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Speaker 1: This is Master's in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: Her name is Annie Duke and she is the author

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Speaker 1: of Thinking in Bets. This conversation is not so much

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Speaker 1: about poker, although clearly as a world champion in poker

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Speaker 1: and at one point the winningest of female poker player

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Speaker 1: for that period, poker does come up. But it's all

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Speaker 1: about thought process. It's all about not looking at outcomes,

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Speaker 1: but thinking about how you think about what you're doing,

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Speaker 1: whether this is business or finance or investing or what

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Speaker 1: have you. Uh, there is a run of cognitive issues

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Speaker 1: and there is a run of miss focus on what

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Speaker 1: we do, how we do it, what we don't know.

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Speaker 1: But should our own blind spots, our own cog the

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Speaker 1: neveras that really applies to everything. This isn't just a

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Speaker 1: tele poker book. In fact, I would say poker is

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Speaker 1: really a minor part of the book. Um it's the

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Speaker 1: leaping off point for discussing human cognition, decision making theory

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Speaker 1: and how we think about the worlds or should think

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Speaker 1: about the world probabilistically, and we very often don't determined.

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Speaker 1: Poker is called resulting looking at outcomes as opposed to process.

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Speaker 1: Anybody who manages other people's money for living, anybody who

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Speaker 1: engages in behavior where there's a decent amount of risk

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Speaker 1: and uncertainty, should really uh, not only listen to this conversation,

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Speaker 1: which I found fascinating, but get the book and plow

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Speaker 1: through it. You will learn so much. It's it's absolutely fascinating. So,

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Speaker 1: with no further ado, my conversation with Annie Duke. I'm

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Speaker 1: Barry Rihults. You're listening to Masters in Business on Bloomberg Radio.

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Speaker 1: My special guest today is Annie Duke. At one point

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Speaker 1: she was the winningest female poker player in history. She

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Speaker 1: won the World Series of Poker in two thousand and four,

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Speaker 1: and she is the author of a fascinating new book,

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Speaker 1: Thinking in Bets, Making Smarter decisions when you don't have

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Speaker 1: all the facts. Annie Duke, Welcome to Bloomberg. Thanks for

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Speaker 1: having me so. I was struck by some of your

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Speaker 1: definitions in the book and how much they reminded me

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Speaker 1: of of investing. And my definition of investing is deploying

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Speaker 1: capital on the basis of limited information about an unknowable future.

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Speaker 1: That sounds a lot like the way you described playing poker,

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Speaker 1: it's almost exactly the way that I described playing poker. So, Uh,

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Speaker 1: the kind of loose definition is decision making under conditions

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Speaker 1: of uncertainty over time. Uh. So that would be a

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Speaker 1: relatively loose definition of poker where you're just as you said,

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Speaker 1: you're deploying capital based on limited information. That's one source

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Speaker 1: of uncertainty, um about some sort of uncertain future. Uh.

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Speaker 1: That would be luck um intervening, which is the other

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Speaker 1: source of uncertainty. So when we talk about decision making

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Speaker 1: under conditions of uncertainty, those are the two sources which

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Speaker 1: are very nicely put into your definition of investing. And

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Speaker 1: you also spend a lot of time describing the focus

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Speaker 1: on results um and and outcomes rather than the process

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Speaker 1: that led us do those results. Tell us a little

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Speaker 1: bit about what led you to that analysis and why

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Speaker 1: so many people look at a bad result and think

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Speaker 1: immediately it's a bad process when that may not be

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Speaker 1: the case. Well, I think that it's really hard. So

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Speaker 1: we have this very uncertain relationship between uh, decision quality

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Speaker 1: and outcome quality. UM. So for example, in poker, I

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Speaker 1: can have the very best hand and I can still lose.

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Speaker 1: I could get dealt asis, and you could have a

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Speaker 1: seven in a two and the turn of the cards

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Speaker 1: make it so that you win the hand, or vice versa.

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Speaker 1: I could have the seven in two and make terrible

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Speaker 1: decisions in playing that hand and still win. And that's

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Speaker 1: really really similar to the kind of decisions that we

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Speaker 1: make in life and investing in business. And the problem

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Speaker 1: is that getting to be able to see the process

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Speaker 1: isn't transparent. The thing that we can see is the

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Speaker 1: outcome of the process. We can see did it work

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Speaker 1: out or did it not work out? Did I win

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Speaker 1: the hand, did I not? Did the you know, did

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Speaker 1: the stock I invested in go up in value or

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Speaker 1: down in value? And that's what we can see. And

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Speaker 1: now working backwards from that into what was the decision

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Speaker 1: process is really hard. It's very opaque and very often, uh,

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Speaker 1: the quality of the decision doesn't reveal itself except over time.

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Speaker 1: So we can see the outcome right then, but very

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Speaker 1: often whether the decision process is good it takes a

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Speaker 1: lot of time to reveal itself. So what do we

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Speaker 1: do under those conditions of uncertainty? We have this bias,

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Speaker 1: we have this heuristic which is outcome was bad? Okay,

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Speaker 1: that must mean the decision was bad. I'll take that

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Speaker 1: as a signal outcome was good. Okay, decision was good. Um.

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Speaker 1: And the problem is that that's a really poor strategy

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Speaker 1: for learning from your outcomes. It's great if you're playing chess,

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Speaker 1: but it's terrible. If you're playing poker, it's terrible. If

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Speaker 1: you're investing, it's terrible. If you're running a business, it's terrible.

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Speaker 1: If you're choosing a romantic partner, it's terrible if you're driving.

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Speaker 1: That's that's the thing about it. So that I love

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Speaker 1: the term for this in the book that poker players use.

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Speaker 1: They call it resulting. Yes, So resulting is taking the

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Speaker 1: quality of the result and deciding that that tells you

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Speaker 1: what the quality of the case. That is not the

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Speaker 1: case at all. So I opened the book actually talking

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Speaker 1: about this Pete Carroll um Super Bowl and the what

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Speaker 1: was it? Four yards to go go first down, second,

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Speaker 1: second down, and only a few seconds left, but enough

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Speaker 1: for multiple plays if the clock is managed right, right,

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Speaker 1: So peak four, that's where the number that's yeah, that's

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Speaker 1: where the number four came from. So yeah, they're on

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Speaker 1: they're on the one yard line there against the Patriots.

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Speaker 1: Of course, um, and it's literally seconds to go. It's

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Speaker 1: twenty six seconds left and the Seahawks have exactly one

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Speaker 1: time out. And I think that people remember because it's

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Speaker 1: quite famous that Pete Carroll called and running play there.

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Speaker 1: Uh sorry, Pete Carroll called a pass play there, right,

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Speaker 1: so uh while having the best short yardage UM runner

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Speaker 1: in the game in Marshall Crunch as it is, it

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Speaker 1: was Marshaw Lynch. Marshall Lynch. Yeah, he's a musician. There

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Speaker 1: you go, marsha Unlynch. So so everybody expected him to

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Speaker 1: hand it off to Marsha Lynch. Instead, he called for

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Speaker 1: Russell Wilson to pass. Uh. The past was very famously intercepted.

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Speaker 1: And during the game, actually you can hear Chris Collingsworth

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Speaker 1: call this is I mean, he's just, you know, gobsmacked.

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Speaker 1: This is the worst thing I've ever seen. Uh. And

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Speaker 1: the next day the headlines were equally brutal, and the

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Speaker 1: argument wasn't so much about whether Pete Carroll had made

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Speaker 1: the worst call in Super Bowl history. It was more like,

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Speaker 1: did you make the worst call in Super Bowl history?

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Speaker 1: Or was it the worst call in football history? Period?

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Speaker 1: So we can agree that this was a terrible result.

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Speaker 1: But I make the argument in the opening of the

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Speaker 1: book that this is a really good example of resulting,

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Speaker 1: which is just assuming that the quality of the of

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Speaker 1: the outcome is telling you about the quality of Pete

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Speaker 1: Carroll's decision making. There were, in facts, some outline voices.

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Speaker 1: One of the stronger ones was from Benjamin Morris over A,

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Speaker 1: who argued it was pretty statistically sound. How many remind

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Speaker 1: us how many interceptions were in similar circumstances during the

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Speaker 1: season during that season, zero and historically between one and two.

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Speaker 1: So really this was a high probability. Either there's a

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Speaker 1: touchdown or the passes dropped and you managed the and

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Speaker 1: you still have time for two more running place. Well,

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Speaker 1: I'm gonna put it investing in investing terms. Remember, he

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Speaker 1: has one time out left, so if he calls the

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Speaker 1: running play and Lynch fails to get in, of course

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Speaker 1: the clock is running then, so he has to burn

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Speaker 1: a time out and he'll only have time to run

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Speaker 1: the two running place. Um, there are three possible outcomes

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Speaker 1: from the past play. One is this really really tiny

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Speaker 1: chance of an interception. The other two is the balls dropped,

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Speaker 1: which happens in basically no time, So the clock stops

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Speaker 1: immediately obviously, which then gives them the two running place,

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Speaker 1: or it's caught for a touchdown and you assume that

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Speaker 1: the Seahawks are going to win. So let's say that

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Speaker 1: it's a practically free option at the same two running

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Speaker 1: place that you would get otherwise. And we all know

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Speaker 1: from investing that options that are basically free. I mean,

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Speaker 1: you know, one percent isn't a lot, right, It's a

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Speaker 1: basically free option at these same two running plays. So

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Speaker 1: I think if we think about an investment terms, look,

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Speaker 1: we can quibble right about whether in fact that was

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Speaker 1: a good decision or not, but calling it the worst

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Speaker 1: is as in Super Bowl history, when he was essentially

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Speaker 1: exercising and almost free option, that seems a little much.

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Speaker 1: And I think that we can see how strongly this

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Speaker 1: is coming from this resulting problem when we just do

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Speaker 1: this simple thought experiment. What if he had passed the

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Speaker 1: ball and it was caught in the end zone for

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Speaker 1: a touchdown the Patriots and what little time they had

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Speaker 1: left failed to score and they won the Super Bowl.

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Speaker 1: What do you think the headlines would have looked at

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Speaker 1: It certainly wouldn't be time. I think it would have

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Speaker 1: been something like out Belichick Belichick, Um, you know, wow,

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Speaker 1: that was so unexpected. That's what got them to the

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Speaker 1: super Bowl in the first place, that kind of decision making.

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Speaker 1: And in fact, we kind of know that's true because

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Speaker 1: we sort of just ran the experiment. We just sort

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Speaker 1: of saw that. It come to play with the Philly

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Speaker 1: Special where everybody called Peterson brilliant for not just going

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Speaker 1: for the extra three points at the end of the

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Speaker 1: second down, um and actually throwing this weird path to

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Speaker 1: the quarterback Nick Foles in the end zone and he

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Speaker 1: caught it, and people said he's brilliant, he thought Belichick,

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Speaker 1: and I think that what we can agree is whether

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Speaker 1: you think that that was a good decision or not.

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Speaker 1: The quality of the outcome should have nothing to do

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Speaker 1: with whether the decision quality was good. In reality, it

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Speaker 1: was one try. We flipped the coin one time. So

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Speaker 1: I think that thought experiment is very revealing because you

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Speaker 1: can see how much you as an individual gets sort

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Speaker 1: of pulled around by the outcome because it's we can

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Speaker 1: see it. It's heavy, it's like a gravity. Well, hind

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Speaker 1: signed bias is a powerful thing. We talked a little

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Speaker 1: bit about Pete Carroll's decision. Let's talk about how this

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Speaker 1: applies to poker. You wrote, Poker players who stand the

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Speaker 1: test of time have a variety of talents, but what

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Speaker 1: they share is the ability to execute in the face

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Speaker 1: of these limitations. Explain that a little bit. Well, I

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Speaker 1: think that the issue that we all have is that

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Speaker 1: we're pretty good at understanding what our goals are, right.

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Speaker 1: We can set those pretty well, you know, I want

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Speaker 1: to be I sort of refer in the book to

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Speaker 1: whatever your e er is, smarter, faster, healthier, richer, happier,

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Speaker 1: whatever it is, you figure it out. But the problem

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Speaker 1: isn't so much these kind of like, Okay, I want

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Speaker 1: I know what my long term goal is, in the

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Speaker 1: sense like we can see this with New Year's resolutions, right.

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Speaker 1: The problem is that we have all these small, little

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Speaker 1: executional decisions along the way, and that's where we really

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Speaker 1: get into trouble. If we think about it in terms

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Speaker 1: of say Danny Kanaman's framework from thinking fast and Slow,

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Speaker 1: we could think about the goal setting is happening in

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Speaker 1: system too, in deliberative mind right, and the the actual

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Speaker 1: A lot of these executional decisions are happening in system one,

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Speaker 1: and we know that system one, this more reflective mind

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Speaker 1: is going to be subject to a lot of biases.

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Speaker 1: One of the main biases that's going to get in

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Speaker 1: the way of executing our goals is that we're supposed

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Speaker 1: to learn from the outcomes we have, but it doesn't

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Speaker 1: feel good, for example, to be wrong, so we process,

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Speaker 1: and in fact, people hate being wrong so much that

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Speaker 1: they very often will will take a safe play where

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Speaker 1: they don't end up looking wrong, rather than taking a

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Speaker 1: risk with a binary outcome. How did that affect How

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Speaker 1: did that thinking effect the way you played? Did you

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Speaker 1: see that behavior amongst other poker players? Oh? Absolutely? So

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Speaker 1: if you know that, here's kind of the problem is

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Speaker 1: that you know that the people around you are going

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Speaker 1: to act like the Seahawks fans or the pundits after

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Speaker 1: the play, and if you have some sort of spectacularly

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Speaker 1: spectacularly bad outcome, particularly if it's associated with taking a risk,

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Speaker 1: which Pete Carroll certainly, did you know that they're going

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Speaker 1: to come down on you, right, I mean this the

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Speaker 1: way that the world is going to come down critiquing you.

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Speaker 1: So what ends up happening a lot of times at

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Speaker 1: the table is that when you have these decisions, and

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Speaker 1: remember your cards are faced down, so people can't see right,

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Speaker 1: they can't really see where they're making a good or

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Speaker 1: bad decision. There, they'll very often come down on the

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Speaker 1: side of well, I'm not going to really do the

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Speaker 1: risky thing, which is say maybe try to bluff here. Instead,

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Speaker 1: I'm just going to quietly fold this hand away and

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Speaker 1: nobody's ever going to know um. And that's just one

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Speaker 1: of the many problems that can come where you're sort

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Speaker 1: of defending yourself against outcomes. The other way that you

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Speaker 1: can defend yourself against outcomes is that when things happen

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Speaker 1: to you where you lose, what's one of the best defenses. Well,

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Speaker 1: we know that one of the sources of uncertainty that

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Speaker 1: we have is luck. Right, that's one of the things

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Speaker 1: that we can't we I can choose. I can make

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Speaker 1: a decision that's going to have some set of possible

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Speaker 1: futures that's going to occur, say an interception, a drop

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Speaker 1: past catch, whatever it might be. But I can't control

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Speaker 1: which one right. And that's what I can't control. So

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Speaker 1: we know that luck is going to be a really

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Speaker 1: good defense as well. And so what we'll do is

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Speaker 1: we'll have an outcome it's bad, and we'll say well,

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Speaker 1: I just got unlucky. And the problem with that is

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Speaker 1: that if you got unlucky, there's nothing to be learned

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Speaker 1: from it. And so how are you going to improve

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Speaker 1: your execution of decisions in the future, because you're just

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Speaker 1: offloading that because it just doesn't feel good. It doesn't

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Speaker 1: feel good to say, well, maybe that bad outcome came

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Speaker 1: from the fact that maybe I didn't actually play the

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Speaker 1: hand that well, maybe I made poor decisions in in

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Speaker 1: my investment strategy or in driving in my car down

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Speaker 1: the street when I got in that accident. You describe

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Speaker 1: that process of self evaluation of was this a good

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Speaker 1: decision or was this luck as one of the more

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Speaker 1: difficult things that you have to learn when you're playing poker.

