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Speaker 1: Pushkin.

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Speaker 2: Hey everybody, Nate here, I'm dropping into your feed on

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Speaker 2: a Tuesday because Marie and I were recently guests on

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Speaker 2: Cautionary Tales. Normally, on that show, economist Tim Harford tells

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Speaker 2: us stories of real world mistakes, things like the Birmingham

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Speaker 2: Lab League and the Tenrife air disaster, and what lessons

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Speaker 2: we can learn from them. When Marie and I appeared,

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Speaker 2: Tim had us answer questions from Cautionary Tales and Risky

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Speaker 2: Business listeners. They're smart, they're fun, and we're excited to

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Speaker 2: be sharing the full episode with you today. Hope you

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Speaker 2: enjoy and be sure to check out the rist of

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Speaker 2: Cautionary Tales wherever you're listening to this.

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Speaker 3: Hello, and welcome to another episode of Cautionary Questions. I

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Speaker 3: team up with clever people to answer you clever questions,

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Speaker 3: and this time I'm joined by Nate Silver and Maria Konnikova.

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Speaker 3: They are the hosts of the Risky Business podcast and

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Speaker 3: they are here to answer questions from both sets of listeners.

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Speaker 3: Nate is a political analyst. He's the author of On

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Speaker 3: the Edge, and Maria is the best selling author of

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Speaker 3: the Biggest Bluff. How I Learned to Pay attention master

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Speaker 3: myself and win both fantastic books. Both Nate and Maria

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Speaker 3: are also journalists, and they are both high stakes poker players.

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Speaker 3: I'm a journalist too and an undercover economist, and I'm

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Speaker 3: not much of a poker player. But between us, we

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Speaker 3: should have some good answers for all of the smart

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Speaker 3: questions that you've been sending in. We will be tackling

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Speaker 3: smartphones in schools, tariffs, and how to use game theory

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Speaker 3: to win at sports. Nate, Maria, welcome to Cautionary Tales.

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Speaker 2: Thank you, Tom, Thanks so much.

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Speaker 1: Tom.

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Speaker 3: Well, should it be cautionary business or risky tales, risky

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Speaker 3: risky cars, risky cassion. That's good. You've been po casting

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Speaker 3: for how long?

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Speaker 1: About a year?

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Speaker 2: About a year? Yeah, we're coming up on the World

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Speaker 2: Series of Poker begins in every May, and so yeah,

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Speaker 2: we launched it just about a year ago. When we're

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Speaker 2: coming up in our second World Series season, excellent, When

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Speaker 2: Nate and I.

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Speaker 1: Are both at the final table of the main event,

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Speaker 1: that'll be really great for our Risky Business podcast audience growth.

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Speaker 3: Would you be podcasting from the World Series if you're

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Speaker 3: on the final table?

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Speaker 1: Not at the final table? Since there are no phones allowed, but.

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Speaker 3: We'll find a way.

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Speaker 2: I mean, we do have these long days right where

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Speaker 2: we both have real jobs and then maybe you'll do

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Speaker 2: several hours of real work quote unquote, and then play

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Speaker 2: poker for twelve hours and then it's fine your two

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Speaker 2: am Mexican food hangout or something. But they are long days.

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Speaker 2: We do not get vacation when we're in Vegas.

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Speaker 3: Basically we do not. Right enough poker chat, let us

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Speaker 3: get into the virtual postbag. After we listened to the

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Speaker 3: theme music for Cautionary Tales. I am here with Nate

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Speaker 3: Silver and Maria Konikova, and here is a good question

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Speaker 3: to start with from listener Alex. Alex asks, have any

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Speaker 3: personal experiences or academic theories really shaped your decision making.

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Speaker 3: Let me put this one to Nate first, and I'd

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Speaker 3: love to hear what both of you think.

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Speaker 2: I would say, my experience with risk taking and decision

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Speaker 2: making is very hands on for the most part. Right

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Speaker 2: when I was a kid, we would play Fantasy Baseball

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Speaker 2: and have little NFL betting pools and things like that. Obviously,

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Speaker 2: poker there's good instructional material, but you know, most people

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Speaker 2: are ultimately kind of largely self taught, you know, in

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Speaker 2: my book On the Edge. Then game theory plays a

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Speaker 2: really large role, and game theory plays a very large

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Speaker 2: role in poker. There are not computer programs called solvers

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Speaker 2: that literally have solved the Nash equilibrium for poker. If

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Speaker 2: you've seen a beautiful mind. A lot of things in

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Speaker 2: life is like what's the game theory equilibrium of, Like

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Speaker 2: what traffic patterns look like in New York, or how

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Speaker 2: often you should eat one type of cuisine versus another,

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Speaker 2: or any type of competitive situation where there are lots

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Speaker 2: of optionality. I'd say game theory is kind of the

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Speaker 2: most important variable there.

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Speaker 3: And game theory, we should say, is really the use

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Speaker 3: of mathematical equations to model strategic into actions. So a

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Speaker 3: very simple example of game theory is the game of

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Speaker 3: rock paper scissors. Now, if I play rock, you're going

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Speaker 3: to play paper and beat me. If I play paper,

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Speaker 3: you're going to play scissors and beat me. And so

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Speaker 3: game theory says, oh, you have to randomize, you have

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Speaker 3: to have a one third chance of playing each strategy.

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Speaker 3: I mean, that's kind of obvious. Game theory can be

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Speaker 3: used to model much more complex situations, including famously, because

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Speaker 3: it inspired one of the creators of game theory poker.

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Speaker 1: Yeah, I would second Nate on game theory. It was

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Speaker 1: one of the pinnacles of my academic work. And I'd

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Speaker 1: also say the psychological theories of Danny Conneman have been

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Speaker 1: hugely influential in how I think about decision making. He

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Speaker 1: was an absolutely brilliant man who really unearthed a lot

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Speaker 1: of the ways that our brains misprocess. I don't think

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Speaker 1: that's a word, but I'm going to make it one

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Speaker 1: misprocess information and the biases and heuristics that we're up against.

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Speaker 1: And it's incredibly powerful because even if you know that

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Speaker 1: those biases and heuristics exist in theory, in practice, you

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Speaker 1: still exhibit them. Right, You have the game theory kind

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Speaker 1: of that mathematical side, and then the psychological side of

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Speaker 1: Cooman and Poker, in my mind, is the life experience

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Speaker 1: that has cemented these more than anything else else in

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Speaker 1: my brain and has made me internalize how this actually

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Speaker 1: works in reality and how you can try to decide

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Speaker 1: optimally and then take that from the poker table to

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Speaker 1: real life, because poker's an environment where you can really

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Speaker 1: sample thousands and thousands and thousands of decisions, sample correctly.

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Speaker 1: And as you know, Tim, that's incredibly important when it

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Speaker 1: comes to learning and trying to get a sense of

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Speaker 1: for probabilities and in real life, not the poker table,

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Speaker 1: we never sample correctly right. So that's where a lot

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Speaker 1: of these biases come from. So I think poker is

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Speaker 1: a very powerful teaching tool to help cement these theories

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Speaker 1: into practice.

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Speaker 3: Okay, I've got a great question from Drew loyal listeners

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Speaker 3: to caution me tales. Now, I'm a big fan of

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Speaker 3: board games, and Drew wants to know Maria and Nate

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Speaker 3: do you play board games. If you play board games,

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Speaker 3: do you bring a poker lens to the table or

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Speaker 3: is it a different vibe?

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Speaker 1: So I hate board cames. I do not play board games.

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Speaker 1: My sisters a little over six years older than I am,

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Speaker 1: and she always loved board games. They bored me. Haha.

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Speaker 1: They're just not interesting. And you know when someone brings out,

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Speaker 1: you know, sellers of Katan or anything like that, I'm like,

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Speaker 1: oh God, no, no, no, no, please to get to

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Speaker 1: ride as a favorite in my family and I just

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Speaker 1: want to die, you know, for me, if there's game night,

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Speaker 1: please let it be poker. Otherwise, please don't make me

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Speaker 1: play and let me read my book on the side

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Speaker 1: and cheer you on while you play.

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Speaker 2: Yeah. I mean, look, some of the games get quite advanced,

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Speaker 2: and so it's like, where do you go from a

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Speaker 2: game that takes ten hours to learn something that's kind

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Speaker 2: of a broken, overly simple game, Right, But like, yeah,

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Speaker 2: I like games because I'm very competitive.

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Speaker 1: Right.

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Speaker 2: If I'm playing, it's like, oh, people, we're like, don't

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Speaker 2: have that background in games or poker. Then I'm just

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Speaker 2: gonna be honest. I tend to I tend to figure

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Speaker 2: out the strategy pretty quick. And when you.

