going to work out. And so those

827
00:44:53,170 --> 00:44:56,810
Speaker 2: percentages are not something that you want to rely on. Now,

828
00:44:56,810 --> 00:44:59,370
Speaker 2: if my incentive is if my percentage is wrong, I'm

829
00:44:59,410 --> 00:45:03,250
Speaker 2: getting fired. Right. If my percentage is wrong, then I'm

830
00:45:03,290 --> 00:45:07,930
Speaker 2: making zero dollars. Then all of a sudden, I come

831
00:45:08,010 --> 00:45:10,930
Speaker 2: up with different percentages, I use different inputs. My model

832
00:45:10,930 --> 00:45:14,130
Speaker 2: looks very very different. But Wall Street didn't work that way,

833
00:45:14,490 --> 00:45:17,610
Speaker 2: still doesn't work that way. That's not how you're incentivized.

834
00:45:17,810 --> 00:45:22,530
Speaker 4: Yeah, look, I think there's a risk of laundering subjective

835
00:45:22,570 --> 00:45:25,650
Speaker 4: opinions through probabilities and forgetting their subjective right. If I'm

836
00:45:25,690 --> 00:45:28,250
Speaker 4: running link the airport, there's traffic on the van Wick

837
00:45:28,330 --> 00:45:30,370
Speaker 4: or whatever, right, I might say to myself it's a

838
00:45:30,370 --> 00:45:32,050
Speaker 4: five percent chance is going to miss my flight. Right,

839
00:45:32,090 --> 00:45:35,570
Speaker 4: It's not coming out of any regression analysis or anything.

840
00:45:36,490 --> 00:45:38,050
Speaker 4: You know, I've probably used the phrase in this show,

841
00:45:38,130 --> 00:45:42,250
Speaker 4: like quoting Vice President Harris. A model does not fall

842
00:45:42,290 --> 00:45:44,090
Speaker 4: out of a coconut tree. It exists in the context

843
00:45:44,090 --> 00:45:47,210
Speaker 4: of that which became before it, or whatever else you're

844
00:45:47,250 --> 00:45:49,930
Speaker 4: trying to get at the truth with the model. And

845
00:45:49,970 --> 00:45:54,890
Speaker 4: I think mediocre modelers in particular will publish a number

846
00:45:55,490 --> 00:45:58,170
Speaker 4: and then forget how many assumptions are driven into that.

847
00:45:58,250 --> 00:46:01,730
Speaker 3: Right. Look, I still think it's worth quantifying things.

848
00:46:01,770 --> 00:46:03,970
Speaker 4: I mean, at the end of the day, probability is

849
00:46:03,970 --> 00:46:08,410
Speaker 4: defined as a number between point zero and one, and.

850
00:46:09,130 --> 00:46:11,370
Speaker 3: You have to make decisions even in conditions of uncertainty.

851
00:46:12,250 --> 00:46:13,890
Speaker 4: But to Tim's point, yeah, I think there are cases

852
00:46:13,890 --> 00:46:19,010
Speaker 4: where people don't forget how provisional a guess is when

853
00:46:19,010 --> 00:46:19,890
Speaker 4: you put a number on it.

854
00:46:20,930 --> 00:46:23,770
Speaker 2: Yeah, And I think it is always important to not

855
00:46:23,850 --> 00:46:26,610
Speaker 2: have a false sense of certainty. And there's also a

856
00:46:26,610 --> 00:46:30,970
Speaker 2: lot of data that shows that when you have numbers,

857
00:46:31,010 --> 00:46:32,570
Speaker 2: and when you have a lot of these things, it

858
00:46:32,610 --> 00:46:35,050
Speaker 2: does give you a false sense of certainty, right that

859
00:46:35,370 --> 00:46:37,610
Speaker 2: you often do become a little bit over confident when

860
00:46:37,610 --> 00:46:39,290
Speaker 2: you're like, well, I have this model in this model,

861
00:46:39,370 --> 00:46:42,930
Speaker 2: so it must be and there you have the bias

862
00:46:42,970 --> 00:46:45,130
Speaker 2: that we've talked about a lot, where it goes from

863
00:46:45,170 --> 00:46:48,290
Speaker 2: being probabilistic to seeming much more certain like this will

864
00:46:48,290 --> 00:46:53,570
Speaker 2: not default, This will not happen because you forget that

865
00:46:55,050 --> 00:46:55,730
Speaker 2: it ain't zero.

866
00:46:57,050 --> 00:46:59,330
Speaker 1: Thank you so much, guys, fantastic Tim.

867
00:46:59,370 --> 00:47:01,290
Speaker 2: Thank you so much for coming on the pod today.

868
00:47:01,290 --> 00:47:03,050
Speaker 2: It's been such a pleasure having you.

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00:47:03,170 --> 00:47:06,210
Speaker 1: Oh it's been really really fun. Thank you for sharing

870
00:47:06,770 --> 00:47:08,770
Speaker 1: your wisdom. And if people want to hear more about

871
00:47:08,850 --> 00:47:12,330
Speaker 1: Sound Israel or any other stories of things going disastrously

872
00:47:12,410 --> 00:47:15,490
Speaker 1: wrong and me trying to investigate the social science behind

873
00:47:15,490 --> 00:47:18,850
Speaker 1: why they went wrong cause New Tales with Tim Harford

874
00:47:19,010 --> 00:47:22,930
Speaker 1: is one of Risky Business's sister podcasts on pushkin.

875
00:47:23,650 --> 00:47:26,450
Speaker 2: It is and there are lots of tales of risk

876
00:47:26,490 --> 00:47:29,450
Speaker 2: taking assessments that do not turn out quite the way

877
00:47:29,490 --> 00:47:49,010
Speaker 2: the risk taker thought they would. Risky Business is hosted

878
00:47:49,010 --> 00:47:52,250
Speaker 2: by me Maria Kunakova and me Mate Silver. The show

879
00:47:52,330 --> 00:47:56,330
Speaker 2: is a co production of Pushkin Industries and iHeartMedia. This

880
00:47:56,410 --> 00:48:00,210
Speaker 2: episode was produced by Isabel Carter. Our associate producer is

881
00:48:00,250 --> 00:48:03,690
Speaker 2: Gabriel Hunter Chang. Our executive producer is Jacob Goldstein.

882
00:48:04,250 --> 00:48:06,610
Speaker 3: And if you want to listen to and add free version,

883
00:48:06,650 --> 00:48:08,890
Speaker 3: sign up for Pushkin Plus. For six seventy nine a

884
00:48:08,930 --> 00:48:11,930
Speaker 3: month you can access to ad free listening. Thanks for

885
00:48:12,010 --> 00:48:12,450
Speaker 3: tuning in

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00:48:28,210 --> 00:48:28,250
Speaker 1: H