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Speaker 1: Pushkin. Can anyone or anything be truly altruistic? Can incredible

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Speaker 1: acts of kindness cause more harm than good? And what's

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Speaker 1: the most effective way of doing good? I'm sure that

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Speaker 1: the best selling author Michael Lewis will have some interesting

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Speaker 1: thoughts because the world's most notorious effective altruist, Sam Banguin Freed,

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Speaker 1: is the subject of Michael's book Going Infinite. I'm going

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Speaker 1: to be talking to Michael about that relationship and his

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Speaker 1: latest book, Who Is Government. But I want your help

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Speaker 1: to explore the theme of altruism with Michael Lewis, so

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Speaker 1: please send all of your kindness related questions from Michael

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Speaker 1: Lewis to Tales at Pushkin dot FM. Hello and welcome

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Speaker 1: to another episode of Caution Questions, where I team up

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Speaker 1: with clever people to answer your clever questions. And this

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Speaker 1: time I'm joined by Nate Silver and Maria Konnikova. They

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Speaker 1: are the hosts of the Risky Business podcast and they

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Speaker 1: are here to answer questions from both sets of listeners.

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Speaker 1: Nate is a political analyst. He's the author of On

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Speaker 1: the Edge, and Maria is the best selling author of

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Speaker 1: the Biggest Bluff. How I Learned to Pay Attention? Master

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Speaker 1: myself and win both fantastic books. Both Nate and Maria

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Speaker 1: are also journalists, and they are both high stakes poker players.

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Speaker 1: I'm a journalist too, and an undercover economist, and I'm

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Speaker 1: not much of a poker player. But between us, we

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Speaker 1: should have some good answers for all of the smart

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Speaker 1: questions that you've been sending in. We will be tackling

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Speaker 1: smartphones in schools, tariffs, and how to use game theory

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Speaker 1: to win at sports. Nate, Maria, welcome to Cautionary Tales.

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Speaker 1: Thank you, Tom, Thanks so much.

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Speaker 2: Tim.

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Speaker 1: Well, should it be cautionary business or risky Tales?

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Speaker 3: Risky cash, risky caution?

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Speaker 2: That's good, that's good.

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Speaker 1: You've been podcasting for how long?

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Speaker 2: About a year? About a year?

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Speaker 3: Yeah, we're coming up on the World Series of Poker

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Speaker 3: begins in every May, and so yeah, we launched it

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Speaker 3: just about a year ago. When we're coming up in

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Speaker 3: our second World Series season, excellent, When Nate and I.

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Speaker 2: Are both at the final table of the main event,

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Speaker 2: that'll be really great for our Risky Business podcast audience growth.

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Speaker 1: Would you be podcasting from the World Series if you're

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Speaker 1: on the final table?

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Speaker 2: Not at the final table, since there are no phones allowed, but.

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Speaker 1: We'll find a way.

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Speaker 3: I mean, we do have these long days, right, we

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Speaker 3: both have real jobs, and then maybe you'll do several

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Speaker 3: hours of real work quote unquote, and then play poker

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Speaker 3: for twelve hours and then it's fine your two am

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Speaker 3: Mexican food hangout or something. But they are long days.

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Speaker 3: We do not get vacation when we're in Vegas.

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Speaker 2: Basically we do not.

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Speaker 1: Right enough poker chat, let us get into the virtual postbag.

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Speaker 1: After we listened to the theme music for Portionary Tales.

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Speaker 1: I am here with Nate Silver and Maria Konikova, and

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Speaker 1: here is a good question to start with from listener Alex.

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Speaker 1: Alex asks, have any personal experiences or academic theories really

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Speaker 1: shaped your decision making? Let me put this one to

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Speaker 1: Nate first, and I'd love to hear what both if

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Speaker 1: you think, I.

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Speaker 3: Would say my experience with risk taking and decision making

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Speaker 3: is very hands on for the most part. Right when

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Speaker 3: I was a kid, we would play fantasy baseball and

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Speaker 3: have little NFL betting pools and things like that. Obviously poker,

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Speaker 3: there's instructural material, but you know, most people are ultimately

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Speaker 3: kind of largely self taught, you know, in my book

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Speaker 3: on the Edge, then game theory plays a really large role,

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Speaker 3: and game theory plays a very large role in poker.

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Speaker 3: There are not computer programs called solvers that literally have

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Speaker 3: solved the Nash equilibrium for poker. If you've seen a

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Speaker 3: beautiful mind. A lot of things in life is like

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Speaker 3: what's the game theory equilibrium of, Like what traffic patterns

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Speaker 3: look like in New York, or how often you should

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Speaker 3: eat one type of cuisine versus another, or any type

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Speaker 3: of competitive situation. When there are lots of optionality, I'd

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Speaker 3: say game theory is kind of the most important variable there.

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Speaker 1: And game theory, we should say, is really the use

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Speaker 1: of mathematical equations to model strategic into actions. So a

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Speaker 1: very simple example of game theory is the game of

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Speaker 1: rock paper scissors. Now, if I play rock, you're going

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Speaker 1: to play paper and beat me. If I play paper,

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Speaker 1: you're going to play scissors and beat me. And so

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Speaker 1: game theory says, oh, you have to randomize, you have

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Speaker 1: to have a one third chance of playing each strategy.

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Speaker 1: I mean, that's kind of obvious. Game theory can be

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Speaker 1: used to model much more complex situations, including famously, because

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Speaker 1: it inspired one of the creators of game theory poker.

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Speaker 2: Yeah, I would second Nate on game theory. It was

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Speaker 2: one of the pinnacles of my academic work. And I'd

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Speaker 2: also say the psychological theories of Danny Conneman have been

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Speaker 2: hugely influential in how I think about decision making. He

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Speaker 2: was an absolutely brilliant man who really unearthed a lot

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Speaker 2: of the ways that our brains misprocess. I don't think

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Speaker 2: that's a word, but I'm going to make it one

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Speaker 2: misprocess information and the biases and heuristics that we're up against.

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Speaker 2: And it's incredibly powerful because even if you know that

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Speaker 2: those biases and heuristics exist in theory, in practice, you

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Speaker 2: still exhibit them. Right. You have the game theory kind

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Speaker 2: of that mathematical side, and then the psychological side of

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Speaker 2: Conoman and poker, in my mind is the life experience

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Speaker 2: that has cemented these more than anything else in my

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Speaker 2: brain and has made me internalize how this actually works

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Speaker 2: in reality and how you can try to decide optimally

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Speaker 2: and then take that from the poker table to real life,

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Speaker 2: because poker's an environment where you can really sample thousands

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Speaker 2: and thousands and thousands of decisions, sample correctly, and as

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Speaker 2: you know, Tim that's incredibly important when it comes to

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Speaker 2: learning and trying to get a sense of probabilities and

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Speaker 2: in real life, not the poker table. We never sample correctly, right,

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Speaker 2: So that's where a lot of these biases come from.

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Speaker 2: So I think poker is a very powerful teaching tool

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Speaker 2: to help cement these theories into practice.

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Speaker 1: Okay, I've got a great question from Drew loyal listeners

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Speaker 1: to caution me tales. Now, I'm a big fan of

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Speaker 1: board games, and Drew wants to know Maria and Nate

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Speaker 1: do you play board games. If you play board games,

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Speaker 1: do you bring a poker lens to the table or

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Speaker 1: is it a different vibe?

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Speaker 2: So I hate board casese I do not play board games.

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Speaker 2: My sister is a little over six years older than

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Speaker 2: I am, and she always loved board games. They bored me. Haha.

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Speaker 2: They're just not interesting. And you know when someone brings out,

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Speaker 2: you know, sellers of Katan or anything like that, I'm like,

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Speaker 2: oh God, no, no, no no, please Ticket to ride

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Speaker 2: as a favorite in my family and I just want

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Speaker 2: to die, you know. For me, if there's game night,

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Speaker 2: please let it be poker. Otherwise, please don't make me

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Speaker 2: play and let me read my book on the side

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Speaker 2: and cheer you on while you play. Yeah.

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Speaker 3: I mean, look, some of the games get quite advanced,

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Speaker 3: and so it's like, where do you go from a

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Speaker 3: game that takes ten hours to learn something that's kind

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Speaker 3: of a broken, overly simple game, Right, But like, yeah,

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Speaker 3: I like games because I'm very competitive. Right, if I'm playing,

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Speaker 3: it's like, oh, people, we're like, don't have that background

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Speaker 3: in games or poker. Then I'm just gonna be honest.

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Speaker 3: I tend to I tend to figure out the strategy

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Speaker 3: pretty quick.

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Speaker 1: And when you.

