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Advancing Behavioral Economics with Colin Camerer · Masters in Business

0:02
Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Master's in
0:11
Speaker 1: Business with Barry Ridholds on Bloomberg Radio.
0:16
Speaker 2: This week on the podcast finally I get Colin Camera
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Speaker 2: in the studio to talk about neuroeconomics, behavioral finance, and
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Speaker 2: really all the fascinating things he's been doing at Caltech
0:30
Speaker 2: for the past. Gee's been there for almost thirty years.
0:34
Speaker 2: Is that about right? He's really an interesting guy, not
0:37
Speaker 2: just because he has the mathematical and behavioral finance background,
0:42
Speaker 2: but because he essentially asked the question, what's going on
0:47
Speaker 2: inside our brains when we make decisions? What's happening before
0:51
Speaker 2: we even have a degree of awareness of our own decisions.
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Speaker 2: I just find what he does fascinating, not just fmyes,
1:00
Speaker 2: but eye tracking and eg and galvanomic responses of the skin,
1:06
Speaker 2: and just on and on, all these different ways to
1:08
Speaker 2: measure what's going on with your hormones, what's going on
1:11
Speaker 2: pharmacologically within your body. It's both fascinating and terrifying because
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Speaker 2: you come to realize what you think is a decision
1:23
Speaker 2: you're making, very often is a decision your brain is
1:27
Speaker 2: making with or without you. I found our conversation to
1:30
Speaker 2: be absolutely fascinating, and I think you will also with
1:34
Speaker 2: no further ado my sit down with Caltech Colin camera.
1:40
Speaker 1: Thanks for having me so.
1:41
Speaker 2: I've been looking forward to having this conversation with you
1:45
Speaker 2: for a long time, not just because of my interest
1:49
Speaker 2: in behavioral finance, but because of the space you occupy
1:54
Speaker 2: in neuroeconomics. We'll talk a little bit about that in
1:57
Speaker 2: a bit, but let's start with your back, which is
2:01
Speaker 2: kind of astonishing. You get a bachelor's in quantitative studies
2:05
Speaker 2: from john Hopkins at seventeen, and then an MBA in
2:09
Speaker 2: finance and a PhD in decision theory from the University
2:13
Speaker 2: of Chicago at twenty one. That's a lot of school
2:17
Speaker 2: really quickly. What were the career plans? Were you thinking academia?
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Speaker 2: Were you thinking finance?
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Speaker 1: I was actually kind of not quite sure, So I
2:25
Speaker 1: got in. I went to Chicago Grad School for PhD
2:29
Speaker 1: in the now Booth School of Business. Because I had
2:33
Speaker 1: learned a little bit about finance. I took an independent
2:36
Speaker 1: study from Carl Christ who's a famous econometrician at Johns
2:40
Speaker 1: Hopkins when Gene Fama's book Foundations of Finance had just
2:46
Speaker 1: come out. In fact, I literally worked in the college
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Speaker 1: bookstore part time, and I remember unpacking the box to
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Speaker 1: have this Fama book, and so I immediately bought one,
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Speaker 1: and you know, I was going to do this independent
2:56
Speaker 1: study and read through. And by the way, it really
2:58
Speaker 1: is some books often called Foundations of Blank. It really
3:01
Speaker 1: was Foundations, right, you know, it was the It was
3:04
Speaker 1: a summary in the nineteen seventy six, right, very early days.
3:09
Speaker 1: And so Carl christ had said, well, you should think
3:11
Speaker 1: about Chicago. That's a powerhouse place for finance. And so
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Speaker 1: I started studying finance there and passed the prelam which
3:19
Speaker 1: is no which is no small feat that's very selective.
3:23
Speaker 1: And then I got interested in behavioral science because finance
3:29
Speaker 1: was really obsessed with market efficiency and you know, there
3:33
Speaker 1: was no behavioral science, behavioral finance in sight at that time.
3:37
Speaker 1: But there were other folks at at Chicago.
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Speaker 2: Well, if I recall correctly, Dick Taylor was there early
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Speaker 2: in the behavioral finance or did he end up there later, Yeah,
3:47
Speaker 2: he came later.
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Speaker 1: He came later. So when I came in the late seventies,
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Speaker 1: a lot of Nobel Prize winners were their Fama, Miller Shoals.
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Speaker 1: I think Fisher Black might have just left for MIT
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Speaker 1: and when I came, but it was pre Andre Schleifer
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Speaker 1: and Ravishni who did a lot of interesting behavioral finance,
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Speaker 1: and then Dick Taylor came, I think around nineteen ninety five,
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Speaker 1: nineteen to.
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Speaker 2: Six, and you were at cal Tech by then, right,
4:15
Speaker 2: just correct?
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Speaker 1: So yeah, so Dick and I had just passed like
4:18
Speaker 1: ships in the night, and I regard that sometimes not
4:21
Speaker 1: having just stayed and you know it's been part of
4:23
Speaker 1: a new vanguard.
4:26
Speaker 2: Well, but you actually are part of a new vanguard
4:29
Speaker 2: because the work you do in neuroeconomics, which we're going
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Speaker 2: to get into, especially fMRIs and all the other things
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Speaker 2: you've done more or less created that space. I mean,
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Speaker 2: that's pretty foundational. Behavioral finance has a number of fathers,
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Speaker 2: including Dick Taylor and Danny Kahneman. So well, let's circle
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Speaker 2: back to the neuroeconomics in a little bit. But I
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Speaker 2: want to ask what led you into decision making research?
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Speaker 2: How did you find yourself taking the background you had
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Speaker 2: in quantitative studies and your PhD and m b a
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Speaker 2: and and go into decision making.
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Speaker 1: So I some of it was when I was in
5:13
Speaker 1: college at Johns Hopkins. I studied physics and math that
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Speaker 1: was too abstract, and number theory was just too mind blowing,
5:20
Speaker 1: you know for me, like I'm just not going to
5:22
Speaker 1: work at that level. And then I studied psychology and
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Speaker 1: that seemed like just kind of a list of things
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Speaker 1: that happened to people, but there was no unifying wish squishy.
5:32
Speaker 1: And then economics, which I really only took a little
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Speaker 1: bit of a lot fewer than my peers I later
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Speaker 1: competed with in grad school, was kind of in between,
5:41
Speaker 1: like the Three Little Bears, you know, there was I
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Speaker 1: love that, and there was people right you know, physics
5:45
Speaker 1: didn't have people, psychology didn't have math.
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Speaker 2: Economics was kind of the right mix.
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Speaker 1: Exactly exactly. And I think a lot of a lot
5:51
Speaker 1: of social scientists may feel that way, and the people
5:54
Speaker 1: who like math lest stay in psychology or go to
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Speaker 1: sociology or something where the mathematical structure isn't You found
6:00
Speaker 1: the canon and the foundation.
6:03
Speaker 2: So what led you into game theory? You end up
6:06
Speaker 2: writing a book Behavioral game Theory that was published in
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Speaker 2: three How does that relate to economics and decision making
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Speaker 2: and investing?
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Speaker 1: So in graduate school when I pivoted away from finance,
6:19
Speaker 1: there was a couple of psychologist Hilly Einhern and Robin Hogarth,
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Speaker 1: who were interested in judgment decision making. They were doing
6:27
Speaker 1: things very similar to konomen and diversity. It was sort
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Speaker 1: of somewhat mathematical attempts to understand actual human decision making,
6:34
Speaker 1: not really stylized like Bay's rule and optimization. You know,
6:38
Speaker 1: those are good things to know, but they were interested
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Speaker 1: in deviations from those and what that might tell us
6:43
Speaker 1: and what the practical value. So that's what I ended
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Speaker 1: up doing in grad school. Game theory came a little
6:48
Speaker 1: bit later because at Chicago at that time, in the
6:53
Speaker 1: late seventies, there was hardly any interest in game theory
6:55
Speaker 1: for peculiar reasons. They were, you know, the economic world
6:59
Speaker 1: was dominated by price theory supplying demand like Gary Becker,
7:02
Speaker 1: you know, there was a lot going on. Game theory
7:04
Speaker 1: just was not flourishing there. But my first job was
7:07
Speaker 1: as an assistant professor in Northwestern and that happened to be,
7:12
Speaker 1: through just historical coincidence, a hotbed of great game theory.
7:16
Speaker 1: Paul Milgram was there, Banked Holmestrom was there, Robert Weber,
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Speaker 1: who worked on lots of things on auction theory, Dave Barren,
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Speaker 1: who was interested in political economy, and you know, political
7:28
Speaker 1: systems as games. So Milgrim and Holstrom went on to
7:32
Speaker 1: win Nobel prizes and went to other places. So it
7:35
Speaker 1: was sort of this incubator place that then, you know,
7:40
Speaker 1: like a incubator like Hewlett Packard and things like that,
7:44
Speaker 1: where people then went off to do other stuff. And
7:46
Speaker 1: so I basically learned game theory in my first job
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Speaker 1: as AISTM professor, and that game theory is similar to
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Speaker 1: behavor economics. The standard theory that everyone teaches in every
7:57
Speaker 1: introductory course is people arend and make the best choices
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Speaker 1: given what they think others will do, and they're correct
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Speaker 1: guessing about what others do. Like a bunch of people
8:08
Speaker 1: who played poker with each other, you know, every Friday
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Speaker 1: night for decades, they kind of know what the tells are.
8:14
Speaker 1: But we were interested in what happens before you get
8:17
Speaker 1: to this kind of what's called Nash equilibrium, you know,
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Speaker 1: where everyone is guessed correctly what everyone's going to do.
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Speaker 1: And so to me, there was a huge room for
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Speaker 1: understanding the psychology of strategic thinking in game theory.
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Speaker 2: So that's really interesting to me. I always found the
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Speaker 2: traditional economic homo economists of humans as rational calculating profit
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Speaker 2: maximizing actors is just complete contradiction of real life experience.
8:47
Speaker 2: How did you go from your initial interest in behavioral
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Speaker 2: finance into neuroeconomics, where you're looking at the biological underpinnings
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Speaker 2: of what happens as people make decisions.
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Speaker 1: Yeah, So the neuroeconomics to me was sort of a
9:02
Speaker 1: natural extension of behavior economics, which was We're going to
9:06
Speaker 1: grab from any interesting data and different ways of thinking
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Speaker 1: about humans outside of standard economics and kind of pull
9:12
Speaker 1: it in and try to, you know, generate a kind
9:14
Speaker 1: of hybrid. It was almost like an import export business.
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Speaker 1: And I'm going to import some psychology or dicktale or
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Speaker 1: imported from konomon and what is this going to tell
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Speaker 1: us about fairness and reference points and loss aversion what
9:25
Speaker 1: have you? And neur economics seem to me like just
9:27
Speaker 1: another thing to do. Part of it is my personality
9:30
Speaker 1: is kind of like intellectual entrepreneurship. So I liked you know,
9:33
Speaker 1: doing different things. You know, over the years, I've worked
9:36
Speaker 1: on lots of different methods and with different groups of people,
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Speaker 1: and neureconomics was just a chance to do something even
9:41
Speaker 1: more dramatic.
9:43
Speaker 2: And tell us about your patent on active learning decision engines.
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Speaker 2: What on earth is that?
9:50
Speaker 1: So active learning is the commuter scientist term is sometimes
9:53
Speaker 1: called dynamic adaptive learning. For basically, like if I was
9:57
Speaker 1: going to try to figure out how much you like risk,
10:00
Speaker 1: like you were a client, and if a financial advisor
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Speaker 1: is asking you know, I might start by saying, well,
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Speaker 1: here's a portfolio. Is this too risky or not risky enough?
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Speaker 1: And if you say, nah, that's not risky enough, you know,
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Speaker 1: I'd rather go for more, and then I would give
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Speaker 1: you a better one that's a little has a little
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Speaker 1: more risk in it. And in chemistry it's called titration.
10:20
Speaker 1: You know, you kind of change the mixture of the chemicals.
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Speaker 1: And so for each person, you're asking them a dynamic,
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Speaker 1: customized set of questions to get to the best answer
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Speaker 1: as quickly as possible, and that's called active learning. So
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Speaker 1: one of my colleagues at Caltech at that time, Andreas Krauss,
10:37
Speaker 1: was studying he was a Gonna Better scientist. So they're
10:39
Speaker 1: always on the frontier of how to get the truth
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Speaker 1: faster and subject to computational constraints, like you know, because
10:47
Speaker 1: sometimes it's not just a question. I'm getting there, but
10:50
Speaker 1: can you do it in real time so you don't
10:52
Speaker 1: have to wait half an hour, you know, to ask
10:55
Speaker 1: the next highly unformative question. And so the patent was
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Speaker 1: just a a method that Andreas and another guy who
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Speaker 1: now works like Google, I believe Daniel Goldman and me
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Speaker 1: had worked on to apply this in a particular way.
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Speaker 1: And so it was basically a software pattern. There was
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Speaker 1: an Advazon pattern on an algorithm.
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Speaker 2: So you're asking people questions, how do you know they're
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Speaker 2: giving you honest answers? And I asked that question for
11:21
Speaker 2: very specific reasons that will be evident in a moment.
