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Speaker 1: Pushkin. Hi, everyone, welcome back to Risky Business, our show

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Speaker 1: about making better decisions. I'm Maria Kanikova and I'm Nate Silver.

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Speaker 1: So today we have a slightly different and fun show

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Speaker 1: for you. We're going to start off talking about some

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Speaker 1: of our usual things, so we'll do a quick politics recap.

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Speaker 1: So we're taping this on Tuesday, August twenty seventh. That

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Speaker 1: DNC has concluded, Kamala Harris has accepted the nomination, and

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Speaker 1: a lot of things are going on with Nate's election models,

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Speaker 1: so we're going to talk quickly about that, and then

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Speaker 1: we're going to be bringing on a very special guest,

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Speaker 1: Tim Harford, who hosts another Pushkin podcast, Cautionary Tales, and

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Speaker 1: we'll be talking about the intersection of risk and cautionary

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Speaker 1: details from history. All right, before we bring Tim on,

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Speaker 1: Nate's let's talk a little bit about politics and what

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Speaker 1: has happened since the DNC. Last time you and I

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Speaker 1: talked the DNC was on day one and we weren't

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Speaker 1: quite sure what to expect. We weren't quite sure how

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Speaker 1: it was going to go. So now here we are

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Speaker 1: a week later. Let's recap and talk about what has

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Speaker 1: happened since the convention, and also RFK Junior obviously dropping out,

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Speaker 1: So two big things have happened since we last spoke

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Speaker 1: to potentially affect the model.

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Speaker 2: We still don't know that much, but so far it's

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Speaker 2: been kind of a bunch of rand not crap, but

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Speaker 2: like kind of by definition, the polls that are done

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Speaker 2: in one or two days are either online tracking polls

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Speaker 2: which are fine but sometimes don't really capture change some

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Speaker 2: voter sentiment. It's gonna be very stable or quick and

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Speaker 2: dirty fly by night polls that are also not very good.

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Speaker 2: So by Thursday, well, no, so far it seems like

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Speaker 2: Harris is poling as well. She has all campaign, But

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Speaker 2: is it gonna Is she gonna three points or four

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Speaker 2: points or five or six or seven? The model will

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Speaker 2: care about that a lot, but like we don't. We

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Speaker 2: just don't know quite yet. I mean, I thought it

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Speaker 2: was an effective convention, you know, maybe will. I thought

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Speaker 2: the gender politics of her speech were interesting. It was

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Speaker 2: a very male coded speech talking of like the like

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Speaker 2: the most lethal military force. It was a very risk

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Speaker 2: taking speech. Actually right, She's like it was we're privileged,

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Speaker 2: so we should fucking kick ass America. Like I'm like, oh,

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Speaker 2: my gosh, I love this.

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Speaker 1: It was a it was a good speech to speak,

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Speaker 1: It was.

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Speaker 2: Interesting, it was like not a typical speech. And again

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Speaker 2: I'm a fan of good strategy, and so anybody who

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Speaker 2: shows good strategic instincts I think Harris has repeatedly. Now

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Speaker 2: at this point, I just kind of my estimation of

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Speaker 2: their intellect and their skills goes up a little bit.

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Speaker 2: But but we'll see. I mean, if a speech appeals

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Speaker 2: to me is probably somewhat negatively correlated with it's appealing

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Speaker 2: to the general population, I'd.

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Speaker 1: Say, I don't know. It appealed to me as well.

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Speaker 2: Well.

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Speaker 1: I think she she walked a fine line in I

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Speaker 1: think talking about all of the things that matter, having

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Speaker 1: it actually been heavier on policy than I think a

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Speaker 1: lot of people thought it was going to be, and

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Speaker 1: appealing to a lot of different voters. Right. She didn't.

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Speaker 1: She didn't hew to just one audience or kind of

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Speaker 1: one one topic, and I think that that was actually

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Speaker 1: quite an effective way for her to go.

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Speaker 2: No, she avoided some of the presumptuousness, and I think

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Speaker 2: we saw with Hillary. I watched her speech in Hillary

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Speaker 2: Clinton's intersperse with one another, and she avoided that presumptuousness,

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Speaker 2: and she is playing to the center right, kind of unapologized.

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Speaker 2: I mean that's the basic gto game, theoreptical strategy. Most

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Speaker 2: of the time, you want to you want to appear

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Speaker 2: more moderate.

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Speaker 1: Yeah. And then, of course after the convention, we had

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Speaker 1: news on Friday of last week with RFK Junior dropping

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Speaker 1: out of the race and throwing his support.

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Speaker 2: What is Cheryl Hines doing? Can we talk about that?

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Speaker 1: Okay, yes, I don't know.

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Speaker 2: I mean, what is Cheryl Hines doing?

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Speaker 1: I don't know.

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Speaker 2: Yeah. Anyway, when you have polls that test the matchup

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Speaker 2: both with him without RFK Junior, then Trump does better

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Speaker 2: in polls without him. In other words, RFK is taking

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Speaker 2: more votes from Trump than from Harris in our model.

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Speaker 2: We adjusted the model on Saturday to make it rfkless.

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Speaker 2: It didn't hurt Harris kind of like coincidental reasons, which

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Speaker 2: his name was. Like, she is getting like somewhat of

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Speaker 2: a convention bounce. I just said, like, oh, it's like

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Speaker 2: too early to pay attention to it. But like, but

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Speaker 2: she's doing okay, and that was enough to offset this

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Speaker 2: RFK maneuver. But yeah, it's a little bit of a

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Speaker 2: risk factor. Look I think anything involving RFK is kind

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Speaker 2: of over it. He was getting like, you know, three

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Speaker 2: and a half four percent in the polls. A lot

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Speaker 2: of those books will go to other third party candidates.

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Speaker 2: By the way, he'll still be on the ballot technically

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Speaker 2: speaking in a number of states. So anyway, I think

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Speaker 2: it's like an overrated storyline. But you know, it's one

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Speaker 2: of the better things Trump has going for him right now.

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Speaker 2: I suppose after a very bad month, you know, whatever,

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Speaker 2: he whatever quid pro quo there was, and I'm not

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Speaker 2: accusing anybody of anything, but like, look, RFK Junior was

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Speaker 2: negotiating with both campaigns for some type of position. What

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Speaker 2: ambassadorship do you predict that he'll get. Malta might be one.

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Speaker 1: Yeah, No, I think I think getting an ambassadorship to

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Speaker 1: a country like Malta might might be a yeah, might

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Speaker 1: be a way forward for our FKA. He was very

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Speaker 1: mad that. You know, in the past he has been

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Speaker 1: promised positions in the Trump administration that never materialized.

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Speaker 2: Anyway, future ambassador to Malta RFK Junior and doors Stunnel Trump,

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Speaker 2: which should help Trump a little bit, but probably not

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Speaker 2: that big a deal.

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Speaker 1: And one more question for you. Last time we spoke,

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Speaker 1: I kind of I don't know if this was last time,

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Speaker 1: but I've definitely asked you whether you thought that Trump

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Speaker 1: was actually going to be debating Kamala in September, and

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Speaker 1: I express skepticism. Now we see that he might be

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Speaker 1: paving the way for not doing that. Have you changed

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Speaker 1: your thinking about it at all?

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Speaker 2: You know, Marie, you can go to polymarket dot com,

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Speaker 2: to which I'm a paid advisor, by the way, and

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Speaker 2: bet on this.

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Speaker 1: I know I can'tnate because I live in the United States.

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Speaker 2: Okay, not if you're an American. I think Donald Trump

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Speaker 2: would be incredibly stupid not to debate he needs at

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Speaker 2: this point. Currently he's losing, right. The reason why we

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Speaker 2: have it closer to fifty to fifty in the model

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Speaker 2: is because you know, there are reasons the model expects

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Speaker 2: the race to tighten up again, and Trump has electoral

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Speaker 2: college advantage if it's really close. But if you have

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Speaker 2: the election today, Harris might be a two to one

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Speaker 2: favorite or something like that. So Trump wants to introduce

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Speaker 2: obstacles for her and variants. So I think it's a bluff.

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Speaker 2: I mean, I think I don't know for sure. Trump's

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Speaker 2: been acting pretty irrationally. I think it's a bluff. If

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Speaker 2: it's not, I mean, if I were the Harris campaign

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Speaker 2: and be like, Okay, don't yo, bro, don't take your

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Speaker 2: coverage too far because you could be a good excuse

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Speaker 2: not to debate you, then we'll just ride this momentum

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Speaker 2: train through to November. And so he has very little leverage.

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Speaker 2: If he ducked the debate, he would look very weak. Yeah,

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Speaker 2: it has to be emasculating. I mean there are gender

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Speaker 2: things here too, right, Like he can't debate Kamala Harris.

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Speaker 2: Like he looks so weak that he has like very

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Speaker 2: little leverage. And if you stupd enough to do that,

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Speaker 2: then you know, probably he doesn't deserve to win. But

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Speaker 2: I'm skeptical that this is anything other than like tactical negotiation.

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Speaker 1: That makes sense. I'll be very, very curious to see

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Speaker 1: how this plays out. And if I could bet on polymarket,

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Speaker 1: I would, but but I can't.

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Speaker 2: You're going to Barcelona, right.

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Speaker 1: I mean I am in Barcelona, Barcelona. Yes, this is

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Speaker 1: this is my Barcelona hotel room, nice with my very

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Speaker 1: own mini bar.

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Speaker 2: I was gonna say it's a lot of booze for yes, yes,

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Speaker 2: it is for in the afternoon for me. All right,

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Speaker 2: we're we're gonna bring Tim Harford on. Then maybe you

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Speaker 2: can have a drink Maria, four pm, Barcelona.

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Speaker 1: That sounds great. It is just about cocktail hour here

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Speaker 1: in Barcelona. But first, yes, let's welcome Tim onto the show. Tim,

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Speaker 1: welcome to the show.

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Speaker 3: To be on the show, thank you so for.

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Speaker 1: The listeners of Pushkin. You're probably familiar with Tim as

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Speaker 1: the host of Cautionary Tales. I've been lucky enough to

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Speaker 1: be on the show and to have been a listener

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Speaker 1: of the show. I think it's wonderful. My episodes are

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Speaker 1: obviously the best, but it's all pretty good. Tim is

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Speaker 1: an economist, a journalist. He has a column in the

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Speaker 1: Financial Times. He's written multiple amazing books. You know, longtime

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Speaker 1: friend of the two of us, Nate, I don't know

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Speaker 1: if you want to add anything else, but we're so

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Speaker 1: happy to be here and to do kind of an

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Speaker 1: episode where Cautionary Tales and Risky Business intersect, where we

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Speaker 1: talk about cautionary tales that are about risky business, that

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Speaker 1: are about risk, about taking risks, about how risk taking

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Speaker 1: can go wrong, and how sometimes you know the lines

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Speaker 1: between legitimate risks and cons and deceptions might get blurred

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Speaker 1: a little bit and cross over into territory that goes

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Speaker 1: from legitimate to illegitimate very quickly.

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Speaker 2: Do you mean to say that campling doesn't always work out, Maria?

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Speaker 1: It's weird. It's so weird.

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Speaker 3: The chance of loss is in fact one hundred percent,

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Speaker 3: which I guess we'll get we'll get to that. But yes, that.

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Speaker 1: Is absolutely true. So when we were kind of thinking

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Speaker 1: about ways to make this episode work, Tim, you thought

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Speaker 1: about one particular story where you and I have actually

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Speaker 1: intersected on this because we've both thought about this person

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Speaker 1: and he's someone who I actually had on my previous podcast,

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Speaker 1: The Grift, and he is a well, let's let's have

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Speaker 1: you lay him out. Let us meet Sam Israel, the

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Speaker 1: first kind of our first subject for today.