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Speaker 2: Crush them, you crush their souls and their dreams, and

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Speaker 2: they wish they'd never asked you to play. Day to

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Speaker 2: your point about the complexity, there's a New Yorker cartoon

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Speaker 2: that I absolutely love and it's a group of people

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Speaker 2: sitting around a table and one of them is reading

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Speaker 2: a game instruction manual and the rest of them are

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Speaker 2: all skeletons. He says, and so those are the rules?

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Speaker 2: Shall we start playing?

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Speaker 3: I love it.

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Speaker 1: I had the privilege of interviewing Klaus Touber, the creator

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Speaker 1: of Settlers of Katan, and he said something that really

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Speaker 1: stuck with me, which I suspect will resonate with you

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Speaker 1: poker players. He said, you can know somebody their whole

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Speaker 1: lives and play a game with them and you will

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Speaker 1: see something that you've never seen.

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Speaker 2: That's wonderful. And I actually that's how I feel about poker,

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Speaker 2: and your character comes out. And by the way, I

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Speaker 2: do have a very strong interest in games for my

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Speaker 2: next book when it comes to cheating, so I'm interested in, like,

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Speaker 2: what kind of a person you know cheats up Monopoly

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Speaker 2: at home game night, People who want to win Just

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Speaker 2: totally like mind boggling to me that you would, that

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Speaker 2: you would do that, but that there you have it.

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Speaker 3: Yeah, My partner is also very competitive, but his main

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Speaker 3: objective is making sure that I don't win, right. He

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Speaker 3: will go out, You'll go out of his way to

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Speaker 3: lose himself if it's a murder suicide mission basically, which

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Speaker 3: changes the strategy quite a bit.

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Speaker 1: I feel seen that's amazing.

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Speaker 3: So here's one for you, Tim. This question is from

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Speaker 3: Carrie K E R R I E. I know it's

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Speaker 3: Irish or something, Carrie, but anyway, Hi Tim and team.

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Speaker 3: I worry that a big problem with the productivity gap

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Speaker 3: in the UK is a lack of high quality data

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Speaker 3: and data driven decision making. I see a lot of

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Speaker 3: investment in data that is overseen by technical experts in

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Speaker 3: a given domain, but very little consultation of data scientists.

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Speaker 3: Company and organizational boards also seem to lack the sort

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Speaker 3: of expertise which is often rolled into a catch all

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Speaker 3: it function. I work in the public sector, and so

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Speaker 3: I have framed my question accordingly. Should we have a

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Speaker 3: government department devoted to data or is it all a

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Speaker 3: bit nineteen eighty four, Best wishes, Carrie Well.

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Speaker 1: I love the question, and we do kind of have

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Speaker 1: a government department devoted to data. It's called the Office

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Speaker 1: for National Statistics, but it's focused on very old school

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Speaker 1: statistics rather than modern data science, and old school statistics

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Speaker 1: tends to focus on smaller data sets, very well behaved,

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Speaker 1: well formatted data sets. For example, you might go out

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Speaker 1: and do a survey rather than just say, hey, well,

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Speaker 1: we're going to just draw administrative data, or we're going

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Speaker 1: to look at data that's coming out of people's smartphones,

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Speaker 1: which you know, there's a lot more information in there,

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Speaker 1: but it's a lot messier, to be honest. What really

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Speaker 1: concerns me at the moment is that data is systematically

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Speaker 1: underrated by governments. I think they don't realize how powerful

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Speaker 1: it can be, that they don't realize how dangerous it

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Speaker 1: can be, and it tends to be given a low

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Speaker 1: political priority or misused. So at the moment in the US,

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Speaker 1: for example, we've got this sort of sudden disappearance of

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Speaker 1: all these government websites. It's unclear how much data has

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Speaker 1: permanently disappeared, or is it just that it's temporarily gone down.

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Speaker 1: Is somebody archiving it? Will the archives work or not?

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Speaker 1: Archives don't always work. Are we going to have interrupted

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Speaker 1: data sets? And it's very unclear what's gone and what hasn't.

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Speaker 1: So that's a particularly extreme example, but generally, I'm working

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Speaker 1: on a history of data. At the moment. It's so powerful,

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Speaker 1: it's so important. It is the foundation of our health systems.

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Speaker 1: It's the foundation of many of our economic activities, it's

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Speaker 1: the foundation of science. It's also the foundation of some

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Speaker 1: terrible abuses of human rights in the past. And yet

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Speaker 1: it's kind of boring and it's kind of geeky, and

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Speaker 1: so it just gets overlooked, and that's what really worries me.

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Speaker 3: Yeah, I mean, it's interesting that you have, you know,

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Speaker 3: one partisan faction in the US that seems to think

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Speaker 3: that less data is better instead of having better data. Right,

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Speaker 3: Some types of data you can go back and recollect,

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Speaker 3: but some things you can't.

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Speaker 2: Right.

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Speaker 3: The UK, for example, has a data set of economic

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Speaker 3: statistics like GDP that goes back like almost a thousand

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Speaker 3: years or something, right, because they were collecting records for

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Speaker 3: tax purposes and estate purposes and everything in real time.

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Speaker 1: Yeah, well, we call the Doom Doomsday Book ten eighty six.

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Speaker 1: I'm old enough to remember the nine hundredth anniversary of

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Speaker 1: the Doomsday Book. There's an interesting little caution retail there.

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Speaker 1: So for the nine hundredth anniversary of the Doomsday Book,

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Speaker 1: which was in nineteen eighty six, the BBC organized this

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Speaker 1: kind of a proto Wikipedia. They said, we're going to

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Speaker 1: encourage this project where school kids across the country are

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Speaker 1: going to act ass citizen journalists. They're going to go around,

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Speaker 1: they'll interview people, they'll gather data, they'll write little essays.

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Speaker 1: We've got maps, we'll have photographs, and we'll put it

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Speaker 1: all on a laser disc. Remember laser disk. Well, you

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Speaker 1: can see the problem, right, And we've got these highly

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Speaker 1: sophisticated and expensive computer systems they're going to be accessible

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Speaker 1: in every school. And it was called Doomsday nineteen eighty six.

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Speaker 1: I think it was super cool. I remember it really well.

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Speaker 1: And then by about nineteen ninety eight, it was just

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Speaker 1: impossible to even read the laser disks. So this whole

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Speaker 1: project just went offline. Meanwhile, by the way, the original

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Speaker 1: Doomsday books from ten eighty six still available.

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Speaker 2: Paper has a certain appeal and has lasted for centuries

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Speaker 2: for a reason. You know, the flip side tim of

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Speaker 2: what you were saying about what's happening in the US

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Speaker 2: and kind to address the nineteen eighty four element of

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Speaker 2: the question is so you know, yes, a lot of

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Speaker 2: data is disappearing, and then data that was never meant

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Speaker 2: to be aggregated and linked and personalized is being shared,

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Speaker 2: and profiles of people of US citizens and non citizens

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Speaker 2: are being created that should have never been created. Right.

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Speaker 2: There were rules in place to keep those data sets

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Speaker 2: separate for a reason, for private sup protections, and now

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Speaker 2: the government is trying to roll back those rules and

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Speaker 2: collect and de anonymize the data that was shared by

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Speaker 2: people with the assumption that that would never happen. That's

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Speaker 2: really bad, right, So there's this kind of dystopian thing

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Speaker 2: going on.

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Speaker 1: And there's there's history here. I mean, yeah, this has

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Speaker 1: happened before. This happened in the nineteen forties.

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Speaker 2: It's not good. Nineteen forties was not a good time

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Speaker 2: in history if people remember.

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Speaker 1: No, I think we can all agree on that one.

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Speaker 1: Thank you very much, Nate and Maria. We will be

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Speaker 1: answering more of your questions after this shortbreak. We are back.

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Speaker 1: I'm here with Nate Silver and Maria Konikova of Risky Business,

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Speaker 1: and we have an interesting question from a Risky Business

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Speaker 1: listener who asks, one thing that happened during the tariffs

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Speaker 1: which didn't get much attention, is the ten years treasury

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Speaker 1: rate spiked from four to four and a half percent.

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Speaker 1: I should say, by the way I work for the

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Speaker 1: Financial Times, we were paying a lot of attention to that,

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Speaker 1: but understand that outside geek land it was ignored. That

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Speaker 1: basically means, the listener continues, there is less belief that

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Speaker 1: US debt is a safe bet. It probably means somebody

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Speaker 1: cough China, cough issued a cell order for a large

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Speaker 1: amount of US treasury with the intent to move the

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Speaker 1: interest rate as retaliation for tariffs. To cut to the Chase.

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Speaker 1: The listener asks, I would be extremely interested in hearing

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Speaker 1: what are your thoughts on the likelihood of the United

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Speaker 1: States defaulting?

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Speaker 2: Nay, this one's yere.

