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Speaker 1: This is Masters in Business with Barry Ridholds on Bloomberg Radio.

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

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Speaker 1: I know you guys give me grief for saying that

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Speaker 1: every week, but I really have an extra special guest,

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Speaker 1: Professor Danny Kahneman, winner of the two thousand and two

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Speaker 1: Nobel Prize in Economics, which is quite a feat when

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Speaker 1: you realize that he's not an economist. He's a cognitive psychologist,

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Speaker 1: author of the book Thinking Fast and Slow before we

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Speaker 1: get to the podcast, which is really quite fascinating. Just

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Speaker 1: a quick funny story. So we finished doing the interview

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Speaker 1: and we're heading out of the Bloomberg building where you're

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Speaker 1: heading to, Oh, I'm going over here. Oh I'm heading

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Speaker 1: in that same direction, I tell Danny, which is what

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Speaker 1: he insists everybody call him. And so we take a

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Speaker 1: subway downtown and we're get out at Grand Central and

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Speaker 1: we're walking someplace and and he said, as to me, listen,

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Speaker 1: you don't have to walk me to where I'm going.

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Speaker 1: I'm in New York right now where I am? And

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Speaker 1: I said, well, to tell you the truth, it's two o'clock.

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Speaker 1: I haven't had lunch yet. A block from where your

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Speaker 1: destination is. Is this lovely um sandwich shop called Alidoro,

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Speaker 1: just open the midtown. There's a Soho branch. It's really good.

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Speaker 1: I'm just heading in your direction anyway. So he looks

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Speaker 1: at me and says, the sandwich is really that good. Yes,

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Speaker 1: they're delicious. So long story short, we go into this place.

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Speaker 1: It's essentially a takeout joint with one long picnic table

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Speaker 1: on the back. Most people um take sandwiches out. And

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Speaker 1: it's two o'clock so the rush lunch hour rush is over,

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Speaker 1: so I we order sandwiches. Takes a few minutes. I

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Speaker 1: kind of put it, sit him down at the table,

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Speaker 1: and I said, I'll go get the sandwiches. And as

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Speaker 1: I'm coming back to the table, there is Professor Khneman

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Speaker 1: sitting at a picnic table surrounded by I don't know

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Speaker 1: a dozen millennials, kids who could have been in his

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Speaker 1: class a couple of years ago at Princeton, and they're

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Speaker 1: just holy oblivious to this kindly professor sitting amongst them.

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Speaker 1: Here's a brilliant thinker who changed the way we think

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Speaker 1: about how we think, and just everybody completely wholly unaware

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Speaker 1: that that brilliance is in their midst. Then it's the

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Speaker 1: whole thing just cracked me up. I found the interview

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Speaker 1: to be absolutely fascinating. I think you will also, so,

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Speaker 1: with no further ado, my conversation with Danny Khneman. This

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Speaker 1: is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: Our special guest this week. And I know I say

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Speaker 1: this all too often, but we really do have an

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Speaker 1: extra special us this week. His name is Professor Daniel Kaneman.

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Speaker 1: He taught at the Woodrow Wilson School at Princeton, both

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Speaker 1: in psychology and public affairs. He is a fellow at

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Speaker 1: the Center for Rationality at Hebrew University, winner of the

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Speaker 1: Nobel Prize in Economic Sciences as well as the Lifetime

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Speaker 1: Contribution Award of the American Psychology Association UH and he

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Speaker 1: is a recipient of the Presidential Medal of Freedom. I

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Speaker 1: could go on and on about his curriculum vita, but

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Speaker 1: I would rather jump right into this. Danny Kahneman, Welcome

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Speaker 1: to Bloomberg. Pleased to be here, so I'm excited to

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Speaker 1: talk to you about so many different things. Let's let's

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Speaker 1: start way back at the beginning. You you began your

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Speaker 1: academic career in Israel. Did did you expect to spend

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Speaker 1: the rest of your life working in academia? Oh? Yeah,

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Speaker 1: I mean I expected to be a professor when I

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Speaker 1: was a kid. And so you win the Nobel Prize

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Speaker 1: in two thousand and two, not for psychology, which is

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Speaker 1: your field of study, but but for economics. How does

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Speaker 1: the psychologist win a prize for economics? Well, I mean

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Speaker 1: there is no Nobel in psychology, but we wanted in

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Speaker 1: economics for work that is psychological. Uh, when it's you

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Speaker 1: and he didn't actually win this because they don't give

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Speaker 1: it posthumously, but you know, I always felt it it's

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Speaker 1: a joint prize because the prize was awarded for work

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Speaker 1: we had done together, and it was work that influenced economics.

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Speaker 1: So it's under influence in general that you get a Nobel,

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Speaker 1: not on the quality of the work necessary. So so

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Speaker 1: let's talk about your colleague, a most diverse key in

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Speaker 1: your book Thinking Fast and Slow, you described first seeing

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Speaker 1: him speak. I think it was around nineteen sixty nine

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Speaker 1: and the subject was, uh, do people make good intuitive

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Speaker 1: statistical assumptions? Did you know right away the two of

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Speaker 1: you were destined to be research partners? No? But what

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Speaker 1: happened was he visited seminario was teaching. I mean I

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Speaker 1: invited him and he spoke about some research that was

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Speaker 1: being done in Michigan at the time on whether people

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Speaker 1: are good intuitive statisticsians, and their conclusion was that they are.

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Speaker 1: And so we had very counterintuitive well it's now counterintuitive,

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Speaker 1: at that time, it sounded quite intuitive, and we had

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Speaker 1: a very heated discussion. You know, they had those in Israel,

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Speaker 1: and it was a very Israeli type conversation and we

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Speaker 1: enjoyed it, both of us. We decided to have lunch

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Speaker 1: together the following Friday, and there we we discussed ideas

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Speaker 1: over that lunch, and we didn't know that it was

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Speaker 1: going to shape our lives, but we had a pretty

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Speaker 1: good inkling of what we wanted to do next. So

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Speaker 1: the two you established a cognitive basis for analyzing common

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Speaker 1: human errors and how they arrive from biases. How did you, guys,

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Speaker 1: happen across across that discovery. We started, you know, from

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Speaker 1: a combination of what Amos knew and what I was

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Speaker 1: specialized in, and my specialty at the time was visual perception,

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Speaker 1: and in visual perception you have illusions and analyzing the

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Speaker 1: illusions is interesting because the illusions teach you something about

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Speaker 1: the mechanism of normal perception. And Amos was an expert

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Speaker 1: on decision theory and on formal analysis, and we complemented

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Speaker 1: each other very well. And the idea that the research

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Speaker 1: that we developed eventually was really a research on cognitive

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Speaker 1: illusions and where they come from. And we proposed a

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Speaker 1: mechanism and mechanisms we call we call those heuristics, and

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Speaker 1: now have a somewhat different interpretation, not very different, but

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Speaker 1: slightly different from the one we had at that time.

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Speaker 1: I mean, the general idea is quite simple. You ask

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Speaker 1: people a complicated question like what is the probability of

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Speaker 1: an event? And they can't answer it because it's very difficult.

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Speaker 1: But there are easier questions that are related to that

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Speaker 1: one that they can answer, such as, this is a

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Speaker 1: surprising event that is something that people know right away?

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Speaker 1: Is it a typical result of that kind of mechanism?

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Speaker 1: And people can answer that right away. So, and what

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Speaker 1: happens is people take the answer to the easy question,

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Speaker 1: they use it to answer the difficult question, and they

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Speaker 1: think they have answered the difficult question. In fact, they haven't.

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Speaker 1: They answered an easier one. So that's why the mechanism

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Speaker 1: that we studied. You mentioned that your view on heuristics

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Speaker 1: have changed somewhat over the years. What's the difference between

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Speaker 1: what you live today and and way back when. Well,

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Speaker 1: we started out with what we call a limited number

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Speaker 1: of heuristics, and they became quite well known because we

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Speaker 1: have a paper that we published in nine four that

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Speaker 1: in we published it in Science magazine, so it was

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Speaker 1: widely read across many disciplines. And in that paper we

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Speaker 1: spoke about three major heuristics, and we analyze the biases

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Speaker 1: that these heuristics lead to. And many people would know

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Speaker 1: their name from if they went to business school. So

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Speaker 1: it's representativeness, it's availability and anchoring. What I'm thinking of

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Speaker 1: now is that there is a more general process, and

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Speaker 1: that's the process I described a minute ago. They called

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Speaker 1: that attribute substitution. It's to substitute one question for another's.

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Speaker 1: So instead of answering the complex, difficult question, you answer

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Speaker 1: what you can, which is the easier question. That's right.

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Speaker 1: So if I ask you an example that comes to mind,

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Speaker 1: how happy are you these days? Now? You know your

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Speaker 1: mood right now? You're very likely to tell me your

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Speaker 1: mood right now. And think that you have answered the

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Speaker 1: more general question of how happy are you these days?

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Speaker 1: So that's that's another example. There are many like them.

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Speaker 1: I'm Barry Ridholts. You're listening to Masters in Business on

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Speaker 1: Bloomberg Radio. My special guest today is Nobel Prize winning

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Speaker 1: psychologist Danny Kahneman, who has specialized in both cognitive and

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Speaker 1: decision making processes. Let's let's jump right into some of

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Speaker 1: the things that you discussed in Thinking Fast and Slow,

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Speaker 1: a book that I found to be just right in

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Speaker 1: the sweet spot of my confirmation bias. Who was everything

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Speaker 1: I hoped it to be. You talked about what you

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Speaker 1: see is all there is? When when this cussing systems

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Speaker 1: one and two, it's almost a theme throughout the book,

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Speaker 1: explain what that is? What you see is all there is? Well,

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Speaker 1: people are really not aware of information that they don't have,

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Speaker 1: and so the the idea which is emphasized in the

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Speaker 1: book is that you take whatever information you have and

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Speaker 1: you make the best story possible out of that information.

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Speaker 1: And the information you don't have you don't feel that

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Speaker 1: it's necessary. And I have an example that that I

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Speaker 1: think brings that out. If I tell you about a

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Speaker 1: national leader, that she is intelligent and firm. Now do

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Speaker 1: you have an impression already whether she's a good leader

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Speaker 1: or a bad leader, And you certainly do. She's a

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Speaker 1: good leader. Now the third word that I was about

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Speaker 1: to say is corrupt. But you know, the point being

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Speaker 1: that you didn't wait for information that you didn't have,

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Speaker 1: who formed an impression as we were going with the

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Speaker 1: information you did have, And this is what you see,

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Speaker 1: is all there is the working assumption amongst people who

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Speaker 1: are trying to draw a conclusion from available information is

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Speaker 1: they failed to calculate the impact of data they're either

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Speaker 1: unaware of, or don't know or simply haven't encountered. That's

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Speaker 1: I mean, people, If what people are trying to do

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Speaker 1: is to make the best story possible out of the

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Speaker 1: information they have, then this is what they're going to do.