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Speaker 3: Sam Israel, the third I think is his full name. Third,

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Speaker 3: absolutely remarkable gentlemen if gentlemen is the right term, and

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Speaker 3: it probably isn't. So we did a Cautionary Tales episode

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Speaker 3: live on stage about pyramid schemes and Ponzi schemes and

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Speaker 3: white people fall for them, but also white people set

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Speaker 3: them up, and this kind of strange snowball of disaster

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Speaker 3: where the Ponzi scheme becomes increasingly difficult to cover. And

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Speaker 3: the most amazing example I've ever come across is by

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Speaker 3: You Capital, which was set up by Sam Israel. I

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Speaker 3: saw one writer described Sam Israel and by You Capital.

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Speaker 3: It's like somebody took the Bernie made Off story but

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Speaker 3: was told to write a Hollywood script and to punch

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Speaker 3: it up a bit, you know, make it sing a

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Speaker 3: bit more. And everything that made made Off's Ponzi scheme

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Speaker 3: notorious applies to Sam Israel. But it just all gets crazier.

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Speaker 3: So Sam came from a wealthy family I think of

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Speaker 3: commodity traders in Louisiana, but he wanted to show he

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Speaker 3: could make it himself. And he got into Wall Street

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Speaker 3: at an early age, absorbed that Wall Street culture, you know,

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Speaker 3: all that kind of broish culture of Wall Street in

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Speaker 3: the nineteen eighties. And so he takes all this in,

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Speaker 3: He keeps his mouth shut, He watches various dubious activities,

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Speaker 3: and not just the kind of the sex work and

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Speaker 3: the excess, but also illegality, financial illegality, observes people kind

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Speaker 3: of insider trading, for example, and then he sets up

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Speaker 3: his own firm, by You Capital, which is a hedge fund,

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Speaker 3: and very quickly By You Capital turns from being a

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Speaker 3: hedge fund into being a ponzi scheme. And just to

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Speaker 3: remind people what a ponzi scheme is, it's very simple.

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Speaker 3: Investors give you money, and then you announce you've made

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Speaker 3: massive profits, and then more investors give you money, and

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Speaker 3: you announce you made even more massive profit, and then

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Speaker 3: more investors give you money, and you keep saying you've

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Speaker 3: made massive profits. And if anybody ever says, that's great,

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Speaker 3: I'd like my money back, well that's easy. You can

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Speaker 3: give them the money back with the profits because more

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Speaker 3: people keep giving you, keep giving you their money. And

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Speaker 3: the insight that Sam Israel had was with a hedge fund,

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Speaker 3: it's kind of open ended. The money keeps growing, and

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Speaker 3: why would anybody ever want their money back if you

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Speaker 3: keep telling them they made another twenty percent this year,

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Speaker 3: they made another twenty five percent this year, Like no

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Speaker 3: one ever asks for their money back. They just leave

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Speaker 3: the money with you. And so the fraud went on

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Speaker 3: for a very long time, and then things started to unravel.

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Speaker 3: But Maria, you met Sam So, and you met Sam

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Speaker 3: in prison. So a spoiler. So tell us what did

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Speaker 3: you make for as a person? How did he get

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Speaker 3: into this? Why did he get into this?

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Speaker 1: Well, it's funny because my interview with him was from

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Speaker 1: a while back, you know, twenty seventeen something like that.

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Speaker 1: I honestly don't remember.

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Speaker 3: We should say he was arrested, I think in about

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Speaker 3: two thousand and seven, two thousand and eight something like that.

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Speaker 1: Wasn't yeah, exactly exactly, but so I was trying to

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Speaker 1: refresh my memory, and you know, just looked at some

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Speaker 1: of the transcripts and then also looked at an interview

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Speaker 1: that he did with Andrew Ross Sorkin, and he told

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Speaker 1: us the exact same thing at the beginning, which is like,

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Speaker 1: don't believe me, I'm a liar. And it's so funny

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Speaker 1: because I think that he thinks that that absolves him, right,

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Speaker 1: if he puts that disclaimer up top, then he can

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Speaker 1: sort of charm his way out and say, but actually,

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Speaker 1: I'm a good guy, right, I'm not like that bad

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Speaker 1: guy made off.

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Speaker 3: So I should say there's one amazing scene. Gee Lawson

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Speaker 3: wrote this book The Octopus, which is like the quintessential

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Speaker 3: account of Sammue's wail. But there's a scene in that

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Speaker 3: book where his wife walks in and catches him bent

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Speaker 3: over his desk snorting cocaine through a fifty dollar bill

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Speaker 3: and says, why are you taking cocaine? And he goes,

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Speaker 3: how dare you accuse me of taking drugs? So yeah, sorry,

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Speaker 3: I interrupted.

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Speaker 1: He it is the kind of guy he is, and

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Speaker 1: you know, he was incredibly charismatic, and he said, I

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Speaker 1: was doing really really well on Wall Street. Right, he

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Speaker 1: kind of got in. He didn't want to compete with

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Speaker 1: his siblings, so he wanted to do it on his own,

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Speaker 1: so he didn't want to go into the family business. Instead,

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Speaker 1: he had this opportunity to go into Wall Street, worked

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Speaker 1: at a very successful hedge fund and was actually making money.

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Speaker 1: By the way, this is all according to Sam Israel, Right,

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Speaker 1: I haven't actually looked at his returns. I did not

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Speaker 1: look at his balance sheets. I don't know how he

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Speaker 1: did as a trader. He assures me that he was

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Speaker 1: making you know, millions for people and for himself in

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Speaker 1: his prior Wall Street days. So let's just we'll take

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Speaker 1: that on faith. But what I've learned working with con

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Speaker 1: artists is you can't take anything on faith. So asterisk

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Speaker 1: but he was very successful on Wall Street, and I

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Speaker 1: assume he must have been to a certain degree because

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Speaker 1: he started his own hedge fund right and he was

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Speaker 1: able to raise I think about three hundred million to

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Speaker 1: begin with of outside money, which back then, this was

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Speaker 1: nineteen ninety six, I want to say something around somewhere

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Speaker 1: around there in ninety so that was a lot of money.

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Speaker 1: And he went in with a partner who was a

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Speaker 1: close friend of his, who was a disgraced fund manager

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Speaker 1: whose fund had just gone under. But Sam believed in

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Speaker 1: him and thought that, you know, that he was a

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Speaker 1: good guy and that this would work. So the way

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Speaker 1: Sam told it to me was that when they started

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Speaker 1: by you in nineteen ninety six, he kind of relied

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Speaker 1: on this guy, you know, to be an equal partner,

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Speaker 1: and that this guy was started losing a lot of money,

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Speaker 1: and Sam said, well, I'm a good trader, I'm good

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Speaker 1: at this. I'll be able to kind of work my

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Speaker 1: way out of the hole. But he couldn't, and the

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Speaker 1: hole kept getting bigger, and so at some point he realized,

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Speaker 1: you know, shit, we've lost twelve percent in our first year.

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Speaker 1: You know, it's even worse in our second year. This

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Speaker 1: is looking bad, and our ability to raise money is

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Speaker 1: going down because our returns are shit. They're absolutely horrible.

301
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Speaker 1: And so that's when it became a Ponzi scheme.

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Speaker 3: And his his his accountant, who was a guy called

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Speaker 3: Dan Marino. He there is footnote foroobody. Well, if anybody

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Speaker 3: so think about it, this is nineteen ninety six. If

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Speaker 3: anybody googles as pre google, right, if anybody who tries

306
00:16:30,684 --> 00:16:34,204
Speaker 3: to search on the internet for Dan Marino, they get

307
00:16:34,244 --> 00:16:37,284
Speaker 3: the football player. So he's working with this accountant who

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Speaker 3: is completely invisible to internet searches. And the accountants are smart.

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Speaker 2: Yeah, it's really smart.

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Speaker 1: It's very smart.

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Speaker 3: The accountant's completely crooked and basically sets up his own

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Speaker 3: fake Uh.

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Speaker 2: Sounds like a cricket I had a stereotype based on

314
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Speaker 2: last but sounds like a cricket account accountant or a quarterback.

315
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Speaker 3: So he sets up his own fake auditing firm. So

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Speaker 3: he's basically auditing his own accounts. And if anybody sort

317
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Speaker 3: of were to really go and check the go the

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Speaker 3: guy who says that Dan Marino's accounts are genuine is

319
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Speaker 3: an orditor called Dan Marino, and they're the same guy.

320
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Speaker 3: So that was kind of an important part of this fraud.

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Speaker 3: But yeah, Dan Marinos told the author Gee Lawson that

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Speaker 3: one of the problems was, there's this idea, Oh, we're

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Speaker 3: going to have a really good year. We're going to

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Speaker 3: make a lot of money for real. And when we

325
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Speaker 3: make a lot of money for real, then it will

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Speaker 3: no longer be a Ponzi scheme. You know, we'll have

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Speaker 3: you know, it's all genuine, It'll all come out good

328
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Speaker 3: in the end. Dan Marino said. The problem is sometimes

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Speaker 3: they did have good years, but whenever they had a

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Speaker 3: good year, they would claim it was an even better year,

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Speaker 3: and whenever they had a bad year, they would never

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Speaker 3: admit that they had a bad year. So there was

333
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Speaker 3: a you know, whatever it was, vanity, fear of the consequences,

334
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Speaker 3: whatever it was, he just made it completely impossible for

335
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Speaker 3: him ever to catch up with his own life.

336
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Speaker 1: And I think that that's very typical of con artists,

337
00:18:00,164 --> 00:18:02,724
Speaker 1: right where they say I'll fix it, I'll make it better,

338
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Speaker 1: this isn't this is just temporary, but it never is.

339
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Speaker 1: But the most incredible thing about I'm Israel is that

340
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Speaker 1: once the scheme kind of comes undone, right, which happens

341
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Speaker 1: at some point, just like with SBF night. Right, at

342
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Speaker 1: some point people are going to ask for their money

343
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Speaker 1: and people are going to get spooked, and when they

344
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Speaker 1: get spooked, like there's going to be a day of reckoning.

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Speaker 1: And he had a moment where he said, oh shit,

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Speaker 1: you know I'm going to jail. And he thought he

347
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Speaker 1: would get seven or eight years, which was pretty typical

348
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Speaker 1: for white collar criminals. And then they switched judges and

349
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Speaker 1: the judge gave him two hundred and forty months, so

350
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Speaker 1: twenty years right instead, And he was like, oh shit,

351
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Speaker 1: you know I can't do twenty years. I'm going to

352
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Speaker 1: be an old man. This is this is not cool.

353
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Speaker 1: I was ready for seven I can't do twenty. And

354
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Speaker 1: so instead of you know, figuring out how do I

355
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Speaker 1: deal with this, he decides to fake his own death.

356
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Speaker 3: Yeah, which he's obviously going to work out really well.

357
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Speaker 1: Oh it's going to work out great, we'll be back

358
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Speaker 1: in a minute. He only has a few months to plan, right,

359
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Speaker 1: It's not like he has been thinking, oh, I'm going

360
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Speaker 1: to fake my own death. This is my exit strategy.

361
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Speaker 1: Because a lot of CONN artists they don't think that

362
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Speaker 1: far ahead, right, they don't think of the exit strategy.

363
00:19:32,884 --> 00:19:35,004
Speaker 1: They think it's always going to work out. So he says,

364
00:19:35,004 --> 00:19:36,724
Speaker 1: I'm going to I'm going to fake my own death.