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Speaker 3: I used to know more about credit default swaps and

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Speaker 3: things like this. I mean, look, in the US runs

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Speaker 3: up very large deficits. Neither party has any interest in

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Speaker 3: doing what it would take to cut those deficits potentially. Right,

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Speaker 3: we got lucky in kind of the financial crisis in

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Speaker 3: COVID era of lowering rest rates where you can borrow

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Speaker 3: very cheaply. It's no longer true now, in fact, borrowing

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Speaker 3: he's getting more expensive. And so you know, I trust

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Speaker 3: the market's fairly efficient here. But I guess the US

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Speaker 3: is still a bit too big to fail.

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Speaker 1: Well, normally, when we say too big to fail, we're

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Speaker 1: referring to things that the US is going to bail

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Speaker 1: out if necessary. Right, Yeah, so US is too big

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Speaker 1: to save, that's your problem.

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Speaker 3: You know, the world has profoundly been affected by Trump's

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Speaker 3: second term in a way it wasn't in Trump's first term.

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Speaker 3: Our neighbors to the north, Canada, had an election that

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Speaker 3: was entirely upended. The Liberals want to back in power,

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Speaker 3: even though Trudeau had been very unpopular. Right, I would

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Speaker 3: argue that the election of the new pope was in

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Speaker 3: part a response to Trump and the changing geopolitical world, right,

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Speaker 3: and say, Okay, we want to have some influence on

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Speaker 3: the United States. Have figure who's not Trump? It can

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Speaker 3: balance them out somehow, And so yeah, we're way a

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Speaker 3: different world. And I suppose I trust markets and investors

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Speaker 3: are intelligent about that. And I'm not sure it's it's

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Speaker 3: China so much as the fact we're in a new regime.

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Speaker 1: Yeah, I certainly agree. I don't think China needed to

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Speaker 1: do anything to make the US Treasury rate spike. When

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Speaker 1: the rate spikes, by the way, it means people are

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Speaker 1: selling US government debt. When they sell it, the price

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Speaker 1: goes down, the interest rates goes up. But yeah, plenty

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Speaker 1: of people willing to do that without any coordinated action

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Speaker 1: from China. I mean, there is absolutely zero need for

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Speaker 1: the US government to default, because the US government can

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Speaker 1: always print more dollars. I mean, it could be the

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Speaker 1: US government debt gets less attractive, it could be that

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Speaker 1: inflation takes off, it could be there all kinds of problems,

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Speaker 1: but there's never a need to default. I think the

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Speaker 1: decision to default would be just that, it would be

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Speaker 1: a decision the US government would have to actively decide

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Speaker 1: we want to stiff our creditors. So you just have

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Speaker 1: to look at the presidents and say, does this look

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Speaker 1: like a man who stiffs his creditors? I don't think.

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Speaker 1: I don't think. So how long does it sounds like

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Speaker 1: Donald J? Trump?

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Speaker 2: To me?

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Speaker 3: This is President Trump, which I can go out to

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Speaker 3: him and the ft.

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Speaker 1: We have a new acronym. It's Taco. Taco stands for

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Speaker 1: Trump always chicken out. So the bet is that he

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Speaker 1: will not in fact default, even though he's talking about

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Speaker 1: various things that already look like default, because the consequences

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Speaker 1: would be so catastrophic that he would look at the

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Speaker 1: consequences and he would back away. That doesn't mean that

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Speaker 1: he couldn't do some damage. So fingers crossed, all right?

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Speaker 2: So listener Jack wants us to explore the thorny issue

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Speaker 2: of cell phones in school and writes Australia has been

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Speaker 2: running an experiment with New South Wales banning mobile phone

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Speaker 2: use in all schools from October twenty twenty three. This

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Speaker 2: is following the implementation of a ban in Victoria in

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Speaker 2: twenty twenty, which itself cites research from England and other justifications.

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Speaker 2: So this listener has kids in school. This is not

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Speaker 2: where I thought the question was going to go. Actively

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Speaker 2: helps the fourteen year old subvert this ban, so they

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Speaker 2: have an old phone in a locked pouch and their

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Speaker 2: actual phone in their bag. This is because they must

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Speaker 2: use their phone to hotspot the internet to their laptop.

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Speaker 2: Jack writes, My opinion is the whole thing as stupid,

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Speaker 2: as most kids have some kind of workaround and are

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Speaker 2: on their laptops all day. Anyway, if parents are on

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Speaker 2: their phones all the time, why are we expecting kids

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Speaker 2: not to be. I still see teachers in playgrounds face

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Speaker 2: and phone, and all the injustices and hypocrisy of my

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Speaker 2: own school days come flooding back. I mean, I know

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Speaker 2: I'm asking this question. I have so many thoughts, but

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Speaker 2: I'll open it up to you guys first.

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Speaker 1: I also have thoughts. I think the hypocrisy thought is real.

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Speaker 1: I have three children myself. My oldest child is twenty one,

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Speaker 1: the youngest is thirteen. They see me being distracted by

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Speaker 1: my phone all the time, and of course they're going

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Speaker 1: to be influenced by that. I think that this is

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Speaker 1: an evil device, useful but evil that we all of

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Speaker 1: us need to fight together because it's capable of ruining

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Speaker 1: all of our lives. I feel like I should set

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Speaker 1: a good example, and I don't feel that the fact

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Speaker 1: that the grown ups are being distracted by the phones

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Speaker 1: is particularly a good reason to say the kids should

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Speaker 1: have the phones too. I mean, we would never say, hey,

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Speaker 1: I see the grown ups drinking vodka and smoking, so

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Speaker 1: why surely it's fine for the nine year olds to

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Speaker 1: also drink vodka and smoke. I mean, I don't think

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Speaker 1: that follows, but.

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Speaker 2: It's hypocritical term. It's hypocritical that you do not like

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Speaker 2: your child.

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Speaker 1: Yeah, it will. I think it suggests that maybe the

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Speaker 1: grown up shouldn't bring so much vodka and shouldn't smoke

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Speaker 1: so much, rather than the other way around. But anyway, yeah,

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Speaker 1: we are setting an example, whether we like it or not.

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Speaker 2: I completely agree with that. And to add to it,

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Speaker 2: the use of screens in general, it's very different depending

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Speaker 2: on your age. There's emerging research, but now we have

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Speaker 2: a few decades worth of what phones do to developing

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Speaker 2: brains and kind of the habits that you create from

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Speaker 2: a young age. So being on the phone as an

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Speaker 2: adult who grew up without that kind of access is

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Speaker 2: very different from growing up and actually constantly being connected

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Speaker 2: to a scream. By the way, there's also research that

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Speaker 2: shows that taking notes by hand is much more powerful

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Speaker 2: than taking notes on a laptop. You retain more information,

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Speaker 2: you learn better because there's a brain link between the

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Speaker 2: hand and the brain that does not exist when you're typing.

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Speaker 2: It would be better. I totally agree with you, Tim,

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Speaker 2: Adults should be on their phones much less than I

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Speaker 2: write about this. I think it's actually really great practice

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Speaker 2: to not have your phone. I've sometimes left my phone

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Speaker 2: at home on purpose to kind of have a day

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Speaker 2: without cell phones. I have programs that turn the Internet

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Speaker 2: off on my computer because I lack willpower if I don't,

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Speaker 2: forcibly kind of have it turned off to try to

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Speaker 2: have some deep concentration, deep writing time. This is all

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Speaker 2: kind of related to our executive function, our self control ability,

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Speaker 2: our ability to learn, our memory. All of these things

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Speaker 2: are interconnected. And don't you want your child to actually

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Speaker 2: emerge as a kind of smart adult who's capable of

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Speaker 2: all of these things as opposed to someone who is

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Speaker 2: constantly distracted.

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Speaker 1: Nate strikes me as the three screens that wants going

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Speaker 1: nate anythought.

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Speaker 3: You know, I just have my laptops. People think I

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Speaker 3: have some big, elaborate, minor setup. I just have my

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Speaker 3: little laptop. Actually, yeah, Look, is it paternalistic to tell

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Speaker 3: kids they can't use their phones? I mean, yeah, but

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Speaker 3: they're kids, right, so you're allowed, You're allowed to be

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Speaker 3: paternalistic toward them. And I don't know. I mean, I

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Speaker 3: guess the one caveat I have is I was chronically

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Speaker 3: pretty bored in school, right, and so I'm sympathetic to

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Speaker 3: the kids that are like, Okay, I'm bored. I'm learning

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Speaker 3: things I already know. But like, it is a situation

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Speaker 3: where I think you have to have some type of

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Speaker 3: consistent enforcement in the rules. But when you're in school,

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Speaker 3: blame your teacher's not making you interested, But don't blame

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Speaker 3: them for not letting you be on Instagram while you're

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Speaker 3: supposed to be learning algebra.

