Speaker 1: This is Masters in Business with Barry Ridholds on Bloomberg Radio.
Speaker 1: This week on the show, I have an extra special guest.
Speaker 1: I know you guys give me grief for saying that
Speaker 1: every week, but I really have an extra special guest,
Speaker 1: Professor Danny Kahneman, winner of the two thousand and two
Speaker 1: Nobel Prize in Economics, which is quite a feat when
Speaker 1: you realize that he's not an economist. He's a cognitive psychologist,
Speaker 1: author of the book Thinking Fast and Slow before we
Speaker 1: get to the podcast, which is really quite fascinating. Just
Speaker 1: a quick funny story. So we finished doing the interview
Speaker 1: and we're heading out of the Bloomberg building where you're
Speaker 1: heading to, Oh, I'm going over here. Oh I'm heading
Speaker 1: in that same direction, I tell Danny, which is what
Speaker 1: he insists everybody call him. And so we take a
Speaker 1: subway downtown and we're get out at Grand Central and
Speaker 1: we're walking someplace and and he said, as to me, listen,
Speaker 1: you don't have to walk me to where I'm going.
Speaker 1: I'm in New York right now where I am? And
Speaker 1: I said, well, to tell you the truth, it's two o'clock.
Speaker 1: I haven't had lunch yet. A block from where your
Speaker 1: destination is. Is this lovely um sandwich shop called Alidoro,
Speaker 1: just open the midtown. There's a Soho branch. It's really good.
Speaker 1: I'm just heading in your direction anyway. So he looks
Speaker 1: at me and says, the sandwich is really that good. Yes,
Speaker 1: they're delicious. So long story short, we go into this place.
Speaker 1: It's essentially a takeout joint with one long picnic table
Speaker 1: on the back. Most people um take sandwiches out. And
Speaker 1: it's two o'clock so the rush lunch hour rush is over,
Speaker 1: so I we order sandwiches. Takes a few minutes. I
Speaker 1: kind of put it, sit him down at the table,
Speaker 1: and I said, I'll go get the sandwiches. And as
Speaker 1: I'm coming back to the table, there is Professor Khneman
Speaker 1: sitting at a picnic table surrounded by I don't know
Speaker 1: a dozen millennials, kids who could have been in his
Speaker 1: class a couple of years ago at Princeton, and they're
Speaker 1: just holy oblivious to this kindly professor sitting amongst them.
Speaker 1: Here's a brilliant thinker who changed the way we think
Speaker 1: about how we think, and just everybody completely wholly unaware
Speaker 1: that that brilliance is in their midst. Then it's the
Speaker 1: whole thing just cracked me up. I found the interview
Speaker 1: to be absolutely fascinating. I think you will also, so,
Speaker 1: with no further ado, my conversation with Danny Khneman. This
Speaker 1: is Masters in Business with Barry Ridholts on Bloomberg Radio.
Speaker 1: Our special guest this week. And I know I say
Speaker 1: this all too often, but we really do have an
Speaker 1: extra special us this week. His name is Professor Daniel Kaneman.
Speaker 1: He taught at the Woodrow Wilson School at Princeton, both
Speaker 1: in psychology and public affairs. He is a fellow at
Speaker 1: the Center for Rationality at Hebrew University, winner of the
Speaker 1: Nobel Prize in Economic Sciences as well as the Lifetime
Speaker 1: Contribution Award of the American Psychology Association UH and he
Speaker 1: is a recipient of the Presidential Medal of Freedom. I
Speaker 1: could go on and on about his curriculum vita, but
Speaker 1: I would rather jump right into this. Danny Kahneman, Welcome
Speaker 1: to Bloomberg. Pleased to be here, so I'm excited to
Speaker 1: talk to you about so many different things. Let's let's
Speaker 1: start way back at the beginning. You you began your
Speaker 1: academic career in Israel. Did did you expect to spend
Speaker 1: the rest of your life working in academia? Oh? Yeah,
Speaker 1: I mean I expected to be a professor when I
Speaker 1: was a kid. And so you win the Nobel Prize
Speaker 1: in two thousand and two, not for psychology, which is
Speaker 1: your field of study, but but for economics. How does
Speaker 1: the psychologist win a prize for economics? Well, I mean
Speaker 1: there is no Nobel in psychology, but we wanted in
Speaker 1: economics for work that is psychological. Uh, when it's you
Speaker 1: and he didn't actually win this because they don't give
Speaker 1: it posthumously, but you know, I always felt it it's
Speaker 1: a joint prize because the prize was awarded for work
Speaker 1: we had done together, and it was work that influenced economics.
Speaker 1: So it's under influence in general that you get a Nobel,
Speaker 1: not on the quality of the work necessary. So so
Speaker 1: let's talk about your colleague, a most diverse key in
Speaker 1: your book Thinking Fast and Slow, you described first seeing
Speaker 1: him speak. I think it was around nineteen sixty nine
Speaker 1: and the subject was, uh, do people make good intuitive
Speaker 1: statistical assumptions? Did you know right away the two of
Speaker 1: you were destined to be research partners? No? But what
Speaker 1: happened was he visited seminario was teaching. I mean I
Speaker 1: invited him and he spoke about some research that was
Speaker 1: being done in Michigan at the time on whether people
Speaker 1: are good intuitive statisticsians, and their conclusion was that they are.
Speaker 1: And so we had very counterintuitive well it's now counterintuitive,
Speaker 1: at that time, it sounded quite intuitive, and we had
Speaker 1: a very heated discussion. You know, they had those in Israel,
Speaker 1: and it was a very Israeli type conversation and we
Speaker 1: enjoyed it, both of us. We decided to have lunch
Speaker 1: together the following Friday, and there we we discussed ideas
Speaker 1: over that lunch, and we didn't know that it was
Speaker 1: going to shape our lives, but we had a pretty
Speaker 1: good inkling of what we wanted to do next. So
Speaker 1: the two you established a cognitive basis for analyzing common
Speaker 1: human errors and how they arrive from biases. How did you, guys,
Speaker 1: happen across across that discovery. We started, you know, from
Speaker 1: a combination of what Amos knew and what I was
Speaker 1: specialized in, and my specialty at the time was visual perception,
Speaker 1: and in visual perception you have illusions and analyzing the
Speaker 1: illusions is interesting because the illusions teach you something about
Speaker 1: the mechanism of normal perception. And Amos was an expert
Speaker 1: on decision theory and on formal analysis, and we complemented
Speaker 1: each other very well. And the idea that the research
Speaker 1: that we developed eventually was really a research on cognitive
Speaker 1: illusions and where they come from. And we proposed a
Speaker 1: mechanism and mechanisms we call we call those heuristics, and
Speaker 1: now have a somewhat different interpretation, not very different, but
Speaker 1: slightly different from the one we had at that time.
Speaker 1: I mean, the general idea is quite simple. You ask
Speaker 1: people a complicated question like what is the probability of
Speaker 1: an event? And they can't answer it because it's very difficult.
Speaker 1: But there are easier questions that are related to that
Speaker 1: one that they can answer, such as, this is a
Speaker 1: surprising event that is something that people know right away?
Speaker 1: Is it a typical result of that kind of mechanism?
Speaker 1: And people can answer that right away. So, and what
Speaker 1: happens is people take the answer to the easy question,
Speaker 1: they use it to answer the difficult question, and they
Speaker 1: think they have answered the difficult question. In fact, they haven't.