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Speaker 1: Why is it so different to untangle skill and luck,

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Speaker 1: be it in gambling or or business or professional sports

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Speaker 1: for that matter. Well, again, because we we generally just

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Speaker 1: don't have enough data to do it, and we tend

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Speaker 1: to uh field outcomes one at a time. We don't

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Speaker 1: wait wait around an aggregate, even aggregate even if we

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Speaker 1: did so. For example, I mean, if we think about

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Speaker 1: choosing a romantic partner, say you know, a spouse, how

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Speaker 1: many tries at that do we have. It's not like

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Speaker 1: were doing a whole lot of data collection there, right,

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Speaker 1: We only date a few people in our lives. The

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Speaker 1: end is quite small. The sample size is small. Um,

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Speaker 1: It's sort of like most of the decisions we're doing

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Speaker 1: is saying we're going to flip a coin four times

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Speaker 1: and then try to say something about whether the coin

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Speaker 1: is fair or not, and it's just hard and and

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Speaker 1: we can't see. Nobody sort of pulls back the curtain

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Speaker 1: and says to us, you know, this portion of the

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Speaker 1: outcome was due to luck, and this person portion of

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Speaker 1: the outcome was due to skill. We just know that

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Speaker 1: we sort of live in this noisy system where sometimes

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Speaker 1: we drive through red lights and we get through just fine,

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Speaker 1: and sometimes we go through green lights and we get

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Speaker 1: in an accident. And it's just not perfectly linked together.

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Speaker 1: Like like it is saying a game like chas, if

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Speaker 1: I play poorly, I probably lose if I play. Not

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Speaker 1: a lot of random luck involved in the of poker exactly,

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Speaker 1: but poker and investing there is a lot of random

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Speaker 1: luck and we can't see it. We need like a

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Speaker 1: big sample size in order to be able to get

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Speaker 1: down to say something about those statistics, and even then

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Speaker 1: we're probably making some assumptions as we're doing our analysis.

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Speaker 1: And that's why it's so hard, because again, all we

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Speaker 1: can see is the outcome. Right, we can make a

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Speaker 1: bad decision and it can converge on a win. We

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Speaker 1: can make a good decision and it can converge on

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Speaker 1: the same win. So how then do you just take

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Speaker 1: this thing and now try to work backwards and untangle

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Speaker 1: luck from skill when it's very hard to see down

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Speaker 1: into it and we generally don't have a big sample size.

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Speaker 1: So let's talk about that untangling. Undergraduate, you go to

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Speaker 1: Columbia University graduate school, you go to University of Pennsylvania,

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Speaker 1: you almost get a PhD psychology. How far away from

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Speaker 1: were you from from a degree? I needed to defend it, okay,

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Speaker 1: and that was done. But you you so you know

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Speaker 1: you're practically you go to the ninth inning with the

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Speaker 1: one yard line. Yes, I was on the one and

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Speaker 1: then you moved to Billings, Montana. So two questions, A,

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Speaker 1: what motivated the move? And be how important is psychology

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Speaker 1: to your successful track record in poker? Well, let me say,

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Speaker 1: you know, there's been a lot of moments of luck

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Speaker 1: intervening in my life, which I think have been pretty interesting.

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Speaker 1: So one of them is the moment that I moved

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Speaker 1: to Montana, which came from what might look like an

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Speaker 1: unlucky event, but I guess that it turned out pretty

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Speaker 1: well for me. Um, which was that right at the end,

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Speaker 1: as I was going out for my UH interviews to

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Speaker 1: become a professor, I had been struggling with some stomach

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Speaker 1: issues the last year in graduate school, and they really

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Speaker 1: came to a head, um right at that time, and

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Speaker 1: I landed in the hospital for two weeks with this

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Speaker 1: very bad stomach problem. So I needed to take some

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Speaker 1: time off. And during that time off, I discovered that

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Speaker 1: you you, oh, okay, Now I don't have a fellowship.

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Speaker 1: I need money. Um. The man I was married to

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Speaker 1: at the time had a house in Montana and said,

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Speaker 1: let's go. We'll just go to Montana for the summer

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Speaker 1: and you can recuperate there. Um. And during that summer

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Speaker 1: I started playing poker. And that's kind of how that happened. Now,

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Speaker 1: when you say during that summer you started playing poker,

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Speaker 1: I'm going to correct you slightly, because you grew up

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Speaker 1: in a house old. Your brother is a multiple uh

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Speaker 1: poker champion, and you grew up in a very competitive

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Speaker 1: household where there were card games and other things going on. Uh,

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Speaker 1: that you played competitively within the family. Am I wrong

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Speaker 1: in assuming you were playing poker at home against your

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Speaker 1: future champion brother. Well, not exactly wrong, but also not

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Speaker 1: exactly right. I would say that the poker chips came

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Speaker 1: out in the house maybe once or twice a year,

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Speaker 1: and it was some games like I don't know if

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Speaker 1: you've ever played past the trash. I think the more

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Speaker 1: common name for my dad called it past the trash.

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Speaker 1: I think the more common name is anaconda that you've

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Speaker 1: played so you're passing like three cards to the right

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Speaker 1: three card. You would never play this in the casino,

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Speaker 1: of course, UM, so we'd play things like that. So

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Speaker 1: mostly actually we're playing um games like hearts, um oh Hell,

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Speaker 1: which is a sort of simplified version of bridge. Uh.

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Speaker 1: I played bridge starting when I was a teenager. Uh,

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Speaker 1: A lot of like gin um. And the thing about

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Speaker 1: that is, while you don't necessarily have this kind of

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Speaker 1: ongoing betting element that you do in poker, you have

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Speaker 1: a lot of the same elements of uncertainty and luck.

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Speaker 1: So being able to reason around. Those kinds of games

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Speaker 1: certainly helped me, uh with poker, So that was, you know,

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Speaker 1: that was the first, definitely the first moment of luck. UM.

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Speaker 1: The The interesting thing with how do psychology help? Is

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Speaker 1: that when I first started UM playing, I think that

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Speaker 1: people thought that I studied clinical psychology, which would be

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Speaker 1: like understanding like why are you depressed or why are

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Speaker 1: you anxious? Or those kinds of things, And they thought

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Speaker 1: that would be really helpful because I would have some

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Speaker 1: sort of analysis of your personality. I suppose that might

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Speaker 1: have been helpful, but I've never taken a class in

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Speaker 1: clinical psychology in my life, so I know no more

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Speaker 1: than from my own going to a therapist. UM We're

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Speaker 1: Actually was really helpful was incognitive psychology. What you're studying

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Speaker 1: is these issues of how do you learn and uncertain systems?

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Speaker 1: How do we process information? What are the biases and

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Speaker 1: heuristics that get in our way? Now? That was incredibly

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Speaker 1: ful for understanding not only the learning problem that I

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Speaker 1: was about to face, in terms of you know, I'm

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Speaker 1: getting this feedback at the table and these outcomes are

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Speaker 1: coming my way, and how do I actually become better?

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Speaker 1: How do I figure out how to be a better

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Speaker 1: decision maker in this game, but also how might other

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Speaker 1: people bias be expressed at the table in a way

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Speaker 1: that I could strategically use to my advantage. Um, and

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00:20:23,480 --> 00:20:26,240
Speaker 1: that was incredibly helpful. Let's talk a little bit about

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Speaker 1: your actual poker career, couch, because you've obviously want a

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Speaker 1: lot of money, and the World Series of Poker is

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Speaker 1: a big event each year. At what point in your

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Speaker 1: casual poker playing did you reach the understanding, hey, I

385
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Speaker 1: could I could earn a living this way. So when

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Speaker 1: I was in graduate school, I my and I you know,

387
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Speaker 1: I was living on my little fellowship. My brother was

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Speaker 1: already a professional poker player, so he would bring me

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Speaker 1: out to Las Vegas maybe once or twice a year,

390
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Speaker 1: and I, I mean, I enjoyed watching him play, but

391
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Speaker 1: when he was in tournaments, you can't actually sit behind somebody,

392
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Speaker 1: and so you were just sort of standing on the rail,

393
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Speaker 1: and that's pretty boring. So I told him I was

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Speaker 1: kind of bored. Uh, And he actually gave me a

395
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Speaker 1: tiny bit of money. I mean it was like three

396
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Speaker 1: dollars or something and sent me off to play with

397
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Speaker 1: a little napkin of hand rankings. Basically, Um, like literally written, Yeah,

398
00:21:15,920 --> 00:21:19,720
Speaker 1: it was well, I knew that part. It was here

399
00:21:19,720 --> 00:21:22,119
Speaker 1: the hands are allowed to play right, And it was

400
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Speaker 1: written in a on a with a keynote crayon, like

401
00:21:25,000 --> 00:21:27,240
Speaker 1: literally on on a napkin. This is what I had.

402
00:21:27,240 --> 00:21:30,680
Speaker 1: And I walked over to the Freemont Casino. Um, you

403
00:21:30,720 --> 00:21:32,040
Speaker 1: know the poke room I think was next to the

404
00:21:32,080 --> 00:21:35,080
Speaker 1: Carls Jr. And that that's where I played, so that certainly,

405
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Speaker 1: you know, that was when I was in academic, so

406
00:21:36,840 --> 00:21:38,879
Speaker 1: it never occurred to me. I would say that pretty

407
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Speaker 1: early on. Um, in sitting down at these tables in Montana,

408
00:21:42,480 --> 00:21:44,160
Speaker 1: I figured out that I could make a living. But

409
00:21:44,400 --> 00:21:47,200
Speaker 1: you know, my reference was to what the starting salary

410
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Speaker 1: of a professor was, which I think at the time

411
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Speaker 1: I was applying for jobs that were about twenty three

412
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Speaker 1: dollars a year. So uh, that's you know, it's a

413
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Speaker 1: low bar for for what you felt the living was

414
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Speaker 1: going to be. I had this really big advantage, of course,

415
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Speaker 1: which is that I had these great mentors. I mean,

416
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Speaker 1: my brother among them, Eric sidell Um was one who

417
00:22:05,359 --> 00:22:08,920
Speaker 1: was really really amazing and very generous with his thoughts.

418
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Speaker 1: And uh that cut a few years off the beginning

419
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Speaker 1: of my learning curve. I would say I had a

420
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Speaker 1: big leg up, and I was playing against people who

421
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Speaker 1: weren't you know, who weren't world class players. They were

422
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Speaker 1: playing in a in a game where you had about

423
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Speaker 1: three hundred dollars maybe six hundred, maybe six d playing

424
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Speaker 1: with ranchers and farmers and truckers. And yeah, and I

425
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Speaker 1: have world champions coaching me, so you know, the ringer

426
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Speaker 1: at the table. I mean, as it turned out, yeah, So,

427
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Speaker 1: I mean, I think the first month that I played,

428
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Speaker 1: I remember, I had a little notebook, and I've made

429
00:22:41,359 --> 00:22:43,240
Speaker 1: that month. Obviously, that doesn't mean that that was my

430
00:22:43,280 --> 00:22:45,800
Speaker 1: expected value, but it looked pretty good to somebody who

431
00:22:45,840 --> 00:22:49,960
Speaker 1: was applying for dollar year jobs. That's right. So we're ready.

432
00:22:49,960 --> 00:22:52,600
Speaker 1: You're up to thirty plus, right, So I was like, whoa,

433
00:22:52,680 --> 00:22:55,520
Speaker 1: I'm I'm gonna make it. You know. So from from

434
00:22:55,520 --> 00:22:57,679
Speaker 1: that point when you realized, hey, I can make some

435
00:22:57,760 --> 00:23:00,880
Speaker 1: money doing this, how long was it for you realized

436
00:23:00,960 --> 00:23:03,960
Speaker 1: I'm a world class player, I am a poker expert.

437
00:23:04,320 --> 00:23:07,720
Speaker 1: What was that timeline? Like? That was never? I retired

438
00:23:07,800 --> 00:23:09,960
Speaker 1: in two thousand twelve, and I never I don't I

439
00:23:10,760 --> 00:23:13,479
Speaker 1: would think that even even after you won, you never thought, oh,

440
00:23:13,480 --> 00:23:15,960
Speaker 1: I'm a world class player, or were you thinking, well,

441
00:23:16,000 --> 00:23:17,879
Speaker 1: there was a decent amount of random locking this, it

442
00:23:17,920 --> 00:23:20,560
Speaker 1: could have not been my skill. So I think that

443
00:23:20,600 --> 00:23:22,760
Speaker 1: this is actually really important for anything that you do

444
00:23:22,800 --> 00:23:24,960
Speaker 1: in life in terms of what my mindset about it was,

445
00:23:25,680 --> 00:23:29,560
Speaker 1: which was that I think that there's a difference between

446
00:23:29,760 --> 00:23:31,440
Speaker 1: sort of being humble in the face of the game

447
00:23:31,480 --> 00:23:33,199
Speaker 1: that you're playing and humble in the face of the

448
00:23:33,200 --> 00:23:38,520
Speaker 1: opponents that you're facing. Yeah, so the interesting thing with

449
00:23:38,560 --> 00:23:42,560
Speaker 1: poker is that you know it's certainly for a human being.

450
00:23:42,960 --> 00:23:45,280
Speaker 1: The more that you play, the more you kind of

451
00:23:45,320 --> 00:23:47,880
Speaker 1: realize that you have no idea what you're doing at

452
00:23:47,880 --> 00:23:52,120
Speaker 1: this table. That it's such a complex decision problem, right,

453
00:23:52,119 --> 00:23:54,800
Speaker 1: I mean, all the cards are faced down, and there

454
00:23:54,880 --> 00:23:56,800
Speaker 1: is a lot of luck, and it's an incredibly hard

455
00:23:56,880 --> 00:23:58,840
Speaker 1: learning problem, and you're really just trying to get a

456
00:23:58,880 --> 00:24:01,480
Speaker 1: little bit better. You're trying to get closer to understanding

457
00:24:01,480 --> 00:24:03,479
Speaker 1: what the primary line of play is. And it's very

458
00:24:03,520 --> 00:24:05,359
Speaker 1: different than in chess. And chess you can really go

459
00:24:05,400 --> 00:24:08,879
Speaker 1: back and you can deconstruct with great certainty. You know

460
00:24:09,000 --> 00:24:11,520
Speaker 1: what the possible lines of play were, what your responses

461
00:24:11,600 --> 00:24:13,639
Speaker 1: might be, and you can really kind of work that

462
00:24:13,680 --> 00:24:15,199
Speaker 1: game to the end. In that sense, it's more of

463
00:24:15,200 --> 00:24:19,160
Speaker 1: a mathematical calculation. But isn't recognizing the degree of luck

464
00:24:19,200 --> 00:24:23,080
Speaker 1: in randomness in outcomes and a certain humble uncertainty of

465
00:24:23,119 --> 00:24:26,800
Speaker 1: your own skill set that meta cognition. Isn't that a

466
00:24:26,920 --> 00:24:31,280
Speaker 1: channel Dunning Krueger effect. Isn't that a wrecking uh an

467
00:24:31,320 --> 00:24:34,000
Speaker 1: acknowledgment of your own skill that you reach a point

468
00:24:34,000 --> 00:24:36,720
Speaker 1: of saying, oh, there's this much randomness in it, and

469
00:24:37,119 --> 00:24:39,520
Speaker 1: I'm aware of it absolutely, and there's this much that

470
00:24:39,600 --> 00:24:42,760
Speaker 1: I don't know and can't know. And I mean even

471
00:24:43,119 --> 00:24:46,280
Speaker 1: towards the end of my career. So I retired in

472
00:24:46,280 --> 00:24:48,080
Speaker 1: two thousand twelve. I think it was two thousand ten.