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Speaker 1: I think it might also be a collective action problem. Yes,

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Speaker 1: I think it's perfectly possible that you've got tea majors

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Speaker 1: who would be quite happy if nobody in the school

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Speaker 1: had Instagram or nobody had Snapchat. But if other people

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Speaker 1: are using Snapchat to communicate, to arrange to meet up. Well,

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Speaker 1: then I need Snapchat too, and just talking to teenagers,

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Speaker 1: I think that viewpoint is not unusual. Some of them

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Speaker 1: are like, please, can you just ban everybody from using

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Speaker 1: it and then I'll be happy? Yeah, Okay, let's move

434
00:23:33,610 --> 00:23:36,610
Speaker 1: on because I I've got a question specifically from Maria

435
00:23:37,050 --> 00:23:41,290
Speaker 1: from a long, long time listener from Quebec. They would

436
00:23:41,410 --> 00:23:44,850
Speaker 1: like to hear Maria talk about a particular point of

437
00:23:44,850 --> 00:23:48,330
Speaker 1: her book, The Biggest Bluff, where she describes the breakfast

438
00:23:48,410 --> 00:23:51,010
Speaker 1: she had with her mentor you were trying to find

439
00:23:51,010 --> 00:23:52,610
Speaker 1: somebody who would teach her to play poker so that

440
00:23:52,610 --> 00:23:54,410
Speaker 1: you could write a book about learning to play poker.

441
00:23:55,250 --> 00:23:58,170
Speaker 1: He only became interested when he realized you don't know

442
00:23:58,330 --> 00:24:00,610
Speaker 1: anything at all about this subject. You don't even know

443
00:24:00,650 --> 00:24:03,490
Speaker 1: how a deck of cards works. And our listener says,

444
00:24:03,610 --> 00:24:05,970
Speaker 1: I am sixty five years old and I went through

445
00:24:06,010 --> 00:24:08,450
Speaker 1: a couple of career changes and I find this story

446
00:24:08,530 --> 00:24:11,610
Speaker 1: very moving and so true that what it means to

447
00:24:11,690 --> 00:24:14,770
Speaker 1: learn something and to teach something. So what are your

448
00:24:14,770 --> 00:24:15,330
Speaker 1: thoughts on that?

449
00:24:15,890 --> 00:24:19,930
Speaker 2: Yeah? I think so. This conversation took place the first

450
00:24:19,930 --> 00:24:23,370
Speaker 2: time I ever met Eric Seidell, who is a legend

451
00:24:23,890 --> 00:24:26,370
Speaker 2: of the poker world, and he had never taken on

452
00:24:26,410 --> 00:24:29,010
Speaker 2: a poker student before. And what stood out about me

453
00:24:29,210 --> 00:24:31,330
Speaker 2: was that I was a complete blank slate. I didn't

454
00:24:31,370 --> 00:24:34,050
Speaker 2: have any bad habits. I didn't have any habits, right,

455
00:24:34,090 --> 00:24:37,130
Speaker 2: I didn't know anything. And so for him, I think

456
00:24:37,210 --> 00:24:40,330
Speaker 2: it was a very interesting experiment, just like for me,

457
00:24:40,370 --> 00:24:43,130
Speaker 2: it was an interesting experiment from scratch. You know, what

458
00:24:43,370 --> 00:24:46,810
Speaker 2: can he do with me given my background in psychology

459
00:24:47,170 --> 00:24:50,330
Speaker 2: in a short period of time these days, when you

460
00:24:50,330 --> 00:24:53,530
Speaker 2: know I've got all these math wizards and selvers and

461
00:24:53,570 --> 00:24:57,810
Speaker 2: all of these things, is kind of psychology hard work.

462
00:24:57,850 --> 00:25:01,330
Speaker 2: And if my background isn't enough, right, could I do well?

463
00:25:01,730 --> 00:25:03,970
Speaker 2: And so if I could, that would be an amazing,

464
00:25:04,010 --> 00:25:06,690
Speaker 2: kintive proof of concept for the way that he thinks

465
00:25:06,690 --> 00:25:09,330
Speaker 2: about the game. And so I think to him, that's

466
00:25:09,370 --> 00:25:12,810
Speaker 2: what made this interesting and that's what made it appealing Nate.

467
00:25:12,890 --> 00:25:17,170
Speaker 1: Why is Maria so good at poker? Is it the coach?

468
00:25:17,730 --> 00:25:21,410
Speaker 1: Is it her training as a psychologist? Is it something else?

469
00:25:21,970 --> 00:25:25,450
Speaker 3: I think it's her and not the coaching. Although Maria

470
00:25:25,810 --> 00:25:28,450
Speaker 3: is a wonderfully well connected person who has access to

471
00:25:29,170 --> 00:25:31,050
Speaker 3: some of the best poker minds in the world. But yeah, look,

472
00:25:31,050 --> 00:25:33,010
Speaker 3: it's the balance of the people reading skills and the

473
00:25:33,050 --> 00:25:37,690
Speaker 3: mathematical skills and the discipline. Really, you know, it's rare

474
00:25:37,810 --> 00:25:41,330
Speaker 3: to see Maria tilt. I'm sure she does it right,

475
00:25:41,370 --> 00:25:43,010
Speaker 3: but I think she is playing more than I am.

476
00:25:43,050 --> 00:25:46,250
Speaker 1: Frankly, Tilting is basically where you get emotional and it

477
00:25:46,290 --> 00:25:47,410
Speaker 1: affects your decisions.

478
00:25:47,530 --> 00:25:49,810
Speaker 3: Tilt doesn't have to be anger, right. It can be like, oh,

479
00:25:49,850 --> 00:25:52,170
Speaker 3: I've had a great day, so I'm gonna not fight

480
00:25:52,250 --> 00:25:54,250
Speaker 3: for this plot that you're supposed to bluff everything. It

481
00:25:54,250 --> 00:25:56,250
Speaker 3: can be a lot of different formats of it. But

482
00:25:56,370 --> 00:25:58,690
Speaker 3: like you know, we no one always plays their A game, right.

483
00:25:58,730 --> 00:26:01,490
Speaker 3: But if you're always playing your B plus game or better,

484
00:26:01,650 --> 00:26:04,210
Speaker 3: and you're really smart in lots of different ways and

485
00:26:04,250 --> 00:26:07,730
Speaker 3: read people well and maybe defy stereotypes a little bit too,

486
00:26:07,730 --> 00:26:10,250
Speaker 3: maybe are more aggressive than people are expecting. So yeah,

487
00:26:10,410 --> 00:26:13,650
Speaker 3: I would say she's a well rounded poker player. By

488
00:26:13,650 --> 00:26:16,690
Speaker 3: the way, It's part of like learning the game later

489
00:26:16,930 --> 00:26:19,210
Speaker 3: is that you might wind up being a little bit

490
00:26:19,210 --> 00:26:21,650
Speaker 3: more well rounded. When I used to play for a

491
00:26:21,690 --> 00:26:24,330
Speaker 3: living back god twenty years ago, now I'm getting old,

492
00:26:24,410 --> 00:26:26,730
Speaker 3: I played a game called limit Hold them, which the

493
00:26:26,770 --> 00:26:29,770
Speaker 3: same amount as bet on every hand or as a

494
00:26:29,770 --> 00:26:32,450
Speaker 3: no limit you can be anywhere from one chip to

495
00:26:32,490 --> 00:26:35,130
Speaker 3: the entire size of your stack, and it's a much

496
00:26:35,130 --> 00:26:36,930
Speaker 3: different game. And so I had to spend a lot

497
00:26:36,930 --> 00:26:39,850
Speaker 3: of time unlearning habits from limit hold them, which can

498
00:26:39,850 --> 00:26:41,770
Speaker 3: be bad habits in no limit holding.

499
00:26:42,530 --> 00:26:44,930
Speaker 1: We've got more questions coming up, but first let's take

500
00:26:44,970 --> 00:26:59,290
Speaker 1: a quick break. Welcome back to caush me questions with me,

501
00:26:59,490 --> 00:27:00,650
Speaker 1: Tim Harford.

502
00:27:00,290 --> 00:27:02,890
Speaker 2: And me Maria Kannakova and me Nate Silver.