Speaker 1: They answered an easier one. So that's why the mechanism
Speaker 1: that we studied. You mentioned that your view on heuristics
Speaker 1: have changed somewhat over the years. What's the difference between
Speaker 1: what you live today and and way back when. Well,
Speaker 1: we started out with what we call a limited number
Speaker 1: of heuristics, and they became quite well known because we
Speaker 1: have a paper that we published in nine four that
Speaker 1: in we published it in Science magazine, so it was
Speaker 1: widely read across many disciplines. And in that paper we
Speaker 1: spoke about three major heuristics, and we analyze the biases
Speaker 1: that these heuristics lead to. And many people would know
Speaker 1: their name from if they went to business school. So
Speaker 1: it's representativeness, it's availability and anchoring. What I'm thinking of
Speaker 1: now is that there is a more general process, and
Speaker 1: that's the process I described a minute ago. They called
Speaker 1: that attribute substitution. It's to substitute one question for another's.
Speaker 1: So instead of answering the complex, difficult question, you answer
Speaker 1: what you can, which is the easier question. That's right.
Speaker 1: So if I ask you an example that comes to mind,
Speaker 1: how happy are you these days? Now? You know your
Speaker 1: mood right now? You're very likely to tell me your
Speaker 1: mood right now. And think that you have answered the
Speaker 1: more general question of how happy are you these days?
Speaker 1: So that's that's another example. There are many like them.
Speaker 1: I'm Barry Ridholts. You're listening to Masters in Business on
Speaker 1: Bloomberg Radio. My special guest today is Nobel Prize winning
Speaker 1: psychologist Danny Kahneman, who has specialized in both cognitive and
Speaker 1: decision making processes. Let's let's jump right into some of
Speaker 1: the things that you discussed in Thinking Fast and Slow,
Speaker 1: a book that I found to be just right in
Speaker 1: the sweet spot of my confirmation bias. Who was everything
Speaker 1: I hoped it to be. You talked about what you
Speaker 1: see is all there is? When when this cussing systems
Speaker 1: one and two, it's almost a theme throughout the book,
Speaker 1: explain what that is? What you see is all there is? Well,
Speaker 1: people are really not aware of information that they don't have,
Speaker 1: and so the the idea which is emphasized in the
Speaker 1: book is that you take whatever information you have and
Speaker 1: you make the best story possible out of that information.
Speaker 1: And the information you don't have you don't feel that
Speaker 1: it's necessary. And I have an example that that I
Speaker 1: think brings that out. If I tell you about a
Speaker 1: national leader, that she is intelligent and firm. Now do
Speaker 1: you have an impression already whether she's a good leader
Speaker 1: or a bad leader, And you certainly do. She's a
Speaker 1: good leader. Now the third word that I was about
Speaker 1: to say is corrupt. But you know, the point being
Speaker 1: that you didn't wait for information that you didn't have,
Speaker 1: who formed an impression as we were going with the
Speaker 1: information you did have, And this is what you see,
Speaker 1: is all there is the working assumption amongst people who
Speaker 1: are trying to draw a conclusion from available information is
Speaker 1: they failed to calculate the impact of data they're either
Speaker 1: unaware of, or don't know or simply haven't encountered. That's
Speaker 1: I mean, people, If what people are trying to do
Speaker 1: is to make the best story possible out of the
Speaker 1: information they have, then this is what they're going to do.
Speaker 1: And that the measure of confidence that people have in uh,
Speaker 1: in their beliefs, in their opinions, there's really a reflection
Speaker 1: of the quality of the story that they've told them. So,
Speaker 1: so let's talk about that narrative because it's it's one
Speaker 1: of my favorite errors in investing is that we tell
Speaker 1: ourselves these complex narratives that seem to fit whatever information
Speaker 1: is in front of us, and that very much creates
Speaker 1: a risk that the narrative, as emotional and compelling as
Speaker 1: it might be, is possibly misleading. How does that fit
Speaker 1: into your work? I think the same. Talib has a
Speaker 1: very nice example in his in his book The Black Swan.
Speaker 1: It's an example of that happened at Bloomberg on Bloomberg News,
Speaker 1: and it happened the day that Saddam was court, and
Speaker 1: something happened in the bond market shortly thereafter. I forget
Speaker 1: whether it rose or it felt and the headline was
Speaker 1: Saddam Court and investors are fleeing from risk. And then
Speaker 1: a few hours later the market reversed and there was
Speaker 1: a headline that said, Saddam Court investors feel more secure
Speaker 1: taking risk. So now what happened was obviously it's not
Speaker 1: plausible that that you could explain to opposite consequences by
Speaker 1: the same thing, but you can make a very good
Speaker 1: story and what happens and commentators and Bloomberg Radio and
Speaker 1: all other pundits what they do there is a salient
Speaker 1: event today. So when you're looking at what happened in
Speaker 1: the market, you're looking back and you see that salient event,
Speaker 1: and irresistibly you think there is a causal connection. So
Speaker 1: whatever happened in the market you attribute to the salient event,
Speaker 1: went up, went down, whatever it's always it makes a
Speaker 1: good story. I have a very vivid recollection early in
Speaker 1: the two thousand and three Iraq invasion there was a important,
Speaker 1: highly regarded mosque that was blown up by American warplanes.
Speaker 1: And the headline, and I don't remember it was the
Speaker 1: Times of the Wall Street Journal, but it was a
Speaker 1: big publication oil prices skyrocket on mosque accidentally being blown up.
Speaker 1: And later that day, so you're looking at this online
Speaker 1: and later that day the oil prices had come back down,
Speaker 1: and it was the same headline, only changing the conclusion
Speaker 1: mass destroyed, oil prices remains stable. Well, if it's going
Speaker 1: up and down after the event, it means the event
Speaker 1: isn't really significant, is it? Well, clearly. So let's talk
Speaker 1: a little bit about some of the biases, those three
Speaker 1: biases you discovered early on availability, anchoring, and representativeness. First,
Speaker 1: these are three enormous biases. First question, why publish in
Speaker 1: Discover magazine or Science magazine as opposed to some staid
Speaker 1: academic journal. Oh, we had done our homework. I mean
Speaker 1: we published in State academic journalist first, and then what
Speaker 1: we published in Science, which you know is a scientific magazine.
Speaker 1: I mean it's not it's a popular magazine. No it's not. No,
Speaker 1: it's a it's a Science is a professional magazine, but
Speaker 1: it's a it's addressed to scientists of all disciplines. But
Speaker 1: you know, it's a it's a perfectly respectable scientific magazine. No,
Speaker 1: we did. We we were academics. We published academically. We
Speaker 1: didn't publish and discover. They looked us up and they
Speaker 1: interviewed us and they wrote about us, but we didn't
Speaker 1: seek that out. We were really academics. We were not
Speaker 1: trying to influence the world. And whatever happened happened, but
Speaker 1: that wasn't really what we intended. Did you have any
Speaker 1: sense of the enormity of these three particular biases early on, Well,
Speaker 1: when we wrote for Science, we had a sense that
Speaker 1: this was an important story. We really did not anticipate
Speaker 1: the impact that it had on many fields, including investment
Speaker 1: and the law, and and and even economics. It had
Speaker 1: a lot of influence and that we didn't anticipate. Really.
Speaker 1: So so let's talk about a couple of these. You
Speaker 1: mentioned availability bias, it's just the information you have. Anchoring
Speaker 1: is an enormous factor in things like negotiations, in pricing.