473
00:24:48,760 --> 00:24:52,840
Speaker 1: I won the NBC National Heads Up Championship and I

474
00:24:54,040 --> 00:24:56,960
Speaker 1: think that was five thousand dollars. I think was first place.

475
00:24:57,000 --> 00:24:59,879
Speaker 1: I don't really remember. Who can remember each half million dollars.

476
00:25:00,000 --> 00:25:03,760
Speaker 1: It's drink about poker. It's it's not really about the money.

477
00:25:03,800 --> 00:25:05,760
Speaker 1: It's about like, how do I make the best decisions

478
00:25:05,760 --> 00:25:07,440
Speaker 1: to get to where I'm going? So you actually sort

479
00:25:07,440 --> 00:25:10,239
Speaker 1: of it's actually a necessary factor to separate yourself from

480
00:25:10,280 --> 00:25:12,680
Speaker 1: the money, because otherwise the money can cause you to

481
00:25:13,640 --> 00:25:16,800
Speaker 1: why surgeons don't operate on their own family either, exactly

482
00:25:16,800 --> 00:25:18,800
Speaker 1: like you need a separation, so you actually kind of

483
00:25:18,840 --> 00:25:20,960
Speaker 1: don't think about things like money. So I mean, I

484
00:25:20,960 --> 00:25:22,879
Speaker 1: could look it up for you what I want, But

485
00:25:23,160 --> 00:25:25,480
Speaker 1: I think that even then, like right before that tournament,

486
00:25:25,520 --> 00:25:27,760
Speaker 1: my game had made a big change, like I discovered

487
00:25:27,760 --> 00:25:30,280
Speaker 1: new things about the game. So it's I think that's

488
00:25:30,320 --> 00:25:34,120
Speaker 1: it's this paradox, which is that in order to get better,

489
00:25:34,160 --> 00:25:36,320
Speaker 1: you sort of have to recognize your actual lack of

490
00:25:36,359 --> 00:25:39,800
Speaker 1: expertise again in relation to the game. Now that's different

491
00:25:39,800 --> 00:25:41,520
Speaker 1: than me feeling like if I sat down at a

492
00:25:41,520 --> 00:25:44,119
Speaker 1: poker table that I would be better at this problem

493
00:25:44,160 --> 00:25:46,960
Speaker 1: than you, which I do. And then also I think

494
00:25:46,960 --> 00:25:48,960
Speaker 1: that allows you to recognize when you're sitting at a

495
00:25:48,960 --> 00:25:52,119
Speaker 1: table with someone who's probably better than you at this problem.

496
00:25:52,160 --> 00:25:55,480
Speaker 1: And in fact, the NBC National Heads Up Championship gives

497
00:25:55,480 --> 00:25:57,119
Speaker 1: a good example of that. I ended up facing Eric

498
00:25:57,160 --> 00:26:01,080
Speaker 1: Sidewn my mentor um in the fine and I kind

499
00:26:01,119 --> 00:26:04,240
Speaker 1: of and I knew he's going to outthink me. I mean,

500
00:26:04,280 --> 00:26:06,560
Speaker 1: he's just a much better player than I am. So

501
00:26:06,600 --> 00:26:09,560
Speaker 1: I injected a lot of luck into that match because

502
00:26:09,560 --> 00:26:12,280
Speaker 1: I understood that if I have to execute a lot

503
00:26:12,320 --> 00:26:14,639
Speaker 1: of decisions against him where he has an edge on

504
00:26:14,680 --> 00:26:17,960
Speaker 1: every single decision, right, I'm going to be in big trouble.

505
00:26:18,040 --> 00:26:19,840
Speaker 1: But if I could get my money in one time

506
00:26:20,400 --> 00:26:22,440
Speaker 1: hoping to sort of get close to a fifty fifty,

507
00:26:22,520 --> 00:26:23,919
Speaker 1: that I would be better off because I could just

508
00:26:24,000 --> 00:26:26,240
Speaker 1: win that. So he couldn't just sort of chip away

509
00:26:26,280 --> 00:26:28,520
Speaker 1: at me with his edge. Your play was, let's go

510
00:26:28,640 --> 00:26:31,120
Speaker 1: for the random outcome, and I have a much better shot. Yeah,

511
00:26:31,160 --> 00:26:33,080
Speaker 1: because you're a better decision maker than I am. And

512
00:26:33,080 --> 00:26:35,560
Speaker 1: so if I let you make a hundred decisions against me,

513
00:26:36,000 --> 00:26:38,480
Speaker 1: that's a sure loss for me. But if I if

514
00:26:38,480 --> 00:26:41,080
Speaker 1: we just go for one big decision, you know you

515
00:26:41,119 --> 00:26:43,800
Speaker 1: can get lucky on the one coin toss. In investing,

516
00:26:43,960 --> 00:26:47,160
Speaker 1: we call it relative and absolute returns, and you're you're

517
00:26:47,200 --> 00:26:49,560
Speaker 1: making it that way. Let's talk a little bit about

518
00:26:49,800 --> 00:26:53,560
Speaker 1: blind spot bias and you you actually write in the book,

519
00:26:53,600 --> 00:26:57,600
Speaker 1: as have other people, blind spot bias is greater the

520
00:26:57,680 --> 00:27:01,760
Speaker 1: smarter you are. Why is that? Well, let me ask

521
00:27:01,800 --> 00:27:05,200
Speaker 1: you a question. If you had to bring somebody in

522
00:27:05,440 --> 00:27:08,560
Speaker 1: to argue your case for you, do you want like

523
00:27:08,600 --> 00:27:10,480
Speaker 1: the smart guy or the not so smart guy to

524
00:27:10,480 --> 00:27:16,360
Speaker 1: do that. You want the most accomplished, successful Harvard Law

525
00:27:16,400 --> 00:27:19,919
Speaker 1: school professor who can argue that case as opposed to

526
00:27:19,960 --> 00:27:22,320
Speaker 1: a person who's not very good. Okay, so this gives

527
00:27:22,400 --> 00:27:25,760
Speaker 1: us the reason why being smart makes it worse. Let's

528
00:27:25,800 --> 00:27:32,680
Speaker 1: think about us as we're all arguing our case. That's

529
00:27:32,720 --> 00:27:36,920
Speaker 1: the thing. We all have beliefs that we think are

530
00:27:37,000 --> 00:27:41,159
Speaker 1: true or false. Right, we all make predictions about the future,

531
00:27:41,720 --> 00:27:45,639
Speaker 1: and we are really good at making a case for

532
00:27:45,680 --> 00:27:48,159
Speaker 1: our stuff. And in fact, we kind of know that

533
00:27:48,200 --> 00:27:50,800
Speaker 1: because we can see when other people are like clearly

534
00:27:50,880 --> 00:27:53,719
Speaker 1: just like arguing their side and leaving data out and

535
00:27:53,800 --> 00:27:56,280
Speaker 1: spinning the facts and putting a particular frame on it.

536
00:27:56,760 --> 00:28:00,600
Speaker 1: So here's kind of the problem when you're really statistically

537
00:28:00,600 --> 00:28:03,920
Speaker 1: adapt or you're very mentally agile, is that we all

538
00:28:03,920 --> 00:28:06,720
Speaker 1: want our beliefs to be true. We all want our

539
00:28:06,760 --> 00:28:11,919
Speaker 1: predictions to be right, and so we will argue in

540
00:28:12,040 --> 00:28:13,920
Speaker 1: order to make our case. We're kind of our own

541
00:28:14,000 --> 00:28:17,640
Speaker 1: best pr agents. And the smarter you are, the better

542
00:28:17,720 --> 00:28:20,080
Speaker 1: you are at the spin. So if I have to

543
00:28:20,119 --> 00:28:24,080
Speaker 1: go and say make a statistical case for say gun

544
00:28:24,119 --> 00:28:27,480
Speaker 1: control lowers crime or gun control increases crime, give me

545
00:28:27,520 --> 00:28:30,400
Speaker 1: those two cases to make. Depending on what my beliefs are,

546
00:28:30,720 --> 00:28:33,120
Speaker 1: I can go and find lots and lots of support

547
00:28:33,160 --> 00:28:36,399
Speaker 1: for either side. And if I if I believe the

548
00:28:36,440 --> 00:28:38,720
Speaker 1: first thing that it lowers crime, I'll go find lots

549
00:28:38,720 --> 00:28:40,800
Speaker 1: and lots of reasons why that might be true, and

550
00:28:40,800 --> 00:28:42,560
Speaker 1: I'll give you very good arguments, and I'll show you

551
00:28:42,600 --> 00:28:44,840
Speaker 1: studies that show it, and it will be a compelling case,

552
00:28:44,880 --> 00:28:46,360
Speaker 1: and I'm going to be pretty good at it, the

553
00:28:46,400 --> 00:28:48,640
Speaker 1: smarter than I am. Likewise, if I think that gun

554
00:28:48,680 --> 00:28:51,000
Speaker 1: control increases crime, I'll go just find lots of stuff

555
00:28:51,000 --> 00:28:53,040
Speaker 1: for that. And the interesting thing about it is I

556
00:28:53,080 --> 00:28:56,280
Speaker 1: won't know I'm doing it necessarily, so that confirmation bias

557
00:28:56,320 --> 00:29:00,520
Speaker 1: is completely on subconscious and you're unaware of Well, there

558
00:29:00,560 --> 00:29:03,000
Speaker 1: are definitely people who are spinning. I mean, it's not

559
00:29:03,040 --> 00:29:06,280
Speaker 1: like people aren't sometimes purposely doing that. But I think

560
00:29:06,280 --> 00:29:08,320
Speaker 1: that the really insidious part of this is that we

561
00:29:08,480 --> 00:29:12,760
Speaker 1: generally don't know that we're doing it. And that's where

562
00:29:12,880 --> 00:29:14,720
Speaker 1: I think that you know, I think that we have

563
00:29:14,840 --> 00:29:17,920
Speaker 1: this idea that if I tell you about confirmation bias

564
00:29:18,080 --> 00:29:20,920
Speaker 1: or the larger problem of motivated reasoning, which is reasoning

565
00:29:20,960 --> 00:29:24,400
Speaker 1: toward a conclusion, that you already believe is true, which

566
00:29:24,400 --> 00:29:28,320
Speaker 1: would mean both finding confirming evidence but also discrediting evidence

567
00:29:28,360 --> 00:29:31,200
Speaker 1: that disagrees with you. Um, if I tell you about

568
00:29:31,240 --> 00:29:34,080
Speaker 1: this problem and you're a pretty smart person, that you're

569
00:29:34,080 --> 00:29:36,040
Speaker 1: going to walk away thinking, oh, well, now I know

570
00:29:36,080 --> 00:29:37,800
Speaker 1: about this problem and I'm really smart, so now I

571
00:29:37,800 --> 00:29:39,960
Speaker 1: won't do it. But the thing is that that's just

572
00:29:40,040 --> 00:29:42,080
Speaker 1: not the way the brain works. These processes are very

573
00:29:42,120 --> 00:29:45,640
Speaker 1: deeply embedded in again system one a reflective mind. Um,

574
00:29:45,680 --> 00:29:47,520
Speaker 1: and we can't do that. And that that really, I

575
00:29:47,560 --> 00:29:50,960
Speaker 1: think gives us this really good clue, which is if

576
00:29:51,000 --> 00:29:54,400
Speaker 1: you're doing it, I spot it right away, right, I'm like, uh, Barry, like,

577
00:29:54,600 --> 00:29:58,000
Speaker 1: come on, you're missing this study, or you're clearly spinning this,

578
00:29:58,120 --> 00:30:01,200
Speaker 1: or you're distorting this fact. If we know that, what

579
00:30:01,240 --> 00:30:03,640
Speaker 1: we can do is use that to our advantage and

580
00:30:03,720 --> 00:30:08,560
Speaker 1: bring other people in essentially to watch our bias back. Right.

581
00:30:08,640 --> 00:30:10,720
Speaker 1: And if I can get if we can make an agreement,

582
00:30:10,720 --> 00:30:13,400
Speaker 1: and I can say, look, I want to have this

583
00:30:13,920 --> 00:30:17,080
Speaker 1: kind of charter with you as my friend that when

584
00:30:17,080 --> 00:30:20,640
Speaker 1: you spot me in this place where calling call me

585
00:30:20,680 --> 00:30:23,720
Speaker 1: out for spinning, if you have information that disagrees with me,

586
00:30:23,840 --> 00:30:26,400
Speaker 1: please offer it to me. I will try my hardest

587
00:30:26,440 --> 00:30:29,000
Speaker 1: to not be defensive. If you think that I'm being defensive,

588
00:30:29,040 --> 00:30:30,960
Speaker 1: you'll call me out on that too, And we're going

589
00:30:31,000 --> 00:30:32,680
Speaker 1: to really help each other. And then I'll do the

590
00:30:32,720 --> 00:30:35,120
Speaker 1: same for you and get some people to go do

591
00:30:35,160 --> 00:30:37,040
Speaker 1: that for yourselves, because we're good at seeing it in

592
00:30:37,080 --> 00:30:40,120
Speaker 1: other people. We invest so much time and effort and

593
00:30:40,200 --> 00:30:44,400
Speaker 1: energy creating a model of the universe around us that

594
00:30:44,440 --> 00:30:48,720
Speaker 1: it's always a challenge when something undercuts that belief system.

595
00:30:48,760 --> 00:30:52,160
Speaker 1: In fact, here's a quote from the book, instead of

596
00:30:52,200 --> 00:30:55,760
Speaker 1: altering our beliefs to fit new information, we do the opposite,

597
00:30:56,280 --> 00:30:59,440
Speaker 1: alter our interpretation of that information to fit our beliefs.