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Speaker 2: How do you know the answers are legitimate?
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Speaker 1: Okay, So in experiment economics, one of the main rules,
11:31
Speaker 1: like a commandment, is we almost always pay people unless
11:35
Speaker 1: we can't, like with children sometimes or what have you.
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Speaker 1: We almost always pay people money or something we know
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Speaker 1: they value based on the decisions they made. So when
11:43
Speaker 1: we do these kind of risk assessments, again not with clients,
11:46
Speaker 1: but say in a simple experiment for modest amounts of
11:48
Speaker 1: money twenty bucks, fifty bucks, what we'll do is we
11:51
Speaker 1: say at the end, we're going to pick one of
11:52
Speaker 1: the things you said you wanted, and we're going to
11:54
Speaker 1: actually play that for money. And if you if you
11:57
Speaker 1: don't tell us what you really wanted, you're gonna get
11:59
Speaker 1: stuck with something.
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Speaker 2: So you're creating an incentive for them to be somewhat honest.
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Speaker 1: Correct.
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Speaker 2: The reason I ask we're recording this about two weeks
12:08
Speaker 2: before the twenty twenty four presidential election. I wrote something
12:12
Speaker 2: a month ago about why polling errors are really a
12:16
Speaker 2: behavioral problem because when you ask people who you're going
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Speaker 2: to vote for, what you're really asking is not just
12:22
Speaker 2: their preference, but hey, you're gonna get your lazy butt
12:26
Speaker 2: off the couch and go to the library and vote.
12:28
Speaker 2: And I assumed, hey, there's an era of five, six
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Speaker 2: seven percent built into that, and that's why polls are
12:34
Speaker 2: so bad. Researching your work about hypothetical bias, I was
12:39
Speaker 2: shocked the data that you came is when you ask
12:43
Speaker 2: people if they're going to vote, about seventy percent say
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Speaker 2: they will, In reality, just forty five percent of them do.
12:49
Speaker 2: That's a massive error of twenty five percent. What value
12:54
Speaker 2: is there in polls when people have no idea what
12:56
Speaker 2: they're really going to do?
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Speaker 1: Yeah, So I mean I think the best post are
13:01
Speaker 1: know that, and so they try to phrase the question
13:03
Speaker 1: or gather some other data. But this is often called
13:06
Speaker 1: acquiescence or yes bias. Right, so you say, people, are
13:09
Speaker 1: you planning to vote? Oh, yeah, I'm planning to vote, Well,
13:11
Speaker 1: you're going to Are you going to not vote because
13:13
Speaker 1: it's too Yeah, I may not vote.
13:14
Speaker 2: What happens if it rains, what happens if you're busy.
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Speaker 1: So you can often get numbers in it up to
13:19
Speaker 1: more than one hundred percent. Are you having to vote? No,
13:21
Speaker 1: you have seventy percent. Yeah, I probably won't vote fifty
13:24
Speaker 1: five percent. That's one hundred and twenty five percent current.
13:26
Speaker 1: The math doesn't math, and you see it. Particularly. One
13:29
Speaker 1: of the things we study was product purchases. So when
13:32
Speaker 1: you show people new products and say, you know, you
13:34
Speaker 1: think you'd be interested in this, you get way too
13:37
Speaker 1: many yes's. And that's one recent new products fail. It
13:40
Speaker 1: is because somebody who's the product champion inside the firm,
13:43
Speaker 1: like in a consumer products company, looks at this polling
13:46
Speaker 1: date and says, see, see you know, give me money
13:48
Speaker 1: to roll this out in a test market. So what
13:52
Speaker 1: one of the things we have done is to try
13:53
Speaker 1: to see if we didn't, we'd wro'te a few papers
13:56
Speaker 1: on this, but I don't feel like we exactly crack
13:58
Speaker 1: the nut was to see if a combination of what
14:02
Speaker 1: people look at if you measure where their eyes are looking,
14:05
Speaker 1: like how often they look back and forth between a
14:08
Speaker 1: price and a product, and maybe brain signals can help
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Speaker 1: us predict when they say, yeah, I'm going to vote,
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Speaker 1: are they really going to vote or not?
14:16
Speaker 2: And neuroeconomics, as as I've learned about it through you
14:21
Speaker 2: is you're putting people in a functional MRI machine. You're
14:24
Speaker 2: asking them a series of questions, and you're identifying what
14:27
Speaker 2: parts of the brain are actually lighting up.
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Speaker 1: Correct exactly so that so and and by the way,
14:32
Speaker 1: ephraimri is glamorous and fantastic, but there's lots of other
14:37
Speaker 1: methode that they are used as well. You know, it's
14:39
Speaker 1: unnatural because people are in this tube. It's very loud.
14:43
Speaker 1: You know, if you want to study a phobic, if
14:44
Speaker 1: you want to study close to probi, you cannot, you know,
14:46
Speaker 1: because the closer roobics won't go in there. But it
14:50
Speaker 1: does give you a picture of the whole brain. And
14:52
Speaker 1: in the in the case of the we need some
14:55
Speaker 1: experiments where we show people to consumer good and in
14:58
Speaker 1: one condition. The first part of the experiment we say,
15:01
Speaker 1: you don't have to actually buy this, but just tell us,
15:02
Speaker 1: you know if it was on sale for this price,
15:04
Speaker 1: like yes, no, strong, yes, weak yess. So we get
15:08
Speaker 1: a four point scale and then we surprise them and say,
15:11
Speaker 1: now we're going to show you some different products and
15:13
Speaker 1: these are going to actually buy. So if you say
15:15
Speaker 1: yes and we choose that one out of this.
15:18
Speaker 2: Bin, you get it.
15:19
Speaker 1: You have you have to buy it. Well, we give
15:22
Speaker 1: you some money and we're going to take the price
15:24
Speaker 1: out and give you the residual money and the product,
15:27
Speaker 1: and you're going to leave here with this product or
15:29
Speaker 1: I think some of them we have we mail it
15:30
Speaker 1: to them at Amazon is something we actually had products
15:33
Speaker 1: there in a box. And so the question is what's
15:37
Speaker 1: going on in the brain when they're seriously thinking about
15:40
Speaker 1: buying something for real versus hypothetical, which is like a survey, right,
15:45
Speaker 1: And what we found was the tricky part is to
15:48
Speaker 1: predict when people say yes, hypothetical, but the brain says no,
15:53
Speaker 1: you know, can you can you see a brain?
15:55
Speaker 2: Can you identify that?
15:56
Speaker 1: Uh? Modestly well?
15:59
Speaker 2: Right?
15:59
Speaker 1: And it turns out the most. There's two interesting markers.
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Speaker 1: One is there's a very old area in the brain,
16:06
Speaker 1: old you know, evolutionary yes, called the mid brain, which
16:10
Speaker 1: is actually where all of the dopamina drid neurons live
16:14
Speaker 1: and then and then connect to middle areas of the
16:18
Speaker 1: brain called basoganglia that are kind of computing reward and value.
16:21
Speaker 1: And then frontal cortex, which is really putting together the
16:24
Speaker 1: modern the modern exactly like it's like a thinking cap
16:27
Speaker 1: on top of the monkey brain. And in the mid
16:31
Speaker 1: brain there's a stronger signal when they say yes and
16:36
Speaker 1: they actually do do yes hypothetical and it's a yes reel.
16:41
Speaker 1: There's a stronger signal than when they say yes hypothetical
16:44
Speaker 1: no real. So it's almost like way upstream in the
16:47
Speaker 1: brain if if if in that region they say yes,
16:52
Speaker 1: I'm gonna buy it hypothetically, there's enough activity they're gonna
16:55
Speaker 1: buy it.
16:56
Speaker 2: So my general sense of this, and I'm curious as
17:00
Speaker 2: to how you what the reality is, my sense of
17:03
Speaker 2: it is, on the one hand, people are social animals
17:06
Speaker 2: and they want to be agreeable and say yes to people.
17:10
Speaker 2: On the other hand, we really don't know what the
17:13
Speaker 2: hell we want, especially if you're talking about something six
17:16
Speaker 2: months from now. I guess the tricky part is how
17:19
Speaker 2: do you get people in MRI machines when you have
17:22
Speaker 2: a question for them. We can't even get people to
17:24
Speaker 2: pick up their phone to answer polls. How difficult is
17:28
Speaker 2: it to get subjects to go through this process? Or
17:31
Speaker 2: are these all mostly undergraduates and you know their lab rats?
17:34
Speaker 2: You can do whatever you want.
17:35
Speaker 1: Some of them are undergraduates, although in Caltech they're very
17:39
Speaker 1: unusual human beings because they're actually useful. They're very useful
17:44
Speaker 1: labrats who pay for economics because the media and matthe
17:47
Speaker 1: Is et Is eight hundred, they're the most mathematically skilled except.
17:51
Speaker 2: For that's a perfect score, isn't it?
17:52
Speaker 1: Like exactly, that's the perfect score, like Harvey mud Mit.
17:55
Speaker 1: There are other places that have, you know, similarly hyper
17:58
Speaker 1: analytical kids. So if like if they can't do something
18:02
Speaker 1: like a computation easily, nobody can. So it's very useful
18:07
Speaker 1: establishing like bounds on rationality. You know that people. We
18:10
Speaker 1: often get critiques like well, you wouldn't get bubbles if
18:13
Speaker 1: people were smart enough, Like, well, we have the smartest
18:16
Speaker 1: people and you get bubbles.
18:18
Speaker 2: It's got less to do with the frontal cortex and
18:20
Speaker 2: intelligence and everything with that, the limbic system and the
18:24
Speaker 2: lizard brain back.
18:25
Speaker 1: Yes, exactly, so they have they have all the things
18:27
Speaker 1: in the brain they have, they have other skills that
18:29
Speaker 1: are cordically expressed. But so in a lot of these
18:33
Speaker 1: MRI studies we also use. We work pretty hard actually
18:36
Speaker 1: to get regular folks from the community who and who
18:40
Speaker 1: you know are different ages. You know, we we don't
18:42
Speaker 1: really have a representative sample, although you could, you could
18:45
Speaker 1: try to get pretty close in southern California, and then
18:49
Speaker 1: we we we almost always never do a study this
18:51
Speaker 1: just take outing undergrads because we worry about the robustness
18:54
Speaker 1: across right, It is true in the case of something
18:57
Speaker 1: like trying to get brain signals to break when people
19:00
Speaker 1: will actually buy products. The other type of study we've
19:03
Speaker 1: used to involves eye tracking and things like that, and
19:05
Speaker 1: it turns out that when when you ask people hypothetical questions,
19:09
Speaker 1: would you buy that? You don't really have to buy this,
19:11
Speaker 1: but would you, they just don't look at the price
19:13
Speaker 1: that much, and when they're really shopping, they really look
19:17
Speaker 1: at the price. So one way to tell whether people
19:19
Speaker 1: are being serious in expressing a genuine what I'm going
19:23
Speaker 1: to really do, it is just something like how much
19:26
Speaker 1: time they spend looking at the price and looking back
19:28
Speaker 1: and forth. And there may be other like if if
19:32
Speaker 1: if it was consumer products company was trying to use
19:35
Speaker 1: FROMRI or other methods. There are others that are much
19:38
Speaker 1: more portable, like EEG, and you can get a pair
19:41
Speaker 1: of glasses you walk around and it, you know, it
19:44
Speaker 1: records where your eyes looking. So there are there are
19:46
Speaker 1: things you can do outside of the confines of a
19:49
Speaker 1: campus lab. I think we would just look for things
19:52
Speaker 1: that are that are easy, easily seen biomarkers of this
19:56
Speaker 1: mid brain activity of f MRI, because we're never gonna
19:59
Speaker 1: be able to do that, you know, at scale in
20:01
Speaker 1: a shopping mall or something.
20:03
Speaker 2: So let's go through each of these. We know what
20:05
Speaker 2: fMRI is, right, you're in an MRI machine. EEG and SCR.
20:10
Speaker 2: Tell us what those do.
20:11
Speaker 1: So eg's electro and cephalography and it's basically all the
20:15
Speaker 1: little things on your electrodes. If your ball like me,
20:19
Speaker 1: that's good for seasons. You know, if you're a supermodel
20:23
Speaker 1: with big puffy Texas beauty pageant hair, then no good,
20:27
Speaker 1: no good.
20:28
Speaker 2: So you're measuring electrical activity in the brain, and you
20:31
Speaker 2: could really specify where it is by you know, just
20:35
Speaker 2: triangulating with all the different leads that you have spect.
20:39
Speaker 1: Exactly so the you know, you can put sixteen to
20:42
Speaker 1: one hundred and twenty eight different electrodes. The signals are
20:45
Speaker 1: very weak, but the advantage of EG is it's really fast.
20:49
Speaker 1: So if you want to study something like thinking fast
20:51
Speaker 1: and slow, you know, like if I show you a
20:53
Speaker 1: picture of a person, you have a snap reaction that
20:55
Speaker 1: they're scary or they're someone you want to vote for,
20:58
Speaker 1: then FRI is too slow because it measures these blood
21:01
Speaker 1: flow signals that take like one or two seconds to
21:03
Speaker 1: show up.