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Speaker 1: And that the measure of confidence that people have in uh,

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Speaker 1: in their beliefs, in their opinions, there's really a reflection

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Speaker 1: of the quality of the story that they've told them. So,

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Speaker 1: so let's talk about that narrative because it's it's one

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Speaker 1: of my favorite errors in investing is that we tell

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Speaker 1: ourselves these complex narratives that seem to fit whatever information

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Speaker 1: is in front of us, and that very much creates

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Speaker 1: a risk that the narrative, as emotional and compelling as

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Speaker 1: it might be, is possibly misleading. How does that fit

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Speaker 1: into your work? I think the same. Talib has a

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Speaker 1: very nice example in his in his book The Black Swan.

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Speaker 1: It's an example of that happened at Bloomberg on Bloomberg News,

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Speaker 1: and it happened the day that Saddam was court, and

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Speaker 1: something happened in the bond market shortly thereafter. I forget

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Speaker 1: whether it rose or it felt and the headline was

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Speaker 1: Saddam Court and investors are fleeing from risk. And then

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Speaker 1: a few hours later the market reversed and there was

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Speaker 1: a headline that said, Saddam Court investors feel more secure

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Speaker 1: taking risk. So now what happened was obviously it's not

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Speaker 1: plausible that that you could explain to opposite consequences by

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Speaker 1: the same thing, but you can make a very good

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Speaker 1: story and what happens and commentators and Bloomberg Radio and

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Speaker 1: all other pundits what they do there is a salient

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Speaker 1: event today. So when you're looking at what happened in

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Speaker 1: the market, you're looking back and you see that salient event,

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Speaker 1: and irresistibly you think there is a causal connection. So

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Speaker 1: whatever happened in the market you attribute to the salient event,

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Speaker 1: went up, went down, whatever it's always it makes a

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Speaker 1: good story. I have a very vivid recollection early in

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Speaker 1: the two thousand and three Iraq invasion there was a important,

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Speaker 1: highly regarded mosque that was blown up by American warplanes.

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Speaker 1: And the headline, and I don't remember it was the

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Speaker 1: Times of the Wall Street Journal, but it was a

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Speaker 1: big publication oil prices skyrocket on mosque accidentally being blown up.

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Speaker 1: And later that day, so you're looking at this online

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Speaker 1: and later that day the oil prices had come back down,

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Speaker 1: and it was the same headline, only changing the conclusion

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Speaker 1: mass destroyed, oil prices remains stable. Well, if it's going

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Speaker 1: up and down after the event, it means the event

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Speaker 1: isn't really significant, is it? Well, clearly. So let's talk

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Speaker 1: a little bit about some of the biases, those three

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Speaker 1: biases you discovered early on availability, anchoring, and representativeness. First,

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Speaker 1: these are three enormous biases. First question, why publish in

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Speaker 1: Discover magazine or Science magazine as opposed to some staid

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Speaker 1: academic journal. Oh, we had done our homework. I mean

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Speaker 1: we published in State academic journalist first, and then what

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Speaker 1: we published in Science, which you know is a scientific magazine.

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Speaker 1: I mean it's not it's a popular magazine. No it's not. No,

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Speaker 1: it's a it's a Science is a professional magazine, but

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Speaker 1: it's a it's addressed to scientists of all disciplines. But

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Speaker 1: you know, it's a it's a perfectly respectable scientific magazine. No,

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Speaker 1: we did. We we were academics. We published academically. We

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Speaker 1: didn't publish and discover. They looked us up and they

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Speaker 1: interviewed us and they wrote about us, but we didn't

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Speaker 1: seek that out. We were really academics. We were not

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Speaker 1: trying to influence the world. And whatever happened happened, but

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Speaker 1: that wasn't really what we intended. Did you have any

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Speaker 1: sense of the enormity of these three particular biases early on, Well,

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Speaker 1: when we wrote for Science, we had a sense that

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Speaker 1: this was an important story. We really did not anticipate

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Speaker 1: the impact that it had on many fields, including investment

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Speaker 1: and the law, and and and even economics. It had

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Speaker 1: a lot of influence and that we didn't anticipate. Really.

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Speaker 1: So so let's talk about a couple of these. You

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Speaker 1: mentioned availability bias, it's just the information you have. Anchoring

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Speaker 1: is an enormous factor in things like negotiations, in pricing.

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Speaker 1: I love the story of just asking people. Ask a

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Speaker 1: group of people in a room what the first digit

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Speaker 1: of their phone number is, and then ask them a separate,

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Speaker 1: unrelated question. A higher digit will yield a higher number

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Speaker 1: on the unrelated question, and a lower first digit yields

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Speaker 1: a low How did how did that discovery come about? Well,

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Speaker 1: actually it takes a little more than that, but I'll

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Speaker 1: give you an example. Uh, in the example of negotiation,

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Speaker 1: many people think that you have an advantage if you

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Speaker 1: go second, but actually the advantage is going first throughout

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Speaker 1: the first number, and that's where you're throughout. And and

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Speaker 1: the reason is something in the way the mind works.

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Speaker 1: And the mind works, it tries to make sense whatever

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Speaker 1: you put before it. So this built in tendency that

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Speaker 1: we have of trying to make sense of everything that

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Speaker 1: we encounter, that is a mechanism for anchoring. I'm Barry Ridholts.

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

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Speaker 1: special guest today is Nobel Prize winning psychologist Danny Kahneman,

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Speaker 1: who has specialized in both cognitive and decision making processes.

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Speaker 1: Let's talk a little bit about biases and money. Since

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Speaker 1: this is Bloomberg and many of our listeners are investors,

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Speaker 1: I want to start with prospect theory, which is something

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Speaker 1: that's I find absolutely fascinating. The basic idea is that

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Speaker 1: we feel losses much more severely than we enjoy gains.

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Speaker 1: The first question I have for you about this is

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Speaker 1: how did you discover this? Well, the idea that came

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Speaker 1: first or the was that the standard financial analysis and

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Speaker 1: which called the rational age model, fails in an important way.

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Speaker 1: And in the rational agent model, you should always be

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Speaker 1: thinking in terms of wealth. You know, what will be

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Speaker 1: my wealth if this happens? What will be my wealth

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Speaker 1: if that happens? And all the theory is based on

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Speaker 1: the idea that you are thinking in terms of your wealth.

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Speaker 1: But it turns out you're not thinking in terms of

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Speaker 1: your wealth at all. You're thinking in terms of gains

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Speaker 1: and losses. You're thinking in terms of changes of wealth.

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Speaker 1: So that was the first thing that that was important

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Speaker 1: to establish. It was clear Homo economists is what the

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Speaker 1: professors call it, not a really good representation of how

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Speaker 1: people behave. That's right, and you know that this is

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Speaker 1: sort of immediately obvious that people don't think in terms

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Speaker 1: of their wealth. And it's easy to show that that

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Speaker 1: they don't because you can describe the same decision which

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Speaker 1: in the same in terms of wealth. You can give

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Speaker 1: it two different descriptions and people will make different choices

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Speaker 1: depending on whether it's resented in gains and losses. So

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Speaker 1: that were the first discovery, if you will, I mean,

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Speaker 1: you know it had been stated before by the way

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Speaker 1: Marco Witch Harry Marco which he had he had written.

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Speaker 1: He had written one article on this about changes being important.

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Speaker 1: He didn't follow through, and we came upon us and

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Speaker 1: we did follow through. Now, once you have that that

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Speaker 1: people are thinking gains and losses, it's easy to see

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Speaker 1: that there is an asymmetry. That is that losses, as

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Speaker 1: we said, loom larger than gains. And we even have

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Speaker 1: a pretty good idea of by how much do they

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Speaker 1: loom larger than gains about two to one. And you

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Speaker 1: know an example how to bring that out is I'll

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Speaker 1: offer you a gamble on the toss of a coin.

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Speaker 1: You know, if it chose tales, you lose one hundred

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Speaker 1: and if it chose heads, you win X. Now what

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Speaker 1: would ex have to be for that gamble to become

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Speaker 1: really attractive to you? Well, more than a hundred, so

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Speaker 1: it's more than Actually you're a professional. There is a

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Speaker 1: difference of professionals than others most people, and that's been

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Speaker 1: well established. The men more than two hundred really, so

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Speaker 1: they want two to one odds, which is it's not

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Speaker 1: the odds, I mean the odds I went to one.

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Speaker 1: It's equal chance to payout. It's the payout has to

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Speaker 1: be to to one, meaning that it takes two hundred

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Speaker 1: dollars of potential gain to compensate for one hundred dollars

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Speaker 1: of potential loss when the chances of the two are equal.

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Speaker 1: So that's loss of version. Terms out that loss of

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Speaker 1: version has enormous consequence, enormous absolutely so. So that leads

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Speaker 1: to a couple of really interesting questions. The first one

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Speaker 1: is what is it about losses that makes them so

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Speaker 1: much more painful than gets are pleasurable? In other words,

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Speaker 1: why does this two to one risk of loss of version?

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Speaker 1: Why does this even exist? Well, I mean you know this,

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Speaker 1: this is evolutionary. I mean you you would imagine that

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Speaker 1: in evolution, threats are more important than opportunities. That makes

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Speaker 1: a lot of sense, And so it's a very general

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Speaker 1: phenomenon that sort of bad things. So the preemptal are

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Speaker 1: stronger than good things in our experience. So loss of

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Speaker 1: version is a special case of something much broader. So

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Speaker 1: there's always another opportunity coming along, another game, another deer

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Speaker 1: coming by, but an actual genuine loss. Hey, that's permanent.

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Speaker 1: You don't recover from that. That's right anyway, you take

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Speaker 1: it most seriously. So there is a deer in your

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Speaker 1: sights and a lion, you are going to be busy

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Speaker 1: about the lion and not about the deer. Makes sense.

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Speaker 1: That leads to the obvious question, well, what can investors

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Speaker 1: do to protect themselves against this hardwired Uh? Well, loss

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Speaker 1: of her Uh there's everything they can do. One is

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Speaker 1: not to look at their results, not to look too

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Speaker 1: often at how well they're doing. And today you could

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Speaker 1: look tick by tick, minute by minute. The worst thing,

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Speaker 1: it's a very very bad idea to look too often.

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Speaker 1: When you look very often, you attempted to make changes.

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Speaker 1: And where individual investors lose money is when they make

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Speaker 1: changes in their allocation virtually on average. Whenever an investor

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Speaker 1: makes a move, it's likely to lose money because they

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Speaker 1: are professionals on the other side, betting against the kind

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Speaker 1: of moves that individual investors. I'm Barry Hults. You're listening

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Speaker 1: to Masters in Business on Bloomberg Radio. My special guest

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Speaker 1: today is Danny Kahneman. He is the Nobel Prize winning

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Speaker 1: psychologist formerly professor at Princeton who has had an enormous

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Speaker 1: influence on the fields of economics and investing. Let's get

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Speaker 1: right back to the question of investing and the professionals

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Speaker 1: versus the amateurs. Do we see the same sort of

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Speaker 1: biases and errors amongst the professionals. They're clearly much attenuated

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Speaker 1: among professionals. So I'll give you an example. I mean,

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Speaker 1: and that's the biggest example. It's what makes lots of

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Speaker 1: version important. If I ask you, would you take a

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Speaker 1: gamble if I ask a regular person in the street,

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Speaker 1: Not you, because you're a professional, But if I ask

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Speaker 1: a regular person in the street, would you take a

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Speaker 1: gamble that if you lose, you lose a hundred dollars.