365
00:19:37,764 --> 00:19:40,044
Speaker 1: I've watched this really cool movie it's called r V,

366
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Speaker 1: and I'm going to buy an RV and I'm going to,

367
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Speaker 1: you know, just go around the country and maybe make

368
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Speaker 1: my way to go oh, by the way. One of

369
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Speaker 1: the things he told me was that as after his sentencing,

370
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Speaker 1: as he was walking out, an FBI agent, one of

371
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Speaker 1: the ones he'd gotten friendly with, looked at him and said,

372
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Speaker 1: I have two words for you, Costa Rica. I find

373
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Speaker 1: it very difficult to believe that that actually has.

374
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Speaker 2: Yes out Mano Dan Marino. Would you Costa Rica? I

375
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Speaker 2: don't know what.

376
00:20:14,204 --> 00:20:15,444
Speaker 1: I think You've got a career.

377
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Speaker 3: This so but I'm curious. So, Maria, you said that

378
00:20:19,044 --> 00:20:21,164
Speaker 3: this is very this is short term thinking.

379
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Speaker 2: He didn't.

380
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Speaker 3: He wasn't really think to the consequences of his actions,

381
00:20:24,804 --> 00:20:31,324
Speaker 3: and that that's true. Any Ponzi scheme inevitably becomes completely unsustainable.

382
00:20:31,364 --> 00:20:34,484
Speaker 3: You cannot possibly keep it going. It will come to

383
00:20:34,524 --> 00:20:37,044
Speaker 3: an end. So what is the way out? And then

384
00:20:37,044 --> 00:20:39,164
Speaker 3: the faking the suicide again like of course he's going

385
00:20:39,244 --> 00:20:40,804
Speaker 3: to get caught. Of course, that's not going to work.

386
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Speaker 1: So he decided he was going to jump off a bridge.

387
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Speaker 3: Well, not just he was decided he was going to

388
00:20:46,244 --> 00:20:48,444
Speaker 3: pretend that he had jumped off a bridge, right, he

389
00:20:48,444 --> 00:20:52,884
Speaker 3: actually jumped off a bridge because he because he then

390
00:20:52,924 --> 00:20:55,484
Speaker 3: he did actually actually claimed.

391
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Speaker 1: Actually he actually jumped off a bridge. He did. So

392
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Speaker 1: this is the research he was doing. When you're trying

393
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Speaker 1: to fake your own death, and I interviewed this woman,

394
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Speaker 1: Elizabeth Greenwood, who wrote about faking your own death, you

395
00:21:07,364 --> 00:21:09,284
Speaker 1: have to think very far ahead. You have to figure

396
00:21:09,284 --> 00:21:11,284
Speaker 1: out money. You know, how am I going to be

397
00:21:11,404 --> 00:21:13,164
Speaker 1: off the grid right these days? Like how am I

398
00:21:13,164 --> 00:21:15,164
Speaker 1: going to survive? How am I going to get out

399
00:21:15,204 --> 00:21:18,124
Speaker 1: of the country, and you know where, where's my cash

400
00:21:18,164 --> 00:21:20,964
Speaker 1: going to come from? All of these things. Instead, what

401
00:21:21,084 --> 00:21:23,804
Speaker 1: he spends his time thinking is researching all of the

402
00:21:23,804 --> 00:21:26,364
Speaker 1: bridges in New York to try to figure out which

403
00:21:26,404 --> 00:21:29,524
Speaker 1: one he could conceivably jump off of and not die.

404
00:21:30,644 --> 00:21:34,484
Speaker 1: So he finds a bridge that's under construction, so there

405
00:21:34,484 --> 00:21:37,564
Speaker 1: are nets underneath, and he's like, oh, perfect, Like I'm

406
00:21:37,604 --> 00:21:40,684
Speaker 1: gonna jump and I'm going to land in the net

407
00:21:41,004 --> 00:21:43,844
Speaker 1: and then I'll use the net to scramble up and

408
00:21:44,164 --> 00:21:50,084
Speaker 1: get out. So he does this not realizing that those

409
00:21:50,244 --> 00:21:54,724
Speaker 1: nets are pretty damn hard to climb out of. So

410
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Speaker 1: he manages to make the net. By the way, thinking

411
00:21:56,524 --> 00:22:00,044
Speaker 1: about risk right, risk reward. If you miss the net,

412
00:22:00,244 --> 00:22:04,484
Speaker 1: the you know, the risk reward equation there is not great.

413
00:22:04,604 --> 00:22:06,964
Speaker 3: So the net deigning to catch people or just like

414
00:22:07,884 --> 00:22:09,844
Speaker 3: a spat of that somebody drops, I mean that's the.

415
00:22:10,164 --> 00:22:15,004
Speaker 1: Right, No, it's I think it was. I think it

416
00:22:15,084 --> 00:22:17,244
Speaker 1: was designed to catch bricks and you know, falling debris

417
00:22:17,284 --> 00:22:19,764
Speaker 1: from construction. So when a person goes into it, it

418
00:22:19,884 --> 00:22:22,084
Speaker 1: just like you know, it goes all the way down.

419
00:22:22,324 --> 00:22:23,844
Speaker 1: So then he's stuck in it. And at this point

420
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Speaker 1: he's like, I think I'm going to die anyway, except

421
00:22:25,724 --> 00:22:27,724
Speaker 1: it's going to be much worse because I'm gonna have

422
00:22:27,804 --> 00:22:30,204
Speaker 1: spent my last minutes trying to scramble up this net.

423
00:22:30,644 --> 00:22:34,044
Speaker 1: But he actually does manage to get out, and he

424
00:22:34,124 --> 00:22:36,684
Speaker 1: has a driver waiting for him to take him to

425
00:22:36,684 --> 00:22:41,244
Speaker 1: his RV and he thinks he's going to drive off

426
00:22:41,244 --> 00:22:44,364
Speaker 1: into the sunset. Oh, by the way, another really really

427
00:22:44,404 --> 00:22:47,484
Speaker 1: important thing if you're trying to fake your own death,

428
00:22:47,524 --> 00:22:51,204
Speaker 1: do not tell your mother, your girlfriend, your son, and

429
00:22:51,244 --> 00:22:54,844
Speaker 1: the driver that you're going to be faking your own death.

430
00:22:54,924 --> 00:22:59,884
Speaker 1: Don't worry, momb I'm not actually dead, calling uber.

431
00:23:01,324 --> 00:23:02,124
Speaker 2: Do it in style.

432
00:23:03,724 --> 00:23:06,484
Speaker 1: But that's this is what happened. So you know, he

433
00:23:06,724 --> 00:23:11,364
Speaker 1: he's fucked from the beginning because he has not thought

434
00:23:11,404 --> 00:23:13,924
Speaker 1: any of this through. But he does manage to make

435
00:23:13,964 --> 00:23:16,284
Speaker 1: it out of the net. He's met by a driver,

436
00:23:16,644 --> 00:23:19,124
Speaker 1: makes it to his RV, and for the first few

437
00:23:19,164 --> 00:23:21,644
Speaker 1: weeks it actually seems like everything is kind of okay

438
00:23:22,004 --> 00:23:24,964
Speaker 1: because even though he's living in an RV, living in

439
00:23:25,084 --> 00:23:27,604
Speaker 1: RV parks, you know, he's kind of getting away with it.

440
00:23:28,004 --> 00:23:32,284
Speaker 1: And then he walks into a bar one day and

441
00:23:32,324 --> 00:23:36,324
Speaker 1: he sees himself on TV on America's Most Wanted, and

442
00:23:37,204 --> 00:23:40,444
Speaker 1: he goes and reads about himself on the internet, which

443
00:23:40,484 --> 00:23:42,084
Speaker 1: is a big, big no no if you're faking your

444
00:23:42,084 --> 00:23:45,244
Speaker 1: own death to start googling yourself, but he does that,

445
00:23:45,644 --> 00:23:48,084
Speaker 1: and he sees that his girlfriend has been arrested as

446
00:23:48,084 --> 00:23:50,924
Speaker 1: an accomplice and that they're looking to arrest his mother.

447
00:23:51,484 --> 00:23:53,444
Speaker 1: He doesn't realize that this is a trap, that the

448
00:23:53,484 --> 00:23:55,564
Speaker 1: police do this kind of thing to try to get

449
00:23:55,724 --> 00:23:59,484
Speaker 1: people kind of out of hiding. He thinks this is real,

450
00:23:59,604 --> 00:24:02,604
Speaker 1: and so he gets on a motorcycle, goes to the

451
00:24:02,604 --> 00:24:06,284
Speaker 1: police department to turn himself in, walks in. He says,

452
00:24:06,324 --> 00:24:08,604
Speaker 1: you know, hey, I'm here to turn myself in, and

453
00:24:08,604 --> 00:24:10,964
Speaker 1: the police officer like what you know? In for what?

454
00:24:11,044 --> 00:24:13,684
Speaker 1: You need to use the bathroom? Like it's over there anyway,

455
00:24:13,684 --> 00:24:16,244
Speaker 1: there's all of this miscommunication. He says, no, you know,

456
00:24:16,404 --> 00:24:22,244
Speaker 1: I turning myself in. I'm wanted and I just don't

457
00:24:22,244 --> 00:24:24,484
Speaker 1: want any press here. And at this point the police

458
00:24:24,524 --> 00:24:28,164
Speaker 1: officer actually looks at him, realizes who he is, and

459
00:24:28,364 --> 00:24:31,724
Speaker 1: that is when he gets caught and gets an additional

460
00:24:31,724 --> 00:24:34,484
Speaker 1: two years added on to his sentence for faking his

461
00:24:34,524 --> 00:24:35,564
Speaker 1: own death and running away.

462
00:24:35,964 --> 00:24:39,164
Speaker 3: It's astonishing. I'm not one thing you said, Maria. You

463
00:24:39,244 --> 00:24:42,524
Speaker 3: said that the craziest thing about him, or the most

464
00:24:42,524 --> 00:24:44,484
Speaker 3: amazing thing about him. I'm not even sure that is

465
00:24:44,524 --> 00:24:46,564
Speaker 3: the craziest thing about him. But you know, we haven't

466
00:24:46,564 --> 00:24:49,724
Speaker 3: got all day, so there are other stories you could

467
00:24:49,724 --> 00:24:54,724
Speaker 3: tell about Sam. But I wanted to ask Nate. Given that, Nate,

468
00:24:54,764 --> 00:24:58,684
Speaker 3: you've been thinking about the habits of risk taking individuals,

469
00:24:58,724 --> 00:25:05,044
Speaker 3: these people you call Riverians. Is short termism part of

470
00:25:05,084 --> 00:25:08,084
Speaker 3: the or kind of a side effect or a glitch

471
00:25:08,164 --> 00:25:11,964
Speaker 3: in the Riveria and thinking system. So on the one hand,

472
00:25:12,004 --> 00:25:14,924
Speaker 3: you need that ability to think probabilistically, you need that

473
00:25:14,964 --> 00:25:19,764
Speaker 3: ability to take calculated risks. But I mean Samme's meel

474
00:25:19,844 --> 00:25:21,524
Speaker 3: just seems to have never been able to see past

475
00:25:21,564 --> 00:25:25,244
Speaker 3: the It had no problem taking risks, but just couldn't

476
00:25:25,284 --> 00:25:26,364
Speaker 3: see past the end of his nose.