503
00:27:03,890 --> 00:27:08,370
Speaker 3: So here's a question from Mark. Mark asks, it's no

504
00:27:08,490 --> 00:27:12,210
Speaker 3: secret that political donations are tied to political favors, legislation

505
00:27:12,290 --> 00:27:15,610
Speaker 3: benefiting the highest owners, and so forth. If the politicians

506
00:27:15,650 --> 00:27:17,730
Speaker 3: are the machine, and the machine runs on money, what

507
00:27:17,810 --> 00:27:20,250
Speaker 3: if we tweaked the source of the money. What would

508
00:27:20,250 --> 00:27:23,570
Speaker 3: happen if, by some stroke of magic, Congress received vastly

509
00:27:23,770 --> 00:27:27,450
Speaker 3: larger salaries, increasing with years of service. But with one

510
00:27:27,490 --> 00:27:31,970
Speaker 3: big caveat, politicians cannot accept any donations, gifts, speaking fees,

511
00:27:32,090 --> 00:27:35,450
Speaker 3: or favors in any form other than direct money donations

512
00:27:35,450 --> 00:27:39,010
Speaker 3: from constituents up to the federal limits without forfeiting their

513
00:27:39,010 --> 00:27:43,370
Speaker 3: public service salary. So put, simply, what if public service

514
00:27:43,450 --> 00:27:47,210
Speaker 3: was the bigger, more valuable incentive. Could it even outcompete

515
00:27:47,210 --> 00:27:50,770
Speaker 3: the current incentives? Would governing change for the better or

516
00:27:50,770 --> 00:27:53,490
Speaker 3: for the worse? And why I like it?

517
00:27:53,970 --> 00:27:57,010
Speaker 2: You know, it's a good question, but it's slightly naive

518
00:27:57,090 --> 00:28:01,210
Speaker 2: in the sense that we already have rules against politicians

519
00:28:01,210 --> 00:28:04,890
Speaker 2: accepting all sorts of things that Mark wants them not

520
00:28:04,970 --> 00:28:07,450
Speaker 2: to accept. And there are ways around them, right There

521
00:28:07,450 --> 00:28:11,850
Speaker 2: are packs, which are political action committees which you can

522
00:28:11,970 --> 00:28:16,130
Speaker 2: donate to. There's ways that you can lobby and influence people.

523
00:28:17,010 --> 00:28:20,890
Speaker 2: People would get around these incentives very easily, just like

524
00:28:20,930 --> 00:28:21,650
Speaker 2: they do today.

525
00:28:21,810 --> 00:28:24,170
Speaker 1: This maybe is a naive observation, but I would have

526
00:28:24,210 --> 00:28:28,530
Speaker 1: thought there was a difference between donations to a campaign

527
00:28:28,890 --> 00:28:31,970
Speaker 1: for the purposes of spending money on adverts to get

528
00:28:31,970 --> 00:28:36,130
Speaker 1: reelected versus just donations to a politician's bank account. There

529
00:28:36,170 --> 00:28:37,530
Speaker 1: should be a bright line between the two of them.

530
00:28:37,530 --> 00:28:38,810
Speaker 1: I mean, Nate, you're the experts on this.

531
00:28:39,170 --> 00:28:41,290
Speaker 3: The answers. You are supposed to report to the Federal

532
00:28:41,330 --> 00:28:44,690
Speaker 3: Election Commission every dollar that you spend, so you definitely

533
00:28:44,770 --> 00:28:49,050
Speaker 3: run a risk of expenses being scrutinized if they're not legitimate.

534
00:28:49,130 --> 00:28:51,290
Speaker 3: But of course there are a million things a tranch

535
00:28:51,370 --> 00:28:55,850
Speaker 3: of expenses that are ambiguous between personal and business expenses. Right,

536
00:28:56,130 --> 00:28:59,170
Speaker 3: You can also shuffle money to consultants and pay them

537
00:28:59,170 --> 00:29:01,690
Speaker 3: maybe more than their worth, in exchange for who knows

538
00:29:02,130 --> 00:29:04,690
Speaker 3: favors later on and so forth. I think it's probably

539
00:29:04,690 --> 00:29:07,410
Speaker 3: a good idea to pay members of Congress more. I

540
00:29:07,450 --> 00:29:09,650
Speaker 3: think it might help recruit some people who were not

541
00:29:09,850 --> 00:29:13,330
Speaker 3: already wealthy to enter Congress. I think in general, top

542
00:29:13,490 --> 00:29:17,010
Speaker 3: performing government employees should be paid more too, and make

543
00:29:17,050 --> 00:29:18,890
Speaker 3: it is you're made to fire the poor performers. Though

544
00:29:18,970 --> 00:29:20,690
Speaker 3: I don't I don't understand when we want to go

545
00:29:20,690 --> 00:29:22,650
Speaker 3: into politics. It sounds like miserable to me to be

546
00:29:22,650 --> 00:29:26,610
Speaker 3: a member of Congress. My lizard brain can't crack those incentives.

547
00:29:26,610 --> 00:29:33,450
Speaker 1: Necessarily, it is worth directing some attention to politicians' latitude

548
00:29:33,890 --> 00:29:36,610
Speaker 1: to do things that people might want to bribe them

549
00:29:36,650 --> 00:29:40,090
Speaker 1: to do. So, the more personal discretion of politician has,

550
00:29:40,570 --> 00:29:42,090
Speaker 1: the more it's worth bribing them.

551
00:29:42,810 --> 00:29:42,930
Speaker 2: So.

552
00:29:43,250 --> 00:29:48,210
Speaker 1: One of the interesting things about tariffs is that if

553
00:29:48,250 --> 00:29:53,410
Speaker 1: you put a large tariff on importers, it's then worth

554
00:29:53,450 --> 00:29:56,330
Speaker 1: a great deal of money to particular importers to be

555
00:29:56,370 --> 00:29:58,610
Speaker 1: exempt from that tariff. I mean, it keeps changing. But

556
00:29:58,610 --> 00:30:02,130
Speaker 1: there was this huge tariff on electronic goods coming from China,

557
00:30:02,290 --> 00:30:04,490
Speaker 1: and then it's like, oh, actually, you know, we're not

558
00:30:04,490 --> 00:30:08,330
Speaker 1: going to levy that on iPhones. So Apple benefits from that.

559
00:30:09,170 --> 00:30:11,570
Speaker 1: And I'm not saying, oh, Apple bribed the president. I

560
00:30:11,610 --> 00:30:14,530
Speaker 1: don't have any particular reasons to believe they did. But

561
00:30:14,610 --> 00:30:16,650
Speaker 1: the point is that when you've got that sort of

562
00:30:16,650 --> 00:30:20,930
Speaker 1: personal discression, it becomes hugely valuable, and those interest groups

563
00:30:20,970 --> 00:30:22,490
Speaker 1: are willing to pay a lot of money, and so

564
00:30:22,570 --> 00:30:24,250
Speaker 1: there's the temptation for bribery.

565
00:30:24,970 --> 00:30:27,810
Speaker 2: One of the big differences between first world and third

566
00:30:27,850 --> 00:30:32,210
Speaker 2: world countries is how prevalent bribery is kind of in

567
00:30:32,730 --> 00:30:35,690
Speaker 2: the way that governments function. And yet a lot of

568
00:30:35,730 --> 00:30:39,130
Speaker 2: so called first world countries and second world countries there's

569
00:30:39,210 --> 00:30:42,490
Speaker 2: a lot of bribing and corruption and cheating that goes

570
00:30:42,490 --> 00:30:46,010
Speaker 2: on no matter what. So sure, it would be amazing

571
00:30:46,050 --> 00:30:49,570
Speaker 2: if somehow we could create the an incentive structure where

572
00:30:49,650 --> 00:30:54,930
Speaker 2: that doesn't happen. But I don't see from a psychological standpoint,

573
00:30:55,370 --> 00:30:58,370
Speaker 2: how the same type of personality that wants to become

574
00:30:58,370 --> 00:31:02,050
Speaker 2: a politician and who's successful as a politician, who kind

575
00:31:02,050 --> 00:31:04,010
Speaker 2: of wheels and deals as a part of what they

576
00:31:04,010 --> 00:31:07,610
Speaker 2: did to get there, how they would ever be totally

577
00:31:07,690 --> 00:31:12,010
Speaker 2: above everything and rise to the heights of government needed

578
00:31:12,050 --> 00:31:14,730
Speaker 2: to exert real influence. It's a very cynical tick, but

579
00:31:14,770 --> 00:31:15,570
Speaker 2: that's how I see it.