598
00:30:59,720 --> 00:31:03,320
Speaker 1: Will well known as cognitive dissonance. Why are we so

599
00:31:03,480 --> 00:31:08,160
Speaker 1: hell bent on not changing our minds even when confronted

600
00:31:08,200 --> 00:31:12,040
Speaker 1: facts to the contrary? Well so, I think again, I

601
00:31:12,040 --> 00:31:13,880
Speaker 1: think Conoman really has a lot of great stuff to

602
00:31:13,880 --> 00:31:16,320
Speaker 1: say about this. Generally, we want to have a positive

603
00:31:16,400 --> 00:31:20,360
Speaker 1: narrative of our lives. Now, surely this kind of goes

604
00:31:20,400 --> 00:31:22,600
Speaker 1: back to that long term goal versus you know, what

605
00:31:22,600 --> 00:31:25,200
Speaker 1: are we doing on execution? We can recognize it in

606
00:31:25,240 --> 00:31:28,000
Speaker 1: the long term, the more accurate representation we have with

607
00:31:28,040 --> 00:31:30,080
Speaker 1: the objective truth. In other words, the more that we're

608
00:31:30,120 --> 00:31:32,360
Speaker 1: willing to change our beliefs as we get new information,

609
00:31:32,680 --> 00:31:35,120
Speaker 1: probably the better our long term return is going to be.

610
00:31:35,240 --> 00:31:39,240
Speaker 1: And that means that our decision making more more accurate. Model. Yeah,

611
00:31:39,240 --> 00:31:41,080
Speaker 1: and then we'll have a better narrative of our lives.

612
00:31:41,080 --> 00:31:44,160
Speaker 1: But again, remember I said, we're not very good at aggregating, right.

613
00:31:44,200 --> 00:31:46,200
Speaker 1: We like to do things as it comes in. And

614
00:31:46,240 --> 00:31:49,040
Speaker 1: when we get outcomes that come in, either information that

615
00:31:49,080 --> 00:31:51,120
Speaker 1: disagrees with us or a bad outcome or whatever it

616
00:31:51,200 --> 00:31:54,160
Speaker 1: might be, it's this momentary. It's like, in the moment,

617
00:31:54,720 --> 00:32:00,520
Speaker 1: it's a hit to ourself narrative because being wrong feel bad.

618
00:32:01,160 --> 00:32:04,120
Speaker 1: Finding out that our belief might have need to be

619
00:32:04,160 --> 00:32:07,000
Speaker 1: altered feels like being wrong. Finding out that a prediction

620
00:32:07,040 --> 00:32:09,719
Speaker 1: that we thought would come true doesn't come true feels

621
00:32:09,720 --> 00:32:11,960
Speaker 1: like being wrong. And all of those are hits to

622
00:32:12,000 --> 00:32:14,320
Speaker 1: our positive self narrative. So what do we do in

623
00:32:14,320 --> 00:32:17,600
Speaker 1: the moment? We swatted away in whatever way we can.

624
00:32:18,120 --> 00:32:21,200
Speaker 1: Either we say that something was because of luck, that

625
00:32:21,280 --> 00:32:23,000
Speaker 1: it wasn't our fault, that we couldn't have known, how

626
00:32:23,040 --> 00:32:24,640
Speaker 1: could we have seen that coming? That was out of

627
00:32:24,640 --> 00:32:28,239
Speaker 1: our control, Or we discredit the information that disagrees with us,

628
00:32:28,480 --> 00:32:31,080
Speaker 1: you know, we'll say that person is incredible, or that

629
00:32:31,160 --> 00:32:34,120
Speaker 1: study doesn't have a big enough sample size, or the

630
00:32:34,160 --> 00:32:36,520
Speaker 1: statistics that they ran on it were bad. When we

631
00:32:36,560 --> 00:32:39,440
Speaker 1: see things that do agree with us, we hold no

632
00:32:39,520 --> 00:32:42,160
Speaker 1: critical eye to it whatsoever. It's just like, yeah, that's right.

633
00:32:42,640 --> 00:32:44,840
Speaker 1: So you can think about like I think about this, like,

634
00:32:44,880 --> 00:32:47,520
Speaker 1: if let's say that you're, you know, your big opponent

635
00:32:47,560 --> 00:32:50,560
Speaker 1: of trend following, and you really think that trend following

636
00:32:50,600 --> 00:32:53,400
Speaker 1: is a ridiculous investment strategy, what are you gonna do

637
00:32:53,480 --> 00:32:55,800
Speaker 1: When you see something that's very critical of trend following,

638
00:32:55,840 --> 00:32:58,840
Speaker 1: you're going to go, yeah, great, absolutely. And when you

639
00:32:58,880 --> 00:33:02,280
Speaker 1: see something that suggests it maybe incorporating some trend following

640
00:33:02,800 --> 00:33:06,120
Speaker 1: ideas into your own investment strategy might be a good idea,

641
00:33:06,120 --> 00:33:07,640
Speaker 1: and it has all sorts of reasons for that, you're

642
00:33:07,640 --> 00:33:09,719
Speaker 1: going to literally pick it apart and talk about why

643
00:33:09,800 --> 00:33:12,440
Speaker 1: the author is incredible and everything that's wrong with it,

644
00:33:12,680 --> 00:33:15,320
Speaker 1: because otherwise you have to update your own belief and

645
00:33:15,360 --> 00:33:17,960
Speaker 1: when you update your own belief in in sort of

646
00:33:18,440 --> 00:33:21,600
Speaker 1: you downgrade it, right, it doesn't feel good. And I

647
00:33:21,640 --> 00:33:24,720
Speaker 1: think that what what you need in order to kind

648
00:33:24,720 --> 00:33:26,480
Speaker 1: of get around this, and this is where like having

649
00:33:26,520 --> 00:33:29,240
Speaker 1: people to help you really is good, is to have

650
00:33:29,360 --> 00:33:32,040
Speaker 1: your identity not be driven so much by this idea

651
00:33:32,080 --> 00:33:35,400
Speaker 1: of right, meaning I have these prior beliefs and I

652
00:33:35,440 --> 00:33:37,440
Speaker 1: want to I want those things to be right. I

653
00:33:37,480 --> 00:33:39,680
Speaker 1: just want to reaffirm the things that I already believe

654
00:33:40,400 --> 00:33:42,880
Speaker 1: and switch to this idea of I want to be accurate.

655
00:33:42,920 --> 00:33:45,959
Speaker 1: That accuracy is what feels good. In other words, building

656
00:33:45,960 --> 00:33:48,640
Speaker 1: the most accurate model of the world, which requires that

657
00:33:48,720 --> 00:33:51,640
Speaker 1: you view your beliefs as in progress. And once you

658
00:33:51,960 --> 00:33:54,560
Speaker 1: view your beliefs as in progress, you can't really be

659
00:33:54,640 --> 00:33:58,120
Speaker 1: right or wrong anymore. They're just provisional until the next upade.

660
00:33:58,120 --> 00:34:00,360
Speaker 1: You're just provisional until the next upgrade. And you're gonna

661
00:34:00,400 --> 00:34:02,720
Speaker 1: help me do that, because when when you see me

662
00:34:02,760 --> 00:34:05,200
Speaker 1: start to get defensive and start to discredit something that

663
00:34:05,240 --> 00:34:07,479
Speaker 1: you know disagrees with a belief that I might have had,

664
00:34:07,480 --> 00:34:10,279
Speaker 1: you're gonna say, hey, don't do you think maybe you're

665
00:34:10,280 --> 00:34:12,319
Speaker 1: doing this thing and don't remember the belief is in

666
00:34:12,360 --> 00:34:14,719
Speaker 1: progress and how much would you change your belief now?

667
00:34:15,239 --> 00:34:17,319
Speaker 1: And you know this is going to help you have

668
00:34:17,400 --> 00:34:20,560
Speaker 1: a more accurate view. And this is where the title

669
00:34:20,600 --> 00:34:23,359
Speaker 1: thinking of bets really comes from because the person who

670
00:34:23,360 --> 00:34:26,160
Speaker 1: wins in a bet is not the one who affirms

671
00:34:26,200 --> 00:34:29,040
Speaker 1: their priors. It's the person who has the most accurate

672
00:34:29,080 --> 00:34:33,719
Speaker 1: model of the world. And so thinking about uh, you know,

673
00:34:33,840 --> 00:34:36,480
Speaker 1: decisions as bets, as as you said, you have some

674
00:34:36,560 --> 00:34:39,560
Speaker 1: limited resource that you're investing based unlimited information on an

675
00:34:39,600 --> 00:34:43,520
Speaker 1: uncertain future, allows us to view our beliefs more in

676
00:34:43,640 --> 00:34:46,600
Speaker 1: progress because that just wraps the uncertainty into the whole

677
00:34:46,640 --> 00:34:48,640
Speaker 1: process and allows us to be more accurate in the

678
00:34:48,680 --> 00:34:53,280
Speaker 1: long run. Some of the my favorite writings on probability

679
00:34:53,320 --> 00:34:58,720
Speaker 1: theory and cognition and our own internal models mentioned something

680
00:34:58,760 --> 00:35:01,640
Speaker 1: that you mentioned early on in the book, which is,

681
00:35:01,680 --> 00:35:05,719
Speaker 1: if you want to be better at any probabilistic exercise,

682
00:35:05,800 --> 00:35:10,200
Speaker 1: be gambling or investing or what have you, you have

683
00:35:10,320 --> 00:35:13,200
Speaker 1: to be willing to say I don't know or I

684
00:35:13,239 --> 00:35:17,960
Speaker 1: am uncertain. Explain why that's so important and why so

685
00:35:18,080 --> 00:35:22,239
Speaker 1: few people are willing to do that? Sure, So on

686
00:35:22,400 --> 00:35:26,560
Speaker 1: the why it's so important is I think that top

687
00:35:26,600 --> 00:35:29,160
Speaker 1: of the list is that it's a more accurate representation

688
00:35:29,239 --> 00:35:33,839
Speaker 1: of your beliefs and predictions. Predictions by definition, even if

689
00:35:33,880 --> 00:35:37,359
Speaker 1: you have all of the information, even if everything is known.

690
00:35:37,560 --> 00:35:41,120
Speaker 1: Predictions have to be uncertain just because, uh, we don't

691
00:35:41,120 --> 00:35:43,360
Speaker 1: know how the future is going to turn out. So, uh,

692
00:35:43,480 --> 00:35:45,359
Speaker 1: the best example I can give of that is if

693
00:35:45,360 --> 00:35:47,640
Speaker 1: I have a fair coin, I can tell you for

694
00:35:47,719 --> 00:35:49,959
Speaker 1: sure that it will land heads fifty of the time,

695
00:35:50,280 --> 00:35:53,399
Speaker 1: that doesn't mean that I know it's going to land

696
00:35:53,400 --> 00:35:56,080
Speaker 1: heads on the next try. So that prediction must be uncertain,

697
00:35:56,239 --> 00:35:58,719
Speaker 1: and beliefs are uncertain as well. There's all sorts of

698
00:35:58,719 --> 00:36:01,520
Speaker 1: things that what did believe when you were twenty that

699
00:36:01,600 --> 00:36:04,440
Speaker 1: you were sure of that? Now you look back and

700
00:36:04,480 --> 00:36:06,719
Speaker 1: you say, wow, twenty year old Barry really had that.

701
00:36:07,360 --> 00:36:09,680
Speaker 1: You don't have to go back, go back to four

702
00:36:09,920 --> 00:36:13,560
Speaker 1: or fifty. Yet it seems every five years you look back,

703
00:36:13,719 --> 00:36:15,920
Speaker 1: at least if you're trying to evolve, and you look

704
00:36:15,960 --> 00:36:18,480
Speaker 1: back and say, man, I believed a bunch of junk,

705
00:36:18,680 --> 00:36:22,120
Speaker 1: or I could downgrade some of those beliefs and maybe

706
00:36:22,200 --> 00:36:25,600
Speaker 1: they weren't nearly as important as I previously thought they were.

707
00:36:25,800 --> 00:36:29,600
Speaker 1: That's exactly right. So so so that's the thing is

708
00:36:29,640 --> 00:36:32,279
Speaker 1: that saying I'm not sure, it's just it's just a

709
00:36:32,320 --> 00:36:34,719
Speaker 1: better representation in the world. It's just more accurate to

710
00:36:34,760 --> 00:36:36,680
Speaker 1: what the state of our beliefs is so that that's

711
00:36:36,760 --> 00:36:41,320
Speaker 1: number one. Uh, and then number two acknowledging on certain

712
00:36:41,400 --> 00:36:44,440
Speaker 1: Here's the big problem making decisions when you don't have

713
00:36:44,480 --> 00:36:49,000
Speaker 1: all the facts. We can't control luck, right, luck is okay?

714
00:36:49,320 --> 00:36:51,440
Speaker 1: You know it's gonna land heads fifty of the time.

715
00:36:51,440 --> 00:36:53,400
Speaker 1: I really can't control the coin on the next flip.

716
00:36:53,719 --> 00:36:56,960
Speaker 1: What I can do, though, is get more information. So

717
00:36:57,000 --> 00:36:58,919
Speaker 1: how do I do that? I have to make sure

718
00:36:58,960 --> 00:37:01,200
Speaker 1: that I'm open minded to the information and also let

719
00:37:01,200 --> 00:37:05,480
Speaker 1: them information hungry. So once you start not acknowledging uncertainty,

720
00:37:05,560 --> 00:37:08,400
Speaker 1: what does that do? It creates this information hunger in you.

721
00:37:08,440 --> 00:37:10,960
Speaker 1: Because if I'm certain about a belief, why am I

722
00:37:11,000 --> 00:37:13,360
Speaker 1: doing research on it? Why am I going and seeking

723
00:37:13,360 --> 00:37:15,920
Speaker 1: out information that might help that belief? I'm already sure.

724
00:37:16,400 --> 00:37:19,120
Speaker 1: But if I acknowledge my uncertainty, this is why it's

725
00:37:19,160 --> 00:37:21,279
Speaker 1: so important. It makes me information hungry. It makes me

726
00:37:21,320 --> 00:37:23,720
Speaker 1: want to go find it makes me open up Google.

727
00:37:24,200 --> 00:37:27,440
Speaker 1: Not only that, but here's the great thing is that

728
00:37:27,480 --> 00:37:29,640
Speaker 1: when I acknowledge my own uncertainty in the way that

729
00:37:29,680 --> 00:37:32,319
Speaker 1: I speak, it gets you to start telling me what

730
00:37:32,400 --> 00:37:35,319
Speaker 1: you know. Because when I say for sure, like let's

731
00:37:35,320 --> 00:37:38,319
Speaker 1: say that we'll go back to the trend following. Um,

732
00:37:38,440 --> 00:37:41,320
Speaker 1: let's say that you're somebody who's a real trend following

733
00:37:41,360 --> 00:37:43,680
Speaker 1: person and I and I just say, I don't know

734
00:37:43,719 --> 00:37:45,480
Speaker 1: that about you. And I just say with certainty, well,

735
00:37:45,520 --> 00:37:48,439
Speaker 1: people who are trend followers, they're just idiots. I can't

736
00:37:48,440 --> 00:37:50,320
Speaker 1: believe they think that that's a good thing to do.