477
00:25:27,004 --> 00:25:30,644
Speaker 2: Yeah. No, look, I think the better investors in gamblers

478
00:25:30,644 --> 00:25:32,604
Speaker 2: have a longer time horizon. That's kind of one of

479
00:25:33,364 --> 00:25:36,164
Speaker 2: one of Silicon Valley's secrets, despite their many flaws, that

480
00:25:36,204 --> 00:25:38,884
Speaker 2: they do kind of think ten years ahead. But yeah,

481
00:25:38,924 --> 00:25:41,964
Speaker 2: some of this sounds very familiar, Marie, I know if

482
00:25:41,964 --> 00:25:45,164
Speaker 2: I'm talking to you, the notion of like a good

483
00:25:45,204 --> 00:25:48,444
Speaker 2: business gone bad. I mean, even FTX was a pretty

484
00:25:48,444 --> 00:25:50,244
Speaker 2: good business, right, it was like the leading brand for

485
00:25:50,284 --> 00:25:55,124
Speaker 2: crypto trading. They made legitimate profits, et cetera. But like

486
00:25:55,244 --> 00:25:57,724
Speaker 2: you know, it turns sour or I think you know,

487
00:25:58,124 --> 00:26:01,964
Speaker 2: Sam bacon Free couldn't resist the temptation to take all

488
00:26:01,964 --> 00:26:03,884
Speaker 2: this money sitting on the sideline. He couldn't like resist

489
00:26:03,924 --> 00:26:07,004
Speaker 2: the temptation to go and gamble with it. But yeah,

490
00:26:07,044 --> 00:26:10,444
Speaker 2: the lack of advanced planning coupled with kind of miscalculating

491
00:26:10,484 --> 00:26:14,724
Speaker 2: consequences you know, if you're very charming, which I think

492
00:26:14,764 --> 00:26:17,724
Speaker 2: Sam Israel is more so than SBF, it's a different story.

493
00:26:19,124 --> 00:26:21,164
Speaker 2: You can kind of easle your way out of things, right.

494
00:26:21,204 --> 00:26:22,764
Speaker 2: You can think you can like dance your way out

495
00:26:22,764 --> 00:26:26,684
Speaker 2: of anything, and you can up the con a level

496
00:26:26,764 --> 00:26:29,604
Speaker 2: or two or three, and then you may on some level, no,

497
00:26:29,684 --> 00:26:31,284
Speaker 2: it's not going to work. But I don't know. I mean,

498
00:26:31,364 --> 00:26:35,444
Speaker 2: at some point there's a point of no return, right, there.

499
00:26:35,284 --> 00:26:37,084
Speaker 1: Is a point of no return. Yeah, I think I

500
00:26:37,084 --> 00:26:40,404
Speaker 1: think that's absolutely right, Nate. I think one of the

501
00:26:40,924 --> 00:26:44,844
Speaker 1: this is a characteristic that you have both uh, both

502
00:26:44,844 --> 00:26:48,404
Speaker 1: with raverians and non reveriance and Tim. I'm sure you've

503
00:26:48,524 --> 00:26:51,204
Speaker 1: come across this in other cautionary tales, but I think

504
00:26:51,204 --> 00:26:54,524
Speaker 1: a lot of it is this over confidence in hubris, right,

505
00:26:54,564 --> 00:26:58,044
Speaker 1: that comes with a certain level of success and people.

506
00:26:58,404 --> 00:27:00,924
Speaker 1: And I think to be an entrepreneur and to be

507
00:27:00,964 --> 00:27:04,164
Speaker 1: a risk taker, you need to be over confident to

508
00:27:04,204 --> 00:27:07,564
Speaker 1: a certain extent. You know, as as we all know,

509
00:27:07,684 --> 00:27:10,484
Speaker 1: if you actually know your odds of access, you're not

510
00:27:10,564 --> 00:27:12,804
Speaker 1: gonna you're not gonna start the damn company. You're not

511
00:27:12,884 --> 00:27:15,684
Speaker 1: gonna you're not gonna try it, because it's the risk

512
00:27:15,724 --> 00:27:18,764
Speaker 1: of failure, and the chances of failure are so high,

513
00:27:18,844 --> 00:27:22,564
Speaker 1: so it's a it's this fine balance and I think

514
00:27:22,604 --> 00:27:26,364
Speaker 1: overconfidence so turns into delusion and turns into this thinking

515
00:27:26,444 --> 00:27:31,124
Speaker 1: that actually, you know, I can keep doing this forever.

516
00:27:31,684 --> 00:27:34,764
Speaker 1: And because you've gotten away with it for so long,

517
00:27:35,244 --> 00:27:40,404
Speaker 1: it seems like probabilistically speaking, you know, your base rates change,

518
00:27:40,444 --> 00:27:43,644
Speaker 1: I've gotten you know, okay, you know one year, two years,

519
00:27:43,684 --> 00:27:46,444
Speaker 1: three years, four years, everything's good. This is this is

520
00:27:46,484 --> 00:27:47,244
Speaker 1: all going good.

521
00:27:47,604 --> 00:27:51,084
Speaker 2: Yeah, if the coin comes up heads five times in

522
00:27:51,124 --> 00:27:53,044
Speaker 2: a row, I mean you see it in poker all

523
00:27:53,044 --> 00:27:56,324
Speaker 2: the time, right, Yeah. You know, winner's tilt is something

524
00:27:56,324 --> 00:27:59,604
Speaker 2: which is maybe under discussed loser tilt we all have experienced.

525
00:28:00,004 --> 00:28:03,884
Speaker 2: Uh maybe not a Maria, uh, but winter's tilt where

526
00:28:03,884 --> 00:28:05,724
Speaker 2: you're on a hot streak and you're like, ah, maybe

527
00:28:05,724 --> 00:28:07,924
Speaker 2: I have some gift from God to play poker really

528
00:28:07,924 --> 00:28:10,884
Speaker 2: well or something is also a big deal.

529
00:28:12,204 --> 00:28:15,804
Speaker 1: It absolutely is. One of my favorite psych studies is

530
00:28:16,164 --> 00:28:18,164
Speaker 1: from Ellen Langer, and I think the name of the

531
00:28:18,164 --> 00:28:22,524
Speaker 1: paper is something like heads, I win tails, It's chance

532
00:28:22,684 --> 00:28:29,964
Speaker 1: something like that. Yeah, And she had people be not

533
00:28:30,084 --> 00:28:33,444
Speaker 1: bet but guess the results of a coin toss and

534
00:28:33,524 --> 00:28:35,564
Speaker 1: it was actually not a fair coin. It was rigged,

535
00:28:35,764 --> 00:28:38,764
Speaker 1: and there were different Basically, it came up heads and

536
00:28:38,804 --> 00:28:42,244
Speaker 1: tails the exact same number of times in all of

537
00:28:42,284 --> 00:28:45,124
Speaker 1: the different conditions, but in some of them it was

538
00:28:45,164 --> 00:28:49,324
Speaker 1: pretty random. In others it was clustered near the end,

539
00:28:49,444 --> 00:28:53,564
Speaker 1: and in the most important condition, the correct guesses were

540
00:28:53,604 --> 00:28:56,524
Speaker 1: clustered near the beginning, right, So basically you would say,

541
00:28:56,564 --> 00:28:59,364
Speaker 1: you know, heads, you'd guess, and then they'd make sure

542
00:28:59,364 --> 00:29:01,364
Speaker 1: it landed on heads. It was a rigged toss, so

543
00:29:01,364 --> 00:29:04,084
Speaker 1: that you were right it was yeah, And the people

544
00:29:04,124 --> 00:29:07,364
Speaker 1: who were correct clustered at the beginning would then and

545
00:29:07,404 --> 00:29:10,684
Speaker 1: these were Harvard students, by the way. Then they got

546
00:29:10,724 --> 00:29:15,764
Speaker 1: all sorts of questions like I'm good at predicting the

547
00:29:15,844 --> 00:29:19,644
Speaker 1: outcomes of coin tosses, and they would rate themselves as

548
00:29:19,684 --> 00:29:21,844
Speaker 1: actually quite good at it. They would say, if I

549
00:29:21,884 --> 00:29:24,364
Speaker 1: had more time to practice and to guess, I'd get

550
00:29:24,364 --> 00:29:27,284
Speaker 1: even better. So things that made it very clear that

551
00:29:27,324 --> 00:29:29,964
Speaker 1: they thought that this was a skill and not actually

552
00:29:30,004 --> 00:29:33,164
Speaker 1: completely random. And it was so easy to get people

553
00:29:33,204 --> 00:29:36,524
Speaker 1: to believe that they were skilled at something where it

554
00:29:36,564 --> 00:29:40,644
Speaker 1: was just complete randomness when they had those those things

555
00:29:40,724 --> 00:29:42,524
Speaker 1: happen at the beginning, so Tim, I think this goes

556
00:29:42,564 --> 00:29:44,844
Speaker 1: back to the beginning of your question. This is how

557
00:29:44,884 --> 00:29:47,844
Speaker 1: you get into Ponzi schemes. Think about Bernie Madeoff, right,

558
00:29:47,884 --> 00:29:50,324
Speaker 1: He was successful for far longer than Sam Israel, and

559
00:29:50,324 --> 00:29:52,284
Speaker 1: Sam Israel, by the way, was the single biggest Ponzi

560
00:29:52,324 --> 00:29:53,844
Speaker 1: scheme before Bernie made Off.

561
00:29:54,284 --> 00:29:58,484
Speaker 3: Now I'm curious, we've been talking about people who were

562
00:29:59,404 --> 00:30:02,244
Speaker 3: have an appetite for risk and who it all came

563
00:30:02,284 --> 00:30:05,724
Speaker 3: apart for Maria, I know you've been doing a little

564
00:30:05,724 --> 00:30:10,244
Speaker 3: bit of research into one of the most important gamblers

565
00:30:10,364 --> 00:30:12,684
Speaker 3: in economic history.

566
00:30:13,284 --> 00:30:17,844
Speaker 1: Yeah, John Law. He was. He was someone who I

567
00:30:17,844 --> 00:30:20,364
Speaker 1: wrote about for The Confidence Game and I've come back

568
00:30:20,364 --> 00:30:23,884
Speaker 1: to so my my next book is about cheating, So

569
00:30:24,084 --> 00:30:27,164
Speaker 1: I've I've kind of been thinking about him. But one

570
00:30:27,164 --> 00:30:30,404
Speaker 1: of the reasons I am interested in John Law. So

571
00:30:30,644 --> 00:30:32,684
Speaker 1: when we're talking about someone like Sam Israel, right, it's

572
00:30:32,684 --> 00:30:35,964
Speaker 1: pretty clear con artist, right, Ponzi's game. When you're talking

573
00:30:36,004 --> 00:30:40,004
Speaker 1: about someone like John Law, it becomes much less clear

574
00:30:40,644 --> 00:30:44,804
Speaker 1: because he's someone who was a huge gambler, and we

575
00:30:44,924 --> 00:30:47,644
Speaker 1: know that sometimes he was successful, but he also ran

576
00:30:47,764 --> 00:30:51,364
Speaker 1: his father's business into the ground if I remember correctly,

577
00:30:51,484 --> 00:30:54,524
Speaker 1: through gambling. But I guess he got better with time

578
00:30:55,564 --> 00:31:00,844
Speaker 1: and killed a man literal gambling or like literal gambling.

579
00:31:01,124 --> 00:31:04,284
Speaker 1: So yeah, so he was someone who came from money.

580
00:31:04,764 --> 00:31:08,564
Speaker 1: Who's you know who whose parents had a financial business.

581
00:31:08,684 --> 00:31:10,924
Speaker 3: We should say this is it was seventeen hundreds, just

582
00:31:11,444 --> 00:31:14,524
Speaker 3: to situate, right, Yes, for those small number of listeners

583
00:31:14,524 --> 00:31:16,884
Speaker 3: who don't know who John Lauri is or that everybody should.