Speaker 1: I love the story of just asking people. Ask a
Speaker 1: group of people in a room what the first digit
Speaker 1: of their phone number is, and then ask them a separate,
Speaker 1: unrelated question. A higher digit will yield a higher number
Speaker 1: on the unrelated question, and a lower first digit yields
Speaker 1: a low How did how did that discovery come about? Well,
Speaker 1: actually it takes a little more than that, but I'll
Speaker 1: give you an example. Uh, in the example of negotiation,
Speaker 1: many people think that you have an advantage if you
Speaker 1: go second, but actually the advantage is going first throughout
Speaker 1: the first number, and that's where you're throughout. And and
Speaker 1: the reason is something in the way the mind works.
Speaker 1: And the mind works, it tries to make sense whatever
Speaker 1: you put before it. So this built in tendency that
Speaker 1: we have of trying to make sense of everything that
Speaker 1: we encounter, that is a mechanism for anchoring. I'm Barry Ridholts.
Speaker 1: You're listening to Masters in Business on Bloomberg Radio. My
Speaker 1: special guest today is Nobel Prize winning psychologist Danny Kahneman,
Speaker 1: who has specialized in both cognitive and decision making processes.
Speaker 1: Let's talk a little bit about biases and money. Since
Speaker 1: this is Bloomberg and many of our listeners are investors,
Speaker 1: I want to start with prospect theory, which is something
Speaker 1: that's I find absolutely fascinating. The basic idea is that
Speaker 1: we feel losses much more severely than we enjoy gains.
Speaker 1: The first question I have for you about this is
Speaker 1: how did you discover this? Well, the idea that came
Speaker 1: first or the was that the standard financial analysis and
Speaker 1: which called the rational age model, fails in an important way.
Speaker 1: And in the rational agent model, you should always be
Speaker 1: thinking in terms of wealth. You know, what will be
Speaker 1: my wealth if this happens? What will be my wealth
Speaker 1: if that happens? And all the theory is based on
Speaker 1: the idea that you are thinking in terms of your wealth.
Speaker 1: But it turns out you're not thinking in terms of
Speaker 1: your wealth at all. You're thinking in terms of gains
Speaker 1: and losses. You're thinking in terms of changes of wealth.
Speaker 1: So that was the first thing that that was important
Speaker 1: to establish. It was clear Homo economists is what the
Speaker 1: professors call it, not a really good representation of how
Speaker 1: people behave. That's right, and you know that this is
Speaker 1: sort of immediately obvious that people don't think in terms
Speaker 1: of their wealth. And it's easy to show that that
Speaker 1: they don't because you can describe the same decision which
Speaker 1: in the same in terms of wealth. You can give
Speaker 1: it two different descriptions and people will make different choices
Speaker 1: depending on whether it's resented in gains and losses. So
Speaker 1: that were the first discovery, if you will, I mean,
Speaker 1: you know it had been stated before by the way
Speaker 1: Marco Witch Harry Marco which he had he had written.
Speaker 1: He had written one article on this about changes being important.
Speaker 1: He didn't follow through, and we came upon us and
Speaker 1: we did follow through. Now, once you have that that
Speaker 1: people are thinking gains and losses, it's easy to see
Speaker 1: that there is an asymmetry. That is that losses, as
Speaker 1: we said, loom larger than gains. And we even have
Speaker 1: a pretty good idea of by how much do they
Speaker 1: loom larger than gains about two to one. And you
Speaker 1: know an example how to bring that out is I'll
Speaker 1: offer you a gamble on the toss of a coin.
Speaker 1: You know, if it chose tales, you lose one hundred
Speaker 1: and if it chose heads, you win X. Now what
Speaker 1: would ex have to be for that gamble to become
Speaker 1: really attractive to you? Well, more than a hundred, so
Speaker 1: it's more than Actually you're a professional. There is a
Speaker 1: difference of professionals than others most people, and that's been
Speaker 1: well established. The men more than two hundred really, so
Speaker 1: they want two to one odds, which is it's not
Speaker 1: the odds, I mean the odds I went to one.
Speaker 1: It's equal chance to payout. It's the payout has to
Speaker 1: be to to one, meaning that it takes two hundred
Speaker 1: dollars of potential gain to compensate for one hundred dollars
Speaker 1: of potential loss when the chances of the two are equal.
Speaker 1: So that's loss of version. Terms out that loss of
Speaker 1: version has enormous consequence, enormous absolutely so. So that leads
Speaker 1: to a couple of really interesting questions. The first one
Speaker 1: is what is it about losses that makes them so
Speaker 1: much more painful than gets are pleasurable? In other words,
Speaker 1: why does this two to one risk of loss of version?
Speaker 1: Why does this even exist? Well, I mean you know this,
Speaker 1: this is evolutionary. I mean you you would imagine that
Speaker 1: in evolution, threats are more important than opportunities. That makes
Speaker 1: a lot of sense, And so it's a very general
Speaker 1: phenomenon that sort of bad things. So the preemptal are
Speaker 1: stronger than good things in our experience. So loss of
Speaker 1: version is a special case of something much broader. So
Speaker 1: there's always another opportunity coming along, another game, another deer
Speaker 1: coming by, but an actual genuine loss. Hey, that's permanent.
Speaker 1: You don't recover from that. That's right anyway, you take
Speaker 1: it most seriously. So there is a deer in your
Speaker 1: sights and a lion, you are going to be busy
Speaker 1: about the lion and not about the deer. Makes sense.
Speaker 1: That leads to the obvious question, well, what can investors
Speaker 1: do to protect themselves against this hardwired Uh? Well, loss
Speaker 1: of her Uh there's everything they can do. One is
Speaker 1: not to look at their results, not to look too
Speaker 1: often at how well they're doing. And today you could
Speaker 1: look tick by tick, minute by minute. The worst thing,
Speaker 1: it's a very very bad idea to look too often.
Speaker 1: When you look very often, you attempted to make changes.
Speaker 1: And where individual investors lose money is when they make
Speaker 1: changes in their allocation virtually on average. Whenever an investor
Speaker 1: makes a move, it's likely to lose money because they
Speaker 1: are professionals on the other side, betting against the kind
Speaker 1: of moves that individual investors. I'm Barry Hults. You're listening
Speaker 1: to Masters in Business on Bloomberg Radio. My special guest
Speaker 1: today is Danny Kahneman. He is the Nobel Prize winning
Speaker 1: psychologist formerly professor at Princeton who has had an enormous
Speaker 1: influence on the fields of economics and investing. Let's get
Speaker 1: right back to the question of investing and the professionals
Speaker 1: versus the amateurs. Do we see the same sort of
Speaker 1: biases and errors amongst the professionals. They're clearly much attenuated
Speaker 1: among professionals. So I'll give you an example. I mean,
Speaker 1: and that's the biggest example. It's what makes lots of
Speaker 1: version important. If I ask you, would you take a
Speaker 1: gamble if I ask a regular person in the street,
Speaker 1: Not you, because you're a professional, But if I ask
Speaker 1: a regular person in the street, would you take a
Speaker 1: gamble that if you lose, you lose a hundred dollars.
Speaker 1: If you win, you win a hundred and eighty on
Speaker 1: the toss of a coin. That's a no brainer. Well,
Speaker 1: it's a no brainer for professionals. Most people don't like it.
Speaker 1: Really do that all day long? Yeah, because you're a professional. Now,
Speaker 1: when you ask the same people in the street, Okay,
Speaker 1: don't want this one? Would you take ten? So we'll
Speaker 1: toss tin coins and every time, if you lose, you
Speaker 1: lose a hundred, and if you win, you win a
Speaker 1: hundred and eighty. Everybody wants the tin. Nobody wants the one. So,
Speaker 1: in other words, they think the mathistatistics in repeated play.