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Speaker 1: Crush them, you crush their souls and their dreams, and

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Speaker 1: they wish they'd never asked to play. Day to your

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Speaker 1: point about the complexity, there's a New Yorker cartoon that

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Speaker 1: I absolutely love and it's a group of people sitting

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Speaker 1: around a table and one of them is reading a

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Speaker 1: game instruction manual and the rest of them are all skeletons.

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Speaker 1: He says, and so those are the rules? Shall we

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Speaker 1: start playing?

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Speaker 3: I love it. I had the privilege of interviewing Klaus Toyber,

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Speaker 3: the creator of Settlers of Katan, and he said something

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Speaker 3: that really stuck with me, which I suspect will resonate

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Speaker 3: with you poker players. He said, you can know somebody

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Speaker 3: their whole lives and play a game with them and

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Speaker 3: you will see something that you've never seen that's wonderful.

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Speaker 1: And I actually that's how I feel about poker. Your

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Speaker 1: character comes out. And by the way, I do have

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Speaker 1: a very strong interest in games for my next book

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Speaker 1: when it comes to cheating, so I'm interested in, like,

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Speaker 1: what kind of a person you know cheats a monopoly

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Speaker 1: at home.

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Speaker 3: Game night, people who want to win.

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Speaker 1: It's just totally like mind boggling to me that you

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Speaker 1: would do that. But but there you have it.

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Speaker 2: Yeah, my partner is also very competitive, but his main

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Speaker 2: objective is making sure that I don't win, right, He

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Speaker 2: will go out, You'll go out of his way to

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Speaker 2: lose himself if it's a murder suicide mission basically, which

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Speaker 2: changes the strategy quite a bit.

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Speaker 3: I feel. See that's amazing.

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Speaker 2: So here's one for you, Tim. This question is from

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Speaker 2: Carrie k E r ri E. I know Irish or something, Carrie,

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Speaker 2: but anyway, Hi Tim and team. I worry that a

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Speaker 2: big problem with the productivity gap in the UK is

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Speaker 2: a lack of high quality data and data driven decision making.

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Speaker 2: I see a lot of investment in data that is

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Speaker 2: overseen by technical experts in a given domain, but very

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Speaker 2: little consultation of data scientists. Company and organizational boards also

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Speaker 2: seem to lack the sort of expertise which is often

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Speaker 2: rolled into a catch all it function. I work in

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Speaker 2: the public sector, and so I have framed my question accordingly.

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Speaker 2: Should we have a government department devoted to or is

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Speaker 2: it all a bit nineteen eighty four? Best wishes, Kirie.

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Speaker 3: Well, I love the question, and we do kind of

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Speaker 3: have a government department devoted to data. It's called the

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Speaker 3: Office for National Statistics, but it's focused on very old

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Speaker 3: school statistics rather than modern data science, and old school

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Speaker 3: statistics tends to focus on smaller data sets, very well behaved,

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Speaker 3: well formatted data sets. For example, you might go out

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Speaker 3: and do a survey rather than just say, hey, well,

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Speaker 3: we're going to just draw administrative data, or we're going

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Speaker 3: to look at data that's coming out of people's smartphones,

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Speaker 3: which you know, there's a lot more information in there,

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Speaker 3: but it's a lot messier, to be honest. What really

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Speaker 3: concerns me at the moment is that data is systematically

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Speaker 3: underrated by governments. I think they don't realize how powerful

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Speaker 3: it can be, that they don't realize how dangerous it

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Speaker 3: can be, and it tends to be given a low

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Speaker 3: political priority or misused. So at the moment in the US,

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Speaker 3: for example, we've got this sort of sudden disappearance of

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Speaker 3: all these government websites. It's unclear how much data has

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Speaker 3: permanently disappeared, or is it just that it's temporarily gone down.

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Speaker 3: Is somebody archiving it? Will the archives work or not?

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Speaker 3: Archives don't always work. Are we going to have interrupted

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Speaker 3: data sets? It's very unclear what's gone and what hasn't.

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Speaker 3: So that's a particularly extreme example, but generally, I'm working

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Speaker 3: on a history of data. At the moment. It's so powerful,

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Speaker 3: it's so important. It is the foundation of our health systems.

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Speaker 3: It's the foundation of many of our economic activities. It's

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Speaker 3: the foundation of science. It's also the foundation of some

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Speaker 3: terrible abuses of human rights in the past. And yet

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Speaker 3: it's kind of boring and it's kind of geeky, and

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Speaker 3: so it just gets overlooked, and that's what really worries me.

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Speaker 2: Yeah, I mean, it's interesting that you have, you know,

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Speaker 2: one partisan faction in the US that seems to think

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Speaker 2: that less data is better instead of having better data, right.

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Speaker 2: And some types of data you can go back and recollect,

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Speaker 2: but something as you can't.

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Speaker 3: Right.

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Speaker 2: The UK, for example, has a data set of economic

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Speaker 2: statistics like GDP that goes back like almost a thousand

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Speaker 2: years or something, right, because they were collecting records for

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Speaker 2: tax purposes and estate purposes and everything in real time.

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Speaker 3: Yeah. Well, we call the doom Doomsday Book eighty six.

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Speaker 3: I'm old enough to remember the nine hundredth anniversary of

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Speaker 3: the Doomsday Book. There's an interesting little caution retail there.

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Speaker 3: So for the nine hundredth anniversary of the Doomsday Book,

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Speaker 3: which was in nineteen eighty six, the BBC organized this

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Speaker 3: kind of a proto Wikipedia. They said, we're going to

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Speaker 3: encourage this project where school kids across the country are

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Speaker 3: going to act as citizen journalists. They're going to go around,

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Speaker 3: they'll interview people, they'll gather data, they'll write the little essays.

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Speaker 3: We've got maps, we'll have photographs, and we'll put it

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Speaker 3: all on a laser disc. Remember laser disc. You can

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Speaker 3: see the problem, right. And we've got these highly sophisticated,

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Speaker 3: expensive computer systems they're going to be accessible in every school.

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Speaker 3: And it was called Doomsday nineteen eighty six. I think

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Speaker 3: it was super cool. I remember it really well. And

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Speaker 3: then by about nineteen ninety eight, it was just impossible

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Speaker 3: to even read the laser discs. So this whole project

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Speaker 3: just went offline. Meanwhile, by the way, the original Doomsday

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Speaker 3: books from ten eighty six still available.

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Speaker 1: Paper has a certain appeal and has lasted four centuries

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Speaker 1: for a reason. You know, the flip side tim of

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Speaker 1: what you were saying about what's happening in the US

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Speaker 1: and kind to address the nineteen eighty four element of

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Speaker 1: the question is so, you know, yes, a lot of

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Speaker 1: data is disappearing, and then data that was never meant

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Speaker 1: to be aggregated and linked and personalized is being shared,

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Speaker 1: and profiles of people of US citizens and non citizens

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Speaker 1: are being created that should have never been created.

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Speaker 3: Right.

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Speaker 1: There were rules in place to keep those data sets

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Speaker 1: separate for a reason, for privacy protections, and now the

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Speaker 1: government is trying to roll back those rules and collect

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Speaker 1: and de anonymize the data that was shared by people

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Speaker 1: with the assumption that that would never happen. That's really bad, right,

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Speaker 1: So there's this kind of dystopian thing going on.

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Speaker 3: And there's history here. I mean, this has happened before.

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Speaker 3: This happened in the nineteen forties.

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Speaker 1: It's not good. Nineteen forties was not a good time

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Speaker 1: in history if people remember.

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Speaker 3: No, I think we can all agree on that one.

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Speaker 3: Thank you very much, Nate and Maria. We will be

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Speaker 3: answering more of your questions after this shortbreak. We are back.

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Speaker 3: I'm here with Nate Silver and Maria Konikova of Risky Business,

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Speaker 3: and we have an interesting question from a Risky Business

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Speaker 3: listener who asks, one thing that happened during the tariffs

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Speaker 3: which didn't get much attention, is the ten year treasury

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Speaker 3: rate spiked from four to four and a half percent.

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Speaker 3: I should say, by the way I work for the

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Speaker 3: Financial Times, we were paying a lot of attention to that,

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Speaker 3: but understand that outside geek land it was ignored. That

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Speaker 3: basically means, the listener continues, there is less belief that

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Speaker 3: US debt is a safe bet. It probably means somebody

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Speaker 3: cough China, cough, issued a cell order for a large

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Speaker 3: amount of US treasury with the intent to move the

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Speaker 3: interest rate as retaliation for tariffs. To cut to the

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Speaker 3: chase the listener asks, I would be extremely interested in

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Speaker 3: hearing what are your thoughts on the likelihood of the

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Speaker 3: United States defaulting? Nate?

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Speaker 1: This one's yere.