21:04
Speaker 2: But like one, one or two seconds is too slow for.
21:08
Speaker 1: You know, a lot is going on in the in
21:12
Speaker 1: the first two seconds where people are thinking out of
21:14
Speaker 1: a decision that's really interesting necessarily you know, which mortgage
21:18
Speaker 1: to finance, their refinance their house in.
21:20
Speaker 2: Or literally system one thinking fast is exactly.
21:24
Speaker 1: So it's the term psychologist. Social psychology use is also
21:27
Speaker 1: called thin slicing, which is that and the thin slices
21:30
Speaker 1: on the order of meaning a very aggregate, somewhat confident
21:34
Speaker 1: judgment is made within you know, ten seconds, thirty seconds,
21:38
Speaker 1: there's a big literature, and we're interviewing about this that,
21:42
Speaker 1: you know, face to base interviewing. Unless you're really trained
21:45
Speaker 1: to have a comparable interview for different people, you know,
21:49
Speaker 1: the first couple of minutes of the interview, you're kind
21:51
Speaker 1: of making up your mind. At least a lot of
21:54
Speaker 1: studies indicate that.
21:55
Speaker 2: And SCR is what so SCR.
21:57
Speaker 1: Skin conducted response, also called galvanic skin response. And so
22:02
Speaker 1: basically it turns out when people are aroused in any
22:07
Speaker 1: any direction, it doesn't tell you good or bab, but
22:09
Speaker 1: it just tells you arousal. You have this detectable increase
22:12
Speaker 1: in sweating you can measure in the fingers.
22:15
Speaker 2: So and in all of these things, you're actually taking measurements,
22:19
Speaker 2: not asking people things. And one of the quotes that
22:23
Speaker 2: caught my attention. Since most of our brain activity goes
22:27
Speaker 2: on without our awareness subconsciously, we cannot solely rely on
22:31
Speaker 2: individual's accounts when analyzing their behavior. How important is the
22:37
Speaker 2: concept of the subconscious to neuroeconomics.
22:41
Speaker 1: It's pretty important. So the saying we use is sometimes
22:44
Speaker 1: you want to ask the brain rather than ask the person,
22:46
Speaker 1: uh huh. And there's some there's some extreme ways in
22:50
Speaker 1: which that works. For example, if I show a face
22:52
Speaker 1: of somebody who's expressing fear but only for thirty milliseconds,
22:56
Speaker 1: which is one movie frame right right, and then a
23:00
Speaker 1: mask when you're meeting another face right on top that's neutral,
23:04
Speaker 1: or in another condition, I show a happy face, very enthusiastic,
23:07
Speaker 1: and then neutral mask. If you ask people did you
23:09
Speaker 1: see a happi or fearful face, they say, like, I
23:13
Speaker 1: have no idea, I didn't see I didn't see either one.
23:15
Speaker 1: But if you look at amigdal activity, which is a
23:18
Speaker 1: region that's known to be rapidly detecting potential threats and
23:23
Speaker 1: including fear, the amignal activity will respond to fear, not
23:28
Speaker 1: in thirty milliseconds, not not happiness in the same way.
23:31
Speaker 1: So the brain knows, it's just that it doesn't get
23:35
Speaker 1: to the like the publicists desk, you know, good consciousness.
23:40
Speaker 2: So I'm so glad you said it that way. So
23:41
Speaker 2: don't ask the person, ask the brain. How do you
23:44
Speaker 2: think of the different parts of the brain. So obviously
23:48
Speaker 2: the amygdala and any of the is it fair to
23:51
Speaker 2: say that's part of the limbic system. Yes, So when
23:55
Speaker 2: you're talking about the publicist. What portion of the brain
23:59
Speaker 2: we just discussing, Well.
24:01
Speaker 1: In terms of sheer territory, it's probably not very much.
24:07
Speaker 1: Four brain, hind brain were prefrontal cortex would be. And
24:13
Speaker 1: there's a lot of sensory procection that's going on, you know,
24:16
Speaker 1: pre conscious or like before we could say, you know,
24:21
Speaker 1: motion to something or use words to explain what's going on.
24:24
Speaker 1: I think it's it's it's genuinely hard to pin down
24:27
Speaker 1: a number. Like you know, if I read, for example,
24:29
Speaker 1: it's ninety percent subconscious and ten percent conscious, I don't
24:32
Speaker 1: know if that's right, and it may vary across life cycle.
24:39
Speaker 1: So you know, we usually were reluctant to pin down
24:43
Speaker 1: a number. I think it's fair to say that there's
24:44
Speaker 1: a lot of things that are going on we usually
24:46
Speaker 1: say implicitly that are not People aren't explicitly aware of
24:50
Speaker 1: enough enough to make it very interesting.
24:52
Speaker 2: So whenever I hear people talk about, you know, things
24:54
Speaker 2: happening within the brain that you're not aware of, I
24:57
Speaker 2: always think of the split brain experiments and tell us
25:01
Speaker 2: a little bit, what does that reveal about our decision
25:04
Speaker 2: making process.
25:05
Speaker 1: So the split brain was actually first explored by Roger
25:08
Speaker 1: Sperry at Caltech actually in his student Mike Zaniga, you know,
25:12
Speaker 1: made a big chunk of career over out of it.
25:16
Speaker 1: And so the split brain patients means they don't have
25:19
Speaker 1: much communication between left and right hemispheres.
25:21
Speaker 2: Corpus colosum is that right, So these are the one
25:27
Speaker 2: I remember was it was some seizure or epilepsy, and
25:32
Speaker 2: they found cutting that stop the seizures. But then your
25:36
Speaker 2: left brain and your right brain don't really communicate anymore exactly.
25:39
Speaker 1: So for example, so if you have a breakdown of
25:42
Speaker 1: corpus closum, the right and left aren't really communicating despite
25:47
Speaker 1: the right brain left brain. Most modern ner as signists
25:50
Speaker 1: don't think there's that much specialization. There's some interesting kinds,
25:53
Speaker 1: but one kind that's pretty rugged is languages mostly in
25:58
Speaker 1: the left brain and regions called broke because area of
26:00
Speaker 1: Wernicke's area. And we know that because you know, when
26:03
Speaker 1: you have specialized damage in that area, you can see
26:05
Speaker 1: people start to talk differently, like they can remember they
26:08
Speaker 1: can't remember words.
26:09
Speaker 2: But the aphasia, yeah, I remember reading about people who
26:12
Speaker 2: can speak, could write, but couldn't read. Just all sorts
26:15
Speaker 2: of wacky things happen when when those two areas are
26:18
Speaker 2: down correct exactly.
26:19
Speaker 1: So there are these very localized, pretty well understood A phaseias
26:22
Speaker 1: that have to do with local damage. So there's there's
26:26
Speaker 1: often what we call plasticity where another part of the
26:28
Speaker 1: brain will take over. So if you had some damage
26:30
Speaker 1: as a young child, it might be that the A phaseia,
26:33
Speaker 1: you know, another another part of their brain like takes
26:35
Speaker 1: over that function. But if it happens later in life,
26:38
Speaker 1: not so anyway. So language is somewhat specialized the left region. So,
26:42
Speaker 1: for example, if someone with a and the sensory systems
26:47
Speaker 1: are contralateral, so the right side of the brain sees
26:49
Speaker 1: the left side of a picture, left side sees the
26:52
Speaker 1: right side. So suppose I show you on the left
26:55
Speaker 1: of a picture a picture of a friend of yours,
27:00
Speaker 1: and I asked the person, if you see this friend
27:04
Speaker 1: of yours, what might what what gesture might you do?
27:06
Speaker 1: Or what might you if you see a friend here
27:08
Speaker 1: as opposed to a house or a shovel, what would
27:10
Speaker 1: you do? And the person waves their hand and then
27:14
Speaker 1: you ask them why did you wave your hand? Now,
27:16
Speaker 1: the left side of the brain has to answer the
27:18
Speaker 1: question because that's the language area. But the left side
27:21
Speaker 1: doesn't know that the right side saw a friend and
27:24
Speaker 1: that's why they waved. So the left side makes stuff.
27:27
Speaker 2: Up, confabulates an explanation for why they're waiting exactly.
27:31
Speaker 1: It's like the publicist for you know, for a very
27:33
Speaker 1: guilty person and or Mike is not get calls it
27:37
Speaker 1: the interpreter. So the interpreter says, I don't really know why,
27:41
Speaker 1: so I'll kind of make give a plausible answer, and
27:43
Speaker 1: they'll say something like, oh, I saw somebody I knew
27:46
Speaker 1: walking by out the window outside. So that's an example
27:51
Speaker 1: of where we know what the brain saw and why
27:54
Speaker 1: the wave occurred, but the left part of the brain
27:56
Speaker 1: doesn't know that.
27:58
Speaker 2: That's really that's really fascinating. Let's stay with the idea
28:01
Speaker 2: of tracking eye movement. So you could do this with glasses.
28:05
Speaker 2: You can do with this with a computer. When you're
28:08
Speaker 2: tracking eye movement, asking people about, hey, would you purchase
28:12
Speaker 2: this product? How big of a tael is it? When
28:15
Speaker 2: they look at the price and is it something they
28:18
Speaker 2: just kind of glance at or is it a repeated
28:20
Speaker 2: and obvious they're focusing on the cost.
28:23
Speaker 1: There's there's sort of two interesting markers. For number one,
28:26
Speaker 1: it's not that big of a tell. So if we
28:28
Speaker 1: try to predict whether they're going to actually buy something,
28:31
Speaker 1: we might get say forty two percent right, and with
28:34
Speaker 1: the eye tracking data it might get up to like
28:37
Speaker 1: fifty four, you know. So as academics we think that's
28:42
Speaker 1: kind of a modest effect size. Now, if you're running
28:44
Speaker 1: a business and you want a two percent lift and
28:47
Speaker 1: purchase maybe a billion dollars, right, So sometimes we're a
28:51
Speaker 1: little cautious as academics about is this a big deal
28:53
Speaker 1: or not? I'm going to where's some of these things?
28:55
Speaker 1: The same in the world of nudges and so on.
28:57
Speaker 1: Sometimes a small you know what, a half increase and
29:00
Speaker 1: get out the vote. If we could do that, you know, scientifically,
29:04
Speaker 1: may well decide an election. Right anyway, So the lift
29:08
Speaker 1: is not that big, but the two taels are basically
29:10
Speaker 1: looking at the price, and the other is refixation, which
29:14
Speaker 1: basically means not just looking once but going back and forth.
29:18
Speaker 1: You know. It's the it's the rapid brain equivalent on
29:21
Speaker 1: a one or two second basis of say a couple
29:24
Speaker 1: who's shopping for a house, going to look at a
29:26
Speaker 1: second time and a third time, you know, the repeated looking.
29:29
Speaker 2: Right, usually a good signal exactly tells you the serious Huh.
29:33
Speaker 2: That's really interesting. So give us some examples of what
29:37
Speaker 2: the studies or the experiments look like. When you're doing
29:41
Speaker 2: eye tracking, What are you trying to What parts of
29:44
Speaker 2: the brain are you looking at? Or is it just
29:46
Speaker 2: the eye tracking? Is it is this by itself or
29:50
Speaker 2: can you combine this with other types of neuroeconomics.
29:54
Speaker 1: Yeah, So, actually the eye trackers we use, which are
29:57
Speaker 1: commercially made for some iis basically and sometimes for clinical use,
30:03
Speaker 1: they use cameras to look at what the where the
30:06
Speaker 1: eye is looking and they sync that up with where
30:08
Speaker 1: on the computer screen you're looking. And so besides the
30:13
Speaker 1: location of where the eyes are looking, you also measure
30:16
Speaker 1: pupil dilation. And pupil dilation turns out to be, you know,
30:20
Speaker 1: the eyes that they went into the soul, so that
30:21
Speaker 1: the pupils actually generate a lot of information. Although it's
30:26
Speaker 1: it's crude, what the people dilation is telling you is
30:29
Speaker 1: about cognitive difficulty. Am I having a hard time thinking
30:32
Speaker 1: about this? And arousal, which again may be negative or positive.
30:36
Speaker 2: It's like, so wide pupil is you aroused.