Speaker 1: When the game is repeated, then people see they become
Speaker 1: much closer to risk neutral and they see the advantage
Speaker 1: of gambling. Now professionals are in a repeated play situation.
Speaker 1: That's the biggest difference, And so they view each decision
Speaker 1: that they make out of a stream of decision that
Speaker 1: they're going to make. Now, this, by the way, is
Speaker 1: true for investors as well. One question that I asked
Speaker 1: people when I tell them about that, So you've turned
Speaker 1: down a hundred hundred and eighty now, but you would
Speaker 1: accept ten of those? Now, are you on your deathbed?
Speaker 1: That's the question I ask if that the last decision
Speaker 1: you're going to make, And clearly they are going to
Speaker 1: be more opportunities to gamble, perhaps not exactly the same gamble,
Speaker 1: but there will be many more opportunities. You need to
Speaker 1: have a policy for how you deal with risks and
Speaker 1: then make your decisions, your individual decisions, in terms of
Speaker 1: a broader policy. Then you will be much closer to rationality.
Speaker 1: But on the single flip of a coin, the average
Speaker 1: individual is going to look at this and say, hey,
Speaker 1: I'll feel foolish if I lose a hundred, even though
Speaker 1: the payout is greater than my potential law that's exactly
Speaker 1: what happens, and you shouldn't feel that way. I mean, now,
Speaker 1: it's true that you will feel like a fool. It's
Speaker 1: very closely related to what you see as all. There
Speaker 1: is that as we tend to see decisions in isolation,
Speaker 1: we don't see a decision about whether I take this
Speaker 1: gamble as one of many similar decisions that I'm going
Speaker 1: to make in future. So are people overly outcome focused
Speaker 1: to the detriment of process? What they are We call
Speaker 1: that narrow framing. They viewed the situation narrowly, and that
Speaker 1: is true in all domains. So, for example, we said
Speaker 1: that people on my opic that they have a narrow
Speaker 1: time horizon. To be more rational, you want to look
Speaker 1: further in time, and then you'll make better decisions. If
Speaker 1: you're thinking of where you will be, you know, a
Speaker 1: long time from now. It's completely different from thinking about
Speaker 1: how will I feel tomorrow If I make this bit
Speaker 1: and I lose, I recall reading about a study and
Speaker 1: I hope I don't mangle this too badly where they
Speaker 1: would take a photo of somebody and then using software
Speaker 1: age their face, and then when they were would ask
Speaker 1: the people who had the current photo of their own face.
Speaker 1: They would get a very different answer than the group
Speaker 1: that we're better able to imagine themselves twenty years alter. Yeah,
Speaker 1: that's about saving. I mean what actually happens is that
Speaker 1: when you show people morphed image of their face as
Speaker 1: an old person, their tendency to save increases, so it's
Speaker 1: easier for them to identify with their future self. But
Speaker 1: in general that's not what we do. People aren't especially
Speaker 1: good about that. Let's talk about being wrong and being
Speaker 1: able to admit that you're wrong. John Kenneth gale Breath
Speaker 1: once favor You famously said, given the choice between changing
Speaker 1: one's minds and proving there's no need to do so
Speaker 1: most people get busy on the proof. You called this
Speaker 1: theory induced blindness. So why are we so unwilling to
Speaker 1: admit when we're wrong. You know, you try to make
Speaker 1: the best story possible, and the best story possible includes
Speaker 1: what frequently actually didn't make that mistake. You know, So
Speaker 1: something occurred and in fact I did not anticipate it,
Speaker 1: but in retrospect, I didn't diticipate it. This is called hindsight.
Speaker 1: And that's one of the main reasons that we don't
Speaker 1: admit that we're wrong, is that whatever happens, we have
Speaker 1: a story. We can make a story, we can make
Speaker 1: sense of it. We think we understand it, and when
Speaker 1: we think we understand it, we alter our image of
Speaker 1: what we thought earlier. I'll give you a kind of
Speaker 1: example that so you have two teams that are about
Speaker 1: to play, you know, a football, and the two teams
Speaker 1: are about evenly balanced. Now one of them completely crushes
Speaker 1: the other. Now, after you have just seen that they're
Speaker 1: not equally strong, you perceive one of them as much
Speaker 1: stronger than the other, and that sception gives you the
Speaker 1: sense that you know, this must have been visible in advance,
Speaker 1: that one of them was much stronger than the other.
Speaker 1: So hindsight is that's a big deal. It allows us
Speaker 1: to keep a coherent view of the world. It blinds
Speaker 1: us to surprise us. It prevents us from learning the
Speaker 1: right thing. It allows us to learn the wrong thing.
Speaker 1: That is, whenever we're surprised by something, even if we
Speaker 1: do admit that we make a mistake. So oh, I'll
Speaker 1: never make that mistake again. But in fact, what you
Speaker 1: should learn, but you should learn when when you make
Speaker 1: a mistake because you did not anticipate something, that the
Speaker 1: world is difficult to anticipate. That's the correct lesson to
Speaker 1: learn from surprises that the world is surprising. It's not
Speaker 1: that my prediction is wrong. It's that predicting in general
Speaker 1: is almost as possible, you know. It's it's ironic. During
Speaker 1: the O eight oh nine finance shull crisis, we saw
Speaker 1: very few people beforehand making warnings about housing, about derivatives,
Speaker 1: about the stock markets. A pair of academics from Ryan
Speaker 1: Hart and Rogolf very famously put out a paper in
Speaker 1: January eight widely ignored afterwards. I can't tell you how
Speaker 1: many people have claimed to have seen it coming, never
Speaker 1: in print, there's no recorded history. But they all started
Speaker 1: coming and they knew this was going to go south.
Speaker 1: Pure hindsight. By this is really hindsight bias. It's actually
Speaker 1: very pernicious because it gives you the wrong impression. So
Speaker 1: I love Michael Lewis, and I like The Big Short
Speaker 1: like everybody, but The Big Short really gives you the
Speaker 1: impression that this was as obvious and the nose you know, see,
Speaker 1: when I read that book, my interpretation was here are
Speaker 1: a very small number of very odd characters who were
Speaker 1: amongst the only ones who saw it, and everybody else
Speaker 1: thought they were crazy. I mean, And the movie does
Speaker 1: a nice job portraying that. Yeah, but actually, at any
Speaker 1: one time, there are many people who are predicting that
Speaker 1: the world is going to end tomorrow, that there's going
Speaker 1: to be a crush next week. Now there is no crush.
Speaker 1: Those people get forgotten when there is a crash, the
Speaker 1: sort of geniuses. So we've seen that time and again,
Speaker 1: the broken clock syndrome. Eventually they're right everybody for although
Speaker 1: in the modern world, thanks to Google, you can very
Speaker 1: easily go back and check if someone has said there.
Speaker 1: There's a famous pundit who is for the past seven years,
Speaker 1: has been saying a seven like crash is around the corner.
Speaker 1: It's coming. Well maybe, but it's now seven years in
Speaker 1: a row. You've been saying, and there are many people
Speaker 1: you know, they're saying, is who predicted seven of the
Speaker 1: last three recessions? That's it's that's sort of So speaking
Speaker 1: of Michael Lewis, you and Amos Diversky are the subject
Speaker 1: of his next book that's coming out. Let's talk about that.
Speaker 1: What was that experience? Like Michael Lewis is a very
Speaker 1: lovable character and is very pleasant to talk to, and
Speaker 1: he earns your confidence by his charm and you know,
Speaker 1: he just he is very good at what he does.