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Speaker 2: I used to know more about credit default swaps and

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Speaker 2: things like this. I mean, look, in the US runs

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Speaker 2: up very large deficits. Neither party has any interest in

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Speaker 2: doing what it would take to cut those deficits potentially. Right,

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Speaker 2: we got lucky in kind of the financial crisis in

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Speaker 2: COVID era of lower interest rates where you can borrow

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Speaker 2: very cheaply. It's no longer true now, In fact, borrow

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Speaker 2: he's getting more expensive. And so, you know, I trust

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Speaker 2: the market's fairly efficient here. But I guess the US

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Speaker 2: is still a bit too big to fail.

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Speaker 3: Well, normally, when we say too big to fail, we're

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Speaker 3: referring to things that the US is going to bail

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Speaker 3: out if necessary. Right, Yeah, the US is too big

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Speaker 3: to save. That's your problem.

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Speaker 2: You know. The world has profoundly been affected by Trump's

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Speaker 2: second term in a way it wasn't in Trump's first term.

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Speaker 2: Our neighbors to the north, Canada, had an election that

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Speaker 2: was entirely upended. The Liberals wound to back in power,

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Speaker 2: even though Trudeau had been very unpopular.

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Speaker 3: Right.

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Speaker 2: I would argue that the election of the new pope

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Speaker 2: was in part a response to Trump and the changing

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Speaker 2: geopolitical world, right, and say, Okay, we want to have

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Speaker 2: some influence on the United States. Have figure who's not Trump?

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Speaker 2: It can balance them out somehow, And so yeah, we're

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Speaker 2: way a different world. And I suppose I trust markets

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Speaker 2: and investors are intelligent about that. And I'm not sure

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Speaker 2: it's it's China so much as the fact that we're

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Speaker 2: in a new regime.

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Speaker 3: Yeah, I said in the agree, I think China needed

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Speaker 3: to do anything to make the US treasury rate spike.

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Speaker 3: When the rate spikes, by the way, it means people

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Speaker 3: are selling US government debt. When they sell it, the

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Speaker 3: price goes down, the interest rates goes up. But yeah,

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Speaker 3: plenty of people willing to do that without any coordinated

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Speaker 3: action from China. I mean, there is absolutely zero need

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Speaker 3: for the US government to default, because the US government

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Speaker 3: can always print more dollars. I mean, it could be

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Speaker 3: the US government debt gets less attractive, it could be

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Speaker 3: that inflation takes off, it could be there all kinds

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Speaker 3: of problems, but there's never a need to default. I

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Speaker 3: think the decision to default would be just that it

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Speaker 3: would be a decision the US government would have to

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Speaker 3: actively decide we want to stiff our creditors. So you

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Speaker 3: just have to look at the president and say, because

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Speaker 3: this looked like a man who stiffs his creditors, I

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Speaker 3: don't think. I don't think. So how long does sounds

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Speaker 3: like Donald J. Trump to me?

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Speaker 2: This is President Trump, which I candect him and the ft.

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Speaker 3: We have a new acronym. It's Taco. Taco stands for

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Speaker 3: Trump always chickens out. So the bet is that he

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Speaker 3: will not in fact default, even though he's talking about

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Speaker 3: various things that already looked like default, because the consequences

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Speaker 3: would be so catastrophic that he would look at the

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Speaker 3: consequences and he would back away. But that doesn't mean

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Speaker 3: that he couldn't do some damage. So fingers crossed, all right?

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Speaker 1: So listener Jack wants us to explore the thorny issue

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Speaker 1: of cell phones in school and writes Australia has been

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Speaker 1: running an experiment with New South Wales banning mobile phone

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Speaker 1: use in all schools from October twenty twenty three. This

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Speaker 1: is following the implementation of a ban in Victoria in

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Speaker 1: twenty twenty which itself cites research from England and other justifications.

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Speaker 1: So this listener has kids in school. This is not

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Speaker 1: where I thought the question was going to go. Actively

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Speaker 1: helps the fourteen year old subvert this ban, so they

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Speaker 1: have an old phone and a locked pouch and their

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Speaker 1: actual phone in their back. This is because they must

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Speaker 1: use their phone to hotspot the internet to their laptop.

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Speaker 1: Jack writes, My opinion is the whole thing as stupid,

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Speaker 1: as most kids have some kind of workaround and are

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Speaker 1: on their laptops all day. Anyway, if parents are on

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Speaker 1: their phones all the time, why are we expecting kids

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Speaker 1: not to be. I still see teachers in playgrounds face

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Speaker 1: and phone, and all the injustices and hypocrisy of my

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Speaker 1: own school days come flooding back. I mean, I know

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Speaker 1: I'm asking this question. I have so many thoughts, but

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Speaker 1: I'll open it up to you guests first.

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Speaker 3: I also have thoughts. I think the hypocrisy thought is real.

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Speaker 3: I have three children myself. My oldest child is twenty one,

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Speaker 3: the youngest is thirteen. They see me being distracted by

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Speaker 3: my phone all the time, and of course they're going

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Speaker 3: to be influenced by that. I think that this is

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Speaker 3: an evil device useful but evil that we all of

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Speaker 3: us need to fight together because it's capable of ruining

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Speaker 3: all of our lives. I feel like I should set

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Speaker 3: a good example, and I don't feel that the fact

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Speaker 3: that the grown ups are being distracted by the phones

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Speaker 3: is particularly a good reason to say the kids should

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Speaker 3: have the phones too. I mean, we would never say, hey,

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Speaker 3: I see the grown ups drinking vodka and smoking, so

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Speaker 3: why surely it's fine for the nine year olds to

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Speaker 3: also drink vodka and smoke. I mean, I don't think

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Speaker 3: that follows.

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Speaker 2: It's hypocritical term.

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Speaker 1: It's hypocritical that you do not like your child.

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Speaker 3: Yeah, well, I think it suggests that maybe the grown

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Speaker 3: up shouldn't bring so much vodka and shouldn't smoke so much,

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Speaker 3: rather than the other way around. But anyway, yeah, we

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Speaker 3: are setting an example, whether we like it or not.

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Speaker 1: I completely agree with that. And to add to it,

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Speaker 1: the use of screens in general, it's very different depending

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Speaker 1: on your age. There's emerging research, but now we have

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Speaker 1: a few decades worth of what phones do to developing

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Speaker 1: brains and kind of the habits that you create from

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Speaker 1: a young age. So being on the phone as an

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Speaker 1: adult who grew up without that kind of access is

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Speaker 1: very different from growing up and actually constantly being connected

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Speaker 1: to a scream. By the way, there's also research that

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Speaker 1: shows that taking notes by hand is much more powerful

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Speaker 1: than taking notes on a laptop. Right. You retain more information,

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Speaker 1: you learn better because there's a brain link between the

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Speaker 1: hand and the brain that does not exist when you're typing.

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Speaker 1: It would be better. I totally agree with you, Tim,

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Speaker 1: Adults should be on their phones much less than I

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Speaker 1: write about this. I think it's actually really great practice

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Speaker 1: to not have your phone. I've sometimes left my phone

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Speaker 1: at home on purpose to kind of have a day

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Speaker 1: without cell phones. I have programs that turn the Internet

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Speaker 1: off on my computer because I lack willpower if I don't,

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00:21:48,764 --> 00:21:51,604
Speaker 1: forcibly kind of have it turned off to try to

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Speaker 1: have some deep concentration, deep writing time. This is all

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Speaker 1: kind of related to our executive function, our self controllability,

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00:21:58,204 --> 00:22:00,844
Speaker 1: our ability to learn, our memory. All of these things

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Speaker 1: are interconnected. And don't you want your child to actually

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Speaker 1: emerge as a kind of smart adult who's capable of

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Speaker 1: all of these things as opposed to someone who is

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Speaker 1: constantly distracted.

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Speaker 3: Nate strikes me as the three screens that wants going

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Speaker 3: nate anything.

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Speaker 2: You know, I just have my laptops. If people think

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Speaker 2: I have some big, elaborate, minor setup, I just have

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Speaker 2: my little laptop. Actually, yeah, Look, is it paternalistic to

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Speaker 2: tell kids they can't use their phones? I mean, yeah,

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Speaker 2: but they're kids, right, so you're allowed, You're allowed to

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Speaker 2: be paternalistic toward them. And I don't know. I mean,

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Speaker 2: I guess the one caveat I have is I was

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Speaker 2: chronically pretty bored in school, right, and so I'm sympathetic

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Speaker 2: to the kids that are like, Okay, I'm bored. I'm

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Speaker 2: learning things I already know. But like, it is a

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Speaker 2: situation where I think you have to have some type

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Speaker 2: of consistent enforcement in the rules. But when you're in school,

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Speaker 2: blame your teachers not making you interested, but don't blame

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Speaker 2: them for not letting you be on Instagram while you're

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Speaker 2: supposed to be learning algebra.