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Speaker 1: If you win, you win a hundred and eighty on

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Speaker 1: the toss of a coin. That's a no brainer. Well,

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Speaker 1: it's a no brainer for professionals. Most people don't like it.

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Speaker 1: Really do that all day long? Yeah, because you're a professional. Now,

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Speaker 1: when you ask the same people in the street, Okay,

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Speaker 1: don't want this one? Would you take ten? So we'll

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Speaker 1: toss tin coins and every time, if you lose, you

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Speaker 1: lose a hundred, and if you win, you win a

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Speaker 1: hundred and eighty. Everybody wants the tin. Nobody wants the one. So,

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Speaker 1: in other words, they think the mathistatistics in repeated play.

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Speaker 1: When the game is repeated, then people see they become

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Speaker 1: much closer to risk neutral and they see the advantage

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Speaker 1: of gambling. Now professionals are in a repeated play situation.

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Speaker 1: That's the biggest difference, And so they view each decision

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Speaker 1: that they make out of a stream of decision that

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Speaker 1: they're going to make. Now, this, by the way, is

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Speaker 1: true for investors as well. One question that I asked

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Speaker 1: people when I tell them about that, So you've turned

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Speaker 1: down a hundred hundred and eighty now, but you would

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Speaker 1: accept ten of those? Now, are you on your deathbed?

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Speaker 1: That's the question I ask if that the last decision

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Speaker 1: you're going to make, And clearly they are going to

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Speaker 1: be more opportunities to gamble, perhaps not exactly the same gamble,

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Speaker 1: but there will be many more opportunities. You need to

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Speaker 1: have a policy for how you deal with risks and

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Speaker 1: then make your decisions, your individual decisions, in terms of

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Speaker 1: a broader policy. Then you will be much closer to rationality.

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Speaker 1: But on the single flip of a coin, the average

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Speaker 1: individual is going to look at this and say, hey,

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Speaker 1: I'll feel foolish if I lose a hundred, even though

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Speaker 1: the payout is greater than my potential law that's exactly

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Speaker 1: what happens, and you shouldn't feel that way. I mean, now,

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Speaker 1: it's true that you will feel like a fool. It's

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Speaker 1: very closely related to what you see as all. There

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Speaker 1: is that as we tend to see decisions in isolation,

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Speaker 1: we don't see a decision about whether I take this

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Speaker 1: gamble as one of many similar decisions that I'm going

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Speaker 1: to make in future. So are people overly outcome focused

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Speaker 1: to the detriment of process? What they are We call

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Speaker 1: that narrow framing. They viewed the situation narrowly, and that

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Speaker 1: is true in all domains. So, for example, we said

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Speaker 1: that people on my opic that they have a narrow

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Speaker 1: time horizon. To be more rational, you want to look

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Speaker 1: further in time, and then you'll make better decisions. If

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Speaker 1: you're thinking of where you will be, you know, a

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Speaker 1: long time from now. It's completely different from thinking about

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Speaker 1: how will I feel tomorrow If I make this bit

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Speaker 1: and I lose, I recall reading about a study and

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Speaker 1: I hope I don't mangle this too badly where they

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Speaker 1: would take a photo of somebody and then using software

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Speaker 1: age their face, and then when they were would ask

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Speaker 1: the people who had the current photo of their own face.

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Speaker 1: They would get a very different answer than the group

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Speaker 1: that we're better able to imagine themselves twenty years alter. Yeah,

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Speaker 1: that's about saving. I mean what actually happens is that

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Speaker 1: when you show people morphed image of their face as

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Speaker 1: an old person, their tendency to save increases, so it's

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Speaker 1: easier for them to identify with their future self. But

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Speaker 1: in general that's not what we do. People aren't especially

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Speaker 1: good about that. Let's talk about being wrong and being

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Speaker 1: able to admit that you're wrong. John Kenneth gale Breath

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Speaker 1: once favor You famously said, given the choice between changing

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Speaker 1: one's minds and proving there's no need to do so

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Speaker 1: most people get busy on the proof. You called this

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Speaker 1: theory induced blindness. So why are we so unwilling to

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Speaker 1: admit when we're wrong. You know, you try to make

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Speaker 1: the best story possible, and the best story possible includes

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Speaker 1: what frequently actually didn't make that mistake. You know, So

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Speaker 1: something occurred and in fact I did not anticipate it,

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Speaker 1: but in retrospect, I didn't diticipate it. This is called hindsight.

429
00:28:13,880 --> 00:28:17,920
Speaker 1: And that's one of the main reasons that we don't

430
00:28:17,920 --> 00:28:21,280
Speaker 1: admit that we're wrong, is that whatever happens, we have

431
00:28:21,320 --> 00:28:23,720
Speaker 1: a story. We can make a story, we can make

432
00:28:23,760 --> 00:28:27,359
Speaker 1: sense of it. We think we understand it, and when

433
00:28:27,400 --> 00:28:32,000
Speaker 1: we think we understand it, we alter our image of

434
00:28:32,040 --> 00:28:34,679
Speaker 1: what we thought earlier. I'll give you a kind of

435
00:28:34,760 --> 00:28:39,200
Speaker 1: example that so you have two teams that are about

436
00:28:39,240 --> 00:28:43,160
Speaker 1: to play, you know, a football, and the two teams

437
00:28:43,160 --> 00:28:48,960
Speaker 1: are about evenly balanced. Now one of them completely crushes

438
00:28:49,080 --> 00:28:53,480
Speaker 1: the other. Now, after you have just seen that they're

439
00:28:53,520 --> 00:28:57,280
Speaker 1: not equally strong, you perceive one of them as much

440
00:28:57,360 --> 00:29:01,120
Speaker 1: stronger than the other, and that sception gives you the

441
00:29:01,160 --> 00:29:04,600
Speaker 1: sense that you know, this must have been visible in advance,

442
00:29:05,080 --> 00:29:07,720
Speaker 1: that one of them was much stronger than the other.

443
00:29:08,640 --> 00:29:13,800
Speaker 1: So hindsight is that's a big deal. It allows us

444
00:29:13,840 --> 00:29:18,000
Speaker 1: to keep a coherent view of the world. It blinds

445
00:29:18,120 --> 00:29:22,040
Speaker 1: us to surprise us. It prevents us from learning the

446
00:29:22,160 --> 00:29:25,640
Speaker 1: right thing. It allows us to learn the wrong thing.

447
00:29:25,760 --> 00:29:29,160
Speaker 1: That is, whenever we're surprised by something, even if we

448
00:29:29,360 --> 00:29:31,880
Speaker 1: do admit that we make a mistake. So oh, I'll

449
00:29:31,920 --> 00:29:35,320
Speaker 1: never make that mistake again. But in fact, what you

450
00:29:35,360 --> 00:29:38,920
Speaker 1: should learn, but you should learn when when you make

451
00:29:38,960 --> 00:29:42,400
Speaker 1: a mistake because you did not anticipate something, that the

452
00:29:42,480 --> 00:29:46,200
Speaker 1: world is difficult to anticipate. That's the correct lesson to

453
00:29:46,320 --> 00:29:50,320
Speaker 1: learn from surprises that the world is surprising. It's not

454
00:29:50,480 --> 00:29:53,960
Speaker 1: that my prediction is wrong. It's that predicting in general

455
00:29:54,080 --> 00:29:57,800
Speaker 1: is almost as possible, you know. It's it's ironic. During

456
00:29:57,840 --> 00:30:02,640
Speaker 1: the O eight oh nine finance shull crisis, we saw

457
00:30:03,000 --> 00:30:08,520
Speaker 1: very few people beforehand making warnings about housing, about derivatives,

458
00:30:08,520 --> 00:30:12,080
Speaker 1: about the stock markets. A pair of academics from Ryan

459
00:30:12,120 --> 00:30:15,320
Speaker 1: Hart and Rogolf very famously put out a paper in

460
00:30:15,520 --> 00:30:21,479
Speaker 1: January eight widely ignored afterwards. I can't tell you how

461
00:30:21,520 --> 00:30:25,560
Speaker 1: many people have claimed to have seen it coming, never

462
00:30:25,600 --> 00:30:28,960
Speaker 1: in print, there's no recorded history. But they all started

463
00:30:29,040 --> 00:30:31,400
Speaker 1: coming and they knew this was going to go south.

464
00:30:32,440 --> 00:30:36,520
Speaker 1: Pure hindsight. By this is really hindsight bias. It's actually

465
00:30:36,640 --> 00:30:41,040
Speaker 1: very pernicious because it gives you the wrong impression. So

466
00:30:42,080 --> 00:30:44,760
Speaker 1: I love Michael Lewis, and I like The Big Short

467
00:30:45,480 --> 00:30:48,720
Speaker 1: like everybody, but The Big Short really gives you the

468
00:30:48,720 --> 00:30:53,520
Speaker 1: impression that this was as obvious and the nose you know, see,

469
00:30:53,760 --> 00:30:58,120
Speaker 1: when I read that book, my interpretation was here are

470
00:30:58,160 --> 00:31:02,640
Speaker 1: a very small number of very odd characters who were

471
00:31:02,680 --> 00:31:06,160
Speaker 1: amongst the only ones who saw it, and everybody else

472
00:31:06,160 --> 00:31:08,440
Speaker 1: thought they were crazy. I mean, And the movie does

473
00:31:08,480 --> 00:31:12,040
Speaker 1: a nice job portraying that. Yeah, but actually, at any

474
00:31:12,120 --> 00:31:15,800
Speaker 1: one time, there are many people who are predicting that

475
00:31:15,880 --> 00:31:18,560
Speaker 1: the world is going to end tomorrow, that there's going

476
00:31:18,600 --> 00:31:22,040
Speaker 1: to be a crush next week. Now there is no crush.

477
00:31:22,160 --> 00:31:25,160
Speaker 1: Those people get forgotten when there is a crash, the

478
00:31:26,040 --> 00:31:29,440
Speaker 1: sort of geniuses. So we've seen that time and again,

479
00:31:29,920 --> 00:31:34,160
Speaker 1: the broken clock syndrome. Eventually they're right everybody for although

480
00:31:34,200 --> 00:31:36,880
Speaker 1: in the modern world, thanks to Google, you can very

481
00:31:36,920 --> 00:31:40,080
Speaker 1: easily go back and check if someone has said there.

482
00:31:40,200 --> 00:31:43,680
Speaker 1: There's a famous pundit who is for the past seven years,

483
00:31:43,680 --> 00:31:47,520
Speaker 1: has been saying a seven like crash is around the corner.

484
00:31:47,560 --> 00:31:50,760
Speaker 1: It's coming. Well maybe, but it's now seven years in

485
00:31:50,760 --> 00:31:52,960
Speaker 1: a row. You've been saying, and there are many people

486
00:31:53,040 --> 00:31:56,320
Speaker 1: you know, they're saying, is who predicted seven of the

487
00:31:56,400 --> 00:32:00,880
Speaker 1: last three recessions? That's it's that's sort of So speaking

488
00:32:00,880 --> 00:32:05,560
Speaker 1: of Michael Lewis, you and Amos Diversky are the subject

489
00:32:05,760 --> 00:32:09,600
Speaker 1: of his next book that's coming out. Let's talk about that.