Speaker 1: I haven't seen the book. I have no idea what
Speaker 1: it's what. I don't even want to see it before
Speaker 1: he gets published. You know, when it gets published, I'll
Speaker 1: read it, of course. But the process has been interesting
Speaker 1: because he's made me think about my past and that's
Speaker 1: been quite enjoyable. It has made me think about my
Speaker 1: my collaboration with a Mustwirsky. No I must Firsky died
Speaker 1: twenty years ago, and he is bringing him to life.
Speaker 1: And he also had access to all of Amos's papers,
Speaker 1: including notes that they must talk and conversations we have
Speaker 1: that's forgotten all about. So it's it's been an interesting
Speaker 1: experience reliving that period, but the experience would be about
Speaker 1: reading the book. I don't know yet. You can hang
Speaker 1: around a little bit. We'll keep we'll keep chatting. We've
Speaker 1: been speaking with Professor Danny Kahneman, winner of the two
Speaker 1: thousand and two Nobel Prize in Economics, former psychology professor
Speaker 1: at Princeton. If you've enjoyed this conversation, be sure and
Speaker 1: stick around for our podcast extras, where we keep the
Speaker 1: tape rolling and continue discussing all things biases and heuristics.
Speaker 1: Be sure and check out my daily column on Bloomberg
Speaker 1: dot com or follow me on Twitter at rit Halts.
Speaker 1: I'm Barry rit Halts. You've been listening to Masters in
Speaker 1: Business on Bloomberg Radio. Welcome to the podcast. By the way,
Speaker 1: it's so odd to call you Danny. Danny, if I
Speaker 1: haven't said thank you so much for being so generous
Speaker 1: with your time, let me do that now before before
Speaker 1: I um forget. I have been uh a big fan
Speaker 1: of your work for a long time and in my
Speaker 1: career I started as a trader and pretty early on
Speaker 1: recognized that why are these four guys doing the exact
Speaker 1: same thing and yet they're each obtaining very different results.
Speaker 1: And the first book on the in the space I
Speaker 1: read was by Tom Gilovich and Cornell, who I believe
Speaker 1: you've you've worked with before. Um, how we know it
Speaker 1: isn't so was was that book. But I've loved the
Speaker 1: work that you've put out, and I have a bazillion questions.
Speaker 1: Let's see if we can work our way through through
Speaker 1: some of these. So earlier on we were talking about
Speaker 1: anchoring and availability um. And one of the when I
Speaker 1: mentioned to a friend that you were an upcoming guest,
Speaker 1: the question he suggested is, well, the people who study
Speaker 1: biases and and the pitfall of human cognitions, they must
Speaker 1: be optimal decision makers, right, And I said, I'll ask so, well,
Speaker 1: certainly not, uh, you know. And the way that I
Speaker 1: described it, there's System one and their System two, and
Speaker 1: System one is very difficult to educate, and System one
Speaker 1: is where our intuitions come from. And system too, very
Speaker 1: often is just the pr agent four. System one it
Speaker 1: just explains decisions that were made, rationalizes, it rationalizes. But
Speaker 1: System one does a good job at keeping us alive
Speaker 1: and not only in the savannah, you know, keeping it
Speaker 1: keeps us alive. And most of the things that people do,
Speaker 1: uh are good. You know, I believe I'm to asky
Speaker 1: and I often considered sort of the profits of the irrationality.
Speaker 1: We don't never wanted that label. We don't believe that
Speaker 1: people are irrational. People are not perfectly irrational. They couldn't be,
Speaker 1: because they have the finite mind and perfect rationality demands
Speaker 1: much more. But you know, they're quite reasonable, except they
Speaker 1: make predictable biases in some mistakes in some conditions. So
Speaker 1: by and large, most people get many of their important
Speaker 1: decisions right, and the ones that get wrong really stand out. Well, uh,
Speaker 1: you know, it's hard to count. But most of the time,
Speaker 1: you know, most of the day you are on system one.
Speaker 1: You know, we're doing things that are well practiced, and
Speaker 1: most of them work. So we we managed to get
Speaker 1: through our day without engaging in without thinking too much,
Speaker 1: and well that for sure. Yeah, and most of the
Speaker 1: time it works just fine. That's that's quite so. The
Speaker 1: answer to your question was a long one, But no,
Speaker 1: I I'm not smarter than I was when I began
Speaker 1: this line of research more than fifty years ago. Because
Speaker 1: my system one is just the same way it was.
Speaker 1: I can recognize sometimes I can recognize situations in which
Speaker 1: I'm likely to make a mistake. So, h this person
Speaker 1: is trying to anchor me. Yeah, I can recognize that.
Speaker 1: It still works on me, by the way, but but
Speaker 1: I can recognize it. So well, let's let's talk a
Speaker 1: little bit about that. So I was always under the belief,
Speaker 1: apparently mistakenly, that if you become self enlightened about your
Speaker 1: own biases, then you can undertake a series of steps
Speaker 1: to prevent yourself from doing damage. We were speaking before
Speaker 1: the show started about indexing and global asset allocation. Now
Speaker 1: are you picking stocks and timing markets or have you
Speaker 1: taken your system too and allowed it to prevent system
Speaker 1: from saving I personally don't do anything of the kind
Speaker 1: you know, But I'm a conservative Poston and so there's
Speaker 1: nothing to be learned from the way that I handled
Speaker 1: my money. But I certainly I would not advise speaking
Speaker 1: individual stuff so or any form of very active manage.
Speaker 1: You strike me as a passive indexer who you specifically said,
Speaker 1: I don't want to check my portfolio too often people
Speaker 1: do that, and when they do, they tend to make mistakes.
Speaker 1: So this seems like a little closer to optimal decision making.
Speaker 1: You've identified a couple of biases that affect out people
Speaker 1: invest and you're engaging in the behavior to prevent yourself
Speaker 1: from Well, I'm mostly very lazy, you know. That's it.
Speaker 1: That's you can get to the same point by being
Speaker 1: rational or by being lazy. In that case, it's laziness.
Speaker 1: If that was true, I would have been a much
Speaker 1: better student in high school. Um so uh. Jason's ye
Speaker 1: of the Wall Street Journal is um was an editor
Speaker 1: with you on some work you did, and he said
Speaker 1: you you once said to him, and I want to
Speaker 1: get the quote exactly right. You have no sunk costs.
Speaker 1: So we're all familiar with the sun cost fallacy. How
Speaker 1: do you have no sun costs? I mean I have
Speaker 1: you know, it was in a particular context. It was
Speaker 1: in the context of how many drafts do you write?
Speaker 1: And I have no sun costs in the sense and
Speaker 1: the mere fact that I have written something, even if
Speaker 1: it's twenty or thirty pages. If now I decide it's
Speaker 1: not good, I don't try to fix it. I started over,
Speaker 1: throw it away and started over. That's that's having no
Speaker 1: sun costs. Now, the implication is some people would have said, Hey,
Speaker 1: I spent two hours on this, I'm gonna go back
Speaker 1: and edit this and try and make it. You're that
Speaker 1: that time and effort is spent as far as you're concerned.