737
00:37:50,320 --> 00:37:51,680
Speaker 1: Do you think you're going to share with me any

738
00:37:51,680 --> 00:37:53,720
Speaker 1: information that you have about why you think trend following

739
00:37:53,800 --> 00:37:57,080
Speaker 1: is a smart strategy. I like to argue, so perhaps perhaps,

740
00:37:57,200 --> 00:38:00,760
Speaker 1: But I'm probably shutting you down right because why either

741
00:38:01,360 --> 00:38:03,360
Speaker 1: you don't want me to think that you're an idiot,

742
00:38:03,520 --> 00:38:05,840
Speaker 1: or you maybe you just don't want to embarrass me,

743
00:38:05,840 --> 00:38:07,600
Speaker 1: and so you don't speak up. Either way, you're probably

744
00:38:07,640 --> 00:38:09,919
Speaker 1: kind of mad that I said it. But if I say,

745
00:38:10,400 --> 00:38:12,279
Speaker 1: you know, I've always been a little bit of a

746
00:38:12,280 --> 00:38:15,840
Speaker 1: trend following skeptic, you know, but I'm not like, I'm

747
00:38:15,880 --> 00:38:20,239
Speaker 1: just I'm not sure not etched in stone. Now you

748
00:38:20,400 --> 00:38:23,760
Speaker 1: trend that following enthusiasts, You're now going to start telling

749
00:38:23,800 --> 00:38:25,480
Speaker 1: me all the things about why you think that it's

750
00:38:25,520 --> 00:38:27,040
Speaker 1: it's a great thing. And we're actually going to get

751
00:38:27,040 --> 00:38:29,200
Speaker 1: into a discussion where both of us are going to

752
00:38:29,280 --> 00:38:32,360
Speaker 1: benefit from it because you're going to learn from my skepticism.

753
00:38:32,400 --> 00:38:34,920
Speaker 1: I'm going to learn from your enthusiasm, and we're gonna

754
00:38:34,960 --> 00:38:37,560
Speaker 1: find we're gonna get closer to what the actual truth

755
00:38:37,560 --> 00:38:40,200
Speaker 1: of the matter is because I've opened the door wide

756
00:38:40,800 --> 00:38:43,600
Speaker 1: to say, tell me what you know. And that's the thing,

757
00:38:43,920 --> 00:38:46,000
Speaker 1: that's what we have control over. We can't control luck.

758
00:38:46,640 --> 00:38:48,959
Speaker 1: We can control the quality of our decisions, which means

759
00:38:49,000 --> 00:38:52,920
Speaker 1: getting more information and refining and calibrating our beliefs, which

760
00:38:52,960 --> 00:38:57,440
Speaker 1: I can only do through information coming my way. We

761
00:38:57,480 --> 00:39:00,400
Speaker 1: have been speaking with Annie Duke. She is the author

762
00:39:00,560 --> 00:39:03,640
Speaker 1: of Thinking in Bets, How to make smarter decisions when

763
00:39:03,640 --> 00:39:07,520
Speaker 1: you don't have all the information. If you enjoy this conversation,

764
00:39:07,640 --> 00:39:10,279
Speaker 1: be sure and check out our podcast extras, where we

765
00:39:10,360 --> 00:39:14,320
Speaker 1: keep the tape rolling and continue discussing all things probabilistic.

766
00:39:14,680 --> 00:39:18,920
Speaker 1: You can find that wherever finer podcasts are sold Bloomberg

767
00:39:18,960 --> 00:39:23,040
Speaker 1: dot com, iTunes, overcast in SoundCloud. We love your comments,

768
00:39:23,080 --> 00:39:27,280
Speaker 1: feedback and suggestions right to us at m IB podcast

769
00:39:27,320 --> 00:39:30,000
Speaker 1: at Bloomberg dot net. You can check out my daily

770
00:39:30,040 --> 00:39:32,640
Speaker 1: column on Bloomberg you dot com or follow me on

771
00:39:32,640 --> 00:39:36,480
Speaker 1: Twitter at Rid Halts I'm Barry rid Halts. You're listening

772
00:39:36,520 --> 00:39:53,360
Speaker 1: to Masters in Business on Bloomberg Radio. Welcome to the podcast,

773
00:39:53,680 --> 00:39:55,640
Speaker 1: and he thank you so much for doing this. I

774
00:39:55,719 --> 00:39:58,279
Speaker 1: was taking notes, which I never do or rarely do,

775
00:39:58,760 --> 00:40:01,239
Speaker 1: because I have some things to share with you, which

776
00:40:01,239 --> 00:40:07,360
Speaker 1: you're hilarious. You mentioned trend following as a sample. Um,

777
00:40:07,400 --> 00:40:11,239
Speaker 1: are you familiar with Michael Kobol's book trend Following? Well,

778
00:40:11,280 --> 00:40:13,800
Speaker 1: I am. I've actually been on his podcast three times,

779
00:40:13,840 --> 00:40:16,640
Speaker 1: you have. I really enjoyed the conversations with him. He's

780
00:40:16,640 --> 00:40:19,600
Speaker 1: a fascinating guy in his background. Everything with Vietnam is amazing.

781
00:40:19,920 --> 00:40:22,600
Speaker 1: Have you seen the most recent addition? And who wrote

782
00:40:22,640 --> 00:40:26,200
Speaker 1: the forward to it? No? Isn't you? I love it?

783
00:40:26,320 --> 00:40:29,080
Speaker 1: How hilarious? That is so hilarious. So I actually picked

784
00:40:29,080 --> 00:40:31,440
Speaker 1: trend following as an example because I I kind of

785
00:40:31,440 --> 00:40:35,279
Speaker 1: follow uh financial you know, somewhat, and I see that

786
00:40:35,280 --> 00:40:37,160
Speaker 1: there seems to be a lot of emotion around it.

787
00:40:37,320 --> 00:40:40,480
Speaker 1: So I like to pick topics that are emotional because

788
00:40:40,480 --> 00:40:43,319
Speaker 1: if if I ask your opinion about skin cream, it's

789
00:40:43,320 --> 00:40:48,400
Speaker 1: like you're probably okay. But but the whole thing around

790
00:40:48,400 --> 00:40:51,399
Speaker 1: trend following, it's like people seem to really it's like there,

791
00:40:51,400 --> 00:40:53,680
Speaker 1: it's like the Jets and the sharks and they're gonna

792
00:40:53,800 --> 00:40:58,279
Speaker 1: meet have a riot there. There is something to the

793
00:40:58,360 --> 00:41:02,440
Speaker 1: concept that if you have a systematic approach to investing

794
00:41:02,480 --> 00:41:06,520
Speaker 1: and it's mechanical and you follow it with tremendous diligence

795
00:41:07,320 --> 00:41:09,799
Speaker 1: that you know, if you go back to the original

796
00:41:10,600 --> 00:41:14,279
Speaker 1: UM book, Oh god, I'm doing a blank on it.

797
00:41:15,719 --> 00:41:19,120
Speaker 1: Uh that that the UM One of the turtles that

798
00:41:19,160 --> 00:41:21,799
Speaker 1: were mentioned in Market Wizards is the original book by

799
00:41:21,880 --> 00:41:26,160
Speaker 1: Jack Schweger, the turtles that Mike Covil talks about all

800
00:41:26,200 --> 00:41:29,000
Speaker 1: the time. If we could raise traders the way they

801
00:41:29,040 --> 00:41:32,840
Speaker 1: grow turtles for export out of out of Asia, you know,

802
00:41:32,960 --> 00:41:35,880
Speaker 1: can we teach them mechanically? And it turns out you

803
00:41:35,960 --> 00:41:40,040
Speaker 1: more or less can assuming people are willing to do that. Yeah, well,

804
00:41:40,080 --> 00:41:42,000
Speaker 1: you know, I think it's interesting. I think that when markets,

805
00:41:42,080 --> 00:41:45,640
Speaker 1: when there's some width in the market in particular UM,

806
00:41:45,680 --> 00:41:47,959
Speaker 1: that becomes very easy to to kind of do these

807
00:41:48,400 --> 00:41:50,960
Speaker 1: kind of mechanical strategies. So the way that I was

808
00:41:51,160 --> 00:41:54,080
Speaker 1: related to poker, which I always thought was very interesting,

809
00:41:54,160 --> 00:41:56,320
Speaker 1: was so I used to teach a lot of poker seminars,

810
00:41:56,320 --> 00:41:58,080
Speaker 1: and in fact, I'm a big proponent of if you

811
00:41:58,120 --> 00:42:02,120
Speaker 1: want to get better at anything, try to teach. So, Mike,

812
00:42:02,239 --> 00:42:04,320
Speaker 1: when I started teaching seminars, there were a whole bunch

813
00:42:04,360 --> 00:42:05,960
Speaker 1: of things that I was doing at poker that I

814
00:42:05,960 --> 00:42:07,840
Speaker 1: actually ended up throwing out because I realized that I

815
00:42:07,840 --> 00:42:10,680
Speaker 1: couldn't actually explain it coherently to another human being such

816
00:42:10,719 --> 00:42:14,040
Speaker 1: that they could execute the strategy themselves. Um. But one

817
00:42:14,080 --> 00:42:15,880
Speaker 1: of the things that that the and these were generally

818
00:42:15,920 --> 00:42:18,440
Speaker 1: intermediate players would would come up in One of the

819
00:42:18,440 --> 00:42:22,120
Speaker 1: most common questions they would say to me is don't

820
00:42:22,160 --> 00:42:25,200
Speaker 1: you hate playing against beginners? Is it don't you hate

821
00:42:25,200 --> 00:42:28,319
Speaker 1: that because of the potential random outcome? Or I think

822
00:42:28,320 --> 00:42:31,080
Speaker 1: because there was more it was more unpredictable. Um. And

823
00:42:31,120 --> 00:42:33,440
Speaker 1: I would say to them, what, No, I love playing

824
00:42:33,440 --> 00:42:36,040
Speaker 1: against beginners. And my question to them was always this,

825
00:42:36,719 --> 00:42:38,360
Speaker 1: if you had to play like a tennis match for

826
00:42:38,400 --> 00:42:40,160
Speaker 1: your life, like someone's literally going to shoot you in

827
00:42:40,160 --> 00:42:42,040
Speaker 1: the head and kill you if you don't win, do

828
00:42:42,080 --> 00:42:44,719
Speaker 1: you want to play against the beginner or an intermediate?

829
00:42:44,760 --> 00:42:46,440
Speaker 1: And they would say, well, the beginner, of course, and

830
00:42:46,440 --> 00:42:47,960
Speaker 1: I said, well, it should be the same for poker.

831
00:42:48,320 --> 00:42:52,279
Speaker 1: And what I figured out was that the reason why

832
00:42:52,680 --> 00:42:54,719
Speaker 1: they felt that way is because there's this thing you

833
00:42:54,760 --> 00:42:59,040
Speaker 1: can do in poker called bluffing, and bluffing is really exciting, right,

834
00:42:59,040 --> 00:43:01,719
Speaker 1: It's like high out smart, did you I had the

835
00:43:01,719 --> 00:43:04,200
Speaker 1: worst hand and I made you fold the best hand,

836
00:43:04,200 --> 00:43:08,200
Speaker 1: and pa, right, I'm great. Well, bluffing can be very

837
00:43:08,200 --> 00:43:10,520
Speaker 1: hard to do against beginners, and the reason is that

838
00:43:10,600 --> 00:43:13,719
Speaker 1: beginners kind of don't know enough about the game yet

839
00:43:13,760 --> 00:43:16,880
Speaker 1: that they understand that your actions should cause them to fold.

840
00:43:17,920 --> 00:43:22,000
Speaker 1: So so the takeaways don't bluff against beginners, right, except

841
00:43:22,040 --> 00:43:25,640
Speaker 1: that that means that you're playing an incredible, incredibly mechanical game.

842
00:43:26,400 --> 00:43:28,319
Speaker 1: So essentially what you're doing is you're just kind of

843
00:43:28,320 --> 00:43:30,759
Speaker 1: waiting for certain kinds of hands. You're waiting until your

844
00:43:30,760 --> 00:43:32,680
Speaker 1: hand is pretty good, and then I'm just getting you

845
00:43:32,760 --> 00:43:35,759
Speaker 1: to bet way more money than you should and you

846
00:43:35,800 --> 00:43:38,160
Speaker 1: know and that that's the way I'm winning. It's it's mechanical.

847
00:43:38,840 --> 00:43:41,799
Speaker 1: And what I discovered was people don't like winning in

848
00:43:41,800 --> 00:43:44,600
Speaker 1: a mechanical way. It's it's almost like they feel like

849
00:43:44,680 --> 00:43:48,239
Speaker 1: they're No. I don't think it's that they feel like

850
00:43:48,239 --> 00:43:51,800
Speaker 1: they're cheating. There's not. It's not them making the decision.

851
00:43:51,840 --> 00:43:55,080
Speaker 1: They're just following the rules, right, So what I would

852
00:43:55,120 --> 00:43:57,760
Speaker 1: try to do. It's like, yes, it's not that they're

853
00:43:57,760 --> 00:44:01,280
Speaker 1: not the ones out thinking anyone, Like they're not playing spectacularly.

854
00:44:01,760 --> 00:44:03,279
Speaker 1: So what I would try to do is get them

855
00:44:03,280 --> 00:44:06,000
Speaker 1: to a more meta place, which I think brings us

856
00:44:06,040 --> 00:44:08,440
Speaker 1: to this what you said about trend following to this

857
00:44:08,520 --> 00:44:11,040
Speaker 1: more meta place of if you view your job in

858
00:44:11,080 --> 00:44:14,120
Speaker 1: the game to make the best decisions that you can

859
00:44:14,760 --> 00:44:17,879
Speaker 1: against the way that the market is behaving, right, because

860
00:44:17,920 --> 00:44:19,800
Speaker 1: the poker table is just a market, right, so the

861
00:44:19,840 --> 00:44:21,759
Speaker 1: market is behaving in a certain way. Let's him at

862
00:44:21,760 --> 00:44:23,759
Speaker 1: a table full of a lot of beginners, it's going

863
00:44:23,840 --> 00:44:26,480
Speaker 1: to behave differently than if I'm at a table full

864
00:44:26,520 --> 00:44:29,480
Speaker 1: of experts. That my job is to come up with

865
00:44:29,520 --> 00:44:32,480
Speaker 1: the best strategy given the market that I'm playing in.