584
00:31:17,084 --> 00:31:19,404
Speaker 1: We're in the seventeen hundreds and he's going to be

585
00:31:19,404 --> 00:31:22,764
Speaker 1: making friends with the Duke of Orleans or the Duke

586
00:31:22,804 --> 00:31:26,764
Speaker 1: of Orleans who was then Regent of France, and he's

587
00:31:26,804 --> 00:31:29,604
Speaker 1: going to be basically setting up France's banking system. So

588
00:31:29,684 --> 00:31:33,484
Speaker 1: the reason why I was fascinated by him is that

589
00:31:34,284 --> 00:31:37,484
Speaker 1: it's actually unclear if he was a con artist or not,

590
00:31:37,804 --> 00:31:40,804
Speaker 1: Like did he believe that? Because it was the kind

591
00:31:40,804 --> 00:31:44,924
Speaker 1: of the end of his time at the height of

592
00:31:44,964 --> 00:31:47,924
Speaker 1: finance was with the establishment of this thing called the

593
00:31:47,964 --> 00:31:55,164
Speaker 1: Mississippi Company, which was a huge bubble and basically bankrupted

594
00:31:55,284 --> 00:31:58,284
Speaker 1: a ton of people. And the question is, you know,

595
00:31:59,084 --> 00:32:01,964
Speaker 1: did he know what he was doing and get unlucky

596
00:32:02,124 --> 00:32:04,204
Speaker 1: or did he like basically, did he do this as

597
00:32:04,244 --> 00:32:06,404
Speaker 1: a kind of Ponzi scheme, as a kind of con

598
00:32:06,604 --> 00:32:06,844
Speaker 1: or not.

599
00:32:07,284 --> 00:32:10,124
Speaker 3: And the fact that we still don't really agree on that,

600
00:32:10,204 --> 00:32:12,124
Speaker 3: I think is fascinating. I mean, we should say so.

601
00:32:13,404 --> 00:32:18,684
Speaker 3: He was originally Scott, a Scott. He killed a guy

602
00:32:18,684 --> 00:32:21,484
Speaker 3: and a duel, was sentenced to death for murder, escaped

603
00:32:21,524 --> 00:32:26,604
Speaker 3: from prison, traveled Europe, wound up in Paris, made a

604
00:32:26,604 --> 00:32:29,764
Speaker 3: few friends with some influential nobles, made a huge amount

605
00:32:29,804 --> 00:32:33,044
Speaker 3: of money gambling because he would set himself up as

606
00:32:33,084 --> 00:32:37,364
Speaker 3: the house and he understood the probability enough that he

607
00:32:37,924 --> 00:32:40,124
Speaker 3: knew he had an edge. So he's gambling with all

608
00:32:40,124 --> 00:32:42,804
Speaker 3: these nobles. He's making a huge amount of money. And

609
00:32:42,844 --> 00:32:48,804
Speaker 3: then he sets up his own bank and he starts

610
00:32:49,204 --> 00:32:52,484
Speaker 3: issuing paper money. This is not the first paper money

611
00:32:52,884 --> 00:32:56,004
Speaker 3: in the history of the world, it's not even the

612
00:32:56,004 --> 00:32:58,284
Speaker 3: first paper money in the history of France, but it is.

613
00:32:58,484 --> 00:33:01,364
Speaker 3: It's pretty new and people are still trying to figure

614
00:33:01,364 --> 00:33:03,884
Speaker 3: out kind of how it works. And of course this

615
00:33:04,044 --> 00:33:07,124
Speaker 3: is revolutionary. He's ahead of his time, like paper money

616
00:33:07,164 --> 00:33:09,084
Speaker 3: is how we do things right. It's kind of amazing.

617
00:33:09,444 --> 00:33:11,524
Speaker 3: And then the whole thing just gets wrapped up with

618
00:33:11,564 --> 00:33:13,804
Speaker 3: French government debt and gets wrapped up with the Mississippi

619
00:33:13,804 --> 00:33:16,924
Speaker 3: bubble and the Mississippi Bubble was it was a stock

620
00:33:17,004 --> 00:33:20,324
Speaker 3: market bubble. One stock was involved, the stock of the

621
00:33:20,364 --> 00:33:24,284
Speaker 3: Mississippi Company, and John Law controlled the Misissippi Company. But

622
00:33:24,324 --> 00:33:27,284
Speaker 3: it was clear that nobody really understood what was going

623
00:33:27,324 --> 00:33:30,244
Speaker 3: on except that number go up. And if number go up,

624
00:33:30,284 --> 00:33:32,044
Speaker 3: everyone everyone gets very excited.

625
00:33:32,164 --> 00:33:35,644
Speaker 2: Yeah, it's very intoxicating when the number goes up. Right.

626
00:33:37,004 --> 00:33:40,284
Speaker 2: I did wonder too, there is some survivorship bias in

627
00:33:40,324 --> 00:33:44,964
Speaker 2: which kind of scams and schemes we discover. You know,

628
00:33:45,164 --> 00:33:47,484
Speaker 2: the best frauds in history, probably nobody knows about it.

629
00:33:47,644 --> 00:33:48,524
Speaker 3: Yeah, a good point.

630
00:33:48,764 --> 00:33:52,284
Speaker 2: SPF was convinced that he could somehow navigate his way

631
00:33:52,324 --> 00:33:55,564
Speaker 2: through bankruptcy, or not through bankruptcy, now his way through

632
00:33:55,604 --> 00:33:58,364
Speaker 2: this downfall and bitcoin and come out on either side

633
00:33:58,364 --> 00:34:00,284
Speaker 2: of it. And maybe people wouldn't really notice, right, Maybe

634
00:34:00,324 --> 00:34:03,164
Speaker 2: it's like a page a sixteen story and not inn

635
00:34:03,164 --> 00:34:06,124
Speaker 2: a one story. If he like, if there's a spontaneous

636
00:34:06,364 --> 00:34:08,924
Speaker 2: rise in bitcoin prices and they recover these losses that

637
00:34:08,964 --> 00:34:11,324
Speaker 2: they have, they were ten million and ten billion in

638
00:34:11,364 --> 00:34:12,884
Speaker 2: a hole, which is pretty hard to overcome.

639
00:34:13,644 --> 00:34:16,644
Speaker 1: It's funny, Nate. I think that's a really important point

640
00:34:16,684 --> 00:34:20,244
Speaker 1: that the best con artists are never caught. When people

641
00:34:20,484 --> 00:34:22,324
Speaker 1: when we talk about conn artists and people ask me,

642
00:34:22,324 --> 00:34:24,764
Speaker 1: you know, why aren't there as many female con artists,

643
00:34:25,684 --> 00:34:29,164
Speaker 1: I say a few things, but one of them is

644
00:34:29,444 --> 00:34:32,204
Speaker 1: kind of a joke but kind of not, which is

645
00:34:32,644 --> 00:34:34,884
Speaker 1: that they're just better at it. So we don't know

646
00:34:34,924 --> 00:34:36,844
Speaker 1: them because they haven't been caught. They don't have as

647
00:34:36,924 --> 00:34:39,604
Speaker 1: much ego, and they know when to disappear and how

648
00:34:39,604 --> 00:34:44,604
Speaker 1: to disappear much better than the sam Is reels of

649
00:34:44,604 --> 00:34:45,004
Speaker 1: the world.

650
00:34:45,284 --> 00:34:47,444
Speaker 2: Or like cheating in poker, a lot of these famous

651
00:34:48,004 --> 00:34:51,564
Speaker 2: cheating scandals, like online cheating scandals, people are are very

652
00:34:51,604 --> 00:34:54,884
Speaker 2: greedy where they win at like, you know, thirteen centered

653
00:34:54,884 --> 00:34:58,204
Speaker 2: deviations above some random rate, whereas if you won at

654
00:34:58,204 --> 00:35:00,764
Speaker 2: two center deviations above random, it would be almost possible

655
00:35:00,764 --> 00:35:03,604
Speaker 2: to detect and you'd have a great life. Although we

656
00:35:03,644 --> 00:35:05,324
Speaker 2: don't find those people though, right, we don't find the

657
00:35:05,364 --> 00:35:07,524
Speaker 2: people that are that are actually good at cheating a

658
00:35:07,524 --> 00:35:07,964
Speaker 2: lot of time.

659
00:35:09,124 --> 00:35:12,404
Speaker 1: Yeah, but so as we you know, wrap up the

660
00:35:12,524 --> 00:35:15,964
Speaker 1: story of John law I do think that it's interesting that,

661
00:35:16,204 --> 00:35:20,444
Speaker 1: I mean, economists don't agree, historians don't agree whether whether

662
00:35:20,564 --> 00:35:23,964
Speaker 1: or not he was you know, greedy, he was obviously greedy.

663
00:35:24,004 --> 00:35:26,604
Speaker 1: I think everyone agrees on that, but whether he thought

664
00:35:26,644 --> 00:35:29,444
Speaker 1: that this could actually all worked out. I found a

665
00:35:29,564 --> 00:35:32,284
Speaker 1: rhyme that I would love to share if you guys

666
00:35:32,284 --> 00:35:37,004
Speaker 1: are in poetry mode, that come from the time about

667
00:35:37,324 --> 00:35:40,124
Speaker 1: what happened with the Mississippi bubble. And it goes like this.

668
00:35:40,884 --> 00:35:44,084
Speaker 1: My shares, which on Monday I bought, were worth millions.

669
00:35:44,084 --> 00:35:47,324
Speaker 1: On Tuesday, I thought so. On Wednesday, I chose my

670
00:35:47,364 --> 00:35:51,364
Speaker 1: abode in my carriage. On Thursday, I rode to the ballroom.

671
00:35:51,444 --> 00:35:54,964
Speaker 1: On Friday I went to the workhouse. Next day I was.

672
00:35:54,964 --> 00:35:59,084
Speaker 2: Sent first poetry reading on the Risky Business podcast.

673
00:35:59,124 --> 00:36:01,844
Speaker 1: I believe it is it is think that should style tradition.

674
00:36:02,084 --> 00:36:02,764
Speaker 3: That's very good.

675
00:36:05,524 --> 00:36:08,044
Speaker 1: And one nobleman of the time said, thus sends the

676
00:36:08,084 --> 00:36:10,924
Speaker 1: system of paper money, which which has enriched a thousand

677
00:36:10,964 --> 00:36:14,644
Speaker 1: beggars and impoverished one hundred thousand men. And obviously that's

678
00:36:14,684 --> 00:36:17,044
Speaker 1: not true, which is why we you know, that was

679
00:36:17,124 --> 00:36:19,284
Speaker 1: not the end of the system of paper money. It

680
00:36:19,364 --> 00:36:22,084
Speaker 1: was just it just happened to be the end of

681
00:36:22,164 --> 00:36:24,684
Speaker 1: John Law. He had to escape France, by the way,

682
00:36:24,724 --> 00:36:27,284
Speaker 1: because he was convicted and was going to be sent

683
00:36:27,324 --> 00:36:29,324
Speaker 1: to prison there. So he dressed up as a beggar

684
00:36:29,844 --> 00:36:33,684
Speaker 1: ran away to Italy and died in Venice, totally impoverished.

685
00:36:34,444 --> 00:36:37,684
Speaker 1: And I guess the cautionary talesary.