Speaker 1: You're starting from scratch, and I think you do the
Speaker 1: things that way. If if you're if you forget about
Speaker 1: the work that you've already invested. But if you if
Speaker 1: you have found a better solution. Now instead of saying, oh,
Speaker 1: I should have written that chapter differently, you just rewrite
Speaker 1: that chapter differently. Now, don't Most people suffer from the
Speaker 1: some cost of fact They feel, well, I already spent
Speaker 1: this money, and now I'm committing. I bought this stock
Speaker 1: and now I'm kind of committed to it. You know,
Speaker 1: I've we're talking about writing, not not the decisions, but
Speaker 1: it's still it's still work process, it's still effort, and
Speaker 1: people tend to think, oh, I mean, most people are
Speaker 1: highly sensitive to some costs, and I would not say
Speaker 1: that I'm not in other domains of life. I said
Speaker 1: I'm free of some costs in the domain of writing.
Speaker 1: And that's what Jason Swag was writing about, because actually
Speaker 1: he thinks that most writers very reluctant, distilled over. Yes, absolutely,
Speaker 1: you put the time and effort in. Gee, I don't
Speaker 1: want to throw this away. I have something to work with. Um.
Speaker 1: It's always easier to do that that second draft than
Speaker 1: it is to start with a plane sheet of paper. No,
Speaker 1: no doubt about that. So we haven't really talked about
Speaker 1: the endowment effect. UM, But I have my favorite example
Speaker 1: that I wanted to share with you because I find
Speaker 1: this such a fascinating subject. Whenever. So I'm a car guy,
Speaker 1: and that means very often friends and family come and
Speaker 1: ask me about I'm thinking about this car or that car.
Speaker 1: What what's your view? And I've noticed it's a fascinating subject,
Speaker 1: like like the two football teams who are so evenly matched.
Speaker 1: When someone comes to you and says, I'm I'm considering
Speaker 1: these two cars. I'm thinking about the Toyota camera or
Speaker 1: the Honda Chord, and the camera has this, this and this,
Speaker 1: but the accord has that, that and that, and it's
Speaker 1: it's pretty evenly matched. And I'm I'm having a hard
Speaker 1: time making a decision. What's your opinion? And so I'll say, well,
Speaker 1: they're both great cars. You won't go wrong with either,
Speaker 1: but I think this one is a little nicer. What
Speaker 1: what do you think? I'll try and ask them some questions.
Speaker 1: Six months later, you see them and they bought car
Speaker 1: A over car B, and you asked them, how do
Speaker 1: you like the car? And the answer is, I can't
Speaker 1: imagine I was even considering that other car. This car
Speaker 1: is fantastic. Now nothing has changed. These are two really
Speaker 1: good automobiles, except for the fact that he now owns
Speaker 1: this car. So what is it about ownership that makes
Speaker 1: us think this is better, more valuable? Whatever? Why do
Speaker 1: we endow these these objects with with superiority. When you've
Speaker 1: owned something for six months, you think highly of it
Speaker 1: because it's become familiar, and almost everything that is familiar
Speaker 1: you like better. Really, So for smiliarity doesn't bring contempt.
Speaker 1: Familiarity makes you like things. So that's a that's a
Speaker 1: big psychological rule in general. Even in the study where
Speaker 1: I think it was a mug with the school's name
Speaker 1: on it, that's a different story. So that I said,
Speaker 1: when you own something for a long time, when you're
Speaker 1: discussing trading, then if you're not a professional, if you're
Speaker 1: an individual and you're thinking of a mug. Then you're
Speaker 1: not thinking of like thinking of wealth. You're not thinking
Speaker 1: of the state of the world in which you have
Speaker 1: that mugs against the state of the world in which
Speaker 1: you have seven dollars in addition to your wealth. You're thinking,
Speaker 1: if you have the mug, do I give it up?
Speaker 1: And if you don't have the mug, do I get
Speaker 1: it and give up the money for it? And giving
Speaker 1: up is more painful than gaining. And that's how loss
Speaker 1: a version gets involved in the endowment defect. It doesn't
Speaker 1: play any role in your story about the car because
Speaker 1: you're not about to trade the car, so you're liking
Speaker 1: for the car is more in effective familiarity and of
Speaker 1: something a psychological process that's called dissonance reduction. It chose
Speaker 1: that car, therefore it must be good. So that is
Speaker 1: true that almost anything that you chose becomes better because
Speaker 1: you chose it. But that's a different process than the
Speaker 1: endowment ef that's totally because it seems there's a big
Speaker 1: overlap between I chose it, therefore it's good, and I
Speaker 1: already own it and therefore it's valuable. Well, because you
Speaker 1: may own it without having chosen it. You know, I
Speaker 1: give you that mug and I ask would you sell it?
Speaker 1: So you just had it for thirty seconds and you
Speaker 1: you get the offer to sell it. So it's a
Speaker 1: different process. That's quite that. It's quite fascinating. Um. So
Speaker 1: I like this vote from thinking fast and slow. Our
Speaker 1: company comforting conviction that the world makes sense rests on
Speaker 1: a secure foundation of our almost unlimited ability to ignore
Speaker 1: our own ignorance. Explain that since you mentioned dissonance, I
Speaker 1: thought that was a good point to bring this up. Well.
Speaker 1: Earlier we talked about something I've read about in the book,
Speaker 1: which is what you see is all there is. And
Speaker 1: the idea that but you don't see is you know
Speaker 1: good refute everything that you believe. That just doesn't occur
Speaker 1: to us. So it's it's another way of telling the story.
Speaker 1: I was telling earlier that we construct the best possible
Speaker 1: narrative about the world, and if we're successful in constructing
Speaker 1: a good story, we believe it, we have a high
Speaker 1: confidence in it, and we don't want to change it.
Speaker 1: That makes perfect sense. Um Let's talk about regression to
Speaker 1: the mean and your conversation with the fighter pilot instructor
Speaker 1: who felt every time he yelled at a cadet they
Speaker 1: would do better and kind of ignored the fact that
Speaker 1: maybe it just happened to be did poorly and he
Speaker 1: was do to do well well the whole. You know,
Speaker 1: it's actually quite remarkable that the statistical fact that you know,
Speaker 1: it's as common as as the air we breathe is
Speaker 1: is very nonintuitive, and that's aggression to the mean. So
Speaker 1: the fact is that, you know, if you look at golfers,
Speaker 1: that's the example of developing the book. So you look
Speaker 1: at the three guys who scored the highest score yesterday,
Speaker 1: chances are they're going to do less well today. And
Speaker 1: that's I'll tell you why. It's because yesterday there's a
Speaker 1: sess was impowered due to luck, and luck is not going,
Speaker 1: not guaranteed to follow them today. So we have to
Speaker 1: think of regression to the mean that what we see
Speaker 1: has already luck built into it, and luck is not
Speaker 1: going to stay. So next time and next time it's
Speaker 1: likely to be less successful. You know, the golfer is
Speaker 1: likely to be less successful than he was because he's
Speaker 1: likely to be less lucky than he was. And people
Speaker 1: dramatically underestimating the impact of chance on their subsilently the
Speaker 1: separating skill from chance, especially in this field, is an
Speaker 1: ongoing battle, and very often what looks like very skillful
Speaker 1: managers turns out to be Hey, they were lucky for
Speaker 1: a couple of quarters. And and and now is that sure?
Speaker 1: And we have a very strong tendency, you know, everybody
Speaker 1: has that. We have that about ourselves, that we attribute
Speaker 1: our success our successes to our skill and failures, you know,
Speaker 1: to bad luck. But actually there is a lot of
Speaker 1: luck in our successes as well that we don't see that.
Speaker 1: That is uh an ongoing issue amongst investors and traders.