30:41
Speaker 1: Exactly exactly? And so I think if you train yourself
30:46
Speaker 1: and maybe depending on the color of the eyes, you
30:49
Speaker 1: might be able to tell, like a poker player might
30:51
Speaker 1: be able to train themselves with a to notice pupil dilation,
30:56
Speaker 1: but just in case. That's why poker players often will
30:58
Speaker 1: wear glasses because these unglasses, right, Because the idea is,
31:03
Speaker 1: if you look at your cards and you have two
31:05
Speaker 1: ass your people will dilate like and it might be
31:09
Speaker 1: hard to see with the naked eye, but the machines
31:11
Speaker 1: we use can definitely see it. That would be a
31:13
Speaker 1: big jump, you know, a big tell. And so we're
31:15
Speaker 1: able to use people dilation and I tracking to judge
31:19
Speaker 1: things like cognitive difficulty. A lot of the early studdies
31:22
Speaker 1: actually were used in game theory because in game theory,
31:25
Speaker 1: the assumption is if I might want to see what
31:28
Speaker 1: my opponent's payoff is in order to decide what they're
31:31
Speaker 1: going to do. And if you ask people what are
31:34
Speaker 1: you looking at on this computer screen? You know, there's
31:36
Speaker 1: there's a four by four matrix of numbers, and I'm
31:39
Speaker 1: trying to think of what you're going to do. There's
31:41
Speaker 1: a lot to look at. And if you ask people
31:43
Speaker 1: for a self report, they're not going to tell you
31:45
Speaker 1: exactly what their eyes are during the whole time, they're
31:47
Speaker 1: probably looking at forty two different things, sometimes very quickly.
31:51
Speaker 1: Sometimes they're going back and looking again and again and again.
31:54
Speaker 1: They just don't have conscious access to that process the
31:57
Speaker 1: way that the eye tracking does.
31:59
Speaker 2: So that's really fascinating that speaking to the brain but
32:03
Speaker 2: not the person gives you a whole lot more insight
32:06
Speaker 2: into the decision making process. Speaking generally, what does this
32:12
Speaker 2: tell us about people, as you know, rational profit seeking
32:20
Speaker 2: actors in the world of finance and investing.
32:24
Speaker 1: I think it's useful to think about, say, young naive investors,
32:28
Speaker 1: or that they may to be young, but people who
32:30
Speaker 1: have less knowledge about the markets, and people who spend
32:33
Speaker 1: a lot more time thinking about estimating fundamentals reading ten K's,
32:39
Speaker 1: you know, having years of trading experience. Because another important
32:43
Speaker 1: facts which we try to keep track of and biro
32:46
Speaker 1: economics is that a lot of decisions and structures people
32:51
Speaker 1: have to make are not things that we're necessarily evolved
32:54
Speaker 1: to be particularly good at. But people are also extremely
32:57
Speaker 1: good at learning and able, you know, to like collect
33:01
Speaker 1: memories and distill things into into knowledge. So let me
33:05
Speaker 1: turn to the concept of price bubbles because I think
33:07
Speaker 1: that's a useful one. So we have a couple of
33:10
Speaker 1: one fMRI study on price bubbles, and we have some
33:12
Speaker 1: new stuff that includes skin conductive's measurement to see if
33:16
Speaker 1: you know, can you kind of predict when a crash
33:18
Speaker 1: is coming from people's hands, you know, reflecting nervousness. It
33:21
Speaker 1: looks like we can predict a little, but not great.
33:24
Speaker 1: You know, that's a high mountain to climb. What we
33:27
Speaker 1: found in our first fMRI study about bubbles was people
33:31
Speaker 1: trade an artificial asset, so we know the value, the
33:34
Speaker 1: fundamental value the asset, which we never know in you know,
33:37
Speaker 1: in natural markets, and that the price is completely what
33:41
Speaker 1: they agree upon. So typically what happens is that the
33:44
Speaker 1: fundamental value is a number that we control, which happens
33:48
Speaker 1: to be fourteen, and the value the asset comes from
33:51
Speaker 1: the fact that if you hold at the end of
33:53
Speaker 1: a period of trading, you get a dividend, or you
33:56
Speaker 1: can invest currency in risk free bonds, and so the
34:00
Speaker 1: trade off between the risk free earnings and the value
34:03
Speaker 1: of the dividends establishes an equlibrium price. It's a very
34:06
Speaker 1: simple equation, and typically the price starts around fourteen, it
34:11
Speaker 1: goes up to maybe twenty or thirty and then crashes.
34:14
Speaker 1: And then in order to bring the experiments to a close,
34:18
Speaker 1: we have them trade for fifty periods or thirty periods,
34:20
Speaker 1: and at the end they were able to cash the
34:22
Speaker 1: assets out at fourteen.
34:24
Speaker 2: So what would you pay for an asset that you'll
34:26
Speaker 2: get fourteen for correct after a series of dividends thirty
34:30
Speaker 2: or fifty trading periods exactly.
34:32
Speaker 1: And so put yourselves in the mind, said of somebody
34:35
Speaker 1: who in period thirty one the price is sixty, and
34:39
Speaker 1: you kind of know that in period fifty nineteen periods
34:42
Speaker 1: from now it's going to be fourteen. So well, unless
34:46
Speaker 1: you think it's going to go up to seventy five, right,
34:49
Speaker 1: So it's true. In fact, that's very helpful for me.
34:54
Speaker 1: So what we found from the brain was that there's
34:57
Speaker 1: two interesting signals, soh sort with the more interesting one.
34:59
Speaker 1: The other one's a little more obvious. The interesting signal
35:02
Speaker 1: is people who sold before the bubble crash, which is
35:06
Speaker 1: the smart thing to do. And again, the bubble crash
35:08
Speaker 1: is not announced. It's something you only see a store
35:10
Speaker 1: cooking back and of your mirror, right.
35:14
Speaker 2: Same in natural markets exactly.
35:15
Speaker 1: Just like a national markets, right, bubbles are only shown
35:18
Speaker 1: in hindsight. Gene Falum has written a lot about this.
35:20
Speaker 1: That's one reason he's skeptical that we should even talk
35:23
Speaker 1: about bubbles, you know, as a scientific phenomens.
35:25
Speaker 2: Okay, I think it goes too far with that, but anyway, anyway.
35:28
Speaker 1: Yeah, you know what I mean. So it turns out
35:30
Speaker 1: the people who are more likely to sell when the
35:33
Speaker 1: price is at sixty and we know it's going to crash,
35:35
Speaker 1: but we're not sure when have heightened activity and insular cortex,
35:40
Speaker 1: which is another region that's involved in emotion and interception.
35:44
Speaker 1: So interception means.
35:46
Speaker 2: Knowing what's going on on the inside of your own body,
35:49
Speaker 2: like a self awareness exactly.
35:50
Speaker 1: So perception is the outside world. Interception is the brain's
35:54
Speaker 1: like the body's ambassadorship to the brain, you know, knowing
35:57
Speaker 1: if I'm nervous or And it's often activated by, particularly
36:02
Speaker 1: by negative emotions. So if you see something disgusting, insula,
36:06
Speaker 1: if you choke a person a little bit, or you
36:08
Speaker 1: you know, you cut off the oxygen, not so it's dangerous,
36:11
Speaker 1: but just to make them uncomfortable. Insula, financial uncertainty, the insula.
36:14
Speaker 1: And so we think of the insula is the early
36:17
Speaker 1: warning signal that there's going to be a crash. And
36:20
Speaker 1: the other interesting brain region is nucleus the cummins, which
36:23
Speaker 1: is basically a reward center and it's called straightum part
36:28
Speaker 1: of basoganglia in the very center of the brain, and
36:31
Speaker 1: that's active in the people who are fueling the bubble.
36:34
Speaker 1: Like when the bubbles, you know, forming the people who
36:38
Speaker 1: have the highest nucleus incumbans activity by the most.
36:41
Speaker 2: So you have a run of traders participating in this,
36:44
Speaker 2: and you could tell by the brain activity who's contributing
36:48
Speaker 2: to the bubble and who's saying, this is getting crazy.
36:51
Speaker 2: I want to take my chips off the tails now.
36:54
Speaker 1: Number one, we can't tell with exquisite precision. You know,
36:58
Speaker 1: you can sort of see these groups, and we're only
37:00
Speaker 1: looking at this X post. So I think it's it's
37:03
Speaker 1: conceivable but challenging to do this in real time, you know.
37:07
Speaker 1: So there's you're watching the market unfold. You're doing real
37:10
Speaker 1: time from eye measurement that can be done, and it's like, okay,
37:14
Speaker 1: traders seven, nine and eleven, you know, we think they're
37:17
Speaker 1: probably going to sell. There the skeptics, they're the bulls
37:20
Speaker 1: and fourteen, seventeen and twenty one. Their new kidcombanis Activity.
37:24
Speaker 1: Seems they're really all in. They're going to be forming
37:27
Speaker 1: the bubble and so on and so on. I mean,
37:28
Speaker 1: we're a few steps away from be able to do it,
37:30
Speaker 1: but we see these what we call proofs of concept
37:33
Speaker 1: like it can be done. It may take a few
37:36
Speaker 1: million dollars if any donors are listening.
37:39
Speaker 2: But it makes perfect sense that that is possible. Different
37:42
Speaker 2: parts of the brain are responding to different inputs, and
37:47
Speaker 2: it's consistent with what we've observed amongst sure, you know,
37:51
Speaker 2: just various investors and traders. There are people with as
37:56
Speaker 2: the you know, in the latter stages of a bull market,
38:00
Speaker 2: they think it's just going to keep going forever and
38:02
Speaker 2: they pile in. And the flip side of that, there
38:06
Speaker 2: are people the famous irrational zuberance speech by Alan Greenspan
38:11
Speaker 2: in nineteen ninety six. You still had a ton of
38:14
Speaker 2: gains until the March two thousand and top. So some people,
38:19
Speaker 2: I'm just curious what drives that. Now that you know
38:23
Speaker 2: what to look for and how to measure it in
38:26
Speaker 2: traders in real time, what do you think is the
38:29
Speaker 2: underlying drivers of whether a person is going to be
38:32
Speaker 2: participating in one tribe or the other.
38:36
Speaker 1: That's a great question. I'll say a little tiny bit
38:38
Speaker 1: more about that. You mentioned the term irrational exuberance, which
38:42
Speaker 1: was coined, as I recall, by Bob Shiller in his
38:44
Speaker 1: book about.
38:46
Speaker 2: I think it was from the Irrational Zuberant speech. Schiller
38:51
Speaker 2: may have helped Greenspan with that speech, if I'm remembering,
38:56
Speaker 2: because I've seen I've seen both, whether it was Schiller's
38:59
Speaker 2: phrase or green Span.
39:00
Speaker 1: It maybe it maybe, you know, it's kind of some
39:03
Speaker 1: you know, some apocryphal. You know, we're not sure exactly
39:06
Speaker 1: who said it first, but certainly there was a kind
39:08
Speaker 1: of meeting of the minds that this was useful. And
39:10
Speaker 1: in fact, when we didn't we use the phrase in
39:12
Speaker 1: our paper, but we didn't put it in the title.
39:14
Speaker 1: It just seemed a little too unscientific. It's okay for
39:17
Speaker 1: USA today or something. But this is the proceedings of
39:20
Speaker 1: the National Academy of Sciences, you know, And but we
39:23
Speaker 1: think of this nucleus incumbents activity. That's that's the measure
39:26
Speaker 1: of irrational exuberance. And the irrational part is, you know,
39:30
Speaker 1: when it's too high, you're gonna end up paying a
39:33
Speaker 1: high price for something it crashes fast. So this irrational
39:37
Speaker 1: is really in there. Literally. But yeah, and and also
39:41
Speaker 1: we when I present this in academic summers and later
39:45
Speaker 1: today i'm meeting some Caltech people, we talk about this
39:48
Speaker 1: famous saying from Warren Buffett. I believe when people are afraid,
39:52
Speaker 1: be greedy, when people ready be afraid. And this brain
39:56
Speaker 1: areas like insula is similar to fear and greed and inclusivecumbents.
40:00
Speaker 1: You know, it's about as close you're going to get
40:03
Speaker 1: to brain areas matching what Warren Buffett had to say,
40:06
Speaker 1: which was such a wise thought.
40:08
Speaker 2: So you really kind of answered the question I was
40:11
Speaker 2: about to ask, which is why has behavioral economics been
40:15
Speaker 2: so successful describing decision making where traditional economics seems to
40:21
Speaker 2: have faltered. But what you're really saying is we don't
40:25
Speaker 2: know what's going on in our brain when we're making
40:27
Speaker 2: decisions as individuals, And when you look underneath the hood,
40:32
Speaker 2: it turns out there's a lot more things happening than
40:35
Speaker 2: at least classical economics seems to imply.
40:38
Speaker 1: Yes, exactly exactly. And also, this isn't something we've carefully researched,
40:43
Speaker 1: but I think it's a good speculation for your audience,
40:46
Speaker 1: which is like when I was going to Chicago in
40:48
Speaker 1: the late seventies, all my gratitude and friends were also
40:51
Speaker 1: kind of critics of Nobody liked Bay of economics at
40:54
Speaker 1: that time, though oh really, oh yeah it was you know,
40:58
Speaker 1: people said things like I think, you know, I'm worried
41:00
Speaker 1: you might be ruining your career because you switched out
41:02
Speaker 1: of finance and well, and what it was was there
41:06
Speaker 1: was a series of critical questions which were, but if
41:10
Speaker 1: people make all these mistakes, couldn't someone profit from you know,
41:14
Speaker 1: arbitrage or from selling them crappy goods, Like well, it
41:18
Speaker 1: seems like that may happen, you know, Or if people
41:21
Speaker 1: make these mistakes, don't they learn over time not to
41:23
Speaker 1: make mistakes? That may also happen, and maybe that there's
41:25
Speaker 1: a sucker born every minute. But there's a you know,
41:27
Speaker 1: a generational process, and markets are always filled with some
41:31
Speaker 1: combination of new investors or you know, sovereign funds of
41:35
Speaker 1: people who aren't very savvy about markets or something like that.