686
00:36:37,724 --> 00:36:41,684
Speaker 3: It is a cautionary tale. And I guess the lesson,

687
00:36:41,764 --> 00:36:44,364
Speaker 3: or maybe the lesson is that the economics lesson is,

688
00:36:44,364 --> 00:36:45,764
Speaker 3: if you're going to have paper money, if you're going

689
00:36:45,804 --> 00:36:49,044
Speaker 3: to have somebody who has the right to just create

690
00:36:49,124 --> 00:36:52,044
Speaker 3: money with a printing press, you've got to make sure

691
00:36:52,044 --> 00:36:54,244
Speaker 3: you have the right controls over that person or over

692
00:36:54,284 --> 00:36:56,884
Speaker 3: that institution. It can't just be some guy who killed

693
00:36:56,924 --> 00:36:59,524
Speaker 3: a guy in a duel and came over what a

694
00:36:59,524 --> 00:37:01,844
Speaker 3: lot of money in gambling, and then he's the guy

695
00:37:01,844 --> 00:37:05,884
Speaker 3: who can do it. You need this institutional scaffolding, which

696
00:37:06,484 --> 00:37:08,564
Speaker 3: you know, when we have it, it seems to work just fine.

697
00:37:10,284 --> 00:37:26,724
Speaker 2: We'll be back right after this. I mean, are there

698
00:37:26,764 --> 00:37:31,564
Speaker 2: like two or three or one big takeaways, like common

699
00:37:31,644 --> 00:37:34,884
Speaker 2: patterns and when you know something is becoming a cautionary

700
00:37:34,924 --> 00:37:35,804
Speaker 2: tale or a can.

701
00:37:38,244 --> 00:37:42,884
Speaker 3: At the risk of quoting that classic opening line of

702
00:37:43,284 --> 00:37:47,564
Speaker 3: Ana Karenina that all happy families are alike, and every

703
00:37:47,684 --> 00:37:51,644
Speaker 3: unhappy family is unhappy in its own way. I think

704
00:37:51,724 --> 00:37:55,044
Speaker 3: one of the striking things about cautionary tales is that

705
00:37:55,684 --> 00:37:57,764
Speaker 3: there are a lot of different ways for things to

706
00:37:57,804 --> 00:38:05,684
Speaker 3: go wrong. There's there are organizational problems, informational problems, engineering problems,

707
00:38:06,684 --> 00:38:12,044
Speaker 3: hubrisk and arrogance, as short sightedness, lots and lots of

708
00:38:12,124 --> 00:38:16,444
Speaker 3: self delusion, lots of wishful thinking. I mean, it's a

709
00:38:16,484 --> 00:38:20,204
Speaker 3: miracle that human civilization survives, actually, given how many different

710
00:38:20,204 --> 00:38:23,244
Speaker 3: ways there are to go wrong. I mean that is

711
00:38:23,284 --> 00:38:27,684
Speaker 3: part of the slightly perverse joy of researching and writing

712
00:38:27,804 --> 00:38:32,604
Speaker 3: these cautionary tales. There is always a new disaster, and

713
00:38:32,764 --> 00:38:34,804
Speaker 3: always a new way for disaster to happen.

714
00:38:35,964 --> 00:38:37,924
Speaker 1: You make that sound almost gleeful.

715
00:38:38,244 --> 00:38:40,684
Speaker 3: Yeah, I mean sometimes I have to remind myself that, like,

716
00:38:41,044 --> 00:38:42,884
Speaker 3: I'm not supposed to be enjoying this, because some of

717
00:38:42,884 --> 00:38:46,484
Speaker 3: them are very very sad. Some of them are straightforwardly

718
00:38:46,564 --> 00:38:50,084
Speaker 3: hilarious and no great harm is done, but a lot

719
00:38:50,084 --> 00:38:52,124
Speaker 3: of them are pretty painful.

720
00:38:53,124 --> 00:38:56,044
Speaker 2: Yeah, Maria, I remember you telling me that, like, cons

721
00:38:56,124 --> 00:38:59,484
Speaker 2: are most likely to occur when you're in an upcycle

722
00:38:59,564 --> 00:39:01,364
Speaker 2: or a down cycle, right, not in the steady state,

723
00:39:01,404 --> 00:39:04,124
Speaker 2: but when there's something new and novel and people are panicking.

724
00:39:04,484 --> 00:39:09,124
Speaker 1: Yeah, moments of transition. So whether those are societal transitions

725
00:39:10,004 --> 00:39:13,844
Speaker 1: or whether their personal transitions is when you're most vulnerable

726
00:39:13,884 --> 00:39:16,244
Speaker 1: to get cons So it's not a personality trade. It's

727
00:39:16,284 --> 00:39:19,844
Speaker 1: not intelligence, it's not it's not anything like that. It

728
00:39:20,004 --> 00:39:23,084
Speaker 1: is this kind of moment of either up or down

729
00:39:23,124 --> 00:39:30,044
Speaker 1: euphoria or you know, despondency. But when things are uncertain

730
00:39:30,124 --> 00:39:35,404
Speaker 1: and your worldview gets challenged, gets shattered, gets displaced, you

731
00:39:35,444 --> 00:39:39,644
Speaker 1: look for certainty, and that's when con artists swoop in

732
00:39:39,924 --> 00:39:41,484
Speaker 1: and give that certainty to you.

733
00:39:41,644 --> 00:39:44,124
Speaker 3: As long as nothing ever changes, we will say from

734
00:39:44,164 --> 00:39:45,484
Speaker 3: cons we're good.

735
00:39:46,044 --> 00:39:50,804
Speaker 2: You know, my great grandfather faked his death and got a.

736
00:39:50,804 --> 00:39:53,844
Speaker 1: Wait what okay, Nate, can we just can we we

737
00:39:53,884 --> 00:39:57,924
Speaker 1: need this story. Sorry, we're pausing everything, Nate, please.

738
00:39:58,124 --> 00:40:00,644
Speaker 2: I mean his name was Ferdinand Thrunn, which is a

739
00:40:00,684 --> 00:40:01,204
Speaker 2: great name.

740
00:40:01,804 --> 00:40:02,644
Speaker 1: It's an amazing name.

741
00:40:02,684 --> 00:40:04,364
Speaker 2: He was written up in the Chicago Tribune and the

742
00:40:04,404 --> 00:40:06,164
Speaker 2: New York Times and places like that. Yeah, he was

743
00:40:06,204 --> 00:40:09,764
Speaker 2: like a insurance fraudster. We should do this as separate

744
00:40:09,964 --> 00:40:14,404
Speaker 2: segments sometime, yeah, But basically his technique was to commit

745
00:40:14,444 --> 00:40:17,244
Speaker 2: crimes so devious that there is no law to charge

746
00:40:17,244 --> 00:40:19,444
Speaker 2: them with because I haven't figured out like this particular

747
00:40:19,524 --> 00:40:21,764
Speaker 2: type of fry. But we should do proper research and

748
00:40:21,804 --> 00:40:22,924
Speaker 2: do an episode on this. Maria.

749
00:40:23,524 --> 00:40:25,964
Speaker 1: Yeah, absolutely so, Maria, Nate.

750
00:40:26,044 --> 00:40:30,284
Speaker 3: I now we've discussed Sam as well, we discussed John Law.

751
00:40:30,484 --> 00:40:33,284
Speaker 3: I actually had a couple of questions, given that you

752
00:40:33,364 --> 00:40:36,524
Speaker 3: guys are absolutely experts on this sort of thing, I

753
00:40:36,564 --> 00:40:38,404
Speaker 3: had a couple of questions for you that I hope

754
00:40:38,444 --> 00:40:42,404
Speaker 3: you won't mind me asking, and that The first one is,

755
00:40:42,604 --> 00:40:47,804
Speaker 3: I have your two most recent books in front of me.

756
00:40:47,964 --> 00:40:50,884
Speaker 3: I have Nate's On the Edge, I have Maria's The

757
00:40:50,924 --> 00:40:55,324
Speaker 3: Biggest Bluff, Stone Cold Classic Amazing. I looked in the index.

758
00:40:57,044 --> 00:41:00,684
Speaker 3: The word experiments does not appear in the index of

759
00:41:00,724 --> 00:41:04,364
Speaker 3: either of your books. Expected value, of course does appear,

760
00:41:04,404 --> 00:41:08,684
Speaker 3: but experiments does not. And I just wondered whether you

761
00:41:08,724 --> 00:41:13,924
Speaker 3: two a brilliant risk takers, analysts poker players. But I

762
00:41:13,924 --> 00:41:17,684
Speaker 3: thought a poker player can't really experiment, like if you

763
00:41:17,724 --> 00:41:20,484
Speaker 3: want to, if you want to find out, you need

764
00:41:20,524 --> 00:41:22,244
Speaker 3: to you need to make the bet. You know, you

765
00:41:22,284 --> 00:41:24,404
Speaker 3: need to put down the money. There's no cheap way

766
00:41:24,844 --> 00:41:29,604
Speaker 3: to find out what the other person's cards are. And

767
00:41:29,684 --> 00:41:34,164
Speaker 3: maybe that is a blind spot in poker playing relative

768
00:41:34,244 --> 00:41:36,844
Speaker 3: to decision making advice in everyday life. Because what I've

769
00:41:37,004 --> 00:41:39,924
Speaker 3: what I've always been saying to people, is okay, if

770
00:41:39,964 --> 00:41:42,604
Speaker 3: you're facing an uncertain situation, you know, you don't know

771
00:41:42,604 --> 00:41:45,204
Speaker 3: what the right thing to do is. Maybe there's an experiment,

772
00:41:45,284 --> 00:41:47,204
Speaker 3: maybe that you can run a little pilot, Maybe you

773
00:41:47,204 --> 00:41:49,484
Speaker 3: can run a little you know, ab test. Maybe there's

774
00:41:49,524 --> 00:41:53,284
Speaker 3: a cheap way to find out without betting all your

775
00:41:53,404 --> 00:41:56,724
Speaker 3: chips metaphorically speaking. So I just wanted to ask why

776
00:41:56,884 --> 00:41:59,124
Speaker 3: why did neither of you talk about experiments? And is

777
00:41:59,564 --> 00:42:02,884
Speaker 3: this in fact a blind spot in the poker player's

778
00:42:02,964 --> 00:42:03,644
Speaker 3: view of the world.

779
00:42:04,804 --> 00:42:08,364
Speaker 2: It's a great point, Tim. I have a home game

780
00:42:08,404 --> 00:42:10,924
Speaker 2: I play maybe every third week. That's a one dollar

781
00:42:11,004 --> 00:42:13,724
Speaker 2: two dollars game, which for me is you know, on

782
00:42:13,764 --> 00:42:15,484
Speaker 2: the lower side of the stakes. I play, and I

783
00:42:15,484 --> 00:42:20,324
Speaker 2: probably am doing some experimenting in that game, right. You know,

784
00:42:20,404 --> 00:42:22,524
Speaker 2: last night I made I had a nut flush straw,

785
00:42:22,564 --> 00:42:24,044
Speaker 2: which some of our artists will know what that means.

786
00:42:24,044 --> 00:42:26,404
Speaker 2: I had the ace I flush straw on a board

787
00:42:26,404 --> 00:42:29,884
Speaker 2: that was King Queen x X and we made like

788
00:42:29,884 --> 00:42:33,684
Speaker 2: a three x overbet shove on the turn. I'm just

789
00:42:33,724 --> 00:42:36,164
Speaker 2: going to experiment, I bet. Probably there's some frequency which

790
00:42:36,164 --> 00:42:37,564
Speaker 2: you're supposed to do. This is in game theory, but

791
00:42:37,684 --> 00:42:40,324
Speaker 2: like but if I lose this pot, then you know,

792
00:42:40,364 --> 00:42:42,684
Speaker 2: I'll win sometimes to catch my flush and like it'll

793
00:42:42,684 --> 00:42:45,444
Speaker 2: be a fun hand to show down and whatever. But

794
00:42:45,484 --> 00:42:48,364
Speaker 2: I did feel I think we do probably experiment a

795
00:42:48,444 --> 00:42:50,284
Speaker 2: little bit, and that kind of like naughty feeling you

796
00:42:50,364 --> 00:42:53,124
Speaker 2: have of like having an experiment and getting away with it,

797
00:42:53,364 --> 00:42:55,164
Speaker 2: like you're kind of taking the piss to use. Is

798
00:42:55,204 --> 00:42:57,484
Speaker 2: that the British term. Yeah, you're taking the piss. You

799
00:42:57,564 --> 00:42:59,284
Speaker 2: kind of get away with it like It's a very

800
00:42:59,284 --> 00:43:02,644
Speaker 2: satisfying feeling, and I think encourages kurrent artistries sometimes.