866
00:44:32,719 --> 00:44:35,719
Speaker 1: And if I understand that, then it's a form of

867
00:44:35,760 --> 00:44:39,920
Speaker 1: outsmarting my opponents. To move to this more mechanical version

868
00:44:39,960 --> 00:44:41,680
Speaker 1: of the game. In the same way that when I

869
00:44:41,680 --> 00:44:45,120
Speaker 1: played against Derek Sidell, I was willing to say, I'm

870
00:44:45,120 --> 00:44:47,320
Speaker 1: not going to try to outthink them. I'm just gonna

871
00:44:47,320 --> 00:44:49,400
Speaker 1: go with luck. And I'm pretty proud of that as

872
00:44:49,440 --> 00:44:52,400
Speaker 1: a strategy because it's a meta strategy. It's like, what

873
00:44:52,400 --> 00:44:55,000
Speaker 1: did you say to you after you you beat him

874
00:44:55,040 --> 00:44:58,080
Speaker 1: with that strategy? What was the subsequent conversation. I think

875
00:44:58,120 --> 00:45:00,440
Speaker 1: it was really proud of me. Really, that's that's a

876
00:45:00,520 --> 00:45:04,240
Speaker 1: great mentor. That's a great mentor who says, so, now

877
00:45:04,440 --> 00:45:07,320
Speaker 1: the student has become the teacher. And well sort of

878
00:45:07,320 --> 00:45:09,839
Speaker 1: because I was admitting that I still couldn't think it, yes,

879
00:45:10,120 --> 00:45:12,200
Speaker 1: but he was. He was very proud of the I

880
00:45:12,200 --> 00:45:14,080
Speaker 1: think of the fact that that was the strategy, that

881
00:45:14,160 --> 00:45:15,600
Speaker 1: I looked at the lay of the land, and you

882
00:45:15,960 --> 00:45:20,160
Speaker 1: chose the smartest route to give yourself the best possibility

883
00:45:20,400 --> 00:45:22,759
Speaker 1: of winning. It's still gonna could have come up against you,

884
00:45:23,120 --> 00:45:25,400
Speaker 1: but you think playing the other way, you would. And

885
00:45:25,400 --> 00:45:27,000
Speaker 1: by the way, I get it when when I play

886
00:45:27,080 --> 00:45:30,200
Speaker 1: against beginners and they win with some hand that like

887
00:45:30,360 --> 00:45:33,920
Speaker 1: no reasonable person on earth whatever, I have that feeling

888
00:45:34,000 --> 00:45:40,200
Speaker 1: of like life is unfair and you know, sure there's

889
00:45:40,239 --> 00:45:42,759
Speaker 1: that blind spot bias rearing it. Yeah. I mean, one

890
00:45:42,760 --> 00:45:44,480
Speaker 1: of the things I really try to get across in

891
00:45:44,480 --> 00:45:47,840
Speaker 1: the book is that I know all of this stuff

892
00:45:47,880 --> 00:45:50,400
Speaker 1: and I still do it. It's just that I do

893
00:45:50,440 --> 00:45:53,120
Speaker 1: it a little less and a little less, as you know,

894
00:45:53,920 --> 00:45:57,840
Speaker 1: just compounds over time into such a higher probability of

895
00:45:57,960 --> 00:46:00,520
Speaker 1: good results. The other thing, it's because I know him

896
00:46:00,560 --> 00:46:03,719
Speaker 1: accountable to the people that I have around me, who

897
00:46:03,760 --> 00:46:06,759
Speaker 1: are going to hold me accountable to bias thinking, who

898
00:46:06,760 --> 00:46:08,960
Speaker 1: are going to say no, no, no, that's not okay.

899
00:46:08,960 --> 00:46:11,319
Speaker 1: That I'm much more likely to catch an error, and

900
00:46:11,360 --> 00:46:13,799
Speaker 1: I'm much more likely to get it faster. Right. So

901
00:46:13,880 --> 00:46:17,440
Speaker 1: instead of looking back on twenty year old Annie and say, wow,

902
00:46:18,320 --> 00:46:20,239
Speaker 1: that was a really crazy decision that she made, or

903
00:46:20,280 --> 00:46:22,640
Speaker 1: that was a crazy belief that she held, I think

904
00:46:22,640 --> 00:46:24,640
Speaker 1: that I can get to that conclusion a lot more

905
00:46:24,719 --> 00:46:27,279
Speaker 1: quickly because I have other people helping me get there.

906
00:46:27,360 --> 00:46:28,920
Speaker 1: I like that you go back to twenty year old

907
00:46:28,960 --> 00:46:31,799
Speaker 1: Annie and not thirty year old name. No, listen seriously, Like,

908
00:46:32,280 --> 00:46:34,799
Speaker 1: how about just like forty five year old Annie, Like

909
00:46:35,000 --> 00:46:39,000
Speaker 1: yesterday Annie? Like yesterday Annie was doing really stupid stuff.

910
00:46:39,160 --> 00:46:41,920
Speaker 1: You know. It's it's amazing that if you stop and

911
00:46:41,960 --> 00:46:46,880
Speaker 1: look back at your life honestly every five years. Uh.

912
00:46:46,920 --> 00:46:49,239
Speaker 1: The one thing I could say about being in my fifties.

913
00:46:49,560 --> 00:46:51,960
Speaker 1: When I went through that exercise in my thirties and forties,

914
00:46:52,000 --> 00:46:56,120
Speaker 1: it was just mortifying. And now it's merely like that

915
00:46:56,239 --> 00:46:58,400
Speaker 1: was a bad decision that was a bad process, but

916
00:46:58,480 --> 00:47:01,000
Speaker 1: it's not like, oh, my god, would idiot you are,

917
00:47:01,800 --> 00:47:04,560
Speaker 1: which was what the experience was in the twenties and thirties. Well,

918
00:47:04,560 --> 00:47:08,200
Speaker 1: I think maybe part of that is because I think

919
00:47:08,239 --> 00:47:11,880
Speaker 1: that if you managed to survive a long time, whatever

920
00:47:11,920 --> 00:47:14,319
Speaker 1: it is, in poker or anything else, by the time

921
00:47:14,360 --> 00:47:17,040
Speaker 1: you're fifty, you have I think you probably have developed

922
00:47:17,040 --> 00:47:19,640
Speaker 1: some more humility around the rightness and wrongness of your

923
00:47:19,640 --> 00:47:21,840
Speaker 1: decisions in the first place. And that's one of the

924
00:47:21,880 --> 00:47:23,759
Speaker 1: things that I try to point out in the book

925
00:47:23,840 --> 00:47:27,880
Speaker 1: is that when you view your beliefs is under under construction,

926
00:47:28,520 --> 00:47:31,200
Speaker 1: you know, in progress, then you don't end up with

927
00:47:31,200 --> 00:47:34,000
Speaker 1: these full on reversals too. I'm an idiot, right, because

928
00:47:34,040 --> 00:47:36,080
Speaker 1: you kind of didn't view yourself as a genius in

929
00:47:36,080 --> 00:47:38,359
Speaker 1: the first place. And so I think it's a place

930
00:47:38,360 --> 00:47:40,080
Speaker 1: where you end up, first of all, being much more

931
00:47:40,120 --> 00:47:43,400
Speaker 1: compassionate to yourself because you sort of realize it's like, okay,

932
00:47:43,400 --> 00:47:45,839
Speaker 1: but I was only like on it anyway, so now

933
00:47:45,840 --> 00:47:51,400
Speaker 1: all right, I'm okay. Right. It's like an adjustment to

934
00:47:51,640 --> 00:47:54,200
Speaker 1: what your level of confidence in the in the whatever

935
00:47:54,200 --> 00:47:56,359
Speaker 1: the belief is that you hold. And I think that

936
00:47:56,360 --> 00:47:58,640
Speaker 1: that's just kind of a nicer way to be to yourself.

937
00:47:58,880 --> 00:48:00,640
Speaker 1: And then I think that what falls from that, once

938
00:48:00,640 --> 00:48:03,000
Speaker 1: you sort of view your own beliefs is under construction,

939
00:48:03,080 --> 00:48:05,680
Speaker 1: is that you are much more compassionate towards other people

940
00:48:06,160 --> 00:48:09,400
Speaker 1: for their own And still I'm still waiting for that

941
00:48:09,440 --> 00:48:12,799
Speaker 1: to at least when I'm driving, I'm waiting, well, my

942
00:48:12,800 --> 00:48:16,400
Speaker 1: wife and so I don't do New Year's resolutions for

943
00:48:16,440 --> 00:48:20,000
Speaker 1: the exact reason you're implying. The decision isn't. It's all

944
00:48:20,040 --> 00:48:24,240
Speaker 1: about the process. But there were two processes I wanted

945
00:48:24,280 --> 00:48:27,399
Speaker 1: to try and spend a little more effort with. One

946
00:48:27,520 --> 00:48:29,880
Speaker 1: was just being nicer on Twitter, because there are some

947
00:48:29,880 --> 00:48:32,600
Speaker 1: people on Twitter who were that started this year around

948
00:48:32,640 --> 00:48:36,240
Speaker 1: the holidays. People are some people are just jerks on Twitter,

949
00:48:36,280 --> 00:48:37,879
Speaker 1: and it's like, you know what, that's a bad look.

950
00:48:38,120 --> 00:48:39,880
Speaker 1: Let me see if I could be less of a jerk.

951
00:48:40,320 --> 00:48:43,600
Speaker 1: And then the other thing was in real life, Hey

952
00:48:43,640 --> 00:48:46,960
Speaker 1: can I be a little nicer on the highway? Just

953
00:48:47,000 --> 00:48:49,759
Speaker 1: pick up whatever karma points letting people in or But

954
00:48:49,880 --> 00:48:55,319
Speaker 1: I still find myself constantly going to say, uh, your

955
00:48:55,440 --> 00:48:58,719
Speaker 1: left directional has been on for about three miles, Either

956
00:48:58,800 --> 00:49:01,320
Speaker 1: make a left or shut that off. And my wife

957
00:49:01,360 --> 00:49:03,000
Speaker 1: is like I thought that was going to stop, and

958
00:49:03,040 --> 00:49:06,080
Speaker 1: it's so hard to make those little change it is.

959
00:49:06,160 --> 00:49:08,839
Speaker 1: But do you think you're doing it slightly less? Oh?

960
00:49:09,080 --> 00:49:13,000
Speaker 1: I am see, And that's what matters. See. That's the

961
00:49:13,040 --> 00:49:15,239
Speaker 1: thing is that I think that in general, it kind

962
00:49:15,280 --> 00:49:17,960
Speaker 1: of goes along with that idea of there's only right

963
00:49:17,960 --> 00:49:20,560
Speaker 1: and wrong, so that if you present me with information

964
00:49:20,600 --> 00:49:23,279
Speaker 1: that disagrees with me, I can only the only choice

965
00:49:23,320 --> 00:49:25,239
Speaker 1: I have is right to wrong right. I have to

966
00:49:25,280 --> 00:49:27,160
Speaker 1: do a full on reversal. If I'm like am six

967
00:49:28,239 --> 00:49:30,399
Speaker 1: and you present me with information now, I'm like, okay,

968
00:49:30,440 --> 00:49:33,120
Speaker 1: I'm for It's just an easier way to be right.

969
00:49:33,640 --> 00:49:36,080
Speaker 1: So I think that to say I'm gonna I have

970
00:49:36,160 --> 00:49:37,799
Speaker 1: this idea that I want to be nicer on the

971
00:49:37,880 --> 00:49:40,880
Speaker 1: role road and to hold yourself to a standard of perfection,

972
00:49:41,440 --> 00:49:44,439
Speaker 1: you're just setting yourself up to feel bad and and

973
00:49:44,520 --> 00:49:48,000
Speaker 1: to lack compassion for yourself and for self recrimination. If

974
00:49:48,000 --> 00:49:50,080
Speaker 1: you say you know what, I'm doing a little better,

975
00:49:50,920 --> 00:49:52,719
Speaker 1: like that's great. I don't want to make it too

976
00:49:52,719 --> 00:49:55,640
Speaker 1: easy on myself. The other the other two notes, you're

977
00:49:55,680 --> 00:49:59,080
Speaker 1: more likely to succeed if you do, though, because it's

978
00:49:59,120 --> 00:50:01,319
Speaker 1: the baby with the bathwater. It's like the what the

979
00:50:01,320 --> 00:50:04,799
Speaker 1: hell effect which Danner ariel It talks about. So it's

980
00:50:04,800 --> 00:50:07,080
Speaker 1: the difference between Okay, I've decided I'm not going to

981
00:50:07,120 --> 00:50:10,400
Speaker 1: eat any bread, and then one morning, like there's donuts

982
00:50:10,400 --> 00:50:14,200
Speaker 1: in the break room and the whole day one calorie

983
00:50:14,239 --> 00:50:17,200
Speaker 1: and it's and that's because you're you're viewing yourself from

984
00:50:17,200 --> 00:50:19,720
Speaker 1: the point of perfection. So once you make a little mistake,

985
00:50:19,840 --> 00:50:21,840
Speaker 1: it tends to it tends to sort of break apart.

986
00:50:22,080 --> 00:50:24,520
Speaker 1: Whereas did you say this is this is a work

987
00:50:24,560 --> 00:50:26,960
Speaker 1: in progress, and all right, I ate half a donut

988
00:50:27,000 --> 00:50:29,200
Speaker 1: this morning, that's okay, and you just have salad for

989
00:50:29,400 --> 00:50:33,280
Speaker 1: lunch because you're not. You haven't broken from the perfection

990
00:50:33,320 --> 00:50:35,919
Speaker 1: that you expected you of yourself. All you were doing

991
00:50:36,000 --> 00:50:38,480
Speaker 1: was trying to do it less, and it's actually a

992
00:50:38,520 --> 00:50:41,160
Speaker 1: better way to get there. I think. I love that

993
00:50:41,520 --> 00:50:45,920
Speaker 1: Arlie's research facility at Duke is called the Institute for

994
00:50:45,960 --> 00:50:49,399
Speaker 1: Advanced Hindsight or something like that. It's it's just it's

995
00:50:49,440 --> 00:50:54,680
Speaker 1: just a hilarious issue. You also mentioned that I'm fascinated

996
00:50:54,680 --> 00:50:57,880
Speaker 1: by this. If you want to learn something, teach it

997
00:50:58,680 --> 00:51:01,319
Speaker 1: and over the past, I don't know it's called it

998
00:51:01,360 --> 00:51:05,399
Speaker 1: a decade, I've been giving presentations around the country, around

999
00:51:05,400 --> 00:51:10,000
Speaker 1: the world, and each presentation almost always leads to the

1000
00:51:10,080 --> 00:51:13,439
Speaker 1: same sets of questions, and it makes me realize I'm

1001
00:51:13,440 --> 00:51:16,120
Speaker 1: doing a bad job teaching this if I get the

1002
00:51:16,120 --> 00:51:19,600
Speaker 1: same questions over and over again. And so each presentation

1003
00:51:19,719 --> 00:51:23,320
Speaker 1: leads to the next presentation and a different set of questions,

1004
00:51:23,719 --> 00:51:26,600
Speaker 1: and I'm kind of coming to the point where I'm

1005
00:51:26,640 --> 00:51:28,839
Speaker 1: not sure if it's that I'm a bit teacher or

1006
00:51:28,880 --> 00:51:32,640
Speaker 1: that I'm just provoking people's thoughts that they're going to ask.

1007
00:51:32,880 --> 00:51:35,879
Speaker 1: But when you consistently get the same questions, it makes

1008
00:51:35,880 --> 00:51:39,919
Speaker 1: you think there's something about this presentation, which often has

1009
00:51:40,000 --> 00:51:43,719
Speaker 1: elements of cognitive issues and confirmation bias in it, that

1010
00:51:43,840 --> 00:51:47,560
Speaker 1: leads people um to the same sort of spot. It's

1011
00:51:47,560 --> 00:51:49,719
Speaker 1: it's kind of fascinating. So yeah, I think that that

1012
00:51:49,920 --> 00:51:52,400
Speaker 1: is and I think that the analysis is really interesting

1013
00:51:52,400 --> 00:51:57,520
Speaker 1: because it could be that, uh, there are some concepts

1014
00:51:57,520 --> 00:51:59,440
Speaker 1: that are hard to get to within the presentation, and

1015
00:51:59,440 --> 00:52:01,640
Speaker 1: you're kind of anticipating that those questions will come and

1016
00:52:01,680 --> 00:52:04,360
Speaker 1: planning to handle them U in Q and A. I

1017
00:52:04,440 --> 00:52:10,360
Speaker 1: do that some because I don't and and sometimes it's

1018
00:52:10,400 --> 00:52:14,480
Speaker 1: it's that you I mean, this sort of comes to Okay,

1019
00:52:14,640 --> 00:52:18,239
Speaker 1: I could anticipate this question, so let me see if

1020
00:52:18,280 --> 00:52:20,680
Speaker 1: I can fit that in with the key concepts. But

1021
00:52:20,760 --> 00:52:22,719
Speaker 1: if there are these big concepts that I'm trying to

1022
00:52:22,719 --> 00:52:25,520
Speaker 1: get across and trying to go down the tangent that

1023
00:52:25,680 --> 00:52:27,360
Speaker 1: where I know this question is going to be coming

1024
00:52:27,360 --> 00:52:30,440
Speaker 1: my way, is actually going to distract from the larger concept.