41:39
Speaker 1: So early in the history of behaval economics, there was
41:41
Speaker 1: really a lot of hostility about it, and then we
41:44
Speaker 1: gradually one thing about Chicago, and the economics profession in
41:48
Speaker 1: general is data do win arguments, so ideology will often persist,
41:54
Speaker 1: like for Gene Fama, for example, he's he'll always be
41:57
Speaker 1: skeptical about behavioral finance for his own reasons, and you know,
42:02
Speaker 1: their ideas. But eventually data went arguments, and there were
42:06
Speaker 1: you know, there were just so many anomalies and ways
42:09
Speaker 1: in which investors were making mistakes. And it wasn't just
42:12
Speaker 1: small investors, you know, who were refinancing their mortgage mistakenly.
42:16
Speaker 1: It was you know, some of these implicit things maybe
42:19
Speaker 1: very big, you know, like venture capitalists joked about how
42:23
Speaker 1: well you know when I think of Mark Zuckerberg and
42:25
Speaker 1: a hoodie, and that's kind of my template for a
42:28
Speaker 1: good founder to invest tens of millions of dollars. Hey, like,
42:32
Speaker 1: that's not as sophisticated, that's not how economics.
42:35
Speaker 2: And I recall reading one of the papers Bob Schiller
42:39
Speaker 2: wrote was looking at divinend yield and saying, if if
42:42
Speaker 2: markets are fully pricing in all data, why does this
42:47
Speaker 2: divin and yield swing around so much? It should be
42:49
Speaker 2: much more consistent than this correct, but apparently it's not.
42:54
Speaker 2: I just I was very amused by Fama and Schiller
42:57
Speaker 2: being awarded the Nobel together. It's almost says if the
43:00
Speaker 2: committee said, look, markets are kind of efficient, and except
43:04
Speaker 2: when they go crazy, you two guys work it out.
43:07
Speaker 1: Yes, yeah, yeah, it was quite a It was kind
43:10
Speaker 1: of a charming and I think sensible award for that reason.
43:13
Speaker 1: And the you know, the journalist said like, well is
43:17
Speaker 1: there you know, one person says a is true, one
43:19
Speaker 1: says A is not always true? Like how could you
43:21
Speaker 1: give that award? The answers they both made made a
43:24
Speaker 1: lot of progress, you know, in different ways.
43:27
Speaker 2: Let's talk about some of the other ways that we
43:31
Speaker 2: can look inside. Are we looking at things like adrenaline
43:35
Speaker 2: or dopamine or any of the sort of hormones that
43:38
Speaker 2: seem to affect our behavior when when we're trying to
43:42
Speaker 2: analyze decision making.
43:43
Speaker 1: Yeah, so, actually that's a very good question, Barry. New
43:48
Speaker 1: economics uses a lot of different methods. The fMRI is
43:51
Speaker 1: sort of like, you know the movie star and a
43:54
Speaker 1: family with four sisters, you know, the glamorous one that
43:56
Speaker 1: everyone pays attention to, but it's actually high maintenance. And
43:59
Speaker 1: then but all the other siblings are you know, kind
44:02
Speaker 1: of contributing in some interesting way. So pharmacology is something
44:05
Speaker 1: people are really interested in.
44:07
Speaker 2: Meaning specifically pharmacology drugs that aren't yes, pharmacolo.
44:11
Speaker 1: So pharmacology is drugs, but some of those, for example,
44:14
Speaker 1: el dopa will actually ramp up dopamine levels and you
44:18
Speaker 1: can see if some interesting things happen.
44:20
Speaker 2: El Dopa is a drug you can consume correct in
44:23
Speaker 2: order to raise your dopamine exactly.
44:25
Speaker 1: So it's it's al Dopa's basically administrative. So Parkinson's patients
44:29
Speaker 1: have a degradation of dopamine and so to kind of
44:33
Speaker 1: ramp them up to normal levels. Al dopa is often
44:36
Speaker 1: used in treatment.
44:37
Speaker 2: Pharmacology is one what are some of the other forces,
44:41
Speaker 2: So we.
44:41
Speaker 1: Do look at neurotransmitters like oxytocin, argoniine Vasopressint is one
44:45
Speaker 1: that we've studied.
44:46
Speaker 2: Isytocin sounds a lot like OxyContin any correct overlap.
44:51
Speaker 1: No exactually, so oxytocin is is sometimes called as like
44:56
Speaker 1: an affiliation hormone. So for example, if you get a
45:00
Speaker 1: really pleasurable massage, you might feel a surge of oxytocin.
45:04
Speaker 1: When my wife was giving birth, they often to induce labor.
45:09
Speaker 1: They often give somebody synthetic oxytocin, and oxytocin is also
45:14
Speaker 1: produced after birth and when the mom is first coming,
45:16
Speaker 1: the baby and probably the dad, although maybe less. You know,
45:20
Speaker 1: it's this very pleasurable thing that makes you want to
45:22
Speaker 1: like hug somebody and feel affiliated. It's affiliated. It's this
45:26
Speaker 1: sort of bio term. So there's a bunch of studies
45:29
Speaker 1: on oxidosins yesting that improved trust. But there's a cautionary tale,
45:34
Speaker 1: which is me and some colleagues went back and looked
45:37
Speaker 1: at those carefully, and it seems that giving people artificial
45:41
Speaker 1: is giving people oxytocin for a modest dose and then
45:45
Speaker 1: see what happens, you know, an hour later. It improves
45:48
Speaker 1: trust a little bit, but it's scientifically very very tricky
45:54
Speaker 1: and some of the standard results if you do the
45:56
Speaker 1: same exact experiment over again, you just don't always get
45:59
Speaker 1: the same result. So we don't know how sturdy oxytocin is.
46:03
Speaker 2: What What are some of the other chemicals you mentioned
46:04
Speaker 2: neurotrano When.
46:06
Speaker 1: We studied I'll say a little bit of was argonon vasopressin,
46:09
Speaker 1: So that's another hormone which is similar to oxytocin, and
46:13
Speaker 1: that when when animals are bonding in groups, this organon
46:18
Speaker 1: vasopressant sort of you know, you'll get a surge and
46:21
Speaker 1: it shows that.
46:22
Speaker 2: So when you say bonding in groups, I'm thinking of
46:24
Speaker 2: a wolf pack or a hyena pack, where yes, they're
46:27
Speaker 2: cooperative species that work together, and there are chemicals that
46:33
Speaker 2: contribute to that. Is that Is that what we're suggesting exactly?
46:35
Speaker 1: So part of me.
46:37
Speaker 2: Wants to say we're just meat sex operating obliviously to
46:42
Speaker 2: what's going on underneath our skin, where we think it's
46:46
Speaker 2: free will, But it sounds like there's a lot of
46:48
Speaker 2: things happening below the surface that's really influencing our decision making.
46:53
Speaker 1: Yeah, oh absolutely. I mean think about things like breathing.
46:56
Speaker 1: You know, breathing is so automatic, then when we stop
47:00
Speaker 1: and do sort of breath work and try to think
47:02
Speaker 1: about it, like the Navy seals might have a breathing
47:04
Speaker 1: exercise to calm down before a terrifying thing they have
47:07
Speaker 1: to take. You know, it actually takes a lot of
47:10
Speaker 1: executive function to think about breathing because we never have.
47:13
Speaker 2: To because it's automated.
47:14
Speaker 1: Because it's so automated. So the fact that it's actually
47:17
Speaker 1: grabs a lot of attention is because the automation is
47:19
Speaker 1: we've completely flipped back in the opposite situation. Let me
47:23
Speaker 1: tell you Urgan investor Pressen's study. We did. So. There's
47:26
Speaker 1: a game similar to prison die Lemma, but not the same,
47:28
Speaker 1: called the Stag hunt game, and the idea is two
47:31
Speaker 1: people decide to show up in the morning and hunt
47:34
Speaker 1: for a stag is a very old fashioned name from
47:37
Speaker 1: the Jean jau Bussau and the sixteen hundreds.
47:40
Speaker 2: We're talking about a male elk or deer.
47:43
Speaker 1: Yeah, an elk or deer. Yeah. The point of the
47:45
Speaker 1: stag is it's so big that no one person can't
47:47
Speaker 1: catch themselves. One person has to spot and the other
47:50
Speaker 1: a shoot or something like that, or they cannot show
47:53
Speaker 1: up in the morning at the appointed spot and just
47:55
Speaker 1: hunt for rabbits on their own. And so the structure
47:58
Speaker 1: of the game when we do it with money or
48:01
Speaker 1: reward with animals is you get one point if you
48:05
Speaker 1: just go for rabbit. If you both hunt for stag,
48:08
Speaker 1: you get two if you hunt for stag. But if
48:10
Speaker 1: you show up by yourself prepared to hunt for stag,
48:13
Speaker 1: you can't catch it and you get zero. And so
48:16
Speaker 1: the choosing a rabbit is choosing one and not helping
48:19
Speaker 1: your friend both showing up for STAG is better for
48:22
Speaker 1: the both of them, but they have to somehow coordinate
48:25
Speaker 1: that activity. And so what we found was when you
48:28
Speaker 1: give people this AVP and it's a crossover design, which
48:32
Speaker 1: means sometimes they get AVP and sometimes they get a
48:34
Speaker 1: placebo because there's a well known placebo effect where if
48:38
Speaker 1: they think maybe they got the a VP, it might
48:40
Speaker 1: subconsciously affect the behavior. So we always control for placebo effects,
48:44
Speaker 1: just like in drug trials, you know, the same thing
48:46
Speaker 1: very routine. When you give them a VP, they're more
48:50
Speaker 1: likely to choose STAG, which is the socially risky and
48:53
Speaker 1: beneficial thing. It's like it generates this willingness to join
48:58
Speaker 1: the group in a way that's going to help better
49:00
Speaker 1: everybody if another if you people join. And the other
49:03
Speaker 1: thing that was really nice in this paper was we
49:07
Speaker 1: also used fhor Mari. So we had two groups of
49:10
Speaker 1: people with administering a VP, one group of scan and
49:14
Speaker 1: one was not scan, which is just to see, like
49:16
Speaker 1: to replicate, do you get the same behavioral thing if
49:18
Speaker 1: they're not. You know, boom boom boom in the scanner
49:20
Speaker 1: and in the scanner you see activity globist palatue, which
49:24
Speaker 1: is known to be it's a small region. It's not
49:27
Speaker 1: one of the more familiar areas you know that show
49:30
Speaker 1: up a lots over and over in economics like Bezo Ganglia,
49:33
Speaker 1: A Magdola, Nsula, PFC. But you do see activity globist
49:38
Speaker 1: paladue when people under a VP are choosing STAG, So
49:44
Speaker 1: it looks like the the a VP is sort of
49:46
Speaker 1: promoting this STAG choice.
49:48
Speaker 2: But when we see people working cooperatively, you see a
49:52
Speaker 2: similar neurotransmitter as you do in the.
49:56
Speaker 1: Path and it's and it's and it's causal. Right, So
49:58
Speaker 1: these are the group of people and sometimes they just
50:01
Speaker 1: get this.
50:01
Speaker 2: Drug and it makes them want to cooperate.
50:04
Speaker 1: And it makes them want to cooperate in a way
50:06
Speaker 1: that with this risky it benefits the group. But we
50:08
Speaker 1: sometimes think of it it overcomes their inhibition to be well,
50:12
Speaker 1: I don't know if you're going to choose STAGG, and
50:14
Speaker 1: I don't know if you're going to show up well.
50:16
Speaker 2: The prisoner's dilemma is you're always better off throwing the
50:19
Speaker 2: other person under the bush.
50:21
Speaker 1: This is not that because here's the other person helps out,
50:24
Speaker 1: you want to help out too. It's the best response.
50:26
Speaker 1: So it's different structurally than the prison's dilemma.
50:29
Speaker 2: So I keep coming back every time I read a
50:33
Speaker 2: new anything about behavioral finance, new economics, anything about this.
50:38
Speaker 2: I can help but come back to the conclusion that
50:42
Speaker 2: all of our evolutionary biology has led us to a
50:47
Speaker 2: state where we're so well adapted to adjusting to changes
50:54
Speaker 2: in the natural world, and all of those things that
50:57
Speaker 2: have developed over the millennia really don't help us in
51:00
Speaker 2: the modern world. If anything, it's probably certainly in investing.
51:05
Speaker 2: It seems to be pretty problematic.
51:07
Speaker 1: Yeah, exactly. In fact, that's called the evolutionary mismatch hypothesis.
51:10
Speaker 2: Oh really, I didn't know it had a name.