580
00:31:16,370 --> 00:31:19,810
Speaker 1: Maria, you mentioned developing countries. This was perceived as being

581
00:31:19,810 --> 00:31:24,850
Speaker 1: a particular problem in Africa, both for historical reasons and

582
00:31:24,890 --> 00:31:27,370
Speaker 1: because a lot of Sub Saharan African countries are very,

583
00:31:27,490 --> 00:31:32,010
Speaker 1: very poor. And so this amazing African entrepreneur called mo Ibrahim,

584
00:31:32,370 --> 00:31:34,810
Speaker 1: who made a huge amount of money in telecommunications in

585
00:31:34,850 --> 00:31:39,050
Speaker 1: the nineteen nineties, became a billionaire. He announced the mo

586
00:31:39,170 --> 00:31:42,010
Speaker 1: Ibrahim Prize he was going to give. It was millions

587
00:31:42,250 --> 00:31:47,570
Speaker 1: to any African leader who surrendered power after losing a

588
00:31:47,570 --> 00:31:50,970
Speaker 1: democratic election. This is about twenty years ago. I was

589
00:31:51,170 --> 00:31:53,650
Speaker 1: googling around. I think the price has been paid at

590
00:31:53,730 --> 00:31:56,570
Speaker 1: least once, but I haven't been able to find any

591
00:31:56,610 --> 00:31:58,570
Speaker 1: survey of whether it's made a difference. But I just

592
00:31:58,610 --> 00:32:00,690
Speaker 1: think it's a clever idea, whether or not it worked

593
00:32:00,690 --> 00:32:02,170
Speaker 1: in practice, it's a clever idea.

594
00:32:03,330 --> 00:32:07,290
Speaker 3: Didn't Sam Beakmin Freed want to pay your bribe Danald

595
00:32:07,330 --> 00:32:09,690
Speaker 3: Trump like a billion dollars not to run for reelection?

596
00:32:09,930 --> 00:32:10,690
Speaker 3: Or something like that.

597
00:32:11,050 --> 00:32:13,730
Speaker 1: Justcas Sam Bangmuanfreed wanted to do it doesn't mean it's

598
00:32:13,770 --> 00:32:14,370
Speaker 1: a bad idea.

599
00:32:14,770 --> 00:32:16,810
Speaker 3: Might I might color my priors a little bit.

600
00:32:16,850 --> 00:32:18,930
Speaker 1: I'd say fair.

601
00:32:18,930 --> 00:32:23,010
Speaker 2: Fair Sometimes you know a broken clock is right twice

602
00:32:23,010 --> 00:32:28,650
Speaker 2: a day. Let's take another question now from Jordan, and

603
00:32:29,090 --> 00:32:33,250
Speaker 2: this one was directed to you, Tim. So first, Jordan

604
00:32:33,410 --> 00:32:37,410
Speaker 2: loves your podcast and has listened to every episode you've

605
00:32:37,450 --> 00:32:37,970
Speaker 2: put out.

606
00:32:38,090 --> 00:32:39,290
Speaker 1: Good to hear, so keep up the.

607
00:32:39,290 --> 00:32:41,770
Speaker 2: Great work and thank you. Jordan is a dedicated phantom.

608
00:32:41,930 --> 00:32:43,730
Speaker 3: It's a lot of episodes it I like it.

609
00:32:43,810 --> 00:32:45,850
Speaker 2: I love your podcast too, but I've missed some. I

610
00:32:45,930 --> 00:32:46,610
Speaker 2: have to admit.

611
00:32:47,130 --> 00:32:50,010
Speaker 1: They're all available on cat shop. So what is Jordan's question?

612
00:32:50,690 --> 00:32:55,290
Speaker 2: Yes, it's about penalty kicks, so if you don't move,

613
00:32:55,330 --> 00:32:57,650
Speaker 2: you're more likely to save them. I wonder about that,

614
00:32:57,770 --> 00:33:00,970
Speaker 2: since if a goalie never moves, then wouldn't the kicker

615
00:33:01,090 --> 00:33:04,570
Speaker 2: behave differently and make it easier to save shots that

616
00:33:04,690 --> 00:33:07,290
Speaker 2: don't go down the middle. I was just thinking about

617
00:33:07,330 --> 00:33:09,290
Speaker 2: it and was wondering how that was controlled.

618
00:33:08,890 --> 00:33:12,090
Speaker 1: For Actually, you two are the perfect people to answer

619
00:33:12,090 --> 00:33:15,890
Speaker 1: this question because it's about game theory, optimality and trying

620
00:33:15,930 --> 00:33:20,410
Speaker 1: to exploit suboptimal behavior in a soccer penalty. Basically, you've

621
00:33:20,410 --> 00:33:23,650
Speaker 1: just got a striker trying to score a goal. You've

622
00:33:23,690 --> 00:33:26,970
Speaker 1: got the goalkeeper standing there, and they can kind of guess.

623
00:33:27,050 --> 00:33:28,810
Speaker 1: They can dive to the left, or they can dive

624
00:33:28,850 --> 00:33:31,250
Speaker 1: to the right, and there's really a fifty to fifty

625
00:33:31,250 --> 00:33:33,610
Speaker 1: they'll guess right, or they could just kind of not

626
00:33:33,730 --> 00:33:36,010
Speaker 1: dive at all and stand in the middle, which makes

627
00:33:36,050 --> 00:33:39,170
Speaker 1: them look passive and like they're not even trying. However,

628
00:33:39,890 --> 00:33:42,570
Speaker 1: it turns out that because goalkeepers don't stand in the

629
00:33:42,570 --> 00:33:46,010
Speaker 1: middle because it makes them look passive, some strikers have

630
00:33:46,090 --> 00:33:48,170
Speaker 1: figured out, oh, if I just boot the ball straight

631
00:33:48,170 --> 00:33:50,850
Speaker 1: at the goalkeeper, they are guaranteed to dive to the

632
00:33:50,930 --> 00:33:53,090
Speaker 1: left or right, and I'm going to kick where they

633
00:33:53,130 --> 00:33:56,970
Speaker 1: were anyway. So Jordan's question is, well, hang on, if

634
00:33:57,010 --> 00:33:59,650
Speaker 1: the strikers figure that out, aren't the goalkeepers also going

635
00:33:59,690 --> 00:34:01,330
Speaker 1: to figure it out. So it's really a question of

636
00:34:01,330 --> 00:34:04,890
Speaker 1: people adapting to each other's strategy and the optimal mix

637
00:34:04,930 --> 00:34:07,530
Speaker 1: of strategies, and you know who better than you two

638
00:34:07,370 --> 00:34:09,090
Speaker 1: to explain the details.

639
00:34:09,490 --> 00:34:12,490
Speaker 2: I mean, that is such a fascinating element of game

640
00:34:12,530 --> 00:34:15,570
Speaker 2: theory that Nate and I talk about quite often, which

641
00:34:15,650 --> 00:34:18,930
Speaker 2: is kind of this recursive element of it. Right if

642
00:34:18,970 --> 00:34:21,170
Speaker 2: I know that you know that, I know that you know,

643
00:34:22,010 --> 00:34:25,290
Speaker 2: And in poker this happens all the time where you're

644
00:34:25,330 --> 00:34:28,090
Speaker 2: trying to figure out, you know, how do I adjust

645
00:34:28,090 --> 00:34:32,170
Speaker 2: to my opponent. If we're both playing GTO game theory

646
00:34:32,290 --> 00:34:36,530
Speaker 2: optimal poker, then technically you know we're we're both executing

647
00:34:36,530 --> 00:34:41,130
Speaker 2: a strategy perfectly. But humans aren't computers and that doesn't happen. Right,

648
00:34:41,170 --> 00:34:43,850
Speaker 2: So if I figure out, for instance, that Nate is

649
00:34:43,930 --> 00:34:48,290
Speaker 2: calling too often, then I should start, you know, changing

650
00:34:48,330 --> 00:34:51,530
Speaker 2: my strategy away from GTO. So even if in this

651
00:34:51,570 --> 00:34:56,490
Speaker 2: particular spot, game theory says that I should check because

652
00:34:56,490 --> 00:34:59,370
Speaker 2: my hand is a little too weak to bet for value,

653
00:34:59,410 --> 00:35:01,690
Speaker 2: I bet again because I know that Nate is gonna

654
00:35:01,730 --> 00:35:05,250
Speaker 2: call me again with a worse hand because he overcalls, right,

655
00:35:05,530 --> 00:35:08,570
Speaker 2: So I'm kind of adapting to his behavior. Now, Nate

656
00:35:08,650 --> 00:35:11,210
Speaker 2: is a smart who also knows a lot about game theory.

657
00:35:11,450 --> 00:35:14,370
Speaker 2: So what if Nate then figures out that I'm doing

658
00:35:14,410 --> 00:35:17,050
Speaker 2: this right because we get to show down and he's like, Maria,

659
00:35:17,130 --> 00:35:19,810
Speaker 2: how did you bet three times with you know, bottom pair?