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Speaker 3: I think it might also be a collective action problem. Yes,

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Speaker 3: I think it's perfectly possible that you've got teenagers who

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Speaker 3: would be quite happy if nobody in this pool had

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Speaker 3: Instagram or nobody had Snapchat. But if other people are

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Speaker 3: using Snapchat to communicate to arrange to meet up. Well,

431
00:23:18,044 --> 00:23:21,404
Speaker 3: then I need Snapchat too, and just talking to teenagers,

432
00:23:21,764 --> 00:23:25,204
Speaker 3: I think that viewpoint is not unusual. Some of them

433
00:23:25,204 --> 00:23:28,124
Speaker 3: are like, please, can you just ban everybody from using

434
00:23:28,124 --> 00:23:30,404
Speaker 3: it and then I'll be happy? Yeah, Okay, let's move

435
00:23:30,444 --> 00:23:33,404
Speaker 3: on because I I've got a question specifically from Maria

436
00:23:33,844 --> 00:23:38,124
Speaker 3: from a long, long time listener from Quebec. They would

437
00:23:38,244 --> 00:23:41,644
Speaker 3: like to hear Maria talk about a particular point of

438
00:23:41,684 --> 00:23:45,084
Speaker 3: her book, The Biggest Bluff, where she describes the breakfast

439
00:23:45,204 --> 00:23:47,804
Speaker 3: she had with her mentor. You were trying to find

440
00:23:47,844 --> 00:23:49,404
Speaker 3: somebody who would teach her to play poker so that

441
00:23:49,444 --> 00:23:51,244
Speaker 3: you could write a book about learning to play poker.

442
00:23:52,084 --> 00:23:55,004
Speaker 3: He only became interested when he realized you don't know

443
00:23:55,164 --> 00:23:57,444
Speaker 3: anything at all about the subject. You don't even know

444
00:23:57,484 --> 00:24:00,324
Speaker 3: how a deck of cards works. And our listener says,

445
00:24:00,444 --> 00:24:02,884
Speaker 3: I'm sixty five years old and I went through a

446
00:24:02,924 --> 00:24:05,564
Speaker 3: couple of career changes and I find this story very

447
00:24:05,604 --> 00:24:08,764
Speaker 3: moving and so true about what it means to learn

448
00:24:08,804 --> 00:24:11,924
Speaker 3: something and to teach something. So what are your thoughts

449
00:24:11,924 --> 00:24:12,124
Speaker 3: on that?

450
00:24:12,684 --> 00:24:13,484
Speaker 1: Yeah?

451
00:24:13,524 --> 00:24:14,204
Speaker 3: I think so.

452
00:24:14,844 --> 00:24:17,604
Speaker 1: This conversation took place the first time I ever met

453
00:24:18,004 --> 00:24:21,644
Speaker 1: Eric Sidell, who is a legend of the poker world,

454
00:24:22,004 --> 00:24:24,284
Speaker 1: and he had never taken on a poker student before.

455
00:24:24,404 --> 00:24:26,484
Speaker 1: And what stood out about me was that I was

456
00:24:26,524 --> 00:24:29,484
Speaker 1: a complete blank slate. I didn't have any bad habits.

457
00:24:29,604 --> 00:24:31,964
Speaker 1: I didn't have any habits, right, I didn't know anything.

458
00:24:32,324 --> 00:24:34,924
Speaker 1: And so for him, I think it was a very

459
00:24:34,964 --> 00:24:37,884
Speaker 1: interesting experiment, just like for me, it was an interesting

460
00:24:37,924 --> 00:24:40,884
Speaker 1: experiment from scratch. You know, what can he do with

461
00:24:40,964 --> 00:24:45,124
Speaker 1: me given my background in psychology in a short period

462
00:24:45,204 --> 00:24:47,764
Speaker 1: of time these days, when you know I've got all

463
00:24:47,764 --> 00:24:51,204
Speaker 1: these math wizards and selvers and all of these things,

464
00:24:51,844 --> 00:24:55,604
Speaker 1: is kind of psychology hard work. And if my background

465
00:24:55,684 --> 00:24:59,204
Speaker 1: isn't enough, right, could I do well? And so if

466
00:24:59,244 --> 00:25:01,404
Speaker 1: I could, that would be an amazing, hittive proof of

467
00:25:01,444 --> 00:25:04,164
Speaker 1: concept for the way that he thinks about the game.

468
00:25:04,484 --> 00:25:06,684
Speaker 1: And so I think to him, that's what made this

469
00:25:06,804 --> 00:25:08,684
Speaker 1: interesting and that's what made it appealing.

470
00:25:09,404 --> 00:25:13,484
Speaker 3: And why is Maria so good at poker? Is it

471
00:25:13,524 --> 00:25:17,364
Speaker 3: the coach? Is it her training as a psychologist? Is

472
00:25:17,404 --> 00:25:18,204
Speaker 3: it something else?

473
00:25:18,804 --> 00:25:21,844
Speaker 2: I think it's her and not the coaching, although Maria

474
00:25:22,604 --> 00:25:25,244
Speaker 2: is a wonderfully well connected person who has access to

475
00:25:26,004 --> 00:25:27,804
Speaker 2: some of the best poker minds in the world. But yeah, look,

476
00:25:27,884 --> 00:25:29,844
Speaker 2: it's the balance of the people reading skills and the

477
00:25:29,884 --> 00:25:34,524
Speaker 2: mathematical skills and the discipline. Really, you know, it's rare

478
00:25:34,644 --> 00:25:38,164
Speaker 2: to see Maria tilt. I'm sure she does it right,

479
00:25:38,164 --> 00:25:39,844
Speaker 2: but I think she is playing more than I am.

480
00:25:39,884 --> 00:25:43,084
Speaker 3: Frankly, Tilting is basically where you get emotional and it

481
00:25:43,084 --> 00:25:44,244
Speaker 3: affects your decisions.

482
00:25:44,364 --> 00:25:46,604
Speaker 2: Tilt doesn't have to be anger, right. It can be like, oh,

483
00:25:46,684 --> 00:25:49,004
Speaker 2: I've had a great day, so I'm gonna not fight

484
00:25:49,044 --> 00:25:50,924
Speaker 2: for this pot that you're supposed to bluff for a thing.

485
00:25:51,004 --> 00:25:52,924
Speaker 2: It can be a lot of different formats of it.

486
00:25:52,964 --> 00:25:55,084
Speaker 2: But like you know, we know one always plays their

487
00:25:55,084 --> 00:25:57,204
Speaker 2: A game, right. But if you're always playing your B

488
00:25:57,284 --> 00:26:00,244
Speaker 2: plus game or better, and you're really smart in lots

489
00:26:00,244 --> 00:26:02,604
Speaker 2: of different ways and read people well and maybe defy

490
00:26:02,684 --> 00:26:05,604
Speaker 2: stereotypes a little bit too, maybe are more aggressive than

491
00:26:05,604 --> 00:26:08,564
Speaker 2: people are expecting. So yeah, I would say she's a

492
00:26:08,564 --> 00:26:11,324
Speaker 2: well rounded poker player. By the way, It's part of

493
00:26:11,364 --> 00:26:14,684
Speaker 2: like learning the game later is that you might wind

494
00:26:14,764 --> 00:26:17,524
Speaker 2: up being a little bit more well rounded. When I

495
00:26:17,604 --> 00:26:20,404
Speaker 2: used to play for a living back god twenty years ago,

496
00:26:20,404 --> 00:26:22,364
Speaker 2: now I'm getting old. I played a game called limit

497
00:26:22,404 --> 00:26:25,324
Speaker 2: Hold Them, which the same amount as bet on every

498
00:26:25,524 --> 00:26:27,804
Speaker 2: hand or as a no limit you can be anywhere

499
00:26:27,804 --> 00:26:31,324
Speaker 2: from one chip to the entire size of your stack,

500
00:26:31,404 --> 00:26:33,124
Speaker 2: and it's a much different game. And so I had

501
00:26:33,164 --> 00:26:36,004
Speaker 2: to spend a lot of time unlearning habits from limit

502
00:26:36,004 --> 00:26:38,564
Speaker 2: hold them, which can be bad habits in no limit holding.

503
00:26:39,364 --> 00:26:41,764
Speaker 3: We've got more questions coming up, but first let's take

504
00:26:41,764 --> 00:26:56,124
Speaker 3: a quick break. Welcome back to caution me questions with me,

505
00:26:56,324 --> 00:26:57,324
Speaker 3: Tim Harford.

506
00:26:57,124 --> 00:26:59,644
Speaker 1: And me Maria Kannakova and me, Mate Silver.