Speaker 1: Um it's it's always bad luck when the trade doesn't
Speaker 1: work out, But when it does work out, it's because
Speaker 1: I'm a genius, and we see that. We see that
Speaker 1: all the time. So, having read a lot of what
Speaker 1: you've written and over over a long time, I was
Speaker 1: surprised to learn that you don't describe yourself as particularly optimistic.
Speaker 1: And I thought that was kind of interesting because you've
Speaker 1: spent what more than a decade now looking at happiness
Speaker 1: and utility. Um, So the first question I have to
Speaker 1: ask is has studying this field made you any more
Speaker 1: optimistic or any any happier? No, I mean, you know,
Speaker 1: optimism is genetic anyway, and yeah largely, Uh you know,
Speaker 1: I'm the son of a very pessimistic mother, and so
Speaker 1: pessimism is sort of genetic too. I could see that
Speaker 1: is is it genetic or is it the home you
Speaker 1: were raised? Then no, I'm just I'm just pretty Actually
Speaker 1: it's actually genetic really so so so studying utility and
Speaker 1: studying happiness, what what conclusions have you reached based on
Speaker 1: that research? Well, the main conclusion was that there is
Speaker 1: a difference between what makes us satisfied and what makes
Speaker 1: us happy. So distinction because the distinction between the two.
Speaker 1: Being happy means having you know, having happy experiences and
Speaker 1: being satisfied is when you look at your life and
Speaker 1: what you've accomplished. Are you content and are you satisfied
Speaker 1: with what you've accomplished? And those two are really not
Speaker 1: the same, which is more important. Well, terms up that
Speaker 1: for most people what they try to do, if they
Speaker 1: try to be satisfied, they go for satisfaction and not
Speaker 1: for they are not thinking how can I maximize the
Speaker 1: quality of the my life? Mm hmm. So and many people,
Speaker 1: I think make big mistakes and that they settle themselves
Speaker 1: the big commute to have a larger home. You know,
Speaker 1: the big commute means an hour or two a day
Speaker 1: that are wasted. You know that counts. Time should count
Speaker 1: for a lot, because it's the only thing we've got basically,
Speaker 1: and people who waste time in order to achieve something
Speaker 1: else are wasting the part of their life. So so
Speaker 1: the studies I've seen is the longer your commute is,
Speaker 1: the less happy you are. Does that also lead to
Speaker 1: the less satisfied you? Not necessarily? Not necessarily, because if
Speaker 1: having a big home is part of your satisfaction, if
Speaker 1: you're sort of proud of the house you've got, uh,
Speaker 1: and you're paying for it by a long commute, you're
Speaker 1: not the way it's satisfied. Although you're not happy. You
Speaker 1: get a bigger house closer to your job, but it's
Speaker 1: going to be a lot more expensive. So so they're
Speaker 1: trading the time for that makes senseense. What what else
Speaker 1: have you what other things have you taken away from
Speaker 1: studying happiness and satisfaction? Well, you know, I think I
Speaker 1: think it turns out that people are happiest and when
Speaker 1: they're in the presence of friends, truly friends, and then
Speaker 1: you know there is a special thrill I think for
Speaker 1: people to be with friends, which is even more than
Speaker 1: to be with family. Really, why is that, Well, there's
Speaker 1: something more relaxing, and you know, when you're in a family,
Speaker 1: there are many obligations and many stresses, and so this
Speaker 1: is a pleasure that you sation being with friends. But
Speaker 1: those are some of the best moments of the week
Speaker 1: when you're not alone. There are there is a particular
Speaker 1: joy for many people in having shared routines with friends.
Speaker 1: You know, the weekly poker game, the weekly dinner and
Speaker 1: movie that that you share with friends. Over a period
Speaker 1: of years, that becomes very precious and it's you know,
Speaker 1: it's becomes an important part of life that that's really
Speaker 1: quite intriguing. Um. You know a lot of the discoveries
Speaker 1: we've talked about have the feeling to me of of epiphanies.
Speaker 1: So one of the things I specifically wanted to ask
Speaker 1: you about when you were doing the officer candidate evaluations
Speaker 1: for the Israeli Army um or when you uh would
Speaker 1: ask colleagues how long they thought it would take for
Speaker 1: for a project to be done. The results of these
Speaker 1: were just really wow, this is much different than everybody
Speaker 1: everybody believed. So what was it about your background and
Speaker 1: your training that let you look at the world so
Speaker 1: differently than everybody else around you. Obviously, these behaviors exist
Speaker 1: amongst everybody. What led you to these really fascinating discoveries
Speaker 1: that everyone else seems to have overlooked? Well, Uh, you know,
Speaker 1: I I I was born to be a psychologist. I
Speaker 1: really believe that, not my faith, but you know, the
Speaker 1: sort the thing was, I was better at that and
Speaker 1: at anything else. And I've always been interested in observing
Speaker 1: people and then trying to figure out, you know, why
Speaker 1: they are, where they feel, what they feel, and and
Speaker 1: so that's sort of curiosity about people I think as
Speaker 1: somehow paid afful me. So um um, so let's get
Speaker 1: to some of our favorite last few questions. I know
Speaker 1: we only have you here for a finite amount of time.
Speaker 1: These are the standard questions we ask all of our guests. Um.
Speaker 1: We we discussed uh, your your you knew right away
Speaker 1: you're going to be in academy or your whole career.
Speaker 1: Who were some of your early mentors? Well, I had professors.
Speaker 1: I had professors I loved when I was an undergraduates
Speaker 1: in Jerusalem. One in particular, I wouldn't call him a mentor,
Speaker 1: but you know, he was your sure life of it.
Speaker 1: It was his name. He was a hero to me
Speaker 1: and too many other people. Really why why is that?
Speaker 1: But what did he do that resonated with you so much?
Speaker 1: He just was a very powerful personality. He had strong
Speaker 1: opinions about everything. He knew a lot, he was, he
Speaker 1: had several doctorates, he was and he had a very
Speaker 1: individual character. So I loved he made a deep impression
Speaker 1: on me. In in graduate school, I had quite a
Speaker 1: few teachers, especially the one who supervised my thesis at Gizelli.
Speaker 1: But I learned from quite a few people when I
Speaker 1: was in graduate school. I don't think I ever had
Speaker 1: a proper mentor in the sense that that many graduate
Speaker 1: students today they get into a lab and they're associated
Speaker 1: with the same person throughout their career. In graduate school,
Speaker 1: that wasn't the case when I was in graduate school.
Speaker 1: You didn't belong to a lab and and so I
Speaker 1: didn't have that experience. So any previous psychologists or or
Speaker 1: clinical experimenters influence your approach to what you did or yeah, many,
Speaker 1: I mean mainly a man named Paul Meal, who who
Speaker 1: was the first to compare algorithms to clinical judgment and
Speaker 1: to reach the conclusion that algorithms are actually more accurate
Speaker 1: in many many cases than than intuitive judgment. Now I'm
Speaker 1: I'm I hope I'm not misremembering it. Um. Something you
Speaker 1: wrote specifically said, even after we show people the success
Speaker 1: ratio of the algorithms, they still wanted to stick with
Speaker 1: their their intuition. Yeah, that's absolutely true. I mean, people
Speaker 1: have people don't love algorithms, and and they better get
Speaker 1: used to them because they're taking over every right, this
Speaker 1: is happening. But you know, when you even think about that,
Speaker 1: when a doctor makes a mistake and the child dies,
Speaker 1: this is terrible, But if it was a piece of
Speaker 1: artificial intelligence that made a mistake and the child dies,
Speaker 1: that would be worse. At least today, because we trust
Speaker 1: the machinery, and the machinery it's more shocking. It's more
Speaker 1: shocking when it's impersonal. You know, when a self driving
Speaker 1: car has an accident, we just learned, we just learned
Speaker 1: about that, But there's something more shocking about the idea.