51:12
Speaker 3: Yes, exactly, So tell us about it, called the Ritholts
51:16
Speaker 3: if only so, this mismatch is simply we evolve to
51:21
Speaker 3: adapt on the savannah, and that doesn't help us figure
51:24
Speaker 3: out which bonds to buy.
51:25
Speaker 2: Is it that simple?
51:26
Speaker 1: Exactly exactly. So another way to think of it is
51:30
Speaker 1: is institutions. Sometimes it's families, it's political advertisement. It might
51:34
Speaker 1: be fine print about fees in a you know, in
51:38
Speaker 1: a financial advertisement. Those are all things that are kind
51:42
Speaker 1: of tricking or exploiting vulnerabilities in our basic ancestral biology.
51:48
Speaker 1: Now again, people are smart too, so there's there is
51:51
Speaker 1: adaptation and kind of plasticity. So over a lifetime you
51:54
Speaker 1: might or maybe in one mba course or even possibly
51:59
Speaker 1: a high schoo of course, you might learn some principles
52:02
Speaker 1: of basic finance that really help you avoid dumb mistakes.
52:05
Speaker 1: You know, like compound interest really compounds quickly. You know,
52:09
Speaker 1: the Caveman brain thinks compounding quickly. I have no idea
52:13
Speaker 1: what that means. My brain can't imagine if I invested
52:16
Speaker 1: in the S and P one thousand dollars forty years ago,
52:19
Speaker 1: how much i'd have. You know, I can't compute that number.
52:21
Speaker 2: Well, we live in an arithmetic world. Exponential numbers, they
52:24
Speaker 2: are hard to comprehend.
52:26
Speaker 1: The brain is mostly linearizing things that and if they're
52:30
Speaker 1: not linear, or they're dramatically nonlinear, like pandemic compound interest,
52:36
Speaker 1: we can learn to overcome it. But we need these
52:38
Speaker 1: kind of external tools. It's almost like exoskeleton, you know,
52:41
Speaker 1: whether it's education advisors and so on.
52:44
Speaker 2: So let's talk a little bit about risk aversion, which
52:47
Speaker 2: has been this behavioral finance concept. People dislike losses twice
52:54
Speaker 2: as much as they enjoy gains. What does a world
52:58
Speaker 2: of neuroeconomics say about loss of version. I've seen a
53:02
Speaker 2: few mathematicians claim, oh, it's just a statistical anomaly. I
53:08
Speaker 2: remain unconvinced that that's the case.
53:11
Speaker 1: Yeah, so, actually I know a lot about loss of persion.
53:13
Speaker 1: We published a meta analysis last year about.
53:16
Speaker 2: There's a reason I'm asking this question. It's not out
53:19
Speaker 2: of left field.
53:19
Speaker 1: Right, you came to the right place. So in the
53:23
Speaker 1: men analysis, we looked at hundreds of studies, basically every
53:25
Speaker 1: study we could find, you know, using informatics, and nowadays
53:29
Speaker 1: you can really do this. It's like a industrial fishing,
53:32
Speaker 1: you know, you throw this net out and you get
53:34
Speaker 1: four thousand studies. Then you win to it down to the
53:36
Speaker 1: ones that are really just all trying to measure the
53:38
Speaker 1: same thing, so you can add them up. There was
53:41
Speaker 1: something like three hundred and seventy estimates of lambda, which
53:44
Speaker 1: is the Greek symbol that means the ratio of the
53:46
Speaker 1: disutility of loss to gain. And as you mentioned, two
53:50
Speaker 1: is sort of a we think it's a little bit
53:52
Speaker 1: smaller like one point sabin, but you know it's comparable. Yeah,
53:55
Speaker 1: it's comparable, and it's not one, which which would be
53:58
Speaker 1: the case in which you're not just finguishing loss and
54:00
Speaker 1: gain at all. You know, they're just like one scale.
54:05
Speaker 1: So the evidence is pretty good. Some other fun facts
54:08
Speaker 1: about loss of version, which is you might think that
54:11
Speaker 1: loss of version is is some kind of handicap, but
54:14
Speaker 1: actually we published a paper with two people who have
54:18
Speaker 1: brain damage and bilateral amigdala, which means neither part of
54:22
Speaker 1: the amgdala can compensate for the other. There's a very
54:25
Speaker 1: unusual disease. It comes from a or a bag via
54:27
Speaker 1: the disease, and they basically the amigdala is kind of
54:30
Speaker 1: like calcified, so it's it's there, but it's like deep freeze,
54:34
Speaker 1: you know, just so work.
54:36
Speaker 2: These people lose the ability to have these emotional responses
54:41
Speaker 2: to stimulus.
54:41
Speaker 1: Correct correct, and a lot has been known about because
54:45
Speaker 1: they've been studied. One of my colleagues, Ralphedelps, has studied
54:48
Speaker 1: several of them for years and they you know, they
54:50
Speaker 1: come back every so often and do a different.
54:52
Speaker 2: Kind of task and let me guess, they're pretty good traders.
54:55
Speaker 1: Generally, they're in disability because, uh, the amygdala damage is
54:59
Speaker 1: not to make they basically take too much risk in
55:02
Speaker 1: a lot of areas of life.
55:05
Speaker 2: So they're risk embracing, not risk averse at all.
55:08
Speaker 1: So the idea that that risk and fear are there
55:11
Speaker 1: to kind of protect you applies to them. Like when
55:15
Speaker 1: you remove that, like one of the patients, Sam makes
55:17
Speaker 1: a lot of poor choices.
55:19
Speaker 2: Give us examples.
55:21
Speaker 1: Well, this example I recall, I hope I'm not getting that.
55:23
Speaker 1: My memory is not mangling it too badly? Is She
55:26
Speaker 1: went on some kind of a date and the person
55:29
Speaker 1: was very sexually aggressive, and she ended up okay, And
55:33
Speaker 1: then somebody said, well, would you want to go out
55:35
Speaker 1: with that person again? She said, yeah, yeah, it was fine,
55:37
Speaker 1: it was fun. You know, she just didn't have this trauma.
55:40
Speaker 1: The amial was not processing. This is really bad. Run away,
55:44
Speaker 1: run away, avoid avoid.
55:46
Speaker 2: So how does this manifest itself amongst investors making risk
55:51
Speaker 2: decisions If their ability to process threats process fear is
55:58
Speaker 2: in present, what happens with those sort of decisions.
56:01
Speaker 1: Well, so for these two patients with imigal damage, they
56:04
Speaker 1: have no loss of version.
56:05
Speaker 2: None whatsoever, And so aggressive traders and investors, Well, so.
56:10
Speaker 1: Yeah, so the way we measures we give them these
56:12
Speaker 1: financial simple financial risks, like you could win. Most people
56:15
Speaker 1: if you say you could win ten, but you might
56:18
Speaker 1: lose eight or might lose seven, they're kind of just
56:20
Speaker 1: indifferent because a loss of seven and a gainer ten.
56:23
Speaker 2: Or you know, if I could, if I could do
56:24
Speaker 2: that on a billion dollars, I would, you know, I'd
56:27
Speaker 2: love to do that.
56:28
Speaker 1: But these two so damage the amgala. No more loss
56:32
Speaker 1: of version. So that's partly a reminder that be careful
56:36
Speaker 1: what you wish for, right, right, Like you.
56:39
Speaker 2: Don't want to react emotionally to everything correct right. The
56:43
Speaker 2: reason it's so hard to do, what Warren Buffett says,
56:46
Speaker 2: is when everybody's clamoring to buy, you get most people
56:49
Speaker 2: get caught up in that enthusiasm where we're social primates,
56:54
Speaker 2: and when the group is screaming bye bye bye, it's
56:57
Speaker 2: very hard to go with the other direction. And then
57:00
Speaker 2: at the bottom, when everybody is selling, the fear is
57:04
Speaker 2: of the alcohols.
57:05
Speaker 1: The fear of school was contagious very much. So right, yeah, yeah, yeah.
57:09
Speaker 2: So you lose that risk aversion. Do you have the
57:13
Speaker 2: ability to just go opposite the crowd because you don't care?
57:17
Speaker 1: It could be I mean, I've I have a feeling
57:21
Speaker 1: successful traders is it's not that they're not loss of verse,
57:25
Speaker 1: but they managed to inhibit it somehow. Or we did
57:29
Speaker 1: a such study in this but it's I don't think
57:31
Speaker 1: the details are all interesting for your readers, but or
57:34
Speaker 1: they're able to do what we call bracketing or kind
57:36
Speaker 1: of portfolio view, which is to say, you have bad
57:39
Speaker 1: days and good days and at the end it's my
57:41
Speaker 1: you know, it's my p and L at the end
57:43
Speaker 1: of the month or at the end of the year
57:45
Speaker 1: or the other quarter, and managed to kind of shrug
57:48
Speaker 1: off a loss. Now, I don't think that's that easy
57:51
Speaker 1: to do if you have intact amygdala right right, So
57:54
Speaker 1: it's it's almost it. It leads into another interesting topic
57:58
Speaker 1: which we've studied a little bit called emotion regular which
58:01
Speaker 1: is the fact that a lot of our emotions are
58:03
Speaker 1: sort of involuntary. You know, if there's a loud boom,
58:06
Speaker 1: you and I are both going to have this fear reaction.
58:08
Speaker 1: You know, haro, stand up will freeze. But you can
58:12
Speaker 1: also learn to regulate emotions. I mean kids are learning
58:15
Speaker 1: that when they learn to, you know, not be too
58:18
Speaker 1: afraid on the first day of school. As people get older,
58:21
Speaker 1: they learn to regulate emotions it's a pretty important skill.
58:25
Speaker 1: And so I think successful trading is probably some kind
58:29
Speaker 1: of cocktail of either a little less natural loss of
58:33
Speaker 1: version but not too little, right, because you don't want
58:35
Speaker 1: to like going crazy. You don't want them to be
58:38
Speaker 1: immune to loss, just like you don't want your hand
58:40
Speaker 1: to be immune to pain, right, because you're going to
58:42
Speaker 1: lean on a hot stoves one day and not notice
58:46
Speaker 1: that your hand is on fire. Right. So you a
58:50
Speaker 1: good trader probably has a little less natural loss of version,
58:53
Speaker 1: and then a really good ability to emotionally regulate, you know,
58:57
Speaker 1: when too much loss is acceptable or getting you into trouble.
59:01
Speaker 2: So the emotional regulation aspect is really interesting. I'm going
59:06
Speaker 2: to push you a little outside of your normal I
59:09
Speaker 2: think of your normal research area. One of the interesting
59:14
Speaker 2: comments that have come up when discussing who's a great
59:19
Speaker 2: fund manager, who's a great trader? Who are these folks
59:22
Speaker 2: that have put together these really impressive track records? A
59:26
Speaker 2: surprising number of neuroatypical folks.
59:30
Speaker 1: Oh yeah.
59:30
Speaker 2: The reason I asked you this is it seems like
59:33
Speaker 2: not only is there a little bit of ability to
59:35
Speaker 2: manage the emotions, but there's that ability to step outside
59:40
Speaker 2: of the crowd and say, I don't care what the
59:42
Speaker 2: rest of the primates are doing.
59:44
Speaker 1: Here.
59:45
Speaker 2: In March two thousand and nine, stocks look really attractive,
59:48
Speaker 2: and I want to be a buyer even though everybody
59:51
Speaker 2: else is selling. Is there an aspect of that to
59:54
Speaker 2: those sorts of ya.
59:56
Speaker 1: That's a fantastic topic. In fact, it is close to something.
59:58
Speaker 2: Oh, it is all right, good, I've been thinking about.
1:00:00
Speaker 1: So one thing is I was going to mention from before.
1:00:04
Speaker 1: So one of the striking things. I was working on
1:00:06
Speaker 1: an economics book and I was reading a lot of
1:00:09
Speaker 1: papers on social conformity. It turns out that almost every
1:00:13
Speaker 1: study finds the typical paradigm is something very stylized and simple, like,
1:00:18
Speaker 1: you know, you see a face and three other people
1:00:21
Speaker 1: see the same face, and you're asked to say is
1:00:23
Speaker 1: this person friendly or unfriendly? And in the conformity case,
1:00:26
Speaker 1: the other three people say friendly, and some other subject
1:00:30
Speaker 1: the other three see unfriendly, and people there seems to
1:00:34
Speaker 1: be a reward activity when you conform to the group.
1:00:38
Speaker 1: And these are not we're not super stress testing, so
1:00:42
Speaker 1: we're not quite something like you know, you're in the
1:00:45
Speaker 1: depth of a crash two thousand and eight crash and
1:00:48
Speaker 1: everyone's selling, and you know, ethically, it's hard for us
1:00:51
Speaker 1: to generate that dramatic an event in the lab. But
1:00:55
Speaker 1: even for these mild effects, and a lot of these people,
1:00:57
Speaker 1: if you ask them, do you follow the craft, they
1:01:00
Speaker 1: would say no, no, no, I kind of go in
1:01:01
Speaker 1: my own way. Like if a bunch of people said
1:01:03
Speaker 1: someone is friendly and you weren't sure, if you thought
1:01:05
Speaker 1: they weren't friendly, would you disagree? H Yeah, yeah, I
1:01:07
Speaker 1: wouldn't bother me. But study after study, the study shows
1:01:10
Speaker 1: there's generally reward value from conformity, which is essentially just
1:01:15
Speaker 1: the modern evidence for what you were talking about, which
1:01:18
Speaker 1: is that part of being a social animal right.