490
00:32:09,640 --> 00:32:12,880
Speaker 1: What was that experience? Like Michael Lewis is a very

491
00:32:12,920 --> 00:32:17,480
Speaker 1: lovable character and is very pleasant to talk to, and

492
00:32:17,800 --> 00:32:22,000
Speaker 1: he earns your confidence by his charm and you know,

493
00:32:22,480 --> 00:32:25,120
Speaker 1: he just he is very good at what he does.

494
00:32:26,080 --> 00:32:28,520
Speaker 1: I haven't seen the book. I have no idea what

495
00:32:28,720 --> 00:32:31,200
Speaker 1: it's what. I don't even want to see it before

496
00:32:31,200 --> 00:32:33,680
Speaker 1: he gets published. You know, when it gets published, I'll

497
00:32:33,720 --> 00:32:37,640
Speaker 1: read it, of course. But the process has been interesting

498
00:32:37,800 --> 00:32:42,720
Speaker 1: because he's made me think about my past and that's

499
00:32:42,760 --> 00:32:46,200
Speaker 1: been quite enjoyable. It has made me think about my

500
00:32:46,560 --> 00:32:49,720
Speaker 1: my collaboration with a Mustwirsky. No I must Firsky died

501
00:32:49,760 --> 00:32:54,120
Speaker 1: twenty years ago, and he is bringing him to life.

502
00:32:55,040 --> 00:32:57,920
Speaker 1: And he also had access to all of Amos's papers,

503
00:32:58,000 --> 00:33:01,040
Speaker 1: including notes that they must talk and conversations we have

504
00:33:01,640 --> 00:33:06,240
Speaker 1: that's forgotten all about. So it's it's been an interesting

505
00:33:06,320 --> 00:33:10,240
Speaker 1: experience reliving that period, but the experience would be about

506
00:33:10,440 --> 00:33:12,920
Speaker 1: reading the book. I don't know yet. You can hang

507
00:33:12,960 --> 00:33:16,120
Speaker 1: around a little bit. We'll keep we'll keep chatting. We've

508
00:33:16,120 --> 00:33:19,400
Speaker 1: been speaking with Professor Danny Kahneman, winner of the two

509
00:33:19,440 --> 00:33:23,920
Speaker 1: thousand and two Nobel Prize in Economics, former psychology professor

510
00:33:24,400 --> 00:33:27,960
Speaker 1: at Princeton. If you've enjoyed this conversation, be sure and

511
00:33:28,040 --> 00:33:31,080
Speaker 1: stick around for our podcast extras, where we keep the

512
00:33:31,080 --> 00:33:35,600
Speaker 1: tape rolling and continue discussing all things biases and heuristics.

513
00:33:36,160 --> 00:33:39,680
Speaker 1: Be sure and check out my daily column on Bloomberg

514
00:33:39,720 --> 00:33:43,520
Speaker 1: dot com or follow me on Twitter at rit Halts.

515
00:33:43,760 --> 00:33:46,880
Speaker 1: I'm Barry rit Halts. You've been listening to Masters in

516
00:33:46,920 --> 00:33:51,640
Speaker 1: Business on Bloomberg Radio. Welcome to the podcast. By the way,

517
00:33:51,800 --> 00:33:53,680
Speaker 1: it's so odd to call you Danny. Danny, if I

518
00:33:53,680 --> 00:33:55,760
Speaker 1: haven't said thank you so much for being so generous

519
00:33:55,760 --> 00:33:59,000
Speaker 1: with your time, let me do that now before before

520
00:33:59,040 --> 00:34:04,200
Speaker 1: I um forget. I have been uh a big fan

521
00:34:04,920 --> 00:34:07,480
Speaker 1: of your work for a long time and in my

522
00:34:07,680 --> 00:34:12,480
Speaker 1: career I started as a trader and pretty early on

523
00:34:12,800 --> 00:34:16,320
Speaker 1: recognized that why are these four guys doing the exact

524
00:34:16,320 --> 00:34:20,520
Speaker 1: same thing and yet they're each obtaining very different results.

525
00:34:20,920 --> 00:34:23,600
Speaker 1: And the first book on the in the space I

526
00:34:23,680 --> 00:34:27,480
Speaker 1: read was by Tom Gilovich and Cornell, who I believe

527
00:34:27,520 --> 00:34:30,960
Speaker 1: you've you've worked with before. Um, how we know it

528
00:34:31,000 --> 00:34:35,200
Speaker 1: isn't so was was that book. But I've loved the

529
00:34:35,239 --> 00:34:38,800
Speaker 1: work that you've put out, and I have a bazillion questions.

530
00:34:38,840 --> 00:34:41,600
Speaker 1: Let's see if we can work our way through through

531
00:34:41,680 --> 00:34:45,080
Speaker 1: some of these. So earlier on we were talking about

532
00:34:45,160 --> 00:34:49,600
Speaker 1: anchoring and availability um. And one of the when I

533
00:34:49,680 --> 00:34:53,080
Speaker 1: mentioned to a friend that you were an upcoming guest,

534
00:34:53,760 --> 00:34:58,120
Speaker 1: the question he suggested is, well, the people who study

535
00:34:58,200 --> 00:35:02,719
Speaker 1: biases and and the pitfall of human cognitions, they must

536
00:35:02,760 --> 00:35:07,800
Speaker 1: be optimal decision makers, right, And I said, I'll ask so, well,

537
00:35:08,239 --> 00:35:11,759
Speaker 1: certainly not, uh, you know. And the way that I

538
00:35:11,880 --> 00:35:15,640
Speaker 1: described it, there's System one and their System two, and

539
00:35:15,719 --> 00:35:20,080
Speaker 1: System one is very difficult to educate, and System one

540
00:35:20,239 --> 00:35:24,239
Speaker 1: is where our intuitions come from. And system too, very

541
00:35:24,280 --> 00:35:28,799
Speaker 1: often is just the pr agent four. System one it

542
00:35:28,960 --> 00:35:34,560
Speaker 1: just explains decisions that were made, rationalizes, it rationalizes. But

543
00:35:34,640 --> 00:35:37,320
Speaker 1: System one does a good job at keeping us alive

544
00:35:37,400 --> 00:35:44,120
Speaker 1: and not only in the savannah, you know, keeping it

545
00:35:44,200 --> 00:35:47,240
Speaker 1: keeps us alive. And most of the things that people do,

546
00:35:48,440 --> 00:35:53,360
Speaker 1: uh are good. You know, I believe I'm to asky

547
00:35:53,440 --> 00:35:57,200
Speaker 1: and I often considered sort of the profits of the irrationality.

548
00:35:57,480 --> 00:36:01,239
Speaker 1: We don't never wanted that label. We don't believe that

549
00:36:01,280 --> 00:36:05,680
Speaker 1: people are irrational. People are not perfectly irrational. They couldn't be,

550
00:36:05,680 --> 00:36:09,680
Speaker 1: because they have the finite mind and perfect rationality demands

551
00:36:09,760 --> 00:36:13,799
Speaker 1: much more. But you know, they're quite reasonable, except they

552
00:36:13,880 --> 00:36:18,640
Speaker 1: make predictable biases in some mistakes in some conditions. So

553
00:36:18,760 --> 00:36:23,000
Speaker 1: by and large, most people get many of their important

554
00:36:23,040 --> 00:36:29,319
Speaker 1: decisions right, and the ones that get wrong really stand out. Well, uh,

555
00:36:29,560 --> 00:36:32,040
Speaker 1: you know, it's hard to count. But most of the time,

556
00:36:32,080 --> 00:36:35,560
Speaker 1: you know, most of the day you are on system one.

557
00:36:35,800 --> 00:36:38,719
Speaker 1: You know, we're doing things that are well practiced, and

558
00:36:38,880 --> 00:36:42,560
Speaker 1: most of them work. So we we managed to get

559
00:36:42,600 --> 00:36:46,520
Speaker 1: through our day without engaging in without thinking too much,

560
00:36:47,400 --> 00:36:51,040
Speaker 1: and well that for sure. Yeah, and most of the

561
00:36:51,120 --> 00:36:55,960
Speaker 1: time it works just fine. That's that's quite so. The

562
00:36:55,960 --> 00:36:59,279
Speaker 1: answer to your question was a long one, But no,

563
00:36:59,600 --> 00:37:03,080
Speaker 1: I I'm not smarter than I was when I began

564
00:37:03,160 --> 00:37:06,680
Speaker 1: this line of research more than fifty years ago. Because

565
00:37:06,760 --> 00:37:08,960
Speaker 1: my system one is just the same way it was.

566
00:37:09,640 --> 00:37:14,160
Speaker 1: I can recognize sometimes I can recognize situations in which

567
00:37:14,360 --> 00:37:18,120
Speaker 1: I'm likely to make a mistake. So, h this person

568
00:37:18,280 --> 00:37:21,160
Speaker 1: is trying to anchor me. Yeah, I can recognize that.

569
00:37:21,320 --> 00:37:23,560
Speaker 1: It still works on me, by the way, but but

570
00:37:23,600 --> 00:37:26,200
Speaker 1: I can recognize it. So well, let's let's talk a

571
00:37:26,239 --> 00:37:29,600
Speaker 1: little bit about that. So I was always under the belief,

572
00:37:29,800 --> 00:37:34,359
Speaker 1: apparently mistakenly, that if you become self enlightened about your

573
00:37:34,400 --> 00:37:37,920
Speaker 1: own biases, then you can undertake a series of steps

574
00:37:37,960 --> 00:37:42,480
Speaker 1: to prevent yourself from doing damage. We were speaking before

575
00:37:42,520 --> 00:37:47,080
Speaker 1: the show started about indexing and global asset allocation. Now

576
00:37:47,080 --> 00:37:51,000
Speaker 1: are you picking stocks and timing markets or have you

577
00:37:52,080 --> 00:37:55,600
Speaker 1: taken your system too and allowed it to prevent system

578
00:37:55,880 --> 00:38:01,560
Speaker 1: from saving I personally don't do anything of the kind

579
00:38:03,440 --> 00:38:06,320
Speaker 1: you know, But I'm a conservative Poston and so there's

580
00:38:06,360 --> 00:38:08,520
Speaker 1: nothing to be learned from the way that I handled

581
00:38:08,520 --> 00:38:11,760
Speaker 1: my money. But I certainly I would not advise speaking

582
00:38:11,800 --> 00:38:15,520
Speaker 1: individual stuff so or any form of very active manage.

583
00:38:15,760 --> 00:38:19,879
Speaker 1: You strike me as a passive indexer who you specifically said,

584
00:38:20,440 --> 00:38:23,200
Speaker 1: I don't want to check my portfolio too often people

585
00:38:23,239 --> 00:38:26,000
Speaker 1: do that, and when they do, they tend to make mistakes.

586
00:38:26,360 --> 00:38:30,520
Speaker 1: So this seems like a little closer to optimal decision making.