507
00:27:00,684 --> 00:27:05,204
Speaker 2: So here's a question from Mark. Mark asks, it's no

508
00:27:05,324 --> 00:27:08,964
Speaker 2: secret that political donations are tied to political favors, legislation

509
00:27:09,004 --> 00:27:12,444
Speaker 2: benefiting the highest owners, and so forth. If the politicians

510
00:27:12,444 --> 00:27:14,564
Speaker 2: are the machine, and the machine runs on money, what

511
00:27:14,644 --> 00:27:17,044
Speaker 2: if we tweaked the source of the money. What would

512
00:27:17,044 --> 00:27:20,404
Speaker 2: happen if, by some stroke of magic, Congress received vastly

513
00:27:20,564 --> 00:27:24,284
Speaker 2: larger salaries, increasing with years of service. But with one

514
00:27:24,324 --> 00:27:28,804
Speaker 2: big caveat, politicians cannot accept any donations, gifts, speaking, fees,

515
00:27:28,924 --> 00:27:32,244
Speaker 2: or favors in any form other than direct money donations

516
00:27:32,284 --> 00:27:35,844
Speaker 2: from constituents up to the federal limits without forfeiting their

517
00:27:35,844 --> 00:27:40,204
Speaker 2: public service salary. So put, simply, what if public service

518
00:27:40,284 --> 00:27:44,044
Speaker 2: was a bigger, more valuable incentive. Could it even outcompete

519
00:27:44,044 --> 00:27:47,564
Speaker 2: the current incentives? Would governing change for the better or

520
00:27:47,604 --> 00:27:50,324
Speaker 2: for the worse? And why I like it?

521
00:27:50,764 --> 00:27:53,844
Speaker 1: You know, it's a good question, but it's slightly naive

522
00:27:53,884 --> 00:27:57,924
Speaker 1: in the sense that we already have rules against politicians

523
00:27:58,044 --> 00:28:01,724
Speaker 1: accepting all sorts of things that Mark wants them not

524
00:28:01,804 --> 00:28:04,244
Speaker 1: to accept. And there are ways around them, right There

525
00:28:04,284 --> 00:28:09,124
Speaker 1: are packs which are political action committees which you can donate,

526
00:28:09,724 --> 00:28:14,124
Speaker 1: There's ways that you can lobby and influence people. People

527
00:28:14,164 --> 00:28:17,924
Speaker 1: would get around these incentives very easily, just like they

528
00:28:17,924 --> 00:28:18,484
Speaker 1: do today.

529
00:28:18,604 --> 00:28:20,924
Speaker 3: This maybe is a naive observation, but I would have

530
00:28:21,044 --> 00:28:25,364
Speaker 3: thought there was a difference between donations to a campaign

531
00:28:25,724 --> 00:28:28,724
Speaker 3: for the purposes of spending money on adverts to get

532
00:28:28,804 --> 00:28:32,764
Speaker 3: re elected, versus just donations to a politician's bank account.

533
00:28:32,884 --> 00:28:34,324
Speaker 3: There should be a bright line between the two of them.

534
00:28:34,364 --> 00:28:35,724
Speaker 3: I mean, ate, you're the experts on this.

535
00:28:36,004 --> 00:28:38,124
Speaker 2: The answers. You are supposed to report to the Federal

536
00:28:38,124 --> 00:28:41,564
Speaker 2: Election Commission every dollar that you spend, so you definitely

537
00:28:41,604 --> 00:28:45,884
Speaker 2: run a risk of expenses being scrutinized if they're not legitimate.

538
00:28:45,964 --> 00:28:48,124
Speaker 2: But of course there are a million things a tranch

539
00:28:48,164 --> 00:28:52,684
Speaker 2: of expenses that are ambiguous between personal and business expenses. Right,

540
00:28:52,924 --> 00:28:55,964
Speaker 2: you can also shuffle money to consultants and pay them

541
00:28:56,004 --> 00:28:58,524
Speaker 2: maybe more than their worth, in exchange for who knows

542
00:28:58,964 --> 00:29:01,484
Speaker 2: favors later on and so forth. I think it's probably

543
00:29:01,484 --> 00:29:04,204
Speaker 2: a good idea to pay members of Congress more. I

544
00:29:04,244 --> 00:29:06,484
Speaker 2: think it might help recruit some people who are not

545
00:29:06,644 --> 00:29:09,604
Speaker 2: already wealthy to enter Congress. I think in general, well

546
00:29:09,964 --> 00:29:13,644
Speaker 2: tap performing government employees should be paid more too, and

547
00:29:13,684 --> 00:29:15,524
Speaker 2: make it is you're made to fire the poor performers.

548
00:29:15,764 --> 00:29:17,524
Speaker 2: I don't I don't understand when we want to go

549
00:29:17,524 --> 00:29:19,484
Speaker 2: into politics. It sounds like miserable to me to be

550
00:29:19,484 --> 00:29:23,364
Speaker 2: a member of Higuras. My lizard brain can't crack those incentives.

551
00:29:23,444 --> 00:29:30,284
Speaker 3: Necessarily, it is worth directing some attention to politicians' latitude

552
00:29:30,724 --> 00:29:33,284
Speaker 3: to do things that people might want to bribe them

553
00:29:33,444 --> 00:29:36,924
Speaker 3: to do. So, the more personal discretion a politician has,

554
00:29:37,404 --> 00:29:40,524
Speaker 3: the more it's worth bribing them. So one of the

555
00:29:40,884 --> 00:29:46,204
Speaker 3: interesting things about tariffs is that if you put a

556
00:29:46,444 --> 00:29:50,804
Speaker 3: large tariff on importers, it's then worth a great deal

557
00:29:50,844 --> 00:29:54,284
Speaker 3: of money to particular importers to be exempt from that tariff.

558
00:29:54,404 --> 00:29:56,164
Speaker 3: I mean, it keeps changing, but there was this huge

559
00:29:56,204 --> 00:30:00,804
Speaker 3: tariff on electronic goods coming from China, and then it's like, oh, actually,

560
00:30:00,884 --> 00:30:03,244
Speaker 3: you know, we're not going to levy that on iPhones.

561
00:30:03,524 --> 00:30:06,804
Speaker 3: So Apple benefits from that. And I'm not saying, oh,

562
00:30:06,844 --> 00:30:10,324
Speaker 3: Apple bribed the president. I don't particular reasons to believe

563
00:30:10,324 --> 00:30:13,084
Speaker 3: they did. But the point is that when you've got

564
00:30:13,084 --> 00:30:16,804
Speaker 3: that sort of personal discression, it becomes hugely valuable, and

565
00:30:16,804 --> 00:30:18,924
Speaker 3: those interest groups are willing to pay a lot of money,

566
00:30:18,964 --> 00:30:21,084
Speaker 3: and so there's the temptation for bribery.

567
00:30:21,764 --> 00:30:24,644
Speaker 1: One of the big differences between first world and third

568
00:30:24,644 --> 00:30:29,044
Speaker 1: world countries is how prevalent bribery is kind of in

569
00:30:29,524 --> 00:30:32,524
Speaker 1: the way that governments function, and yet a lot of

570
00:30:32,564 --> 00:30:35,964
Speaker 1: so called first world countries and second World countries there's

571
00:30:36,044 --> 00:30:39,284
Speaker 1: a lot of bribing and corruption and cheating that goes

572
00:30:39,324 --> 00:30:42,804
Speaker 1: on no matter what. So sure, it would be amazing

573
00:30:42,884 --> 00:30:46,364
Speaker 1: if somehow we could create the an incentive structure where

574
00:30:46,444 --> 00:30:51,764
Speaker 1: that doesn't happen. But I don't see from a psychological standpoint,

575
00:30:52,164 --> 00:30:55,164
Speaker 1: how the same type of personality that wants to become

576
00:30:55,204 --> 00:30:58,844
Speaker 1: a politician and who's successful as a politician, who kind

577
00:30:58,844 --> 00:31:00,844
Speaker 1: of wheels and deals as a part of what they

578
00:31:00,844 --> 00:31:04,484
Speaker 1: did to get there, how they would ever be totally

579
00:31:04,524 --> 00:31:08,764
Speaker 1: above everything and rise to the heights of government needed

580
00:31:08,844 --> 00:31:11,524
Speaker 1: to excert real influence. It's a very cynical tick, but

581
00:31:11,564 --> 00:31:12,364
Speaker 1: that's how I see it.