1:01:20
Speaker 2: The evolution of cooperation has has been very successful for us. Exactly,
1:01:26
Speaker 2: it started to fight the craft, It.
1:01:27
Speaker 1: Did his job, Yeah, exactly. Huh So I thought that
1:01:30
Speaker 1: was quite striking. Again, if you were if you wanted
1:01:32
Speaker 1: to study anti authoritarian personality, it might be a way
1:01:36
Speaker 1: to get into that that there be people who almost pathologically.
1:01:39
Speaker 1: But let's get back to your point about neurotypical people.
1:01:43
Speaker 1: So we're actually working at it beginning that a study
1:01:47
Speaker 1: on autism, so it's autism is called a spectrum disorder,
1:01:50
Speaker 1: which basically means it's not like you have it or
1:01:52
Speaker 1: you don't like schizophrenia. So you know, statistically it's it
1:01:56
Speaker 1: doesn't look like two humps.
1:01:57
Speaker 2: So you have a little, you could have some, you
1:01:59
Speaker 2: could have more, you can have a lot.
1:02:00
Speaker 1: Correct, correct, And there's often differences of symptoms like extreme
1:02:04
Speaker 1: autism often involves catatonia and severe language deficits and what
1:02:08
Speaker 1: have you. And so what people often think about Asperger syndrome,
1:02:12
Speaker 1: which is something that's called high functioning autism, right, which
1:02:15
Speaker 1: is basically you just just socially awkward and hard to
1:02:18
Speaker 1: understand what people do. But a lot of these pathologies
1:02:24
Speaker 1: or disorders, I should say pathology is not the right word.
1:02:27
Speaker 1: A lot of these disorders are accompanied by some enhancement.
1:02:30
Speaker 1: So for example, Asperger's patients have are more, they could
1:02:34
Speaker 1: have perfect pitch for a sound. They are better at
1:02:37
Speaker 1: ignoring some costs, which is a classic Bayer economics. You know,
1:02:41
Speaker 1: I spend so much on this dessert. You know, I
1:02:44
Speaker 1: came to New York. It is eighteen dollars for some flower.
1:02:47
Speaker 1: You know, I have to finish it, right.
1:02:51
Speaker 2: The autism, the money is spent whether you get the
1:02:53
Speaker 2: categories or not.
1:02:54
Speaker 1: So the autists have the right idea and.
1:02:57
Speaker 2: There is a sweet spot. I'm going to get you
1:02:59
Speaker 2: a list of the people who I know in this
1:03:03
Speaker 2: field who have that fantastic impressive numbers and have either
1:03:08
Speaker 2: stated there on the spectrum or it's kind of obvious hey.
1:03:14
Speaker 1: Yeah, yeah, yeah. You could look at film, video or
1:03:17
Speaker 1: written statements and you know, machine learned them and say,
1:03:21
Speaker 1: this person talks or looks.
1:03:22
Speaker 2: I'll ask on Twitter who's on the autism spectrum in
1:03:27
Speaker 2: the world of finance and has a good track record.
1:03:29
Speaker 2: But I have like two dozen names in my head.
1:03:31
Speaker 1: I'll give you a name. Unfortunately, he just died not
1:03:34
Speaker 1: too long ago, Charlie Munger. So of course Charlie a
1:03:36
Speaker 1: few times, right.
1:03:38
Speaker 2: And he doesn't strike me as very spectrumy.
1:03:42
Speaker 1: Well, but what one marker of autism is is like
1:03:46
Speaker 1: poor conversational turn taking, you know. And so when the
1:03:50
Speaker 1: times I met Charlie just twice and if you see
1:03:52
Speaker 1: him at the Berkshire Hathaway, I mean, he's amazing. I
1:03:55
Speaker 1: think it was like the Mark Twain of finance for sure,
1:03:57
Speaker 1: you know, because he was really witty and but also
1:04:00
Speaker 1: there's always like a really deep psychological insight in there.
1:04:04
Speaker 1: You know, it wasn't just funny. It was funny and
1:04:06
Speaker 1: true and often something other people didn't want to say.
1:04:09
Speaker 1: But when I met him, he was just like a
1:04:12
Speaker 1: freight train, and so you had to interrupt, and I
1:04:15
Speaker 1: realized the goal is to not have a conversation. You're
1:04:18
Speaker 1: just going to move the train.
1:04:19
Speaker 2: And different, just nudge him in different. Right.
1:04:22
Speaker 1: Well, you know that reminds me of X boom, and
1:04:24
Speaker 1: then he's often discussing X.
1:04:26
Speaker 2: I never realized that about him.
1:04:27
Speaker 1: So you're saying, that's my non clinical I am not
1:04:30
Speaker 1: a transition, but you know, disclaimer. Part of it is
1:04:33
Speaker 1: reflected and why he was successful. You know, he he
1:04:36
Speaker 1: saw himself as an average person who wasn't making the
1:04:38
Speaker 1: dumb mistakes other people make. But some of those dumb
1:04:41
Speaker 1: mistake people make, you know, he may have not made
1:04:44
Speaker 1: them because he doesn't get caught up in social conformity,
1:04:47
Speaker 1: or because he's very focused on he has good metacognition,
1:04:50
Speaker 1: like if I don't I don't buy a company I
1:04:51
Speaker 1: don't understand, right, you know, that's probably a good strategy.
1:04:56
Speaker 2: So I'm working on a new book. I'm almost done,
1:04:59
Speaker 2: and Munger is great. One of the two people I
1:05:03
Speaker 2: dedicate the book to and the quote of his that
1:05:07
Speaker 2: very much informs the the theme of the book is
1:05:11
Speaker 2: someone once asked him was Berkshire successful because Ewan and
1:05:15
Speaker 2: Warren are so much smarter than everybody else? And his
1:05:18
Speaker 2: response was, it's not that we're smarter than everybody else,
1:05:21
Speaker 2: we were just less stupid, which is such an insightful observation. Hey,
1:05:27
Speaker 2: just fewer Charlie Ellis, make less unforced errors, and you'll
1:05:32
Speaker 2: do better in tennis or investing than the guy trying
1:05:35
Speaker 2: to slam the ace, and most people are not going
1:05:38
Speaker 2: to get it in. Him and Munger had the two
1:05:41
Speaker 2: Charlie's had the same belief system, just be less stupid.
1:05:45
Speaker 2: It's really fascinating. So when you've interviewed Munger, what are
1:05:50
Speaker 2: some of the takeaways you've had from your conversations with him?
1:05:55
Speaker 1: One thing I remember was for so we went and
1:05:57
Speaker 1: looked at our neuroimaging center.
1:06:00
Speaker 2: Did you ever get him in a machine? No?
1:06:02
Speaker 1: I wish. I wish we had. He he may have
1:06:05
Speaker 1: gone for it too. He's you know, he's a pretty
1:06:06
Speaker 1: interesting person and I think very open minds, scientifically curious
1:06:12
Speaker 1: as well as in his financial life. He had gone
1:06:16
Speaker 1: to Celtic for a while. So he was we got
1:06:19
Speaker 1: to run into every so often. Of course, we're always
1:06:20
Speaker 1: people like that. They're always trying to get him to
1:06:22
Speaker 1: give money and or at least show.
1:06:24
Speaker 2: Up and give a speech something.
1:06:26
Speaker 1: Yeah, talk, and so so we showed in the brain scanner.
1:06:30
Speaker 1: He had a really interesting thought which I didn't quite
1:06:32
Speaker 1: appreciate till later, which was he said, what you guys
1:06:37
Speaker 1: should be doing is if you're trying to change behavior,
1:06:40
Speaker 1: like let's say you're trying to get somebody to vote,
1:06:43
Speaker 1: or to wear a mask or you know, quit smoking.
1:06:46
Speaker 1: Opioid's the really hard stuff.
1:06:48
Speaker 2: You know.
1:06:48
Speaker 1: Wait, unless he said, what you should really do is,
1:06:50
Speaker 1: rather than doing one little thing, you should go for
1:06:53
Speaker 1: a lollapalooza, you know, like basically try to add in
1:06:56
Speaker 1: six different things to get the biggest ability to get
1:07:00
Speaker 1: people to quit smoking.
1:07:01
Speaker 2: Let's say it makes sense.
1:07:02
Speaker 1: And so he was thinking as a practitioner like I
1:07:04
Speaker 1: want I'm going to know what's going to work, as
1:07:07
Speaker 1: scientists were often thinking piecemeal, like if we put six
1:07:10
Speaker 1: different things in and it works, we don't know which
1:07:13
Speaker 1: of the six is the active ingredient, but it.
1:07:16
Speaker 2: Could be a different combination for each different exactly.
1:07:18
Speaker 1: So exactly, but and so the reason I was thinking
1:07:21
Speaker 1: about that was nowadays, one of the fallouts or one
1:07:24
Speaker 1: of the products I should say, from Fallow it's definitely
1:07:26
Speaker 1: the wrong word. One of the products from Beata economics
1:07:29
Speaker 1: was this idea of a nudge that often because people
1:07:32
Speaker 1: are often sensitive to very subtle things like opt in
1:07:35
Speaker 1: versus opt out, you know there may be a low cost,
1:07:38
Speaker 1: light touchway to change behavior a little bit.
1:07:41
Speaker 2: Well just look at the four oh one k exactly,
1:07:44
Speaker 2: making the default go to just some specific investment as
1:07:51
Speaker 2: opposed to it just sits there in cash for god
1:07:56
Speaker 2: knows how long. Seems to have really had a big impa.
1:07:59
Speaker 1: Yes, exactly that that was definitely the poster child for
1:08:03
Speaker 1: the simplest nudge, and we kind of understand the psychology
1:08:06
Speaker 1: of it anyway. So now what a lot of people
1:08:08
Speaker 1: are thinking about nudge. This is exactly this lollapaloosa idea
1:08:11
Speaker 1: of Munger's, which is, if we want to get people
1:08:14
Speaker 1: to get out the vote, rather than try six different things,
1:08:17
Speaker 1: we should be trying like six combinations of three things. Statistically,
1:08:22
Speaker 1: it's messy because you'll never really end up knowing which
1:08:25
Speaker 1: of those is the active ingredient. But to just get results,
1:08:29
Speaker 1: that's useful information, it's useful formation. So the Nudge Enterprise,
1:08:34
Speaker 1: which I've been connected to a little bit, is moving
1:08:36
Speaker 1: somewhe in that direction that Munger mentioned many years ago.
1:08:39
Speaker 2: Huh. Really interesting? All right, I only have you for
1:08:41
Speaker 2: a limited amount of time, so let me jump to
1:08:44
Speaker 2: my favorite questions that I ask all of my guests,
1:08:48
Speaker 2: starting with what are you watching or listening to these days?
1:08:51
Speaker 2: What's keeping you entertained?
1:08:54
Speaker 1: So Katie Molkman's podcast Choiceology is one that I've been
1:08:58
Speaker 1: on that I think is quite good. It's basically the
1:09:00
Speaker 1: beaval economics podcasts. There are quite a few others, but
1:09:03
Speaker 1: Katie is a real expert on this and is a
1:09:06
Speaker 1: great interviewer and has.
1:09:07
Speaker 2: Had good guests choice Ology. Choice Ology tell us about
1:09:11
Speaker 2: your mentors who helped to shape your fascinating career.
1:09:15
Speaker 1: So two people who were on my thesis committee, Robin
1:09:18
Speaker 1: Hogarth and Hilly Einhorn were two and there's an interesting story.
1:09:22
Speaker 1: So Robin was Scottish, very verbal, every sentence started with howsoever. Therefore, notwithstanding,
1:09:34
Speaker 1: Hilly was a very blunt jew from Brooklyn, and it
1:09:37
Speaker 1: was the exact opposite so Hilly would mark up my
1:09:40
Speaker 1: thesis and put in all these fancy Hilly would rather
1:09:43
Speaker 1: would take out the whatsoevers and the howevers and that
1:09:45
Speaker 1: thereforece and he was like put in more like boom,
1:09:49
Speaker 1: like short sentences, no sema colons, but like he had
1:09:52
Speaker 1: one punctuation mark period. That's it right, Like you know,
1:09:54
Speaker 1: he like about a million periods at a store, and
1:09:57
Speaker 1: like I'm not likely to use those. And Robin was
1:09:59
Speaker 1: the way around, Oh, this really need to do summa colon,
1:10:02
Speaker 1: you know, let's plump this. And at one point I
1:10:04
Speaker 1: was going back and forth, you know, near the completion
1:10:06
Speaker 1: of my thesis with the two of them were co advisors,
1:10:10
Speaker 1: and I got so frustrated, and I said, how should
1:10:13
Speaker 1: I write this? And we had this this kind of
1:10:16
Speaker 1: like grasshopper moment of it's your thesis, you figure out
1:10:20
Speaker 1: how you want to write it. And I realized they
1:10:22
Speaker 1: were kind of waiting for me to find my voice,
1:10:24
Speaker 1: like they say in writing, you know, like and one
1:10:27
Speaker 1: of the love tables and then the other love graphs.