801
00:43:02,724 --> 00:43:03,364
Speaker 3: Yeah.

802
00:43:03,484 --> 00:43:07,004
Speaker 1: Yeah, I think that there are so two things. One, yes,

803
00:43:07,084 --> 00:43:09,604
Speaker 1: I think that you can experiment this way, and to me,

804
00:43:09,924 --> 00:43:12,044
Speaker 1: I do it when I move down in stakes as well,

805
00:43:12,244 --> 00:43:17,004
Speaker 1: right when the money is not not as meaningful and

806
00:43:17,044 --> 00:43:19,684
Speaker 1: you get to test certain theories out right, So if

807
00:43:19,684 --> 00:43:24,364
Speaker 1: I'm studying and I'm kind of figuring out different possibilities

808
00:43:24,404 --> 00:43:27,244
Speaker 1: for playing similar spots, I might test those out and

809
00:43:27,284 --> 00:43:29,844
Speaker 1: feel comfortable testing them out when I don't care as

810
00:43:29,924 --> 00:43:32,804
Speaker 1: much about the stakes, when it's not kind of as important.

811
00:43:33,044 --> 00:43:37,004
Speaker 1: Now that said, we can't experiment as in the traditional

812
00:43:37,124 --> 00:43:39,764
Speaker 1: so I'm you know, I'm a trained psychologist, right, so

813
00:43:39,884 --> 00:43:42,564
Speaker 1: when I was doing studies for my PhD. You have

814
00:43:42,644 --> 00:43:45,364
Speaker 1: to have a very strict experimental design where you have

815
00:43:45,484 --> 00:43:47,644
Speaker 1: your control group, and you have your test groups, and

816
00:43:47,924 --> 00:43:51,404
Speaker 1: you know, you have all of these things where you're

817
00:43:52,084 --> 00:43:55,924
Speaker 1: trying to subtly change the conditions and see if the

818
00:43:55,964 --> 00:43:59,684
Speaker 1: outcome changes. That obviously you can't do because in some ways,

819
00:43:59,764 --> 00:44:02,204
Speaker 1: you know, my whole book was an experiment. So experiments

820
00:44:02,244 --> 00:44:04,204
Speaker 1: not in the index, but the biggest bluff, all of

821
00:44:04,204 --> 00:44:07,364
Speaker 1: it was was experiment. And I saw poker as kind

822
00:44:07,364 --> 00:44:10,404
Speaker 1: of a psychology laboratory of out a lot of the

823
00:44:10,404 --> 00:44:14,804
Speaker 1: psychological theories that I had kind of known in theory

824
00:44:14,924 --> 00:44:16,964
Speaker 1: and putting them into practice at the table and being

825
00:44:17,044 --> 00:44:18,844
Speaker 1: able to see, oh, you know, this is how this

826
00:44:18,924 --> 00:44:21,924
Speaker 1: plays out, this is how that plays out. And I

827
00:44:22,004 --> 00:44:24,044
Speaker 1: do think that poker is great for that. But of

828
00:44:24,084 --> 00:44:27,964
Speaker 1: course you can't when you're playing in a tournament, when

829
00:44:27,964 --> 00:44:31,724
Speaker 1: you're playing in a game, you can't have the exact

830
00:44:31,764 --> 00:44:36,404
Speaker 1: same conditions where you say, Okay, on this exact same board,

831
00:44:36,404 --> 00:44:38,884
Speaker 1: I'm going to do this. Let's see what happens. All right,

832
00:44:38,924 --> 00:44:41,444
Speaker 1: Pretend I didn't do that. Let's go back. Now I'm

833
00:44:41,444 --> 00:44:43,804
Speaker 1: going to do something else, and we'll see what happens there.

834
00:44:44,004 --> 00:44:47,644
Speaker 1: Because even if Nate and I were thinking, oh, okay,

835
00:44:47,724 --> 00:44:49,844
Speaker 1: let's see what it feels like to over bet three

836
00:44:49,884 --> 00:44:53,364
Speaker 1: times the pot in this particular spot, and Nate says, okay,

837
00:44:53,364 --> 00:44:55,524
Speaker 1: I'm going to do this, and then Maria, you do

838
00:44:55,884 --> 00:45:00,964
Speaker 1: why people that's not in poker, that experiment is actually

839
00:45:00,964 --> 00:45:04,604
Speaker 1: not a valid control group because Nate and I are

840
00:45:04,644 --> 00:45:07,284
Speaker 1: so different. People respond to us differently, and all of

841
00:45:07,284 --> 00:45:10,244
Speaker 1: a sudden, you can't control the environment because it's different environment.

842
00:45:10,284 --> 00:45:12,404
Speaker 1: The moment you switch out the players, right, all of

843
00:45:12,444 --> 00:45:15,684
Speaker 1: a sudden, the experimental conditions change, even if you've changed

844
00:45:15,724 --> 00:45:19,804
Speaker 1: literally nothing except who's sitting in that chair. And I

845
00:45:19,804 --> 00:45:23,644
Speaker 1: think that's actually fascinating and a Really that's how life works.

846
00:45:23,644 --> 00:45:27,324
Speaker 1: That's why sometimes psych studies from the laboratory don't generalize

847
00:45:27,324 --> 00:45:30,124
Speaker 1: well to the real world because the real world does

848
00:45:30,164 --> 00:45:33,484
Speaker 1: get messier and it's much more difficult to control. No. Look,

849
00:45:33,524 --> 00:45:35,684
Speaker 1: I think all of your variables.

850
00:45:35,364 --> 00:45:39,324
Speaker 2: As someone who's self reflectively kind of seen his social

851
00:45:39,364 --> 00:45:44,324
Speaker 2: status rise and fall different times. If you're charming and

852
00:45:44,444 --> 00:45:46,844
Speaker 2: privileged and are seen as being on a winning streak,

853
00:45:46,964 --> 00:45:49,924
Speaker 2: you can get away with a lot. Yeah, people really

854
00:45:49,964 --> 00:45:51,924
Speaker 2: are afraid to call you on your bullshit.

855
00:45:52,484 --> 00:45:56,644
Speaker 3: Yeah, okay, yes, second quick question. Second quick question, because

856
00:45:56,364 --> 00:46:01,524
Speaker 3: I have to take advantage of the opportunity to tap

857
00:46:01,564 --> 00:46:04,924
Speaker 3: into your wisdom. Okay, So you I think I think

858
00:46:04,924 --> 00:46:06,884
Speaker 3: it's fair to say that both of you would advocate

859
00:46:08,244 --> 00:46:10,444
Speaker 3: putting a probability on on something. If you're going to

860
00:46:10,444 --> 00:46:12,404
Speaker 3: make a risky decision, you have to have an idea.

861
00:46:12,644 --> 00:46:14,844
Speaker 3: Is this like a five to one? Is it a

862
00:46:14,884 --> 00:46:17,244
Speaker 3: three to one? Is there twenty percent chances is going

863
00:46:17,284 --> 00:46:19,284
Speaker 3: to happen? Is there are seventy percent chances is going

864
00:46:19,364 --> 00:46:22,764
Speaker 3: to happen? And you know, and there's a difference between

865
00:46:22,804 --> 00:46:25,084
Speaker 3: say a fifty three percent chance and a forty eight

866
00:46:25,124 --> 00:46:28,044
Speaker 3: percent chance, even though they're both close to fifty to fifty.

867
00:46:28,604 --> 00:46:31,684
Speaker 3: So it's important to quantify as much as you can,

868
00:46:31,764 --> 00:46:35,324
Speaker 3: even though you don't always have the data. Okay, So

869
00:46:36,364 --> 00:46:38,764
Speaker 3: two very little stories to get you to reflect on

870
00:46:40,404 --> 00:46:45,244
Speaker 3: the case in favor of quantification. Apparently, when the US

871
00:46:45,364 --> 00:46:49,484
Speaker 3: government was pondering the Bay of Pigs invasion, the Joint

872
00:46:49,524 --> 00:46:52,484
Speaker 3: chiefs of Staff thought that the chance of success was

873
00:46:52,524 --> 00:46:56,324
Speaker 3: thirty percent, and a report was prepared for President Kennedy

874
00:46:56,924 --> 00:46:59,844
Speaker 3: and Kennedy and thirty percent was Presumably they thought, oh,

875
00:46:59,884 --> 00:47:02,804
Speaker 3: the president wanting to understand percentages, so he was told

876
00:47:03,004 --> 00:47:06,444
Speaker 3: there is a fair chance of success, and by fair

877
00:47:06,564 --> 00:47:11,044
Speaker 3: chance of success, they meant well, thirty percent. Now we

878
00:47:11,044 --> 00:47:13,844
Speaker 3: don't know what Kennedy understood by a fair chance of success,

879
00:47:13,844 --> 00:47:15,604
Speaker 3: but he seems to have thought it was a good

880
00:47:15,724 --> 00:47:20,164
Speaker 3: chance of success, and he approved this total fiasco. So

881
00:47:21,084 --> 00:47:25,404
Speaker 3: it might seem more user friendly to express things as well,

882
00:47:25,444 --> 00:47:29,364
Speaker 3: this is common or uncommon, or likely or unlikely. But

883
00:47:29,444 --> 00:47:32,604
Speaker 3: actually none of those words really mean the same thing

884
00:47:32,684 --> 00:47:35,324
Speaker 3: to the person who's uttering the word as to the

885
00:47:35,324 --> 00:47:38,444
Speaker 3: person who's receiving it. So you should always put probabilities

886
00:47:38,484 --> 00:47:42,964
Speaker 3: on things. Here's the counter example, counterexample. Think back to

887
00:47:42,964 --> 00:47:45,604
Speaker 3: the financial crisis two thousand and seven, two thousand and eight.

888
00:47:46,524 --> 00:47:50,964
Speaker 3: You had quants putting probabilities on things. This is the

889
00:47:50,964 --> 00:47:54,004
Speaker 3: probability that such and such a thing will default based

890
00:47:54,044 --> 00:47:55,964
Speaker 3: on what we know about history, based on what we

891
00:47:56,044 --> 00:47:59,764
Speaker 3: know about other things that are correlated with it. But

892
00:47:59,804 --> 00:48:05,044
Speaker 3: actually all of those probabilities were spuriously precise. People had

893
00:48:05,084 --> 00:48:08,364
Speaker 3: too much confidence in the probabilities, and it's fine to say, oh,

894
00:48:08,364 --> 00:48:10,684
Speaker 3: we think there's like antin five percent chance that this

895
00:48:10,724 --> 00:48:13,364
Speaker 3: will default. That's fine. But then the problem is you

896
00:48:13,444 --> 00:48:15,484
Speaker 3: feed the zero point five percent into a model, which

897
00:48:15,524 --> 00:48:18,564
Speaker 3: gets fed into another model, which gets fed into another model,

898
00:48:18,844 --> 00:48:20,444
Speaker 3: and in the end you'd be like, oh, well, we've

899
00:48:20,524 --> 00:48:22,844
Speaker 3: repackaged this thing, and now there's like only a one

900
00:48:22,844 --> 00:48:25,124
Speaker 3: in a trillion chance that this will default. And it

901
00:48:25,164 --> 00:48:27,844
Speaker 3: turns out that that's all dependent on the quality of

902
00:48:27,844 --> 00:48:31,844
Speaker 3: your original assumption and you shouldn't be betting the existence

903
00:48:31,844 --> 00:48:35,924
Speaker 3: of Western civilization on that calculation. And people got it wrong.