587
00:38:31,640 --> 00:38:35,480
Speaker 1: You've identified a couple of biases that affect out people

588
00:38:35,520 --> 00:38:39,920
Speaker 1: invest and you're engaging in the behavior to prevent yourself

589
00:38:39,920 --> 00:38:43,840
Speaker 1: from Well, I'm mostly very lazy, you know. That's it.

590
00:38:44,440 --> 00:38:47,160
Speaker 1: That's you can get to the same point by being

591
00:38:47,640 --> 00:38:51,400
Speaker 1: rational or by being lazy. In that case, it's laziness.

592
00:38:51,960 --> 00:38:53,799
Speaker 1: If that was true, I would have been a much

593
00:38:53,840 --> 00:38:59,200
Speaker 1: better student in high school. Um so uh. Jason's ye

594
00:38:59,239 --> 00:39:03,279
Speaker 1: of the Wall Street Journal is um was an editor

595
00:39:03,440 --> 00:39:07,399
Speaker 1: with you on some work you did, and he said

596
00:39:07,480 --> 00:39:09,480
Speaker 1: you you once said to him, and I want to

597
00:39:09,520 --> 00:39:15,040
Speaker 1: get the quote exactly right. You have no sunk costs.

598
00:39:15,080 --> 00:39:17,719
Speaker 1: So we're all familiar with the sun cost fallacy. How

599
00:39:17,760 --> 00:39:19,960
Speaker 1: do you have no sun costs? I mean I have

600
00:39:20,280 --> 00:39:22,880
Speaker 1: you know, it was in a particular context. It was

601
00:39:22,920 --> 00:39:25,880
Speaker 1: in the context of how many drafts do you write?

602
00:39:26,560 --> 00:39:29,239
Speaker 1: And I have no sun costs in the sense and

603
00:39:29,360 --> 00:39:32,879
Speaker 1: the mere fact that I have written something, even if

604
00:39:32,920 --> 00:39:36,399
Speaker 1: it's twenty or thirty pages. If now I decide it's

605
00:39:36,440 --> 00:39:40,759
Speaker 1: not good, I don't try to fix it. I started over,

606
00:39:40,960 --> 00:39:43,520
Speaker 1: throw it away and started over. That's that's having no

607
00:39:43,680 --> 00:39:48,120
Speaker 1: sun costs. Now, the implication is some people would have said, Hey,

608
00:39:48,160 --> 00:39:50,759
Speaker 1: I spent two hours on this, I'm gonna go back

609
00:39:50,760 --> 00:39:53,080
Speaker 1: and edit this and try and make it. You're that

610
00:39:53,080 --> 00:39:55,880
Speaker 1: that time and effort is spent as far as you're concerned.

611
00:39:56,280 --> 00:40:00,080
Speaker 1: You're starting from scratch, and I think you do the

612
00:40:00,239 --> 00:40:05,000
Speaker 1: things that way. If if you're if you forget about

613
00:40:05,040 --> 00:40:08,680
Speaker 1: the work that you've already invested. But if you if

614
00:40:08,719 --> 00:40:12,160
Speaker 1: you have found a better solution. Now instead of saying, oh,

615
00:40:12,280 --> 00:40:15,800
Speaker 1: I should have written that chapter differently, you just rewrite

616
00:40:15,800 --> 00:40:20,000
Speaker 1: that chapter differently. Now, don't Most people suffer from the

617
00:40:20,200 --> 00:40:23,000
Speaker 1: some cost of fact They feel, well, I already spent

618
00:40:23,120 --> 00:40:25,920
Speaker 1: this money, and now I'm committing. I bought this stock

619
00:40:26,080 --> 00:40:28,359
Speaker 1: and now I'm kind of committed to it. You know,

620
00:40:28,560 --> 00:40:34,759
Speaker 1: I've we're talking about writing, not not the decisions, but

621
00:40:34,800 --> 00:40:38,160
Speaker 1: it's still it's still work process, it's still effort, and

622
00:40:38,280 --> 00:40:40,640
Speaker 1: people tend to think, oh, I mean, most people are

623
00:40:40,719 --> 00:40:43,840
Speaker 1: highly sensitive to some costs, and I would not say

624
00:40:43,880 --> 00:40:47,440
Speaker 1: that I'm not in other domains of life. I said

625
00:40:47,560 --> 00:40:50,160
Speaker 1: I'm free of some costs in the domain of writing.

626
00:40:50,800 --> 00:40:54,560
Speaker 1: And that's what Jason Swag was writing about, because actually

627
00:40:55,440 --> 00:41:01,800
Speaker 1: he thinks that most writers very reluctant, distilled over. Yes, absolutely,

628
00:41:01,880 --> 00:41:04,640
Speaker 1: you put the time and effort in. Gee, I don't

629
00:41:04,640 --> 00:41:07,960
Speaker 1: want to throw this away. I have something to work with. Um.

630
00:41:08,000 --> 00:41:11,600
Speaker 1: It's always easier to do that that second draft than

631
00:41:11,640 --> 00:41:14,040
Speaker 1: it is to start with a plane sheet of paper. No,

632
00:41:14,040 --> 00:41:16,879
Speaker 1: no doubt about that. So we haven't really talked about

633
00:41:16,960 --> 00:41:21,320
Speaker 1: the endowment effect. UM, But I have my favorite example

634
00:41:21,600 --> 00:41:24,879
Speaker 1: that I wanted to share with you because I find

635
00:41:24,880 --> 00:41:29,040
Speaker 1: this such a fascinating subject. Whenever. So I'm a car guy,

636
00:41:29,320 --> 00:41:32,560
Speaker 1: and that means very often friends and family come and

637
00:41:32,600 --> 00:41:35,719
Speaker 1: ask me about I'm thinking about this car or that car.

638
00:41:35,880 --> 00:41:40,759
Speaker 1: What what's your view? And I've noticed it's a fascinating subject,

639
00:41:40,840 --> 00:41:44,200
Speaker 1: like like the two football teams who are so evenly matched.

640
00:41:44,640 --> 00:41:47,200
Speaker 1: When someone comes to you and says, I'm I'm considering

641
00:41:47,360 --> 00:41:50,680
Speaker 1: these two cars. I'm thinking about the Toyota camera or

642
00:41:50,719 --> 00:41:54,160
Speaker 1: the Honda Chord, and the camera has this, this and this,

643
00:41:54,280 --> 00:41:56,759
Speaker 1: but the accord has that, that and that, and it's

644
00:41:56,920 --> 00:41:59,960
Speaker 1: it's pretty evenly matched. And I'm I'm having a hard

645
00:42:00,040 --> 00:42:05,360
Speaker 1: time making a decision. What's your opinion? And so I'll say, well,

646
00:42:05,800 --> 00:42:07,839
Speaker 1: they're both great cars. You won't go wrong with either,

647
00:42:08,200 --> 00:42:10,399
Speaker 1: but I think this one is a little nicer. What

648
00:42:10,400 --> 00:42:12,400
Speaker 1: what do you think? I'll try and ask them some questions.

649
00:42:13,239 --> 00:42:15,840
Speaker 1: Six months later, you see them and they bought car

650
00:42:15,920 --> 00:42:18,360
Speaker 1: A over car B, and you asked them, how do

651
00:42:18,360 --> 00:42:20,879
Speaker 1: you like the car? And the answer is, I can't

652
00:42:20,880 --> 00:42:24,160
Speaker 1: imagine I was even considering that other car. This car

653
00:42:24,239 --> 00:42:27,880
Speaker 1: is fantastic. Now nothing has changed. These are two really

654
00:42:27,920 --> 00:42:32,000
Speaker 1: good automobiles, except for the fact that he now owns

655
00:42:32,040 --> 00:42:36,560
Speaker 1: this car. So what is it about ownership that makes

656
00:42:36,640 --> 00:42:41,160
Speaker 1: us think this is better, more valuable? Whatever? Why do

657
00:42:41,239 --> 00:42:45,799
Speaker 1: we endow these these objects with with superiority. When you've

658
00:42:45,880 --> 00:42:49,719
Speaker 1: owned something for six months, you think highly of it

659
00:42:49,880 --> 00:42:55,759
Speaker 1: because it's become familiar, and almost everything that is familiar

660
00:42:57,239 --> 00:43:02,160
Speaker 1: you like better. Really, So for smiliarity doesn't bring contempt.

661
00:43:02,600 --> 00:43:06,920
Speaker 1: Familiarity makes you like things. So that's a that's a

662
00:43:06,960 --> 00:43:11,200
Speaker 1: big psychological rule in general. Even in the study where

663
00:43:11,200 --> 00:43:13,680
Speaker 1: I think it was a mug with the school's name

664
00:43:13,719 --> 00:43:16,440
Speaker 1: on it, that's a different story. So that I said,

665
00:43:16,480 --> 00:43:20,000
Speaker 1: when you own something for a long time, when you're

666
00:43:20,040 --> 00:43:25,560
Speaker 1: discussing trading, then if you're not a professional, if you're

667
00:43:25,600 --> 00:43:29,560
Speaker 1: an individual and you're thinking of a mug. Then you're

668
00:43:29,600 --> 00:43:33,040
Speaker 1: not thinking of like thinking of wealth. You're not thinking

669
00:43:33,080 --> 00:43:34,960
Speaker 1: of the state of the world in which you have

670
00:43:35,120 --> 00:43:37,479
Speaker 1: that mugs against the state of the world in which

671
00:43:37,480 --> 00:43:42,680
Speaker 1: you have seven dollars in addition to your wealth. You're thinking,

672
00:43:42,920 --> 00:43:45,279
Speaker 1: if you have the mug, do I give it up?

673
00:43:45,719 --> 00:43:48,080
Speaker 1: And if you don't have the mug, do I get

674
00:43:48,120 --> 00:43:51,400
Speaker 1: it and give up the money for it? And giving

675
00:43:51,520 --> 00:43:56,120
Speaker 1: up is more painful than gaining. And that's how loss

676
00:43:56,120 --> 00:43:59,480
Speaker 1: a version gets involved in the endowment defect. It doesn't

677
00:43:59,480 --> 00:44:02,480
Speaker 1: play any role in your story about the car because

678
00:44:02,520 --> 00:44:05,720
Speaker 1: you're not about to trade the car, so you're liking

679
00:44:05,800 --> 00:44:10,360
Speaker 1: for the car is more in effective familiarity and of

680
00:44:10,520 --> 00:44:15,080
Speaker 1: something a psychological process that's called dissonance reduction. It chose

681
00:44:15,120 --> 00:44:18,600
Speaker 1: that car, therefore it must be good. So that is

682
00:44:18,640 --> 00:44:23,680
Speaker 1: true that almost anything that you chose becomes better because

683
00:44:23,719 --> 00:44:26,840
Speaker 1: you chose it. But that's a different process than the

684
00:44:26,920 --> 00:44:29,879
Speaker 1: endowment ef that's totally because it seems there's a big

685
00:44:29,920 --> 00:44:33,600
Speaker 1: overlap between I chose it, therefore it's good, and I

686
00:44:33,640 --> 00:44:39,359
Speaker 1: already own it and therefore it's valuable. Well, because you

687
00:44:39,440 --> 00:44:43,040
Speaker 1: may own it without having chosen it. You know, I

688
00:44:43,200 --> 00:44:46,239
Speaker 1: give you that mug and I ask would you sell it?