582
00:31:13,204 --> 00:31:16,604
Speaker 3: Maria, you mentioned developing countries. This was perceived as being

583
00:31:16,644 --> 00:31:21,644
Speaker 3: a particular problem in Africa, both for historical reasons and

584
00:31:21,724 --> 00:31:24,204
Speaker 3: because a lot of Sub Saharan African countries are very,

585
00:31:24,324 --> 00:31:28,804
Speaker 3: very poor. And so this amazing African entrepreneur called mo Ibrahim,

586
00:31:29,204 --> 00:31:31,644
Speaker 3: who made a huge amount of money in telecommunications in

587
00:31:31,644 --> 00:31:35,844
Speaker 3: the nineteen nineties, became a billionaire. He announced the mo

588
00:31:36,004 --> 00:31:38,844
Speaker 3: Ibrahim Prize he was going to give. It was millions

589
00:31:39,084 --> 00:31:44,404
Speaker 3: to any African leader who surrendered power after losing a

590
00:31:44,404 --> 00:31:47,804
Speaker 3: democratic election. This is about twenty years ago. I was

591
00:31:47,964 --> 00:31:50,484
Speaker 3: googling around. I think the prize has been paid at

592
00:31:50,524 --> 00:31:53,364
Speaker 3: least once, but I haven't been able to find any

593
00:31:53,404 --> 00:31:55,404
Speaker 3: survey of whether it's made a difference. But I just

594
00:31:55,404 --> 00:31:57,524
Speaker 3: think it's a clever idea. Whether or not it worked

595
00:31:57,524 --> 00:31:58,964
Speaker 3: in practice, it's a clever idea.

596
00:31:59,684 --> 00:32:04,484
Speaker 2: Didn't Sam Bankman Freed want to pay your bribe Donald Trump,

597
00:32:04,524 --> 00:32:06,804
Speaker 2: like a billion dollars not to run for reelection or

598
00:32:06,844 --> 00:32:07,324
Speaker 2: something like.

599
00:32:07,284 --> 00:32:09,844
Speaker 3: That, just because Sam Bankmanfreed wanted to do it. It

600
00:32:09,884 --> 00:32:11,324
Speaker 3: doesn't mean it's a bad idea of it.

601
00:32:11,604 --> 00:32:13,524
Speaker 2: Might it might color my priors.

602
00:32:13,124 --> 00:32:13,604
Speaker 3: A little bit.

603
00:32:13,644 --> 00:32:18,764
Speaker 1: I'd say fair fair Sometimes you know a broken clock

604
00:32:18,844 --> 00:32:23,884
Speaker 1: is right twice a day. Let's take another question now

605
00:32:24,044 --> 00:32:28,084
Speaker 1: from Jordan, and this one was directed to you, Tim.

606
00:32:28,724 --> 00:32:32,444
Speaker 1: So first, Jordan loves your podcast and has listened to

607
00:32:32,764 --> 00:32:35,844
Speaker 1: every episode you've put out. Good to hear, so keep

608
00:32:35,924 --> 00:32:37,604
Speaker 1: up the great work and thank you. Jordan is a

609
00:32:37,644 --> 00:32:40,564
Speaker 1: dedicated phantom. It's a lot of episodes it I like it.

610
00:32:40,644 --> 00:32:42,684
Speaker 1: I love your podcast too, but I've missed some. I

611
00:32:42,724 --> 00:32:43,444
Speaker 1: have to admit.

612
00:32:43,964 --> 00:32:46,844
Speaker 3: They're all available on cat shop. So what is Jordan's question?

613
00:32:47,484 --> 00:32:52,124
Speaker 1: Yes, it's about penalty kicks, so if you don't move,

614
00:32:52,164 --> 00:32:54,524
Speaker 1: you're more likely to save them. I wonder about that,

615
00:32:54,604 --> 00:32:57,804
Speaker 1: since if a goalie never moves, then wouldn't the kicker

616
00:32:57,924 --> 00:33:01,404
Speaker 1: behave differently and make it easier to save shots that

617
00:33:01,524 --> 00:33:04,124
Speaker 1: don't go down the middle. I was just thinking about

618
00:33:04,124 --> 00:33:05,804
Speaker 1: it and was wondering how that was controlled for.

619
00:33:06,364 --> 00:33:08,964
Speaker 3: Actually, you two are the perfect people to answer this

620
00:33:09,044 --> 00:33:12,844
Speaker 3: question because it's about game theory, optimality and trying to

621
00:33:12,884 --> 00:33:17,324
Speaker 3: exploit suboptimal behavior in a soccer penalty. Basically, You've just

622
00:33:17,364 --> 00:33:20,684
Speaker 3: got a striker trying to score a goal. You've got

623
00:33:20,684 --> 00:33:23,844
Speaker 3: the goalkeeper standing there, and they can kind of guess.

624
00:33:23,884 --> 00:33:25,644
Speaker 3: They can dive to the left, or they can dive

625
00:33:25,684 --> 00:33:28,084
Speaker 3: to the right, and that's really a fifty to fifty

626
00:33:28,084 --> 00:33:30,444
Speaker 3: they'll guess right, or they could just kind of not

627
00:33:30,564 --> 00:33:32,884
Speaker 3: dive at all and stand in the middle, which makes

628
00:33:32,884 --> 00:33:36,004
Speaker 3: them look passive and like they're not even trying. However,

629
00:33:36,724 --> 00:33:39,404
Speaker 3: it turns out that because goalkeepers don't stand in the

630
00:33:39,404 --> 00:33:42,844
Speaker 3: middle because it makes them look passive, some strikers have

631
00:33:42,884 --> 00:33:45,004
Speaker 3: figured out, oh, if I just boot the ball straight

632
00:33:45,004 --> 00:33:47,684
Speaker 3: at the goalkeeper, they are guaranteed to dive to the

633
00:33:47,764 --> 00:33:49,924
Speaker 3: left or right, and I'm going to kick where they

634
00:33:49,964 --> 00:33:53,764
Speaker 3: were anyways. So Jordan's question is, well, hang on, if

635
00:33:53,804 --> 00:33:56,484
Speaker 3: the strikers figured that out, aren't the goalkeepers also going

636
00:33:56,524 --> 00:33:58,164
Speaker 3: to figure it out? So it's really a question of

637
00:33:58,164 --> 00:34:01,724
Speaker 3: people adapting to each other's strategy and the optimal mix

638
00:34:01,764 --> 00:34:05,404
Speaker 3: of strategies, and who better than you two to explain

639
00:34:05,404 --> 00:34:06,124
Speaker 3: the details here.

640
00:34:06,324 --> 00:34:09,244
Speaker 1: I mean, that is such a fascinating element of game

641
00:34:09,724 --> 00:34:12,524
Speaker 1: that Nate and I talk about quite often, which is

642
00:34:12,564 --> 00:34:15,964
Speaker 1: kind of this recursive element of it. Right, If I

643
00:34:16,004 --> 00:34:18,004
Speaker 1: know that you know that I know that you know.

644
00:34:18,844 --> 00:34:22,124
Speaker 1: And in poker this happens all the time, where you're

645
00:34:22,124 --> 00:34:24,884
Speaker 1: trying to figure out, you know, how do I adjust

646
00:34:24,924 --> 00:34:28,964
Speaker 1: to my opponent. If we're both playing GTO game theory

647
00:34:29,124 --> 00:34:33,364
Speaker 1: optimal poker, then technically, you know, we're we're both executing

648
00:34:33,364 --> 00:34:37,924
Speaker 1: a strategy perfectly. But humans aren't computers and that doesn't happen, right.

649
00:34:38,004 --> 00:34:40,644
Speaker 1: So if I figure out, for instance, that Nate is

650
00:34:40,764 --> 00:34:45,084
Speaker 1: calling too often, then I should start, you know, changing

651
00:34:45,164 --> 00:34:48,364
Speaker 1: my strategy away from GTO. So even if in this

652
00:34:48,404 --> 00:34:53,284
Speaker 1: particular spot, game theory says that I should check because

653
00:34:53,324 --> 00:34:56,204
Speaker 1: my hand is a little too weak to bet for value,

654
00:34:56,244 --> 00:34:58,564
Speaker 1: I bet again because I know that Nate is gonna

655
00:34:58,564 --> 00:35:02,084
Speaker 1: call me again with a worse hand because he overcalls, right,

656
00:35:02,324 --> 00:35:04,844
Speaker 1: So I'm kind of adapting to his behavior.

657
00:35:05,004 --> 00:35:05,124
Speaker 3: Now.

658
00:35:05,204 --> 00:35:07,004
Speaker 1: Nate is a smart guy who also knows a lot

659
00:35:07,044 --> 00:35:10,284
Speaker 1: about game theory. So what if Nate and figures out

660
00:35:10,484 --> 00:35:12,644
Speaker 1: that I'm doing this right, Because we get to show

661
00:35:12,684 --> 00:35:15,044
Speaker 1: down and he's like, Maria, how did you bet three

662
00:35:15,044 --> 00:35:18,204
Speaker 1: times with you know, bottom pair? This is crazy? You

663
00:35:18,284 --> 00:35:22,004
Speaker 1: were right, you won. But Nate figures out that, oh,

664
00:35:22,204 --> 00:35:26,004
Speaker 1: I'm overcalling, So now she's actually playing differently, So now

665
00:35:26,084 --> 00:35:29,004
Speaker 1: I'm going to change my strategy to adjust to that.