1025
00:52:30,840 --> 00:52:33,239
Speaker 1: Maybe it's you're getting those questions because you've given those

1026
00:52:33,400 --> 00:52:36,120
Speaker 1: them the large concepts in such a great way. I'm

1027
00:52:36,160 --> 00:52:37,839
Speaker 1: not saying that that's true. I'm saying, like, we want

1028
00:52:37,840 --> 00:52:39,840
Speaker 1: to sort of think about it from both sides. What

1029
00:52:39,960 --> 00:52:42,480
Speaker 1: I've been finding lately is I've been opening my talks

1030
00:52:42,520 --> 00:52:45,160
Speaker 1: with a video of the Pete Carroll play um, and

1031
00:52:45,200 --> 00:52:47,400
Speaker 1: so my questions tend to be people telling me that

1032
00:52:47,400 --> 00:52:50,239
Speaker 1: I'm an idiot and that Pete Carroll actually made a

1033
00:52:50,239 --> 00:52:54,120
Speaker 1: really horrible decision. And even after You've spent half hour

1034
00:52:54,160 --> 00:52:58,520
Speaker 1: explaining why statistically this was not a terrible decision, and

1035
00:52:58,560 --> 00:53:01,200
Speaker 1: you're just focusing on you. What's interesting is that what

1036
00:53:01,520 --> 00:53:03,520
Speaker 1: I didn't say is that Bill Belichick thinks it was

1037
00:53:03,560 --> 00:53:08,600
Speaker 1: a good call. He no, he said that the formation

1038
00:53:08,840 --> 00:53:12,080
Speaker 1: that that they had had the same defensive formation earlier

1039
00:53:12,080 --> 00:53:14,320
Speaker 1: in the game and it was actually a run protection

1040
00:53:15,239 --> 00:53:17,319
Speaker 1: and so he suspected that Pete Carroll had seen that,

1041
00:53:17,360 --> 00:53:21,840
Speaker 1: and so called the path. Uh. Mike Lombardi, who is

1042
00:53:21,880 --> 00:53:26,399
Speaker 1: a great analyst, actually has worked with Belichick and Bill

1043
00:53:26,440 --> 00:53:31,239
Speaker 1: Walsh and all these guys. Uh, he has it's coming

1044
00:53:31,239 --> 00:53:33,680
Speaker 1: out in September, and it opens with the same play

1045
00:53:34,000 --> 00:53:38,040
Speaker 1: and it discusses it from a formation standpoint as well,

1046
00:53:38,160 --> 00:53:39,960
Speaker 1: where he sort of comes to the same conclusion. And

1047
00:53:40,000 --> 00:53:41,520
Speaker 1: I kind of mentioned that, so I sort of get

1048
00:53:41,600 --> 00:53:44,960
Speaker 1: myself all there. It's usually people who are Seahawks fans,

1049
00:53:44,680 --> 00:53:46,360
Speaker 1: and so the thing is that, I mean, this is

1050
00:53:46,360 --> 00:53:48,040
Speaker 1: why I said, you know, when you asked me, well,

1051
00:53:48,040 --> 00:53:49,680
Speaker 1: why did you choose trend falling, I said, well, I

1052
00:53:49,680 --> 00:53:51,520
Speaker 1: tried to think of something an investment that has a

1053
00:53:51,520 --> 00:53:54,240
Speaker 1: lot of emotion attached to it. Is that when people

1054
00:53:54,239 --> 00:53:56,759
Speaker 1: are really emotionally invested in the beliefs that they have,

1055
00:53:57,200 --> 00:53:59,400
Speaker 1: and particularly in the outcome in this case, right, because

1056
00:53:59,440 --> 00:54:02,960
Speaker 1: the Seahawks lots there's no um it's very very hard

1057
00:54:03,000 --> 00:54:05,800
Speaker 1: when even when you're presented with information that might moderate

1058
00:54:05,800 --> 00:54:07,600
Speaker 1: your belief it's just hard to do it. We're just

1059
00:54:07,719 --> 00:54:10,719
Speaker 1: too emotionally lit up to it. It's that system. One

1060
00:54:10,840 --> 00:54:13,680
Speaker 1: doesn't let it get to system too. Yeah, so there

1061
00:54:13,680 --> 00:54:15,600
Speaker 1: are certain I think that there are many things where

1062
00:54:15,640 --> 00:54:17,439
Speaker 1: someone could get up and give a talk and say

1063
00:54:17,440 --> 00:54:19,160
Speaker 1: all the things that I said, where maybe I would go,

1064
00:54:19,360 --> 00:54:22,319
Speaker 1: you're dumb, that Pete Carroll was was an idiot. It's

1065
00:54:22,360 --> 00:54:24,040
Speaker 1: it's just a matter of like trying to do it less.

1066
00:54:24,040 --> 00:54:25,520
Speaker 1: But I do find that I seem to be getting

1067
00:54:25,520 --> 00:54:27,919
Speaker 1: a lot of that people, people sort of writing into

1068
00:54:27,920 --> 00:54:30,120
Speaker 1: my website and being like, no, that was a horrible call.

1069
00:54:30,400 --> 00:54:32,960
Speaker 1: Are you seeing these all coming from the Pacific Northwest

1070
00:54:33,080 --> 00:54:36,759
Speaker 1: or what a coincidence? All right, So I have a

1071
00:54:36,760 --> 00:54:39,640
Speaker 1: ton of my favorite questions to ask you, and I

1072
00:54:39,680 --> 00:54:42,120
Speaker 1: know I don't have you forever. Let me jump into

1073
00:54:42,160 --> 00:54:45,280
Speaker 1: some of these things because I think, um, your answers

1074
00:54:45,280 --> 00:54:48,000
Speaker 1: to this is gonna be fascinating. Tell us the most

1075
00:54:48,040 --> 00:54:53,040
Speaker 1: important thing that people don't know about your background, the

1076
00:54:53,080 --> 00:54:56,360
Speaker 1: most okay, so well about my background or about me

1077
00:54:56,440 --> 00:54:59,759
Speaker 1: now I'll tell you about me now, is that I

1078
00:54:59,760 --> 00:55:02,680
Speaker 1: think that I spent so much of my life in

1079
00:55:02,680 --> 00:55:08,000
Speaker 1: this really competitive environment. Um So when I was younger, uh,

1080
00:55:08,040 --> 00:55:10,719
Speaker 1: when we were playing cards, my brother was two years

1081
00:55:10,760 --> 00:55:12,960
Speaker 1: older than me, which when year seven is like they

1082
00:55:13,000 --> 00:55:15,439
Speaker 1: might as well. Yeah. Um. And my dad, of course,

1083
00:55:15,520 --> 00:55:17,319
Speaker 1: was a middle aged man, and both of them are

1084
00:55:17,400 --> 00:55:19,280
Speaker 1: very competitive and never let me win. So I would

1085
00:55:19,280 --> 00:55:22,400
Speaker 1: literally like almost every night that we were playing cards,

1086
00:55:22,480 --> 00:55:24,239
Speaker 1: the cards would end up getting thrown against the wall

1087
00:55:24,360 --> 00:55:27,160
Speaker 1: by me at some point. And that was actually my

1088
00:55:27,160 --> 00:55:29,640
Speaker 1: biggest challenge when I was playing poker, was trying to

1089
00:55:29,640 --> 00:55:32,719
Speaker 1: get that emotional component under control, trying to get that

1090
00:55:32,760 --> 00:55:35,680
Speaker 1: competitiveness to a place where I could be smoother and

1091
00:55:35,719 --> 00:55:39,080
Speaker 1: sort of like accept the outcome. Um, because I would

1092
00:55:39,120 --> 00:55:40,919
Speaker 1: just get I mean, when I was young, I would

1093
00:55:40,960 --> 00:55:43,000
Speaker 1: just become completely unhinged by it. You don't want to

1094
00:55:43,040 --> 00:55:46,160
Speaker 1: be rattled at the table, right. I've gone so completely

1095
00:55:46,200 --> 00:55:48,359
Speaker 1: the other way that since I retired, if I were

1096
00:55:48,400 --> 00:55:50,279
Speaker 1: to go out and play tennis with you, I would

1097
00:55:50,320 --> 00:55:53,200
Speaker 1: ask you if we could please not keep score? Yeah,

1098
00:55:53,200 --> 00:55:54,759
Speaker 1: because I just want to play and like have both

1099
00:55:54,800 --> 00:55:56,719
Speaker 1: of us get better the people that I play with

1100
00:55:56,760 --> 00:55:59,160
Speaker 1: I always bring to my tennis lessons because I just

1101
00:55:59,200 --> 00:56:01,640
Speaker 1: want to, like, I'm so much more focused on this

1102
00:56:01,719 --> 00:56:03,240
Speaker 1: kind of win win, which is one of the reasons

1103
00:56:03,280 --> 00:56:05,160
Speaker 1: why I think I love the process of writing the

1104
00:56:05,160 --> 00:56:08,279
Speaker 1: book and giving talks because it feels it's like so

1105
00:56:08,360 --> 00:56:11,000
Speaker 1: not competitive, you know, and I feel like I've sort

1106
00:56:11,000 --> 00:56:13,359
Speaker 1: of like shed a lot of that now. I mean,

1107
00:56:13,440 --> 00:56:15,480
Speaker 1: I don't know if people around me would agree, but

1108
00:56:15,520 --> 00:56:19,200
Speaker 1: I'm certainly it's certainly left. That's very interesting. You mentioned

1109
00:56:19,200 --> 00:56:21,760
Speaker 1: some of your early mentors. Give us a little background

1110
00:56:21,760 --> 00:56:26,080
Speaker 1: on them. Yeah, so I got plugged into through my brother,

1111
00:56:26,280 --> 00:56:29,359
Speaker 1: this amazing group of players in New York. Uh so

1112
00:56:29,480 --> 00:56:31,480
Speaker 1: they all they all started off playing in New York.

1113
00:56:31,560 --> 00:56:34,840
Speaker 1: They had all come from this game's background. Um. Some

1114
00:56:35,080 --> 00:56:39,239
Speaker 1: like Eric Sdel was an amazing uh backgammon player and

1115
00:56:39,239 --> 00:56:41,680
Speaker 1: he was also an options trader. And then you know,

1116
00:56:41,719 --> 00:56:44,440
Speaker 1: my brother came from really gotten there through chas. You

1117
00:56:44,480 --> 00:56:48,640
Speaker 1: had Dan Harrington, who was won the World Series of

1118
00:56:48,680 --> 00:56:52,360
Speaker 1: Poker UM main event, I mean, world champion, amazing player,

1119
00:56:52,400 --> 00:56:55,040
Speaker 1: has written some of the most incredible books on poker

1120
00:56:55,080 --> 00:56:57,520
Speaker 1: that you can get Harrington on hold them if anybody's

1121
00:56:57,560 --> 00:57:00,759
Speaker 1: interested in learning, go get those books. UM guy named

1122
00:57:00,800 --> 00:57:03,440
Speaker 1: Jason Lester who was one of the best back men players,

1123
00:57:03,640 --> 00:57:06,560
Speaker 1: and I got plugged into them as mentors. Now what's

1124
00:57:06,600 --> 00:57:10,880
Speaker 1: amazing is how much money collectively that group of players

1125
00:57:10,880 --> 00:57:13,319
Speaker 1: has made. I have it in a in a end

1126
00:57:13,360 --> 00:57:14,960
Speaker 1: note in the book, but just so that you know,

1127
00:57:15,440 --> 00:57:18,560
Speaker 1: Eric Sidell alone has earned thirty eight million dollars in

1128
00:57:18,600 --> 00:57:22,720
Speaker 1: tournament poker. So these were my mentors and and Eric.

1129
00:57:22,800 --> 00:57:27,120
Speaker 1: I think uh is particularly prominent in the book because

1130
00:57:27,320 --> 00:57:30,280
Speaker 1: when I talked about how do you have people watch

1131
00:57:30,320 --> 00:57:33,080
Speaker 1: your back? He's the one who said this thing to me.

1132
00:57:33,160 --> 00:57:35,480
Speaker 1: I had known him since I was sixteen, when I

1133
00:57:35,520 --> 00:57:37,440
Speaker 1: wasn't a poker player, and we were just like friends

1134
00:57:37,440 --> 00:57:39,680
Speaker 1: because he was friends with my brother. Then all of

1135
00:57:39,720 --> 00:57:41,120
Speaker 1: a sudden, I'm a poker player and I walk up

1136
00:57:41,160 --> 00:57:43,120
Speaker 1: to him like he's still my buddy. One day and

1137
00:57:43,160 --> 00:57:45,800
Speaker 1: I just start moaning to him about this horrible luck

1138
00:57:45,840 --> 00:57:47,640
Speaker 1: that I had had and I can't believe this. I

1139
00:57:47,680 --> 00:57:49,439
Speaker 1: got so unlucky. It's such a bad beat. Blah blah

1140
00:57:49,440 --> 00:57:53,080
Speaker 1: blah blah blah. And he laid it out for me. Boom,

1141
00:57:53,120 --> 00:57:55,000
Speaker 1: He said, why are you even telling me this story?

1142
00:57:55,040 --> 00:57:57,439
Speaker 1: Like do you think I need like your emotional hard

1143
00:57:57,480 --> 00:58:00,600
Speaker 1: luck story? Like voiced it upon me. I've lost with

1144
00:58:00,680 --> 00:58:03,520
Speaker 1: good hands too, Like do you have a question, Because

1145
00:58:03,520 --> 00:58:05,440
Speaker 1: if you have a question, I'll talk to you all day.

1146
00:58:05,560 --> 00:58:06,920
Speaker 1: But I just don't want to hear about your bad

1147
00:58:06,960 --> 00:58:09,640
Speaker 1: luck stories, like literally there's nothing to be learned from it.

1148
00:58:09,880 --> 00:58:12,680
Speaker 1: And and while it sounds a little bit harsh, that

1149
00:58:12,760 --> 00:58:16,600
Speaker 1: was the most important moment of mentorship that I've ever

1150
00:58:16,640 --> 00:58:20,960
Speaker 1: received in my life. That's quite quite interesting. So anyone

1151
00:58:20,960 --> 00:58:24,720
Speaker 1: else influence how you approached the art of gambling? Yeah,

1152
00:58:24,840 --> 00:58:28,520
Speaker 1: I mean I just obviously that group, And then within

1153
00:58:28,600 --> 00:58:30,560
Speaker 1: poker there was also a guy named John Hannigan who's

1154
00:58:30,600 --> 00:58:32,560
Speaker 1: actually mentioned in the book, who was someone I watched

1155
00:58:32,600 --> 00:58:36,400
Speaker 1: really carefully. Um some I think that some of the

1156
00:58:36,440 --> 00:58:39,280
Speaker 1: things most important lessons I learned from people who didn't

1157
00:58:39,360 --> 00:58:41,560
Speaker 1: who probably didn't understand that they were mentoring me in

1158
00:58:41,600 --> 00:58:43,800
Speaker 1: some way where I was just learning from them. And

1159
00:58:43,800 --> 00:58:47,080
Speaker 1: in particularly important lessons for me were people who played

1160
00:58:47,120 --> 00:58:50,680
Speaker 1: in ways that my group had not taught me to play.