660
00:35:19,930 --> 00:35:23,570
Speaker 2: This is crazy? You were right, you won, but Nate

661
00:35:23,610 --> 00:35:27,450
Speaker 2: figures out that, oh, I'm overcalling, so now she's actually

662
00:35:27,530 --> 00:35:30,930
Speaker 2: playing differently. So now I'm going to change my strategy

663
00:35:31,130 --> 00:35:33,810
Speaker 2: to adjust to that. So next time I actually try

664
00:35:33,850 --> 00:35:36,890
Speaker 2: to do that, Nate ends up having just the nuts right,

665
00:35:37,170 --> 00:35:40,610
Speaker 2: the best hand ever. And when I have a good hand,

666
00:35:40,650 --> 00:35:43,010
Speaker 2: you know, he folds right. So he's now adjusted, and

667
00:35:43,050 --> 00:35:45,330
Speaker 2: I say, oh wait, wait, wait, okay, he's changed, so

668
00:35:45,370 --> 00:35:48,850
Speaker 2: now I need to change. And with two good players,

669
00:35:48,970 --> 00:35:53,650
Speaker 2: this process can go on indefinitely, and sometimes you psyche

670
00:35:53,650 --> 00:35:57,170
Speaker 2: yourself out right and you start getting into mind games,

671
00:35:57,170 --> 00:35:59,330
Speaker 2: and sometimes you end up overthinking and just making the

672
00:35:59,370 --> 00:36:03,370
Speaker 2: wrong decision. Also, you're making assumptions about the other person, right,

673
00:36:03,370 --> 00:36:04,930
Speaker 2: and how the other person is seeing you and how

674
00:36:04,930 --> 00:36:07,210
Speaker 2: they're adjusting. Now I'm talking about poker, because I know

675
00:36:07,250 --> 00:36:10,210
Speaker 2: poker and I don't know soccer. But this is exactly

676
00:36:10,570 --> 00:36:14,130
Speaker 2: kind of the problem that you have in this saga question.

677
00:36:14,530 --> 00:36:17,970
Speaker 1: In theoretical terms, as an academic would analyze it, you

678
00:36:18,050 --> 00:36:21,650
Speaker 1: get to an equilibrium, and the equilibrium is a mixed

679
00:36:21,650 --> 00:36:26,290
Speaker 1: strategy where it's unpredictable. So for example, the striker might

680
00:36:26,970 --> 00:36:29,210
Speaker 1: go left forty five percent of the time, go right

681
00:36:29,330 --> 00:36:31,250
Speaker 1: forty five percent of the time, stick it down the

682
00:36:31,250 --> 00:36:34,850
Speaker 1: middle ten percent of the time, and the goalkeeper might

683
00:36:35,050 --> 00:36:36,650
Speaker 1: stay in the middle twenty percent of the time and

684
00:36:36,690 --> 00:36:39,330
Speaker 1: go left forty percent and go right forty percent. But

685
00:36:39,370 --> 00:36:42,410
Speaker 1: the point is they can't be predictable. If they were predictable,

686
00:36:42,410 --> 00:36:45,890
Speaker 1: that could be exploitable, and they can't just be unpredictable

687
00:36:45,970 --> 00:36:48,170
Speaker 1: in a foolish way. So it's like, oh, I could

688
00:36:48,250 --> 00:36:51,290
Speaker 1: dive to the right fifty percent and to the left

689
00:36:51,290 --> 00:36:53,250
Speaker 1: fifty percent and never stand up in the middle. Well,

690
00:36:53,250 --> 00:36:56,130
Speaker 1: that's unpredictable, but it's also not smart because actually I

691
00:36:56,170 --> 00:36:59,450
Speaker 1: need to keep the striker honest and sometimes stand up. Absolutely,

692
00:36:59,570 --> 00:37:03,290
Speaker 1: there's always an equilibrium, but that equilibrium will involve a

693
00:37:03,370 --> 00:37:05,250
Speaker 1: randomized mixture of strategy, right.

694
00:37:05,330 --> 00:37:08,450
Speaker 2: But in practice, so this is called randomization, right, the

695
00:37:08,730 --> 00:37:12,610
Speaker 2: human is very bad at randomizing randomly. Most humans will

696
00:37:12,690 --> 00:37:15,250
Speaker 2: end up having a pattern and will end up actually

697
00:37:15,770 --> 00:37:18,170
Speaker 2: doing one thing more than they're supposed to do, right,

698
00:37:18,250 --> 00:37:20,330
Speaker 2: doing another thing less than they're supposed to do, because

699
00:37:20,330 --> 00:37:23,090
Speaker 2: they're not optimal game theoreticians.

700
00:37:23,370 --> 00:37:26,170
Speaker 1: There was actually a recent example where the English keeper

701
00:37:26,250 --> 00:37:31,170
Speaker 1: Jordan Pickford, in a high stakes penalty shootout, had a

702
00:37:31,330 --> 00:37:34,970
Speaker 1: water bottle with a list of every player on the

703
00:37:34,970 --> 00:37:37,890
Speaker 1: opposing team and whether he should dive to the left

704
00:37:37,970 --> 00:37:40,050
Speaker 1: or to the right, or stand up for that player.

705
00:37:40,290 --> 00:37:44,690
Speaker 1: So they had previously analyzed the play. His coaches had

706
00:37:44,730 --> 00:37:47,770
Speaker 1: basically pre randomized. It's a little risky, so as long

707
00:37:47,770 --> 00:37:50,690
Speaker 1: as nobody finds your water bottle ahead of time, you

708
00:37:51,050 --> 00:37:53,770
Speaker 1: can pre randomize. And I think he saved some goals

709
00:37:53,930 --> 00:37:56,370
Speaker 1: and England won that particular penalty shootout, which is very

710
00:37:56,410 --> 00:37:56,930
Speaker 1: un English.

711
00:37:56,970 --> 00:37:57,410
Speaker 2: That's great.

712
00:37:57,530 --> 00:37:59,970
Speaker 3: If you don't think that athletes are thinking about the

713
00:38:00,010 --> 00:38:02,690
Speaker 3: stuff professional athletes, then then you're wrong.

714
00:38:02,770 --> 00:38:02,930
Speaker 1: Right.

715
00:38:02,970 --> 00:38:05,650
Speaker 3: They may have their coaching staffs encouraging them to do it,

716
00:38:05,650 --> 00:38:07,770
Speaker 3: they may talk about themselves. They may even do some

717
00:38:07,810 --> 00:38:12,250
Speaker 3: of the stuff like fairly intuitively. Right, in an NBA game,

718
00:38:12,530 --> 00:38:14,330
Speaker 3: you have five players and twenty four seconds on the

719
00:38:14,330 --> 00:38:15,930
Speaker 3: shot clock, So it's kind of a game of like

720
00:38:16,570 --> 00:38:20,050
Speaker 3: trying to maximize your expected value with the best shot

721
00:38:20,170 --> 00:38:22,690
Speaker 3: you can, but the defense is trying to minimize your

722
00:38:22,730 --> 00:38:26,050
Speaker 3: expected value. And like the decisions these players make are

723
00:38:26,130 --> 00:38:29,050
Speaker 3: usually pretty smart about, like it's not quite worth taking

724
00:38:29,050 --> 00:38:30,730
Speaker 3: this shot, but now time's running out, and now I

725
00:38:30,770 --> 00:38:31,890
Speaker 3: need to you know, I think a lot of I

726
00:38:31,890 --> 00:38:34,450
Speaker 3: am actually quite high IQ. Frankly, there's a lot of

727
00:38:34,490 --> 00:38:37,970
Speaker 3: money and competitive pride on the line in making the

728
00:38:38,010 --> 00:38:38,690
Speaker 3: right decisions.

729
00:38:39,130 --> 00:38:43,130
Speaker 2: Yeah, so, Nate. Actually, Caleb has been thinking about underhanded

730
00:38:43,170 --> 00:38:46,250
Speaker 2: free throws in the NBA. Here's what he says. From

731
00:38:46,250 --> 00:38:48,810
Speaker 2: what I understand, some of not most players could hit

732
00:38:48,890 --> 00:38:53,610
Speaker 2: a higher free throw percentage if they practiced an underhand technique. Honestly,

733
00:38:53,690 --> 00:38:56,690
Speaker 2: I respect basketball pros less after learning that they don't

734
00:38:56,810 --> 00:38:59,890
Speaker 2: use underhanded free throws because they think it looks goofy.

735
00:39:00,010 --> 00:39:03,730
Speaker 2: It's not a winning mentality. So Nate, should NBA players

736
00:39:03,730 --> 00:39:05,290
Speaker 2: switch to underhanded free throws?