666
00:35:29,324 --> 00:35:31,564
Speaker 1: So next time I actually try to do that, Nate

667
00:35:31,684 --> 00:35:35,524
Speaker 1: ends up having just the nuts right, the best hand ever.

668
00:35:35,764 --> 00:35:37,764
Speaker 1: And when I have a good hand, you know, he

669
00:35:37,844 --> 00:35:41,364
Speaker 1: folds right. So he's now adjusted, and I say, oh wait, wait, wait, okay,

670
00:35:41,404 --> 00:35:44,484
Speaker 1: he's changed, so now I need to change. And with

671
00:35:44,564 --> 00:35:49,124
Speaker 1: two good players, this process can can go on indefinitely,

672
00:35:49,524 --> 00:35:52,764
Speaker 1: and sometimes you psych yourself out right and you start

673
00:35:52,844 --> 00:35:55,404
Speaker 1: getting into mind games, and sometimes you end up overthinking

674
00:35:55,404 --> 00:35:58,804
Speaker 1: and just making the wrong decision. Also, you're making assumptions

675
00:35:58,844 --> 00:36:01,084
Speaker 1: about the other person, right, and how the other person

676
00:36:01,124 --> 00:36:03,044
Speaker 1: is seeing you and how they're adjusting. Now I'm talking

677
00:36:03,084 --> 00:36:05,364
Speaker 1: about poker, because I know poker and I don't know soccer.

678
00:36:05,644 --> 00:36:08,524
Speaker 1: But this is exactly kind of the problem that you

679
00:36:08,644 --> 00:36:10,964
Speaker 1: have in this saga question.

680
00:36:11,364 --> 00:36:14,844
Speaker 3: In theoretical terms, as an academic would analyze it, you

681
00:36:14,884 --> 00:36:18,484
Speaker 3: get to an equilibrium, and the equilibrium is a mixed

682
00:36:18,484 --> 00:36:23,124
Speaker 3: strategy where it's unpredictable. So for example, the striker might

683
00:36:23,804 --> 00:36:26,044
Speaker 3: go left forty five percent of the time, go right

684
00:36:26,124 --> 00:36:28,044
Speaker 3: forty five percent of the time, stick it down the

685
00:36:28,084 --> 00:36:31,644
Speaker 3: middle ten percent of the time. And the goalkeeper might

686
00:36:31,884 --> 00:36:33,484
Speaker 3: stay in the middle twenty percent of the time and

687
00:36:33,524 --> 00:36:36,164
Speaker 3: go left forty percent and go right forty percent. But

688
00:36:36,204 --> 00:36:39,244
Speaker 3: the point is they can't be predictable. If they were predictable,

689
00:36:39,244 --> 00:36:42,724
Speaker 3: that could be exploitable, and they can't just be unpredictable

690
00:36:42,764 --> 00:36:45,044
Speaker 3: in a foolish way. So it's like, oh, I could

691
00:36:45,084 --> 00:36:48,124
Speaker 3: dive to the right fifty percent and to the left

692
00:36:48,124 --> 00:36:50,084
Speaker 3: fifty percent and never stand up in the middle. Well,

693
00:36:50,084 --> 00:36:52,964
Speaker 3: that's unpredictable, but it's also not smart because actually I

694
00:36:52,964 --> 00:36:56,244
Speaker 3: need to keep the striker honest and sometimes stand up. Absolutely,

695
00:36:56,404 --> 00:37:00,124
Speaker 3: there's always an equilibrium, but that equilibrium will involve a

696
00:37:00,204 --> 00:37:02,084
Speaker 3: randomized mixture of strategy, right.

697
00:37:02,164 --> 00:37:05,284
Speaker 1: But in practice, so this is called randomization. Right, The

698
00:37:05,564 --> 00:37:09,204
Speaker 1: human brain is very bad at randomizing randomly. Most humans

699
00:37:09,244 --> 00:37:11,564
Speaker 1: will end up having a pattern and will end up

700
00:37:11,604 --> 00:37:15,244
Speaker 1: actually doing one thing more than they're supposed to doing

701
00:37:15,284 --> 00:37:17,484
Speaker 1: another thing less than they're supposed to, because they're not

702
00:37:17,964 --> 00:37:19,884
Speaker 1: optimal game theoreticians.

703
00:37:20,164 --> 00:37:23,004
Speaker 3: There was actually a recent example where the English keeper

704
00:37:23,084 --> 00:37:28,004
Speaker 3: Jordan Pickford, in a high stakes penalty shootout, had a

705
00:37:28,164 --> 00:37:31,804
Speaker 3: water bottle with a list of every player on the

706
00:37:31,804 --> 00:37:34,724
Speaker 3: opposing team and whether he should dive to the left

707
00:37:34,804 --> 00:37:36,884
Speaker 3: or to the right, or stand up for that player,

708
00:37:37,084 --> 00:37:41,524
Speaker 3: So they had previously analyzed the play. His coaches had

709
00:37:41,564 --> 00:37:44,564
Speaker 3: basically pre randomized. It's a little risky, So as long

710
00:37:44,604 --> 00:37:47,844
Speaker 3: as nobody finds your water bottle ahead of time, you

711
00:37:47,884 --> 00:37:50,524
Speaker 3: can pre randomize. And I think he saved some goals

712
00:37:50,724 --> 00:37:53,204
Speaker 3: and England one that particular penalty shootout, which is very

713
00:37:53,244 --> 00:37:54,244
Speaker 3: un English. That's great.

714
00:37:54,324 --> 00:37:56,804
Speaker 2: If you don't think that athletes are thinking about the

715
00:37:56,844 --> 00:37:59,484
Speaker 2: stuff professional athletes, then you're wrong.

716
00:37:59,604 --> 00:37:59,764
Speaker 3: Right.

717
00:37:59,804 --> 00:38:02,484
Speaker 2: They may have their coaching staffs encouraging them to do it,

718
00:38:02,484 --> 00:38:04,564
Speaker 2: they may talk about themselves. They may even do some

719
00:38:04,604 --> 00:38:09,084
Speaker 2: of the stuff like fairly intuitively. Right, In an NBA game,

720
00:38:09,444 --> 00:38:11,124
Speaker 2: you have five players and twenty four seconds on the

721
00:38:11,124 --> 00:38:12,724
Speaker 2: shot clock, so it's kind of a game of like

722
00:38:13,364 --> 00:38:16,884
Speaker 2: trying to maximize your expected value with the best shot

723
00:38:16,964 --> 00:38:19,524
Speaker 2: you can, but the defense is trying to minimize your

724
00:38:19,524 --> 00:38:22,844
Speaker 2: expected value. And like the decisions these players make are

725
00:38:22,964 --> 00:38:25,884
Speaker 2: usually pretty smart about, like it's not quite worth taking

726
00:38:25,884 --> 00:38:27,564
Speaker 2: this shot, but now time's running out and now I

727
00:38:27,564 --> 00:38:28,724
Speaker 2: need to you know, I think a lot of the

728
00:38:28,804 --> 00:38:31,684
Speaker 2: actually quite high IQ. Frankly, there's a lot of money

729
00:38:31,844 --> 00:38:35,524
Speaker 2: and competitive pride on the line in making the right decisions.

730
00:38:35,964 --> 00:38:39,964
Speaker 1: Yeah, so, Nate. Actually, Caleb has been thinking about underhanded

731
00:38:39,964 --> 00:38:43,044
Speaker 1: free throws in the NBA. Here's what he says. From

732
00:38:43,084 --> 00:38:45,684
Speaker 1: what I understand, some of not most players could hit

733
00:38:45,724 --> 00:38:50,444
Speaker 1: a higher free throw percentage if they practiced an underhand technique. Honestly,

734
00:38:50,524 --> 00:38:53,524
Speaker 1: I respect basketball pros less after learning that they don't

735
00:38:53,604 --> 00:38:56,724
Speaker 1: use underhanded free throws because they think it looks goofy.

736
00:38:56,844 --> 00:39:00,524
Speaker 1: It's not a winning mentality. So, Nate, should NBA players

737
00:39:00,564 --> 00:39:02,164
Speaker 1: switch to underhanded free throws?

738
00:39:02,644 --> 00:39:07,604
Speaker 2: Yeah? Look, there probably are some equilibriums where strategies that

739
00:39:07,644 --> 00:39:12,844
Speaker 2: are considered emasculated, right or embarrassing, somehow it might be

740
00:39:12,924 --> 00:39:17,164
Speaker 2: pursued a bit less right, although maybe that changes too. Right,

741
00:39:17,204 --> 00:39:19,644
Speaker 2: You've now seen a thing in the NBA where there's

742
00:39:19,644 --> 00:39:21,524
Speaker 2: a lot more flopping than there once was, meaning that

743
00:39:21,564 --> 00:39:25,124
Speaker 2: when guys get foul, they'll exaggerate the magnitude of what

744
00:39:25,244 --> 00:39:28,044
Speaker 2: actually happened to them. Right, It used to be something

745
00:39:28,084 --> 00:39:31,684
Speaker 2: people associated with soccer. It's migrated into the NBA as well.