689
00:44:46,640 --> 00:44:50,839
Speaker 1: So you just had it for thirty seconds and you

690
00:44:50,840 --> 00:44:53,400
Speaker 1: you get the offer to sell it. So it's a

691
00:44:53,440 --> 00:44:58,920
Speaker 1: different process. That's quite that. It's quite fascinating. Um. So

692
00:44:59,080 --> 00:45:03,000
Speaker 1: I like this vote from thinking fast and slow. Our

693
00:45:03,200 --> 00:45:07,840
Speaker 1: company comforting conviction that the world makes sense rests on

694
00:45:07,920 --> 00:45:13,280
Speaker 1: a secure foundation of our almost unlimited ability to ignore

695
00:45:13,800 --> 00:45:17,759
Speaker 1: our own ignorance. Explain that since you mentioned dissonance, I

696
00:45:17,760 --> 00:45:20,920
Speaker 1: thought that was a good point to bring this up. Well.

697
00:45:22,040 --> 00:45:25,439
Speaker 1: Earlier we talked about something I've read about in the book,

698
00:45:25,480 --> 00:45:28,640
Speaker 1: which is what you see is all there is. And

699
00:45:28,960 --> 00:45:33,319
Speaker 1: the idea that but you don't see is you know

700
00:45:33,480 --> 00:45:37,680
Speaker 1: good refute everything that you believe. That just doesn't occur

701
00:45:37,760 --> 00:45:43,040
Speaker 1: to us. So it's it's another way of telling the story.

702
00:45:43,080 --> 00:45:46,680
Speaker 1: I was telling earlier that we construct the best possible

703
00:45:46,760 --> 00:45:50,680
Speaker 1: narrative about the world, and if we're successful in constructing

704
00:45:50,719 --> 00:45:53,479
Speaker 1: a good story, we believe it, we have a high

705
00:45:53,520 --> 00:45:55,960
Speaker 1: confidence in it, and we don't want to change it.

706
00:45:56,719 --> 00:46:00,880
Speaker 1: That makes perfect sense. Um Let's talk about regression to

707
00:46:00,960 --> 00:46:05,480
Speaker 1: the mean and your conversation with the fighter pilot instructor

708
00:46:05,880 --> 00:46:09,040
Speaker 1: who felt every time he yelled at a cadet they

709
00:46:09,080 --> 00:46:12,640
Speaker 1: would do better and kind of ignored the fact that

710
00:46:13,520 --> 00:46:15,920
Speaker 1: maybe it just happened to be did poorly and he

711
00:46:16,040 --> 00:46:21,560
Speaker 1: was do to do well well the whole. You know,

712
00:46:21,800 --> 00:46:26,200
Speaker 1: it's actually quite remarkable that the statistical fact that you know,

713
00:46:26,239 --> 00:46:30,759
Speaker 1: it's as common as as the air we breathe is

714
00:46:31,640 --> 00:46:35,400
Speaker 1: is very nonintuitive, and that's aggression to the mean. So

715
00:46:37,360 --> 00:46:42,239
Speaker 1: the fact is that, you know, if you look at golfers,

716
00:46:42,360 --> 00:46:45,160
Speaker 1: that's the example of developing the book. So you look

717
00:46:45,160 --> 00:46:50,320
Speaker 1: at the three guys who scored the highest score yesterday,

718
00:46:51,160 --> 00:46:55,279
Speaker 1: chances are they're going to do less well today. And

719
00:46:55,400 --> 00:47:00,040
Speaker 1: that's I'll tell you why. It's because yesterday there's a

720
00:47:00,080 --> 00:47:04,040
Speaker 1: sess was impowered due to luck, and luck is not going,

721
00:47:04,400 --> 00:47:08,880
Speaker 1: not guaranteed to follow them today. So we have to

722
00:47:08,920 --> 00:47:11,800
Speaker 1: think of regression to the mean that what we see

723
00:47:11,960 --> 00:47:16,120
Speaker 1: has already luck built into it, and luck is not

724
00:47:16,239 --> 00:47:20,439
Speaker 1: going to stay. So next time and next time it's

725
00:47:20,480 --> 00:47:23,920
Speaker 1: likely to be less successful. You know, the golfer is

726
00:47:23,920 --> 00:47:26,839
Speaker 1: likely to be less successful than he was because he's

727
00:47:26,880 --> 00:47:29,640
Speaker 1: likely to be less lucky than he was. And people

728
00:47:29,880 --> 00:47:34,360
Speaker 1: dramatically underestimating the impact of chance on their subsilently the

729
00:47:34,880 --> 00:47:39,319
Speaker 1: separating skill from chance, especially in this field, is an

730
00:47:39,400 --> 00:47:43,400
Speaker 1: ongoing battle, and very often what looks like very skillful

731
00:47:43,440 --> 00:47:46,120
Speaker 1: managers turns out to be Hey, they were lucky for

732
00:47:46,160 --> 00:47:49,360
Speaker 1: a couple of quarters. And and and now is that sure?

733
00:47:49,560 --> 00:47:53,600
Speaker 1: And we have a very strong tendency, you know, everybody

734
00:47:53,680 --> 00:47:57,360
Speaker 1: has that. We have that about ourselves, that we attribute

735
00:47:57,360 --> 00:48:03,239
Speaker 1: our success our successes to our skill and failures, you know,

736
00:48:03,320 --> 00:48:06,719
Speaker 1: to bad luck. But actually there is a lot of

737
00:48:06,800 --> 00:48:10,480
Speaker 1: luck in our successes as well that we don't see that.

738
00:48:10,480 --> 00:48:15,480
Speaker 1: That is uh an ongoing issue amongst investors and traders.

739
00:48:16,040 --> 00:48:19,000
Speaker 1: Um it's it's always bad luck when the trade doesn't

740
00:48:19,000 --> 00:48:21,800
Speaker 1: work out, But when it does work out, it's because

741
00:48:21,880 --> 00:48:24,600
Speaker 1: I'm a genius, and we see that. We see that

742
00:48:24,680 --> 00:48:28,480
Speaker 1: all the time. So, having read a lot of what

743
00:48:28,600 --> 00:48:32,040
Speaker 1: you've written and over over a long time, I was

744
00:48:32,080 --> 00:48:37,200
Speaker 1: surprised to learn that you don't describe yourself as particularly optimistic.

745
00:48:38,000 --> 00:48:40,680
Speaker 1: And I thought that was kind of interesting because you've

746
00:48:40,719 --> 00:48:45,040
Speaker 1: spent what more than a decade now looking at happiness

747
00:48:45,120 --> 00:48:48,440
Speaker 1: and utility. Um, So the first question I have to

748
00:48:48,480 --> 00:48:53,200
Speaker 1: ask is has studying this field made you any more

749
00:48:53,239 --> 00:48:56,920
Speaker 1: optimistic or any any happier? No, I mean, you know,

750
00:48:57,000 --> 00:49:03,279
Speaker 1: optimism is genetic anyway, and yeah largely, Uh you know,

751
00:49:03,920 --> 00:49:07,239
Speaker 1: I'm the son of a very pessimistic mother, and so

752
00:49:07,600 --> 00:49:12,480
Speaker 1: pessimism is sort of genetic too. I could see that

753
00:49:12,640 --> 00:49:14,520
Speaker 1: is is it genetic or is it the home you

754
00:49:14,560 --> 00:49:17,800
Speaker 1: were raised? Then no, I'm just I'm just pretty Actually

755
00:49:18,280 --> 00:49:25,520
Speaker 1: it's actually genetic really so so so studying utility and

756
00:49:25,520 --> 00:49:30,880
Speaker 1: studying happiness, what what conclusions have you reached based on

757
00:49:30,920 --> 00:49:36,360
Speaker 1: that research? Well, the main conclusion was that there is

758
00:49:36,360 --> 00:49:39,680
Speaker 1: a difference between what makes us satisfied and what makes

759
00:49:39,719 --> 00:49:44,200
Speaker 1: us happy. So distinction because the distinction between the two.

760
00:49:44,680 --> 00:49:49,640
Speaker 1: Being happy means having you know, having happy experiences and

761
00:49:49,680 --> 00:49:52,200
Speaker 1: being satisfied is when you look at your life and

762
00:49:52,239 --> 00:49:55,200
Speaker 1: what you've accomplished. Are you content and are you satisfied

763
00:49:55,239 --> 00:49:58,920
Speaker 1: with what you've accomplished? And those two are really not

764
00:49:59,000 --> 00:50:03,560
Speaker 1: the same, which is more important. Well, terms up that

765
00:50:03,840 --> 00:50:08,319
Speaker 1: for most people what they try to do, if they

766
00:50:08,320 --> 00:50:11,840
Speaker 1: try to be satisfied, they go for satisfaction and not

767
00:50:12,000 --> 00:50:15,680
Speaker 1: for they are not thinking how can I maximize the

768
00:50:15,760 --> 00:50:21,000
Speaker 1: quality of the my life? Mm hmm. So and many people,

769
00:50:21,040 --> 00:50:25,600
Speaker 1: I think make big mistakes and that they settle themselves

770
00:50:25,600 --> 00:50:28,759
Speaker 1: the big commute to have a larger home. You know,

771
00:50:28,840 --> 00:50:31,399
Speaker 1: the big commute means an hour or two a day

772
00:50:31,520 --> 00:50:36,600
Speaker 1: that are wasted. You know that counts. Time should count

773
00:50:36,640 --> 00:50:40,080
Speaker 1: for a lot, because it's the only thing we've got basically,

774
00:50:40,640 --> 00:50:45,240
Speaker 1: and people who waste time in order to achieve something

775
00:50:45,320 --> 00:50:49,879
Speaker 1: else are wasting the part of their life. So so

776
00:50:49,960 --> 00:50:52,759
Speaker 1: the studies I've seen is the longer your commute is,

777
00:50:53,360 --> 00:50:56,799
Speaker 1: the less happy you are. Does that also lead to

778
00:50:56,880 --> 00:51:02,160
Speaker 1: the less satisfied you? Not necessarily? Not necessarily, because if

779
00:51:02,280 --> 00:51:05,399
Speaker 1: having a big home is part of your satisfaction, if

780
00:51:05,400 --> 00:51:08,359
Speaker 1: you're sort of proud of the house you've got, uh,

781
00:51:08,360 --> 00:51:11,040
Speaker 1: and you're paying for it by a long commute, you're

782
00:51:11,080 --> 00:51:13,440
Speaker 1: not the way it's satisfied. Although you're not happy. You

783
00:51:13,480 --> 00:51:16,200
Speaker 1: get a bigger house closer to your job, but it's

784
00:51:16,239 --> 00:51:18,879
Speaker 1: going to be a lot more expensive. So so they're

785
00:51:18,920 --> 00:51:23,120
Speaker 1: trading the time for that makes senseense. What what else

786
00:51:23,160 --> 00:51:27,160
Speaker 1: have you what other things have you taken away from

787
00:51:27,200 --> 00:51:33,120
Speaker 1: studying happiness and satisfaction? Well, you know, I think I

788
00:51:33,160 --> 00:51:36,879
Speaker 1: think it turns out that people are happiest and when

789
00:51:36,880 --> 00:51:41,680
Speaker 1: they're in the presence of friends, truly friends, and then

790
00:51:42,080 --> 00:51:44,719
Speaker 1: you know there is a special thrill I think for

791
00:51:44,800 --> 00:51:47,960
Speaker 1: people to be with friends, which is even more than

792
00:51:48,000 --> 00:51:52,120
Speaker 1: to be with family. Really, why is that, Well, there's

793
00:51:52,239 --> 00:51:55,360
Speaker 1: something more relaxing, and you know, when you're in a family,

794
00:51:55,400 --> 00:51:58,799
Speaker 1: there are many obligations and many stresses, and so this

795
00:51:59,000 --> 00:52:01,719
Speaker 1: is a pleasure that you sation being with friends. But

796
00:52:01,960 --> 00:52:04,120
Speaker 1: those are some of the best moments of the week

797
00:52:05,120 --> 00:52:09,799
Speaker 1: when you're not alone. There are there is a particular

798
00:52:10,920 --> 00:52:17,200
Speaker 1: joy for many people in having shared routines with friends.