737
00:39:05,850 --> 00:39:10,810
Speaker 3: Yeah? Look, there probably are some libriums where strategies that

738
00:39:10,850 --> 00:39:16,050
Speaker 3: are considered emasculating, right or embarrassing. Somehow it might be

739
00:39:16,090 --> 00:39:20,370
Speaker 3: pursued a bit less right, although maybe that changes too. Right,

740
00:39:20,370 --> 00:39:22,810
Speaker 3: You've now seen a thing in the NBA where there's

741
00:39:22,810 --> 00:39:24,730
Speaker 3: a lot more flopping than there once was, meaning that

742
00:39:24,770 --> 00:39:28,290
Speaker 3: when guys get foul, they'll exaggerate the magnitude of what

743
00:39:28,410 --> 00:39:31,250
Speaker 3: actually happened to them. Right, it used to be something

744
00:39:31,250 --> 00:39:34,890
Speaker 3: people associated with soccer. It's migrated into the NBA as well.

745
00:39:34,890 --> 00:39:37,490
Speaker 3: But Yeah, sometimes to take strategies that would be seen

746
00:39:37,530 --> 00:39:41,130
Speaker 3: as embarrassing. There's expected value there if you don't care

747
00:39:41,170 --> 00:39:42,210
Speaker 3: about the embarrassment.

748
00:39:43,530 --> 00:39:46,570
Speaker 1: I'm curious, guys, is that an example in Poka of

749
00:39:46,570 --> 00:39:49,690
Speaker 1: a strategy that is positive expected value but everyone thinks

750
00:39:49,730 --> 00:39:51,810
Speaker 1: it's just embarrassing it only an idiot would do it.

751
00:39:51,890 --> 00:39:55,490
Speaker 2: Yeah, there's one that has turned out to be much

752
00:39:55,530 --> 00:39:58,650
Speaker 2: better than people thought. And it's called dunk betting, which

753
00:39:58,690 --> 00:40:01,970
Speaker 2: is leading into the preflop raiser. And so the reason

754
00:40:01,970 --> 00:40:04,330
Speaker 2: it's called dunk betting is because people thought you were

755
00:40:04,370 --> 00:40:07,370
Speaker 2: a dunk donkey if you did it right, because the

756
00:40:07,570 --> 00:40:11,650
Speaker 2: established wisdom is you always check to the pre flop raiser.

757
00:40:12,010 --> 00:40:14,730
Speaker 2: And once we got more advanced game theory, it turns

758
00:40:14,730 --> 00:40:17,130
Speaker 2: out that no, actually there are some spots where you should.

759
00:40:16,970 --> 00:40:19,130
Speaker 1: Lead what is leading into a pre flop raiser.

760
00:40:19,010 --> 00:40:21,730
Speaker 2: Betting first, right, instead of checking your options so that

761
00:40:21,930 --> 00:40:25,850
Speaker 2: letting the other person act you act first. You bet first.

762
00:40:26,250 --> 00:40:29,050
Speaker 2: To this day, though, even though it's now accepted, if

763
00:40:29,090 --> 00:40:33,610
Speaker 2: someone does it right away, it still feels donkeysh even

764
00:40:33,650 --> 00:40:36,930
Speaker 2: when it's not right. It's very hard to kind of

765
00:40:36,930 --> 00:40:39,330
Speaker 2: wrap your mind around the fact that this might actually

766
00:40:39,370 --> 00:40:40,890
Speaker 2: be the correct play.

767
00:40:41,690 --> 00:40:43,850
Speaker 3: One other play that can be hard to make is

768
00:40:44,490 --> 00:40:47,650
Speaker 3: folding when you've already put a lot of money in

769
00:40:47,690 --> 00:40:49,250
Speaker 3: the pot and there's just a little bit more to

770
00:40:49,250 --> 00:40:52,490
Speaker 3: put in and see the showdown. But still sometimes you

771
00:40:52,570 --> 00:40:55,090
Speaker 3: maybe do have a fold potentially. You know, I played

772
00:40:55,850 --> 00:40:58,450
Speaker 3: one unusual hand at a tournament a couple of months

773
00:40:58,490 --> 00:41:02,450
Speaker 3: ago where I raise, a second player called, and a

774
00:41:02,490 --> 00:41:06,690
Speaker 3: third player reraise, and if I was gonna call, I

775
00:41:06,730 --> 00:41:10,330
Speaker 3: was committing about half my stack, right, which means ordinarily

776
00:41:10,330 --> 00:41:12,530
Speaker 3: what you're supposed to was either fold or go ahead

777
00:41:12,570 --> 00:41:14,810
Speaker 3: and go all in. However, I had a hand that

778
00:41:14,850 --> 00:41:17,970
Speaker 3: I wanted to encourage the third player to enter the pot,

779
00:41:18,090 --> 00:41:20,010
Speaker 3: so I just called, and I'm like, well, I'll have

780
00:41:20,050 --> 00:41:21,370
Speaker 3: a lot of good flops and if I don't have

781
00:41:21,410 --> 00:41:23,450
Speaker 3: a good flop then I can get away. It's a

782
00:41:23,450 --> 00:41:26,610
Speaker 3: bad flop and anyway, so yeah, Like so I miss

783
00:41:26,930 --> 00:41:29,250
Speaker 3: the flop, which is the first three cards in poker.

784
00:41:29,810 --> 00:41:33,130
Speaker 3: He bets and I fold getting three and a half

785
00:41:33,370 --> 00:41:34,890
Speaker 3: two one odds. It's like, what are you doing, man?

786
00:41:34,890 --> 00:41:36,770
Speaker 3: You're not allowed to fold there, right, which indicated I

787
00:41:36,810 --> 00:41:38,410
Speaker 3: think that he had a good hand and was hoping

788
00:41:38,410 --> 00:41:40,650
Speaker 3: that I continued, right, But like, you know, I ran

789
00:41:40,730 --> 00:41:42,930
Speaker 3: the math later and I'm like actually this is the

790
00:41:42,970 --> 00:41:44,810
Speaker 3: right play by a couple of percentage points. It was

791
00:41:44,850 --> 00:41:47,170
Speaker 3: just an embarrassing play to make in the moment.

792
00:41:48,130 --> 00:41:51,410
Speaker 1: So I have learned no donk betting for me. And

793
00:41:51,490 --> 00:41:54,610
Speaker 1: I've also learned from Nate that sometimes you have to

794
00:41:54,690 --> 00:41:57,410
Speaker 1: quit whether or not you are ahead. We have to quit.

795
00:41:57,530 --> 00:42:02,050
Speaker 1: We're out of time, Nate, Maria. This has been such fun.

796
00:42:02,130 --> 00:42:03,090
Speaker 3: Thank you so much, Tim.

797
00:42:03,170 --> 00:42:04,050
Speaker 2: Thank you for having us.

798
00:42:04,050 --> 00:42:07,130
Speaker 1: Tim, I always love listening to Whiskey Business, and thank

799
00:42:07,170 --> 00:42:10,050
Speaker 1: you so much for joining us on Calltionary Questions.

800
00:42:10,290 --> 00:42:12,770
Speaker 3: Thank you, Tim. We'll do it again sometime. Thank you.

801
00:42:13,450 --> 00:42:16,570
Speaker 1: I will be back next week with another cautionary tale.

802
00:42:21,930 --> 00:42:25,450
Speaker 1: Cautionary Tales is written by me Tim Harford, with Andrew Wright,

803
00:42:25,650 --> 00:42:29,610
Speaker 1: Alice Fines, and Ryan Dilly. It's produced by Georgia Mills

804
00:42:29,770 --> 00:42:33,570
Speaker 1: and Marilyn Rust. The sound design and original music are

805
00:42:33,570 --> 00:42:37,130
Speaker 1: the work of Pascal Wise. Additional sound design is by

806
00:42:37,210 --> 00:42:41,450
Speaker 1: Carlos San Juan at Brain Audio. Bend A daph Haffrey

807
00:42:41,690 --> 00:42:45,210
Speaker 1: edited the scripts. The show features the voice talents of

808
00:42:45,290 --> 00:42:50,570
Speaker 1: Melanie Guttridge, Stella Harford, Oliver Hembrough, Sarah jupb Messam Monroe,

809
00:42:50,810 --> 00:42:54,810
Speaker 1: Jamal Westman, and rufus Wright. The show also wouldn't have

810
00:42:54,810 --> 00:42:58,250
Speaker 1: been possible without the work of Jacob Weisberg, Gretta Cohene,

811
00:42:58,530 --> 00:43:03,330
Speaker 1: Sarah Nix, Eric Sandler, Carrie Brody, Christina Sullivan, Kira Posey,

812
00:43:03,530 --> 00:43:08,730
Speaker 1: and Owen Miller. Cautionary Tales is a production of Pushkin Industries,

813
00:43:09,170 --> 00:43:13,770
Speaker 1: recorded at Wardour Studios in London by Tom Berry. If

814
00:43:13,810 --> 00:43:17,290
Speaker 1: you like the show, please remember to share, rate and review.

815
00:43:17,370 --> 00:43:19,090
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816
00:43:19,130 --> 00:43:21,850
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817
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818
00:43:25,210 --> 00:43:43,770
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