Speaker 1: So just think about that somebody dying in a in
Speaker 1: an accident, or somebody dying in an accident with a
Speaker 1: self driving car, and and that second idea is some
Speaker 1: more shocking, so people really don't for the time being,
Speaker 1: this is going to change. But there is such a thing.
Speaker 1: It's been called algorithmic version. Really, you know, when you
Speaker 1: look at the statistics, let's use the self driving car,
Speaker 1: by all measures, they're they're safer, they're more reliable, they're
Speaker 1: less likely to be involved in either a major or
Speaker 1: a minor accident. Doesn't mean you're gonna eliminate those sort
Speaker 1: of accidents. So when one happens, it seems to really
Speaker 1: resonate people. I mean, part of that is that there
Speaker 1: are things that a view does natural or as you know,
Speaker 1: sort of acts of God, and those we've learned to accept.
Speaker 1: But when it's acts of men, it's very different. So
Speaker 1: the best example is vaccines. So vaccines, you know, could
Speaker 1: cause sects side effect, they could Let's take a child
Speaker 1: who died from a vaccine. Now, how many children would
Speaker 1: the vaccine have to save to accept the death of
Speaker 1: one child from the vaccine. It's clearly not one to one.
Speaker 1: Now it's a lot of more millions, millions to I
Speaker 1: don't know. I hope it's not millions because that would
Speaker 1: be crazy. But but but it's a more than one.
Speaker 1: And so what what happens here is that anything that
Speaker 1: is man made we have a much stronger reaction to
Speaker 1: then than if the same thing occurs naturally. M that's
Speaker 1: quite fascinating. So so let's talk about books. What are
Speaker 1: some of your favorite books, be it fiction, nonfiction, Michael
Speaker 1: Lewis or otherwise. Well, in nonfiction, the best book I've
Speaker 1: read in in several years is called Sapiens. Sure, it's
Speaker 1: the short history of humankind. You've ash, well, Uh, I
Speaker 1: think it's superb. It's literally on my night table. It's
Speaker 1: the next book up in my queue. If you're telling
Speaker 1: me it's superb, I better started soon it twice? Really,
Speaker 1: I don't. Yeah, and there are many books. Wow, that's
Speaker 1: some endorsement. No, I think it's I think it's very impressive.
Speaker 1: So that's that was one. Uh, certainly in a seem
Speaker 1: Talib's book had a big effect on me, his last one,
Speaker 1: or Fooled by Randomness, mostly the Black Swan, but you know,
Speaker 1: I had liked fool by Randomness too, but the Black
Speaker 1: Swan I learned a lot from. And then obviously their
Speaker 1: works by by people I know like Nudge and Taylor. Taylor,
Speaker 1: I mean, so those are many of some of your favorites. Um,
Speaker 1: so you've mentioned you've been doing this for fifty years.
Speaker 1: What has changed in the field of psychology that really
Speaker 1: stands out to you over over that long period. You know,
Speaker 1: when I started my studies, a lot of psychology was
Speaker 1: still concerned with rats running mazes, and so the what
Speaker 1: it's called the cognitive revolution occurred during my career. Early
Speaker 1: in my career, so people sort of forgot, you know,
Speaker 1: just set rights aside, mostly except for physiological work. And
Speaker 1: when they wanted to study how the mind works, they
Speaker 1: studied how people think. So that that was a big change,
Speaker 1: and and there have been many other changes in recent years.
Speaker 1: Mostly it's the study of the brain that is taking over,
Speaker 1: and you know that's huge. The fm R eyes and
Speaker 1: that's you know this this came too late for me
Speaker 1: to be involved in, but if I had been younger,
Speaker 1: I would have done what younger colleagues of mine did
Speaker 1: and they switched. Is that going to be the biggest
Speaker 1: change going forward? Is the ability to pear into the
Speaker 1: brain while it's in operation. I'm convinced that that's the
Speaker 1: case for the next few decades, really a few decades Wow,
Speaker 1: that's that's amazing. All right. So we're up to our
Speaker 1: last two questions, our final two questions. If a millennial
Speaker 1: or a recent college grad came up to you and said, hey,
Speaker 1: I'm thinking about a career and in experimental psychology, what
Speaker 1: what sort of advice would you give them, whether to
Speaker 1: go into that or in something else or well, they said,
Speaker 1: I'm I'm considering this career. What advice do you have
Speaker 1: for me? What? What would your answer be? You must
Speaker 1: have had people students at Princeton ask you all the time. No,
Speaker 1: I mean I would ask what kind of psychologists you
Speaker 1: want to be? And I assure you want to be
Speaker 1: that kind of psychologist. And there are certain lines of studies,
Speaker 1: like going to graduate school. It's not for everybody, and
Speaker 1: some very very smart people who could succeed in graduate
Speaker 1: school should really not do it because there they will
Speaker 1: be happier doing something else than being an academic. An
Speaker 1: academic is is a life that's good for for a
Speaker 1: minority of people and for most others. You know, it's
Speaker 1: it's not great. So uh, I would never have ready
Speaker 1: made advice. It really depends on it really depends on
Speaker 1: the individual. And in our final question, what is it
Speaker 1: that you know about psychology and the human mind today
Speaker 1: that you wish you knew when you began fifty years ago.
Speaker 1: I can't think of that, actually, I mean it's very odd.
Speaker 1: You know, fifty years ago I didn't know many of
Speaker 1: the things I know today, and today I know a
Speaker 1: negligible amount compared to what people will know, you know,
Speaker 1: fifty years from now. Well, but you've also I don't
Speaker 1: I don't regret not knowing what I knew. I mean,
Speaker 1: discovering things and learning things has been so much fun
Speaker 1: that I have no regret about what they didn't know then. Um,
Speaker 1: maybe regrets the wrong word. What what would have been
Speaker 1: helpful to have known that you discovered later on in
Speaker 1: your career. I mean, there are there There are things
Speaker 1: that I learned even in recent years, in the last
Speaker 1: few years, which is there's a big change in psychology
Speaker 1: that that people are suspicious, and there is what it's
Speaker 1: called the reproducibility crisis. In sure that's across all sciences,
Speaker 1: and it's across all sciences that I wish I had
Speaker 1: been more careful about, in particular because I had thought
Speaker 1: about it, I had all the tools to see it
Speaker 1: and I was not sufficiently aware of it. Dr Danny Khaman,
Speaker 1: this has been absolutely fascinating. Thank you so much for
Speaker 1: being so generous with your time. If you have enjoyed
Speaker 1: this conversation, and be sure and look up an intro
Speaker 1: Down an Inch on Apple iTunes and you could see
Speaker 1: the other one hundred or so such conversations uh we've
Speaker 1: had over the past uh two years. I would be
Speaker 1: remiss if I did not thank my producers, Taylor Riggs
Speaker 1: and Charlie Valmer, Charlie also working as a recording engineer today,
Speaker 1: and of course our head of research, Michael Batnick, who
Speaker 1: has been a huge help in helping to prepare these questions.
Speaker 1: We love your comments and questions and suggestions. Be sure
Speaker 1: and send us an email. You can write me at
Speaker 1: b Ridholt's three at Bloomberg dot net. UM, you've been
Speaker 1: listening to Masters in Business on Bloomberg Radio.