746
00:39:31,724 --> 00:39:34,324
Speaker 2: But yeah, sometimes to take strategies that would be seen

747
00:39:34,364 --> 00:39:37,964
Speaker 2: as embarrassing, there's expected value there if you don't care

748
00:39:38,004 --> 00:39:39,004
Speaker 2: about the embarrassment.

749
00:39:40,364 --> 00:39:43,364
Speaker 3: I'm curious, guys, is there an example in Poka of

750
00:39:43,404 --> 00:39:46,484
Speaker 3: a strategy that is positive expected value but everyone thinks

751
00:39:46,524 --> 00:39:48,644
Speaker 3: he's just embarrassing it. Only an idiot would do it.

752
00:39:48,724 --> 00:39:52,324
Speaker 1: Yeah, there's one that has turned out to be much

753
00:39:52,364 --> 00:39:55,444
Speaker 1: better than people thought, and it's called dunk betting, which

754
00:39:55,484 --> 00:39:58,764
Speaker 1: is leading into the preflop raiser. And so the reason

755
00:39:58,804 --> 00:40:01,164
Speaker 1: it's called dunk betting is because people thought you were

756
00:40:01,164 --> 00:40:04,164
Speaker 1: a dunk donkey if you did it right, because the

757
00:40:04,404 --> 00:40:08,444
Speaker 1: established wisdom is you always check to the preflop raiser.

758
00:40:09,444 --> 00:40:11,724
Speaker 1: Once we got more advanced game theory, it turns out

759
00:40:11,724 --> 00:40:14,204
Speaker 1: that no, actually there are some spots where you should lead.

760
00:40:14,124 --> 00:40:16,004
Speaker 3: What is leading into a preflup raiser.

761
00:40:15,804 --> 00:40:18,564
Speaker 1: Betting first, right, instead of checking your options so that

762
00:40:18,724 --> 00:40:22,684
Speaker 1: letting the other person act, you act first, you bet first.

763
00:40:23,084 --> 00:40:25,884
Speaker 1: To this day, though, even though it's now accepted, if

764
00:40:25,924 --> 00:40:30,364
Speaker 1: someone does it right away, it still feels donkeyish even

765
00:40:30,444 --> 00:40:33,764
Speaker 1: when it's not right. It's very hard to kind of

766
00:40:33,764 --> 00:40:36,164
Speaker 1: wrap your mind around the fact that this might actually

767
00:40:36,204 --> 00:40:37,724
Speaker 1: be the correct play.

768
00:40:38,524 --> 00:40:40,684
Speaker 2: One other play that can be hard to make is

769
00:40:41,324 --> 00:40:44,484
Speaker 2: folding when you've already put a lot of money in

770
00:40:44,524 --> 00:40:46,044
Speaker 2: the pot and there's just a little bit more to

771
00:40:46,084 --> 00:40:49,284
Speaker 2: put in and see the showdown. But still sometimes you

772
00:40:49,404 --> 00:40:51,884
Speaker 2: maybe do have a fold potentially. You know, I played

773
00:40:52,684 --> 00:40:55,324
Speaker 2: one unusual hand at a tournament a couple of months

774
00:40:55,324 --> 00:40:59,284
Speaker 2: ago where I raise, a second player called, and a

775
00:40:59,324 --> 00:41:03,484
Speaker 2: third player reraise, and if I was gonna call, I

776
00:41:03,524 --> 00:41:07,164
Speaker 2: was committing about half my stack, right, which means ordinarily

777
00:41:07,164 --> 00:41:09,284
Speaker 2: what you're supposed to do is either fold or go ahead,

778
00:41:09,524 --> 00:41:11,764
Speaker 2: go all in. However, I had a hand that I

779
00:41:11,804 --> 00:41:14,804
Speaker 2: wanted to encourage the third player to enter the part,

780
00:41:14,924 --> 00:41:16,804
Speaker 2: so I just called, and I'm like, well, I'll have

781
00:41:16,844 --> 00:41:18,204
Speaker 2: a lot of good flops and if I don't have

782
00:41:18,204 --> 00:41:20,244
Speaker 2: a good flop then I can get away. It's a

783
00:41:20,284 --> 00:41:23,404
Speaker 2: bad flop and anyways, so yeah, like, so I miss

784
00:41:23,764 --> 00:41:26,044
Speaker 2: the flop, which is the first three cards in poker.

785
00:41:26,644 --> 00:41:29,964
Speaker 2: He bets and I falled, getting three and a half

786
00:41:30,164 --> 00:41:31,724
Speaker 2: two one not. She's like, what are you doing, man?

787
00:41:31,724 --> 00:41:33,604
Speaker 2: You're not allowed to fall there, right, which indicated I

788
00:41:33,644 --> 00:41:35,244
Speaker 2: think that he had a good hand and was hoping

789
00:41:35,244 --> 00:41:37,484
Speaker 2: that I continued right, But like, you know, I ran

790
00:41:37,564 --> 00:41:39,724
Speaker 2: the math later and I'm like, actually, this is the

791
00:41:39,804 --> 00:41:41,644
Speaker 2: right play by a couple of percentage points. It was

792
00:41:41,684 --> 00:41:44,004
Speaker 2: just an embarrassing play to make in the moment.

793
00:41:44,964 --> 00:41:48,244
Speaker 3: So I have learned no donk betting for me. And

794
00:41:48,324 --> 00:41:51,764
Speaker 3: I've also learned from Nate that sometimes you have to quit,

795
00:41:51,964 --> 00:41:54,244
Speaker 3: whether or not you are ahead. We have to quit.

796
00:41:54,364 --> 00:41:58,844
Speaker 3: We're out of time, Nate, Maria. This has been such fun.

797
00:41:58,924 --> 00:42:00,844
Speaker 1: Thank you so much, Tim, Thank you for having us.

798
00:42:00,884 --> 00:42:03,964
Speaker 3: Tim, I always love listening to Whiskey Business, and thank

799
00:42:04,004 --> 00:42:07,684
Speaker 3: you so much for joining us on Cautionary Questions. Thank you, Tim.

800
00:42:07,724 --> 00:42:10,364
Speaker 3: We'll do it again sometime. Thank you, Thank you. I

801
00:42:10,484 --> 00:42:19,324
Speaker 3: will be back next week with another cautionary tale. Cautionary

802
00:42:19,364 --> 00:42:22,284
Speaker 3: Tales is written by me Tim Harford, with Andrew Wright,

803
00:42:22,484 --> 00:42:26,444
Speaker 3: Alice Fines, and Ryan Dilly. It's produced by Georgia Mills

804
00:42:26,604 --> 00:42:30,364
Speaker 3: and Marilyn Rust. The sound design and original music are

805
00:42:30,404 --> 00:42:33,964
Speaker 3: the work of Pascal Wise. Additional sound design is by

806
00:42:34,004 --> 00:42:38,284
Speaker 3: Carlos San Juan at Brain Audio. Bend A Dafh Haffrey

807
00:42:38,484 --> 00:42:42,044
Speaker 3: edited the scripts. The show features the voice talents of

808
00:42:42,124 --> 00:42:47,244
Speaker 3: Melanie Guttridge, Stella Harford, Oliver Hembrough, Sarah Jubb, Mas Sam Monroe,

809
00:42:47,644 --> 00:42:51,604
Speaker 3: Jamal Westman, and Rufus Wright. The show also wouldn't have

810
00:42:51,644 --> 00:42:55,284
Speaker 3: been possible without the work of Jacob Weisberg, Gretta Cohene,

811
00:42:55,324 --> 00:43:00,164
Speaker 3: Sarah Nix, Eric Sandler, Carrie Brody, Christina Sullivan, Kira Posey

812
00:43:00,364 --> 00:43:05,564
Speaker 3: and Owen Miller. Cautionary Tales is a production of Pushkin Industries.

813
00:43:05,724 --> 00:43:09,924
Speaker 3: It's recorded at Wardoor Studios in London by Tom Barry.

814
00:43:10,444 --> 00:43:13,284
Speaker 3: If you like the show, please remember to share, rate

815
00:43:13,524 --> 00:43:15,684
Speaker 3: and review. It really makes a difference to us and

816
00:43:15,724 --> 00:43:18,364
Speaker 3: if you want to hear the show, add free sign

817
00:43:18,444 --> 00:43:21,044
Speaker 3: up to Pushkin Plus on the show page on Apple

818
00:43:21,084 --> 00:43:31,964
Speaker 3: Podcasts or at pushkin dot Fm, slash plus