799
00:52:17,719 --> 00:52:20,960
Speaker 1: You know, the weekly poker game, the weekly dinner and

800
00:52:21,040 --> 00:52:24,600
Speaker 1: movie that that you share with friends. Over a period

801
00:52:24,640 --> 00:52:29,160
Speaker 1: of years, that becomes very precious and it's you know,

802
00:52:29,200 --> 00:52:34,360
Speaker 1: it's becomes an important part of life that that's really

803
00:52:34,880 --> 00:52:38,480
Speaker 1: quite intriguing. Um. You know a lot of the discoveries

804
00:52:38,520 --> 00:52:42,640
Speaker 1: we've talked about have the feeling to me of of epiphanies.

805
00:52:43,320 --> 00:52:46,719
Speaker 1: So one of the things I specifically wanted to ask

806
00:52:46,760 --> 00:52:50,320
Speaker 1: you about when you were doing the officer candidate evaluations

807
00:52:50,360 --> 00:52:54,560
Speaker 1: for the Israeli Army um or when you uh would

808
00:52:54,600 --> 00:52:57,920
Speaker 1: ask colleagues how long they thought it would take for

809
00:52:57,920 --> 00:53:01,959
Speaker 1: for a project to be done. The results of these

810
00:53:02,120 --> 00:53:06,640
Speaker 1: were just really wow, this is much different than everybody

811
00:53:06,680 --> 00:53:10,360
Speaker 1: everybody believed. So what was it about your background and

812
00:53:10,480 --> 00:53:14,560
Speaker 1: your training that let you look at the world so

813
00:53:14,760 --> 00:53:19,840
Speaker 1: differently than everybody else around you. Obviously, these behaviors exist

814
00:53:19,960 --> 00:53:24,960
Speaker 1: amongst everybody. What led you to these really fascinating discoveries

815
00:53:25,000 --> 00:53:32,360
Speaker 1: that everyone else seems to have overlooked? Well, Uh, you know,

816
00:53:32,400 --> 00:53:37,160
Speaker 1: I I I was born to be a psychologist. I

817
00:53:37,200 --> 00:53:40,320
Speaker 1: really believe that, not my faith, but you know, the

818
00:53:40,440 --> 00:53:43,680
Speaker 1: sort the thing was, I was better at that and

819
00:53:43,760 --> 00:53:48,799
Speaker 1: at anything else. And I've always been interested in observing

820
00:53:48,880 --> 00:53:52,120
Speaker 1: people and then trying to figure out, you know, why

821
00:53:52,200 --> 00:53:55,600
Speaker 1: they are, where they feel, what they feel, and and

822
00:53:56,080 --> 00:53:59,480
Speaker 1: so that's sort of curiosity about people I think as

823
00:54:00,360 --> 00:54:07,279
Speaker 1: somehow paid afful me. So um um, so let's get

824
00:54:07,320 --> 00:54:10,200
Speaker 1: to some of our favorite last few questions. I know

825
00:54:10,239 --> 00:54:13,319
Speaker 1: we only have you here for a finite amount of time.

826
00:54:13,640 --> 00:54:18,880
Speaker 1: These are the standard questions we ask all of our guests. Um.

827
00:54:19,000 --> 00:54:23,759
Speaker 1: We we discussed uh, your your you knew right away

828
00:54:23,760 --> 00:54:25,920
Speaker 1: you're going to be in academy or your whole career.

829
00:54:26,320 --> 00:54:34,600
Speaker 1: Who were some of your early mentors? Well, I had professors.

830
00:54:34,680 --> 00:54:42,520
Speaker 1: I had professors I loved when I was an undergraduates

831
00:54:42,560 --> 00:54:45,680
Speaker 1: in Jerusalem. One in particular, I wouldn't call him a mentor,

832
00:54:45,840 --> 00:54:49,880
Speaker 1: but you know, he was your sure life of it.

833
00:54:49,920 --> 00:54:52,600
Speaker 1: It was his name. He was a hero to me

834
00:54:52,680 --> 00:54:55,759
Speaker 1: and too many other people. Really why why is that?

835
00:54:55,880 --> 00:54:58,279
Speaker 1: But what did he do that resonated with you so much?

836
00:54:59,120 --> 00:55:02,480
Speaker 1: He just was a very powerful personality. He had strong

837
00:55:02,520 --> 00:55:06,840
Speaker 1: opinions about everything. He knew a lot, he was, he

838
00:55:06,840 --> 00:55:11,560
Speaker 1: had several doctorates, he was and he had a very

839
00:55:11,640 --> 00:55:16,480
Speaker 1: individual character. So I loved he made a deep impression

840
00:55:16,520 --> 00:55:20,360
Speaker 1: on me. In in graduate school, I had quite a

841
00:55:20,400 --> 00:55:25,160
Speaker 1: few teachers, especially the one who supervised my thesis at Gizelli.

842
00:55:25,640 --> 00:55:27,920
Speaker 1: But I learned from quite a few people when I

843
00:55:28,000 --> 00:55:32,240
Speaker 1: was in graduate school. I don't think I ever had

844
00:55:32,600 --> 00:55:38,120
Speaker 1: a proper mentor in the sense that that many graduate

845
00:55:38,200 --> 00:55:43,360
Speaker 1: students today they get into a lab and they're associated

846
00:55:43,400 --> 00:55:47,240
Speaker 1: with the same person throughout their career. In graduate school,

847
00:55:47,440 --> 00:55:50,000
Speaker 1: that wasn't the case when I was in graduate school.

848
00:55:50,280 --> 00:55:53,279
Speaker 1: You didn't belong to a lab and and so I

849
00:55:53,320 --> 00:55:58,680
Speaker 1: didn't have that experience. So any previous psychologists or or

850
00:55:58,880 --> 00:56:04,600
Speaker 1: clinical experimenters influence your approach to what you did or yeah, many,

851
00:56:04,680 --> 00:56:10,080
Speaker 1: I mean mainly a man named Paul Meal, who who

852
00:56:10,200 --> 00:56:15,239
Speaker 1: was the first to compare algorithms to clinical judgment and

853
00:56:15,360 --> 00:56:20,840
Speaker 1: to reach the conclusion that algorithms are actually more accurate

854
00:56:21,920 --> 00:56:27,919
Speaker 1: in many many cases than than intuitive judgment. Now I'm

855
00:56:27,960 --> 00:56:32,160
Speaker 1: I'm I hope I'm not misremembering it. Um. Something you

856
00:56:32,239 --> 00:56:36,920
Speaker 1: wrote specifically said, even after we show people the success

857
00:56:37,040 --> 00:56:40,640
Speaker 1: ratio of the algorithms, they still wanted to stick with

858
00:56:40,760 --> 00:56:45,120
Speaker 1: their their intuition. Yeah, that's absolutely true. I mean, people

859
00:56:45,120 --> 00:56:50,000
Speaker 1: have people don't love algorithms, and and they better get

860
00:56:50,080 --> 00:56:52,319
Speaker 1: used to them because they're taking over every right, this

861
00:56:52,440 --> 00:56:55,839
Speaker 1: is happening. But you know, when you even think about that,

862
00:56:59,160 --> 00:57:01,680
Speaker 1: when a doctor makes a mistake and the child dies,

863
00:57:01,800 --> 00:57:05,800
Speaker 1: this is terrible, But if it was a piece of

864
00:57:05,960 --> 00:57:09,360
Speaker 1: artificial intelligence that made a mistake and the child dies,

865
00:57:09,480 --> 00:57:13,120
Speaker 1: that would be worse. At least today, because we trust

866
00:57:13,200 --> 00:57:17,200
Speaker 1: the machinery, and the machinery it's more shocking. It's more

867
00:57:17,280 --> 00:57:21,600
Speaker 1: shocking when it's impersonal. You know, when a self driving

868
00:57:21,680 --> 00:57:25,120
Speaker 1: car has an accident, we just learned, we just learned

869
00:57:25,120 --> 00:57:28,720
Speaker 1: about that, But there's something more shocking about the idea.

870
00:57:30,280 --> 00:57:33,640
Speaker 1: So just think about that somebody dying in a in

871
00:57:33,680 --> 00:57:37,800
Speaker 1: an accident, or somebody dying in an accident with a

872
00:57:37,840 --> 00:57:42,160
Speaker 1: self driving car, and and that second idea is some

873
00:57:42,520 --> 00:57:46,400
Speaker 1: more shocking, so people really don't for the time being,

874
00:57:46,480 --> 00:57:49,000
Speaker 1: this is going to change. But there is such a thing.

875
00:57:49,040 --> 00:57:52,280
Speaker 1: It's been called algorithmic version. Really, you know, when you

876
00:57:52,320 --> 00:57:54,919
Speaker 1: look at the statistics, let's use the self driving car,

877
00:57:55,480 --> 00:57:59,919
Speaker 1: by all measures, they're they're safer, they're more reliable, they're

878
00:58:00,040 --> 00:58:02,800
Speaker 1: less likely to be involved in either a major or

879
00:58:02,840 --> 00:58:06,800
Speaker 1: a minor accident. Doesn't mean you're gonna eliminate those sort

880
00:58:06,800 --> 00:58:10,040
Speaker 1: of accidents. So when one happens, it seems to really

881
00:58:10,040 --> 00:58:15,200
Speaker 1: resonate people. I mean, part of that is that there

882
00:58:15,200 --> 00:58:18,640
Speaker 1: are things that a view does natural or as you know,

883
00:58:19,040 --> 00:58:23,080
Speaker 1: sort of acts of God, and those we've learned to accept.

884
00:58:23,440 --> 00:58:27,080
Speaker 1: But when it's acts of men, it's very different. So

885
00:58:27,720 --> 00:58:33,800
Speaker 1: the best example is vaccines. So vaccines, you know, could

886
00:58:33,880 --> 00:58:38,880
Speaker 1: cause sects side effect, they could Let's take a child

887
00:58:38,960 --> 00:58:44,360
Speaker 1: who died from a vaccine. Now, how many children would

888
00:58:44,360 --> 00:58:48,800
Speaker 1: the vaccine have to save to accept the death of

889
00:58:48,880 --> 00:58:52,240
Speaker 1: one child from the vaccine. It's clearly not one to one.