904
00:48:36,324 --> 00:48:39,764
Speaker 3: So the case against quantification is once you have a number,

905
00:48:40,244 --> 00:48:42,844
Speaker 3: then you are tempted to rely on that number too

906
00:48:42,924 --> 00:48:46,164
Speaker 3: much and to analyze or to manipulate or to remodel

907
00:48:46,764 --> 00:48:50,204
Speaker 3: or to reanalyze that number too much, and to forget

908
00:48:50,324 --> 00:48:55,084
Speaker 3: that actually the number was always basically just an educated guess.

909
00:48:56,524 --> 00:49:00,004
Speaker 1: Well. I think that when you're talking about quantification and

910
00:49:00,044 --> 00:49:04,924
Speaker 1: when you're talking about probabilities, it's the exact same logic

911
00:49:05,004 --> 00:49:08,084
Speaker 1: that you have to apply to algorithms and to kind

912
00:49:08,124 --> 00:49:10,164
Speaker 1: of building algorithms. Which is something that Nate and I

913
00:49:10,244 --> 00:49:12,844
Speaker 1: have talked about on the pod with AI, which is

914
00:49:13,124 --> 00:49:17,604
Speaker 1: garbage in garbage out right. If your assumptions are garbage,

915
00:49:17,644 --> 00:49:21,124
Speaker 1: then your probabilistic assessment is going to be garbage. So

916
00:49:21,324 --> 00:49:24,564
Speaker 1: I actually think that both of these things, they're not

917
00:49:24,684 --> 00:49:28,764
Speaker 1: counter examples. The thirty percent chance of the Bay of pigs.

918
00:49:29,164 --> 00:49:32,964
Speaker 1: I just think that these were people who had a

919
00:49:33,044 --> 00:49:36,124
Speaker 1: lot of qualifications, had done the research, had done regulous,

920
00:49:36,284 --> 00:49:39,884
Speaker 1: rigorous analysis, and that was not garbage in right there.

921
00:49:40,084 --> 00:49:42,364
Speaker 1: They had assumptions that were there for a good reason.

922
00:49:42,604 --> 00:49:45,724
Speaker 1: They had good historical data. I have no idea how

923
00:49:45,764 --> 00:49:48,644
Speaker 1: they came upon the thirty percent. I'm just see making

924
00:49:48,644 --> 00:49:54,564
Speaker 1: your SUPs. Yeah, hindsight bias exactly, and so you get

925
00:49:54,564 --> 00:49:57,524
Speaker 1: thirty percent. And by the way, you should absolutely have

926
00:49:57,604 --> 00:50:01,004
Speaker 1: told Kennedy thirty percent and not fair chance. There are

927
00:50:01,164 --> 00:50:04,404
Speaker 1: so many psych studies about this that trying to put

928
00:50:04,484 --> 00:50:09,684
Speaker 1: words with percentages backfires because people do not understand. And

929
00:50:10,164 --> 00:50:12,404
Speaker 1: it's like if you have a weather man and says

930
00:50:12,444 --> 00:50:15,084
Speaker 1: a fair chance of rain, right, let's talk about not

931
00:50:15,164 --> 00:50:17,084
Speaker 1: a fair chance that the Bay of pigs is a

932
00:50:17,084 --> 00:50:21,084
Speaker 1: success fair chance of rain? Do you bring an umbrella?

933
00:50:21,244 --> 00:50:23,484
Speaker 1: You want to know what the percentage chances. You want

934
00:50:23,524 --> 00:50:26,924
Speaker 1: to know that actual number, because otherwise a quantification gets

935
00:50:27,124 --> 00:50:30,324
Speaker 1: all out of whack. Financial crisis when you have those

936
00:50:30,404 --> 00:50:34,724
Speaker 1: numbers in the models. Those were people whose incentives were

937
00:50:34,724 --> 00:50:39,924
Speaker 1: not aligned with giving you a correct probabilistic assessment. Their

938
00:50:39,964 --> 00:50:43,044
Speaker 1: incentives were to make money. That's also a question that

939
00:50:43,084 --> 00:50:45,444
Speaker 1: you have to make. You always have to ask, and

940
00:50:45,484 --> 00:50:49,204
Speaker 1: this is something that Nate and I talk about as well.

941
00:50:50,004 --> 00:50:53,604
Speaker 1: When you're making these assumptions, do the incentives align, Where

942
00:50:53,684 --> 00:50:57,884
Speaker 1: are the assumptions coming from, and do you have an

943
00:50:57,884 --> 00:51:02,284
Speaker 1: incentive to be correct? Right? And in this particular case,

944
00:51:02,324 --> 00:51:05,844
Speaker 1: their incentive was to make money for them today and

945
00:51:05,884 --> 00:51:07,964
Speaker 1: to make their company think that this was going to

946
00:51:07,964 --> 00:51:10,804
Speaker 1: be a great bet that it was going to work out.

947
00:51:11,484 --> 00:51:15,324
Speaker 1: And so those percentages are not something that you want

948
00:51:15,364 --> 00:51:18,004
Speaker 1: to rely on. Now, if my incentive is if my

949
00:51:18,044 --> 00:51:21,404
Speaker 1: percentage is wrong, I'm getting fired. Right. If my percentage

950
00:51:21,564 --> 00:51:25,684
Speaker 1: is wrong, then I'm making zero dollars. Then all of

951
00:51:25,724 --> 00:51:29,204
Speaker 1: a sudden, I come up with different percentages, I use

952
00:51:29,244 --> 00:51:32,684
Speaker 1: different inputs. My model looks very, very different. But Wall

953
00:51:32,724 --> 00:51:35,404
Speaker 1: Street didn't work that way, still doesn't work that way.

954
00:51:35,604 --> 00:51:37,364
Speaker 1: That's not how you're incentivized.

955
00:51:37,524 --> 00:51:42,284
Speaker 2: Yeah, look, I think there's a risk of laundering subjective

956
00:51:42,324 --> 00:51:45,364
Speaker 2: opinions through probabilities and forgetting their subjective right. If I'm

957
00:51:45,404 --> 00:51:48,724
Speaker 2: running nak the airport, there's traffic on the Vanwick or whatever. Right,

958
00:51:49,084 --> 00:51:50,684
Speaker 2: I might say myself, Oh, it's a five percent chance

959
00:51:50,804 --> 00:51:52,524
Speaker 2: going to miss my flight. Right, It's not coming out

960
00:51:52,524 --> 00:51:56,844
Speaker 2: of any regression analysis or anything. You know. I've probably

961
00:51:56,884 --> 00:52:00,524
Speaker 2: used the phrase in the show, like quoting Vice President Harris.

962
00:52:01,164 --> 00:52:02,844
Speaker 2: A model does not fall out of a coconut tree.

963
00:52:02,844 --> 00:52:05,284
Speaker 2: It exists in the context of that which became before it,

964
00:52:05,324 --> 00:52:08,364
Speaker 2: or whatever else you're trying to get at the truth

965
00:52:08,884 --> 00:52:11,764
Speaker 2: with the model. And I think mediocre modelers in particular

966
00:52:12,804 --> 00:52:16,724
Speaker 2: will publish a number and then forget how many assumptions

967
00:52:16,924 --> 00:52:20,084
Speaker 2: are driven into that. Right. I still think it's worth

968
00:52:20,524 --> 00:52:22,524
Speaker 2: quantifying things. I mean, at the end of the day,

969
00:52:23,004 --> 00:52:27,324
Speaker 2: probability is defined as a number between point zero and one,

970
00:52:27,764 --> 00:52:31,124
Speaker 2: and you have to make decisions even in conditions of uncertainty.

971
00:52:32,004 --> 00:52:33,604
Speaker 2: But to Tim's point, yeah, I think there are cases

972
00:52:33,644 --> 00:52:38,724
Speaker 2: where people don't forget how provisional a guess is when

973
00:52:38,764 --> 00:52:39,604
Speaker 2: you put a number on it.

974
00:52:40,644 --> 00:52:43,524
Speaker 1: Yeah, And I think it is always important to not

975
00:52:43,604 --> 00:52:46,324
Speaker 1: have a false sense of certainty. And there's also a

976
00:52:46,364 --> 00:52:50,684
Speaker 1: lot of data that shows that when you have numbers,

977
00:52:50,724 --> 00:52:52,324
Speaker 1: and when you have a lot of these things, it

978
00:52:52,324 --> 00:52:55,084
Speaker 1: does give you a false sense of certainty, right that

979
00:52:55,124 --> 00:52:57,364
Speaker 1: you often do become a little bit over confident when

980
00:52:57,364 --> 00:52:59,004
Speaker 1: you're like, well, I have this model, in this model,

981
00:52:59,084 --> 00:53:02,324
Speaker 1: so this it must be. And there you have the

982
00:53:02,324 --> 00:53:04,684
Speaker 1: bias that we've talked about a lot, where it goes

983
00:53:04,684 --> 00:53:07,764
Speaker 1: from being probabilistic to seeming much more certain like this

984
00:53:07,804 --> 00:53:12,604
Speaker 1: will not default, this will not happen because you forget

985
00:53:13,164 --> 00:53:15,484
Speaker 1: that it ain't zero.

986
00:53:16,764 --> 00:53:19,044
Speaker 3: Thank you so much, guys, fantastic Tim.

987
00:53:19,124 --> 00:53:21,044
Speaker 1: Thank you so much for coming on the pod today.

988
00:53:21,044 --> 00:53:22,804
Speaker 1: It's been such a pleasure, havn't you.

989
00:53:23,444 --> 00:53:27,124
Speaker 3: It's been really really fun. Thank you for sharing your wisdom.

990
00:53:27,164 --> 00:53:29,284
Speaker 3: And if people want to hear more about Sam Israel

991
00:53:29,444 --> 00:53:32,644
Speaker 3: or any other stories of things going disastrously wrong and

992
00:53:32,964 --> 00:53:35,604
Speaker 3: me trying to investigate the social science behind why they

993
00:53:35,644 --> 00:53:39,524
Speaker 3: went wrong. Caution ne Tales with Tim Harford is one

994
00:53:39,524 --> 00:53:42,684
Speaker 3: of Risky Business's sister podcasts on Pushkin.

995
00:53:43,404 --> 00:53:46,164
Speaker 1: It is and there are lots of tales of risk

996
00:53:46,244 --> 00:53:49,204
Speaker 1: taking assessments that do not turn out quite the way

997
00:53:49,204 --> 00:54:08,724
Speaker 1: the risk taker thought they would. Risky Business is hosted

998
00:54:08,764 --> 00:54:11,684
Speaker 1: by Me kind of Cova and me Mate Silver. The

999
00:54:11,724 --> 00:54:15,244
Speaker 1: show is a co production of Pushkin Industries and iHeartMedia.

1000
00:54:15,924 --> 00:54:19,804
Speaker 1: This episode was produced by Isabel Carter. Our associate producer

1001
00:54:19,884 --> 00:54:23,404
Speaker 1: is Gabriel Hunter Chang. Our executive producer is Jacob Eldstein.

1002
00:54:23,964 --> 00:54:26,364
Speaker 2: And if you want to listen to an AD free version,

1003
00:54:26,404 --> 00:54:29,044
Speaker 2: sign up for Pushkin Plus. For six seventy nine a month,

1004
00:54:29,244 --> 00:54:32,364
Speaker 2: you get access to ad free listening. Thanks for tuning in.