1161
00:58:50,720 --> 00:58:53,920
Speaker 1: So they were playing strategies that were very different from

1162
00:58:54,160 --> 00:58:56,919
Speaker 1: the kinds of strategies that I I was being sort

1163
00:58:56,920 --> 00:58:59,640
Speaker 1: of taught and that we're being fostered than me. And

1164
00:58:59,800 --> 00:59:03,760
Speaker 1: the initial feeling on my part of they must be

1165
00:59:03,800 --> 00:59:07,280
Speaker 1: really bad, like clearly they're just idiots, and they're winning

1166
00:59:07,280 --> 00:59:09,800
Speaker 1: because they're just getting lucky, because just simply because I

1167
00:59:09,800 --> 00:59:13,120
Speaker 1: didn't understand their strategies, and that shift of learning to

1168
00:59:13,200 --> 00:59:15,720
Speaker 1: sort of dig into their strategies and figure out why

1169
00:59:15,720 --> 00:59:17,600
Speaker 1: it was working for them and what they were doing,

1170
00:59:17,720 --> 00:59:20,240
Speaker 1: even though it wasn't something that I was coming and

1171
00:59:20,360 --> 00:59:23,680
Speaker 1: thinking was sort of the right play, UM was really

1172
00:59:23,880 --> 00:59:28,120
Speaker 1: actually important. It's that openness to things that you don't

1173
00:59:28,200 --> 00:59:31,320
Speaker 1: understand that just because you don't understand them or they're

1174
00:59:31,320 --> 00:59:35,240
Speaker 1: not within your wheelhouse doesn't mean they're bad. Sometimes it does,

1175
00:59:35,680 --> 00:59:37,600
Speaker 1: but it doesn't always. And that was I think some

1176
00:59:37,680 --> 00:59:40,280
Speaker 1: of the most important membership mentorship that I received, And

1177
00:59:40,320 --> 00:59:42,200
Speaker 1: I don't think those people know that they mentored me.

1178
00:59:42,680 --> 00:59:45,160
Speaker 1: Tell us about a time you failed and what you

1179
00:59:45,280 --> 00:59:48,600
Speaker 1: learned from the process. Oh, my gosh, which time? I

1180
00:59:48,640 --> 00:59:52,120
Speaker 1: mean so many? I mean, you know, I feel like

1181
00:59:52,680 --> 00:59:54,520
Speaker 1: for a long time I sort of carried around not

1182
00:59:54,600 --> 00:59:57,600
Speaker 1: finishing my PhD as a failure. For sure. UM, I

1183
00:59:57,640 --> 01:00:00,840
Speaker 1: think that that was a lesson in compassion towards myself. UM,

1184
01:00:00,920 --> 01:00:02,600
Speaker 1: you fail all the time at the poker table, I

1185
01:00:02,640 --> 01:00:04,080
Speaker 1: think you have to learn how to process that. I

1186
01:00:04,120 --> 01:00:07,919
Speaker 1: had a startup UM that I was partnering called Epic

1187
01:00:08,000 --> 01:00:12,800
Speaker 1: Poker that we started right at the end of UM.

1188
01:00:12,040 --> 01:00:15,520
Speaker 1: The poker boom right. So this was right around I

1189
01:00:15,520 --> 01:00:17,280
Speaker 1: think we started the company in two thousand ten and

1190
01:00:17,280 --> 01:00:19,560
Speaker 1: we were trying to create sort of the PGA of poker,

1191
01:00:20,120 --> 01:00:22,840
Speaker 1: and that company failed and it was it was awful.

1192
01:00:22,960 --> 01:00:25,200
Speaker 1: Like I obviously I really wanted it to succeed, and

1193
01:00:25,200 --> 01:00:28,480
Speaker 1: whenever a company fails, there's vendors that are angry. Some

1194
01:00:28,520 --> 01:00:31,640
Speaker 1: of the players were angry. UM, and that was certainly

1195
01:00:31,640 --> 01:00:34,000
Speaker 1: a lot to process. What I try to take from

1196
01:00:34,040 --> 01:00:36,960
Speaker 1: every single time that I fail, whether it's the company

1197
01:00:37,120 --> 01:00:40,200
Speaker 1: or failing on a particular poker hand, or feeling like

1198
01:00:40,240 --> 01:00:42,720
Speaker 1: I failed in a tournament because maybe there were decisions

1199
01:00:42,800 --> 01:00:44,600
Speaker 1: that I could made would have been better, I made

1200
01:00:44,600 --> 01:00:46,960
Speaker 1: a big mistake that caused me to lose, or whatever

1201
01:00:47,000 --> 01:00:50,240
Speaker 1: it might be. Is to say, the only real failure

1202
01:00:50,440 --> 01:00:53,160
Speaker 1: is to not to take lessons from it, to figure

1203
01:00:53,160 --> 01:00:55,240
Speaker 1: out to start trying to parse out what was luck,

1204
01:00:55,240 --> 01:00:57,000
Speaker 1: what was skill? What could I change in the future,

1205
01:00:57,360 --> 01:00:59,720
Speaker 1: How do I find compassion for myself? How do I

1206
01:00:59,760 --> 01:01:03,320
Speaker 1: find compassion for other people? UM? And that's what I'm

1207
01:01:03,440 --> 01:01:08,040
Speaker 1: always trying to work on, is that. UM And hopefully

1208
01:01:08,080 --> 01:01:11,080
Speaker 1: I'm I'm doing an okay job at that. That's quite

1209
01:01:11,160 --> 01:01:14,120
Speaker 1: quite interesting. What do you do to stay mentally or

1210
01:01:14,160 --> 01:01:18,000
Speaker 1: physically fit outside of work. Well, I think that the

1211
01:01:18,680 --> 01:01:21,200
Speaker 1: you know, for me, I think that mental and physical

1212
01:01:21,240 --> 01:01:24,520
Speaker 1: fitness actually go hand in hand. Um. I think that

1213
01:01:24,560 --> 01:01:26,760
Speaker 1: the average view of a poker player is someone who's

1214
01:01:26,800 --> 01:01:29,880
Speaker 1: you know, overweight, with advisor and a cigar or something

1215
01:01:29,920 --> 01:01:32,240
Speaker 1: like that. I'm not sure, but actually most of the

1216
01:01:32,280 --> 01:01:35,280
Speaker 1: really uh top poker players are not that at all

1217
01:01:35,320 --> 01:01:38,760
Speaker 1: and are actually in pretty good physical condition. So I

1218
01:01:38,800 --> 01:01:40,760
Speaker 1: play a lot of tennis, I do a lot of yoga.

1219
01:01:41,320 --> 01:01:43,680
Speaker 1: I do something called solid core, which is like uh,

1220
01:01:43,720 --> 01:01:48,840
Speaker 1: sort of pilates on steroids UM, and I spin UM.

1221
01:01:48,920 --> 01:01:52,400
Speaker 1: So I am working out actually more than seven times

1222
01:01:52,440 --> 01:01:55,800
Speaker 1: a week because I'm often at layering yoga UM on

1223
01:01:55,880 --> 01:01:57,800
Speaker 1: top of that. And I think that that's just really

1224
01:01:57,800 --> 01:02:01,600
Speaker 1: important for mental fitness. You're sitting down at a poker

1225
01:02:01,640 --> 01:02:04,600
Speaker 1: table or doing work like what you do in investing,

1226
01:02:04,640 --> 01:02:07,920
Speaker 1: it's like you're doing math problems all day. You go

1227
01:02:08,040 --> 01:02:10,080
Speaker 1: open up a textbook and do math problems all day.

1228
01:02:10,120 --> 01:02:13,840
Speaker 1: It's really tiring. It could be physically exhaust it's physically exhausting.

1229
01:02:13,840 --> 01:02:15,840
Speaker 1: So I think if your physical self isn't in good shape,

1230
01:02:15,880 --> 01:02:18,880
Speaker 1: then your mental self you know you can't be sharp.

1231
01:02:19,560 --> 01:02:23,040
Speaker 1: If a millennial or recent college grad came to you

1232
01:02:23,120 --> 01:02:27,520
Speaker 1: and said they're thinking about a career as a professional

1233
01:02:27,560 --> 01:02:30,640
Speaker 1: poker player, what sort of advice would you give them.

1234
01:02:30,840 --> 01:02:33,080
Speaker 1: I think that now the advice I would give, because

1235
01:02:33,080 --> 01:02:35,160
Speaker 1: it's been on television, so I think it's considered a

1236
01:02:35,200 --> 01:02:38,840
Speaker 1: little more glam is to know what you're getting into.

1237
01:02:38,880 --> 01:02:41,200
Speaker 1: I mean, it's a grind. You have to put in

1238
01:02:41,240 --> 01:02:44,040
Speaker 1: your hours because the amount of money you're gonna make

1239
01:02:44,160 --> 01:02:45,960
Speaker 1: is gonna be tired to how many hands you get in.

1240
01:02:46,480 --> 01:02:51,600
Speaker 1: So it's long hours. It's tiring. Um. It's it's physically

1241
01:02:51,680 --> 01:02:54,480
Speaker 1: exhausting to be in a casino. It's obviously piped in

1242
01:02:54,600 --> 01:02:59,640
Speaker 1: stale air. There aren't windows, so you're not getting any sunlight. Um,

1243
01:02:59,720 --> 01:03:03,160
Speaker 1: and it's mentally really grueling. So that's number one. Uh,

1244
01:03:03,160 --> 01:03:05,640
Speaker 1: And then number two to recognize that it's a very

1245
01:03:05,720 --> 01:03:09,080
Speaker 1: small portion of people that can really make their living

1246
01:03:09,080 --> 01:03:11,959
Speaker 1: at it. I think that probably the advice I would

1247
01:03:11,960 --> 01:03:14,000
Speaker 1: give would be the same as someone who came and said,

1248
01:03:14,320 --> 01:03:16,480
Speaker 1: I'm going to go make my living being a day trader.

1249
01:03:16,920 --> 01:03:19,200
Speaker 1: It's like, great, there's some people who are amazing at that,

1250
01:03:19,320 --> 01:03:22,720
Speaker 1: and they're amazing at high frequency trading, and they do

1251
01:03:22,760 --> 01:03:25,040
Speaker 1: an incredible job of it. Just to understand that most

1252
01:03:25,080 --> 01:03:28,240
Speaker 1: people don't right and they go broke. And so just

1253
01:03:28,440 --> 01:03:31,560
Speaker 1: go in there with open eyes as to what you're getting,

1254
01:03:31,960 --> 01:03:34,720
Speaker 1: as to what your chances of success are. It's it's

1255
01:03:34,720 --> 01:03:36,560
Speaker 1: not that I don't think that you personally can succeed,

1256
01:03:36,640 --> 01:03:40,080
Speaker 1: just know, um, and be prepared for something that's physically

1257
01:03:40,280 --> 01:03:43,640
Speaker 1: and mentally really grueling. And my final question, what is

1258
01:03:43,680 --> 01:03:48,600
Speaker 1: it that you know about poker, playing, gambling, uh, statistic

1259
01:03:48,840 --> 01:03:52,160
Speaker 1: and luck today that you wish you knew twenty years ago.

1260
01:03:52,760 --> 01:03:56,440
Speaker 1: I think essentially everything that I wrote in the book.

1261
01:03:56,800 --> 01:03:59,640
Speaker 1: When I first started playing, I think that I was

1262
01:04:00,680 --> 01:04:02,439
Speaker 1: I think that I thought that I was much better

1263
01:04:02,480 --> 01:04:05,640
Speaker 1: than I actually was. Um, I thought I was hot,

1264
01:04:06,400 --> 01:04:11,880
Speaker 1: you know yeah, uh, you know I describe this scene

1265
01:04:12,080 --> 01:04:14,400
Speaker 1: and which is I remember so well? You know, so

1266
01:04:14,480 --> 01:04:16,720
Speaker 1: remember I said, my brother wrote all these hands down

1267
01:04:16,720 --> 01:04:19,120
Speaker 1: on a napkin, and my brother was a world champion player,

1268
01:04:19,440 --> 01:04:22,920
Speaker 1: and I somehow thought like I was like way better

1269
01:04:22,960 --> 01:04:24,640
Speaker 1: than these people in the game that I was playing

1270
01:04:24,640 --> 01:04:27,360
Speaker 1: with because I had my napkin from my brother. That's

1271
01:04:27,440 --> 01:04:31,160
Speaker 1: like as Dunning Krueger, as you could get right and people.

1272
01:04:31,160 --> 01:04:33,400
Speaker 1: When I saw people play hands that weren't on my

1273
01:04:33,440 --> 01:04:36,600
Speaker 1: brother's list, I was just like, Wow, that person must

1274
01:04:36,600 --> 01:04:38,680
Speaker 1: be an idiot. They're playing a hand that isn't on

1275
01:04:38,760 --> 01:04:42,240
Speaker 1: my brother's list, and I just said, I'm so embarrassed

1276
01:04:42,240 --> 01:04:45,840
Speaker 1: by that. Oh my gosh. So when I first started playing,

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Speaker 1: I wish I had known how little I knew. I

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Speaker 1: wish I had known that I was not even close,

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Speaker 1: like I was a seventeen universes away from being any

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Speaker 1: kind of expert, and that here's a really importan thing

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Speaker 1: that I wish that I had known that the worst

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Speaker 1: player at the table has something to teach you, and

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01:05:04,760 --> 01:05:06,320
Speaker 1: that thing that they're going to teach you is really

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Speaker 1: really important. And it took me a long time to

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Speaker 1: figure that one out. That's quite fascinating. We have been

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01:05:12,120 --> 01:05:15,360
Speaker 1: speaking with Annie Duke. She is the author of Thinking

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01:05:15,360 --> 01:05:18,680
Speaker 1: in Bets, Making Smarter decisions when you don't have all

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01:05:18,720 --> 01:05:21,960
Speaker 1: the facts. If you enjoy this conversation, be sure and

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01:05:22,120 --> 01:05:24,560
Speaker 1: check Up an Inch or Down an Inch on Apple

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01:05:24,640 --> 01:05:27,360
Speaker 1: iTunes and you could see any of the other At

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Speaker 1: this point almost two hundred podcasts that we've had previously.

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Speaker 1: We enjoy your comments. And feedback and suggestions right to

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Speaker 1: us at m IB podcast at Bloomberg dot Net. I

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Speaker 1: would be remiss if I did not thank our staff

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01:05:42,400 --> 01:05:45,600
Speaker 1: who helps to put together UH this podcast each week.

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01:05:45,760 --> 01:05:50,160
Speaker 1: Medina Parwana is our producer and audio engineer. Taylor Riggs

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01:05:50,240 --> 01:05:53,040
Speaker 1: is our booker. Mike bat Nick is our head of research.

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Speaker 1: I'm Barry Retolts. You've been listening to Masters in Business

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Speaker 1: on Bloomberg Radio. Tempting, tempted to so