890
00:58:52,440 --> 00:58:55,280
Speaker 1: Now it's a lot of more millions, millions to I

891
00:58:55,280 --> 00:58:57,520
Speaker 1: don't know. I hope it's not millions because that would

892
00:58:57,520 --> 00:59:00,840
Speaker 1: be crazy. But but but it's a more than one.

893
00:59:02,000 --> 00:59:05,320
Speaker 1: And so what what happens here is that anything that

894
00:59:05,520 --> 00:59:09,520
Speaker 1: is man made we have a much stronger reaction to

895
00:59:10,360 --> 00:59:15,120
Speaker 1: then than if the same thing occurs naturally. M that's

896
00:59:15,200 --> 00:59:19,120
Speaker 1: quite fascinating. So so let's talk about books. What are

897
00:59:19,160 --> 00:59:22,680
Speaker 1: some of your favorite books, be it fiction, nonfiction, Michael

898
00:59:22,720 --> 00:59:28,919
Speaker 1: Lewis or otherwise. Well, in nonfiction, the best book I've

899
00:59:28,920 --> 00:59:34,480
Speaker 1: read in in several years is called Sapiens. Sure, it's

900
00:59:34,560 --> 00:59:41,040
Speaker 1: the short history of humankind. You've ash, well, Uh, I

901
00:59:41,080 --> 00:59:44,439
Speaker 1: think it's superb. It's literally on my night table. It's

902
00:59:44,480 --> 00:59:46,400
Speaker 1: the next book up in my queue. If you're telling

903
00:59:46,440 --> 00:59:50,720
Speaker 1: me it's superb, I better started soon it twice? Really,

904
00:59:50,720 --> 00:59:54,000
Speaker 1: I don't. Yeah, and there are many books. Wow, that's

905
00:59:54,040 --> 00:59:57,480
Speaker 1: some endorsement. No, I think it's I think it's very impressive.

906
00:59:58,200 --> 01:00:01,800
Speaker 1: So that's that was one. Uh, certainly in a seem

907
01:00:01,920 --> 01:00:06,560
Speaker 1: Talib's book had a big effect on me, his last one,

908
01:00:06,680 --> 01:00:12,200
Speaker 1: or Fooled by Randomness, mostly the Black Swan, but you know,

909
01:00:12,280 --> 01:00:15,760
Speaker 1: I had liked fool by Randomness too, but the Black

910
01:00:15,800 --> 01:00:22,200
Speaker 1: Swan I learned a lot from. And then obviously their

911
01:00:22,320 --> 01:00:29,560
Speaker 1: works by by people I know like Nudge and Taylor. Taylor,

912
01:00:31,080 --> 01:00:35,960
Speaker 1: I mean, so those are many of some of your favorites. Um,

913
01:00:36,000 --> 01:00:39,480
Speaker 1: so you've mentioned you've been doing this for fifty years.

914
01:00:39,520 --> 01:00:43,040
Speaker 1: What has changed in the field of psychology that really

915
01:00:43,120 --> 01:00:47,320
Speaker 1: stands out to you over over that long period. You know,

916
01:00:47,400 --> 01:00:55,320
Speaker 1: when I started my studies, a lot of psychology was

917
01:00:55,360 --> 01:01:01,360
Speaker 1: still concerned with rats running mazes, and so the what

918
01:01:01,520 --> 01:01:06,480
Speaker 1: it's called the cognitive revolution occurred during my career. Early

919
01:01:06,520 --> 01:01:10,680
Speaker 1: in my career, so people sort of forgot, you know,

920
01:01:11,080 --> 01:01:15,520
Speaker 1: just set rights aside, mostly except for physiological work. And

921
01:01:15,560 --> 01:01:17,960
Speaker 1: when they wanted to study how the mind works, they

922
01:01:18,000 --> 01:01:21,560
Speaker 1: studied how people think. So that that was a big change,

923
01:01:22,120 --> 01:01:25,840
Speaker 1: and and there have been many other changes in recent years.

924
01:01:26,320 --> 01:01:30,000
Speaker 1: Mostly it's the study of the brain that is taking over,

925
01:01:30,680 --> 01:01:33,880
Speaker 1: and you know that's huge. The fm R eyes and

926
01:01:34,000 --> 01:01:37,360
Speaker 1: that's you know this this came too late for me

927
01:01:38,080 --> 01:01:40,960
Speaker 1: to be involved in, but if I had been younger,

928
01:01:41,200 --> 01:01:44,480
Speaker 1: I would have done what younger colleagues of mine did

929
01:01:44,840 --> 01:01:48,360
Speaker 1: and they switched. Is that going to be the biggest

930
01:01:48,440 --> 01:01:51,120
Speaker 1: change going forward? Is the ability to pear into the

931
01:01:51,200 --> 01:01:54,640
Speaker 1: brain while it's in operation. I'm convinced that that's the

932
01:01:54,720 --> 01:01:58,160
Speaker 1: case for the next few decades, really a few decades Wow,

933
01:01:58,200 --> 01:02:00,560
Speaker 1: that's that's amazing. All right. So we're up to our

934
01:02:00,680 --> 01:02:05,840
Speaker 1: last two questions, our final two questions. If a millennial

935
01:02:06,160 --> 01:02:09,600
Speaker 1: or a recent college grad came up to you and said, hey,

936
01:02:09,640 --> 01:02:14,680
Speaker 1: I'm thinking about a career and in experimental psychology, what

937
01:02:14,680 --> 01:02:18,600
Speaker 1: what sort of advice would you give them, whether to

938
01:02:18,640 --> 01:02:21,800
Speaker 1: go into that or in something else or well, they said,

939
01:02:21,800 --> 01:02:24,960
Speaker 1: I'm I'm considering this career. What advice do you have

940
01:02:25,080 --> 01:02:27,400
Speaker 1: for me? What? What would your answer be? You must

941
01:02:27,400 --> 01:02:30,680
Speaker 1: have had people students at Princeton ask you all the time. No,

942
01:02:30,760 --> 01:02:35,240
Speaker 1: I mean I would ask what kind of psychologists you

943
01:02:35,240 --> 01:02:37,600
Speaker 1: want to be? And I assure you want to be

944
01:02:37,720 --> 01:02:41,800
Speaker 1: that kind of psychologist. And there are certain lines of studies,

945
01:02:42,200 --> 01:02:46,160
Speaker 1: like going to graduate school. It's not for everybody, and

946
01:02:46,280 --> 01:02:49,680
Speaker 1: some very very smart people who could succeed in graduate

947
01:02:49,760 --> 01:02:54,200
Speaker 1: school should really not do it because there they will

948
01:02:54,280 --> 01:02:57,360
Speaker 1: be happier doing something else than being an academic. An

949
01:02:57,400 --> 01:03:00,760
Speaker 1: academic is is a life that's good for for a

950
01:03:00,840 --> 01:03:04,520
Speaker 1: minority of people and for most others. You know, it's

951
01:03:04,640 --> 01:03:10,480
Speaker 1: it's not great. So uh, I would never have ready

952
01:03:10,520 --> 01:03:14,400
Speaker 1: made advice. It really depends on it really depends on

953
01:03:14,440 --> 01:03:18,360
Speaker 1: the individual. And in our final question, what is it

954
01:03:18,440 --> 01:03:22,600
Speaker 1: that you know about psychology and the human mind today

955
01:03:22,640 --> 01:03:25,880
Speaker 1: that you wish you knew when you began fifty years ago.

956
01:03:32,480 --> 01:03:35,880
Speaker 1: I can't think of that, actually, I mean it's very odd.

957
01:03:36,760 --> 01:03:40,640
Speaker 1: You know, fifty years ago I didn't know many of

958
01:03:40,680 --> 01:03:44,320
Speaker 1: the things I know today, and today I know a

959
01:03:44,440 --> 01:03:47,760
Speaker 1: negligible amount compared to what people will know, you know,

960
01:03:47,840 --> 01:03:51,120
Speaker 1: fifty years from now. Well, but you've also I don't

961
01:03:51,320 --> 01:03:54,240
Speaker 1: I don't regret not knowing what I knew. I mean,

962
01:03:54,320 --> 01:03:58,480
Speaker 1: discovering things and learning things has been so much fun

963
01:03:58,840 --> 01:04:02,240
Speaker 1: that I have no regret about what they didn't know then. Um,

964
01:04:02,320 --> 01:04:04,840
Speaker 1: maybe regrets the wrong word. What what would have been

965
01:04:04,920 --> 01:04:08,440
Speaker 1: helpful to have known that you discovered later on in

966
01:04:08,480 --> 01:04:13,919
Speaker 1: your career. I mean, there are there There are things

967
01:04:14,000 --> 01:04:17,000
Speaker 1: that I learned even in recent years, in the last

968
01:04:17,120 --> 01:04:22,920
Speaker 1: few years, which is there's a big change in psychology

969
01:04:23,400 --> 01:04:27,760
Speaker 1: that that people are suspicious, and there is what it's

970
01:04:27,800 --> 01:04:32,720
Speaker 1: called the reproducibility crisis. In sure that's across all sciences,

971
01:04:32,960 --> 01:04:36,400
Speaker 1: and it's across all sciences that I wish I had

972
01:04:36,440 --> 01:04:41,960
Speaker 1: been more careful about, in particular because I had thought

973
01:04:41,960 --> 01:04:44,680
Speaker 1: about it, I had all the tools to see it

974
01:04:45,040 --> 01:04:51,560
Speaker 1: and I was not sufficiently aware of it. Dr Danny Khaman,

975
01:04:51,720 --> 01:04:54,680
Speaker 1: this has been absolutely fascinating. Thank you so much for

976
01:04:54,760 --> 01:04:59,080
Speaker 1: being so generous with your time. If you have enjoyed

977
01:04:59,120 --> 01:05:01,320
Speaker 1: this conversation, and be sure and look up an intro

978
01:05:01,440 --> 01:05:04,760
Speaker 1: Down an Inch on Apple iTunes and you could see

979
01:05:04,800 --> 01:05:09,280
Speaker 1: the other one hundred or so such conversations uh we've

980
01:05:09,320 --> 01:05:13,080
Speaker 1: had over the past uh two years. I would be

981
01:05:13,120 --> 01:05:18,240
Speaker 1: remiss if I did not thank my producers, Taylor Riggs

982
01:05:18,240 --> 01:05:22,320
Speaker 1: and Charlie Valmer, Charlie also working as a recording engineer today,

983
01:05:22,640 --> 01:05:25,520
Speaker 1: and of course our head of research, Michael Batnick, who

984
01:05:25,520 --> 01:05:29,240
Speaker 1: has been a huge help in helping to prepare these questions.

985
01:05:29,800 --> 01:05:33,960
Speaker 1: We love your comments and questions and suggestions. Be sure

986
01:05:33,960 --> 01:05:36,720
Speaker 1: and send us an email. You can write me at

987
01:05:36,760 --> 01:05:41,439
Speaker 1: b Ridholt's three at Bloomberg dot net. UM, you've been

988
01:05:41,480 --> 01:05:44,800
Speaker 1: listening to Masters in Business on Bloomberg Radio.