1:10:31
Speaker 1: So the drafts of my thesis was the table and
1:10:33
Speaker 1: a graph were exactly the same thing. And I had
1:10:35
Speaker 1: to decide was I a graph person or a table
1:10:37
Speaker 1: person or was I kind of like bilingual? So I
1:10:40
Speaker 1: basically became kind of bilingual in terms of how I
1:10:43
Speaker 1: was thinking. It's night. That was very helpful. The other
1:10:46
Speaker 1: person probably is Dick Taylor, because he he's a very
1:10:50
Speaker 1: good writer. He did exactly what so many academics aspire
1:10:53
Speaker 1: to and we always ask for more of, which is
1:10:55
Speaker 1: to write a small number of extremely high quality papers.
1:11:00
Speaker 1: It's very unusual because for career reasons and stuff, you
1:11:03
Speaker 1: have to get tenure and right, and Dick just couldn't
1:11:07
Speaker 1: really write a bad paper. I don't write as many
1:11:09
Speaker 1: great papers as him, and I as a result, I
1:11:11
Speaker 1: write too many okay papers. But that's something I think
1:11:14
Speaker 1: is useful for everyone.
1:11:15
Speaker 2: He's one of my favorite people in the world. I
1:11:18
Speaker 2: got to interview I don't know half a dozen times,
1:11:21
Speaker 2: only once since he won the Nobel Prize, but I
1:11:24
Speaker 2: always find him so informative and entertaining, and I just
1:11:30
Speaker 2: loved his response to winning the prize. What are you
1:11:33
Speaker 2: gonna do with the money? His answer is, I'm going
1:11:35
Speaker 2: to spend it as irrationally as I possibly can, just
1:11:38
Speaker 2: so so him he enjoys life. He very much does
1:11:42
Speaker 2: he's just also a fascinating, fascinating, charming guy. Let's talk
1:11:47
Speaker 2: about books. What are some of your favorites. What are
1:11:48
Speaker 2: you reading right now?
1:11:50
Speaker 1: I am reading Emma Klein a book called The Guests,
1:11:54
Speaker 1: especially for neworlcus in your audience. It's about a very grifty,
1:11:58
Speaker 1: sketchy woman who goes to the Hamptons and kind of
1:12:02
Speaker 1: cons her way around the Hamptons. It's really it's almost.
1:12:05
Speaker 2: Like a very didn't we have kind of a real
1:12:07
Speaker 2: life thing like that happening a year?
1:12:09
Speaker 1: Yes, exactly. It may be loosely inspired by Anna deel
1:12:13
Speaker 1: Vi in Manhattan or some similar cases. It's basically almost
1:12:17
Speaker 1: like a nineteenth century novel about class, because she's very
1:12:21
Speaker 1: conscious of not belonging in the Hamptons, but she's very
1:12:24
Speaker 1: beautiful and kind of charming in this sort of man
1:12:27
Speaker 1: eater femvatal way. And I'm almost done with that. It's
1:12:30
Speaker 1: really delicious. The other thing, I love movies and books
1:12:34
Speaker 1: about capers and heists and grift, which includes Emma Klein
1:12:38
Speaker 1: The Guest. So I'm reading these books by Jim Swain,
1:12:41
Speaker 1: who's not known. I got onto him. Lee Child, who
1:12:45
Speaker 1: who I.
1:12:45
Speaker 2: Love life reads all of his books plow through all
1:12:48
Speaker 2: of it exactly. Yeah, and that did that include the
1:12:51
Speaker 2: Reacher series.
1:12:52
Speaker 1: The Reacher series, that's what he is most famous for,
1:12:54
Speaker 1: the Lee Child. But so Jim Swain was blurbed by
1:12:57
Speaker 1: Lee Child, saying, Jim Swain's the best at what he does,
1:13:00
Speaker 1: and what he does is he writes about a very
1:13:02
Speaker 1: sophisticated cheater in Las Vegas who cheats casinos, and it's
1:13:08
Speaker 1: you know, I'm going to use recycle this and you're
1:13:11
Speaker 1: very shortly for you. But basically there are procedurals about
1:13:16
Speaker 1: how to cheat a casino, but in the end if
1:13:19
Speaker 1: you get caught. There's also this sort of socio psycho
1:13:23
Speaker 1: political thing of you know, if I make up a
1:13:26
Speaker 1: story about why something happened, like if there's a murder
1:13:29
Speaker 1: in a casino and I make up a story about
1:13:31
Speaker 1: it that helps them act like the murder was freakish
1:13:35
Speaker 1: and won't drive away customers. I'm actually delivering a gift
1:13:38
Speaker 1: to them, and they're going to trade off. They're not
1:13:40
Speaker 1: going to send me to jail. But if I give
1:13:42
Speaker 1: them this gift. So there's a lot of layers of
1:13:44
Speaker 1: This is not Dostoevski, It's not brilliant. This is not
1:13:48
Speaker 1: sum but for me, there's a lot of like psychology,
1:13:52
Speaker 1: and you know, in a way, it's a game theory.
1:13:54
Speaker 1: What if there's an arms race between the Vegas Gaming
1:13:58
Speaker 1: Commission and each of the individual casinos who are very sophisticated.
1:14:01
Speaker 1: They hire a lot of ex cheats, you know, to
1:14:04
Speaker 1: tell them what to look for, and then these cheaters
1:14:07
Speaker 1: who know you know, so truly, there's arms series of
1:14:09
Speaker 1: who's gonna win? I found those really interesting.
1:14:11
Speaker 2: If you like books on griffs and cheats and corruption,
1:14:18
Speaker 2: I'm gonna recommend pretty much anything he's written. I've been
1:14:22
Speaker 2: a fan of his for years. Carlhassen was a reorder
1:14:26
Speaker 2: for the Miami Herald Prime Reporter and then just one
1:14:30
Speaker 2: after another, these series of novels and his one of
1:14:34
Speaker 2: his more recent books is now a TV series on
1:14:37
Speaker 2: Apple Plus Bad Monkey, but all of his books it's
1:14:42
Speaker 2: Bad Monkey in the I think the sequel is called
1:14:45
Speaker 2: Razor Girl. But all his books take place in Florida.
1:14:49
Speaker 2: Everybody's corrupt. The police are corrupt, the building inspectors are corrupt,
1:14:52
Speaker 2: the politicians are corrupt, and there's always one or two
1:14:56
Speaker 2: good people in the heart of the story and it's
1:14:58
Speaker 2: how do they navigate? It's just endless sea of treachery
1:15:02
Speaker 2: and corruption. And he's just a delightful, entertaining writer. If
1:15:07
Speaker 2: you you could randomly pick any of his books and
1:15:10
Speaker 2: they're just all they're great beach reads.
1:15:13
Speaker 1: You know. Le Me also mentioned The Wire because I
1:15:15
Speaker 1: grew up in Baltimore County and a series. Yes, and
1:15:20
Speaker 1: David Simon's book The Corner is a kind of a precursor.
1:15:23
Speaker 1: I mean, he's a very interesting person. He was a
1:15:25
Speaker 1: reporter and I think he may have been Baltimore, Britimore.
1:15:30
Speaker 1: And The Corner is like this beautiful I think it
1:15:34
Speaker 1: was a precursor to the Wire. But it's basically about
1:15:36
Speaker 1: a corner in West Baltimore everyone buys drugs, and it's
1:15:39
Speaker 1: about drug addiction and all the things that's surrounded. So
1:15:42
Speaker 1: as somebody who you know, one of the things we
1:15:44
Speaker 1: study in behavioral economics is habits and addictions, and you know,
1:15:47
Speaker 1: and the neuroscience, of course is fascinating along the way,
1:15:51
Speaker 1: and that one is great. And The Wire having grown
1:15:53
Speaker 1: up in Baltimore County, which is not Baltimore City, The
1:15:56
Speaker 1: Wire is almost like a documentary and it has all
1:15:58
Speaker 1: this Baltimore stuff, as well as all my accents where
1:16:00
Speaker 1: you have people are talking about talking like this, and
1:16:04
Speaker 1: it has Tommy Garcetti is this political character who's sort
1:16:07
Speaker 1: of inspired by Tommy Delsandro, whose daughter is Nancy PELUSI.
1:16:12
Speaker 2: Oh really, that's amazing. I found the series the Wire.
1:16:17
Speaker 2: It's a tough watch. It's a great show. It's brutal. Yeah,
1:16:21
Speaker 2: gritty is mild. I mean some of the stuff that
1:16:24
Speaker 2: goes on, and the show is.
1:16:26
Speaker 1: Just like, yeah, there's a famous scene with the nail
1:16:28
Speaker 1: gun you're which if your listeners have the stomach, that's
1:16:33
Speaker 1: pretty classic.
1:16:34
Speaker 2: Similar in the Jack Reacher series, there's a really something
1:16:39
Speaker 2: not that far off. Yeah, they toned it down for television,
1:16:42
Speaker 2: but the book is is really brutal. All Right, we're
1:16:45
Speaker 2: up to our final two questions. What sort of advice
1:16:48
Speaker 2: would you give to a college grad interested in a
1:16:51
Speaker 2: career in filling the blank neuroeconomics, behavioral finance, or even
1:16:57
Speaker 2: just investing.
1:16:58
Speaker 1: For somebody who say it doesn't want to get a PhD.
1:17:02
Speaker 1: That's a different track and probably of less interest. And
1:17:05
Speaker 1: there's you can get a lot of guests advice on
1:17:07
Speaker 1: how to do that. I would study not just finance
1:17:10
Speaker 1: like straight asset pricing and derivatives, but also behavioral economics
1:17:16
Speaker 1: game theory, I think, because even though game theory is
1:17:19
Speaker 1: usually like two players or small numbers of players, it
1:17:22
Speaker 1: really sharpens the logic of you know, when do I
1:17:25
Speaker 1: know something another person doesn't know? And do I know
1:17:28
Speaker 1: that they don't know it? You know, you have to
1:17:30
Speaker 1: really relentlessly think about the math underlying that. And then
1:17:34
Speaker 1: there's a lot of experimental and real world data. One
1:17:37
Speaker 1: of my I just got a text from our students
1:17:40
Speaker 1: this term, and there's a lot of data from sports
1:17:42
Speaker 1: about whether sports activities are like equilibrium responses to other players,
1:17:49
Speaker 1: So you can actually there's there's a lot of sources
1:17:51
Speaker 1: of data besides just say the lab experiments. I talk
1:17:54
Speaker 1: about my book from two thousand and three, Sneaking the
1:17:56
Speaker 1: Plug in Cognitive science is something I would study too.
1:18:00
Speaker 1: So cognitive science is a modern brand of cognitive psych
1:18:05
Speaker 1: that has more math in it, and a lot of
1:18:07
Speaker 1: it actually goes back to something we spoke about, like
1:18:10
Speaker 1: a mismatch. But they are quite interested in what they
1:18:13
Speaker 1: call resource rationality, which means a lot of the mistakes
1:18:16
Speaker 1: people might make, like anchoring on one number and being
1:18:19
Speaker 1: influenced by that. A famous anchoring adjustment heuristic may actually
1:18:23
Speaker 1: be rational if you only have so much working memory,
1:18:26
Speaker 1: or you're under time pressure, or you're tired. It's also
1:18:29
Speaker 1: closely related to the way economists would think about mistakes,
1:18:33
Speaker 1: which is they may be optimal given some constraint, like
1:18:36
Speaker 1: what is that constraint and can we test that experimentally?
1:18:39
Speaker 1: So I think there's a lot of stuff you could
1:18:41
Speaker 1: learn there that will help you think about markets. The
1:18:43
Speaker 1: other thing I would say is get experience thinking about markets,
1:18:48
Speaker 1: whether in turning or I'll tell you a story about
1:18:52
Speaker 1: what worked for me, which was when I was twelve
1:18:53
Speaker 1: years old in Cockeysville, Maryland, August, there was a one
1:18:59
Speaker 1: month month racing program at a small racetrack called Timonium, Maryland,
1:19:05
Speaker 1: and it was a five eighths of a mile tracks.
1:19:06
Speaker 1: It's like a you know, small I would go with
1:19:09
Speaker 1: my dad and a friend of his who was a stockbroker,
1:19:12
Speaker 1: and we would also go to the big tracks like
1:19:14
Speaker 1: Pimlico where the preak mistakes is. But if you go
1:19:16
Speaker 1: to Timonium, you get to see all the horses. There
1:19:20
Speaker 1: was so much interest. You learned so much about markets.
1:19:24
Speaker 1: Number one. It gives you, I think, a respect for
1:19:26
Speaker 1: market efficiency.
1:19:27
Speaker 2: Couse, the odds are actually not that bad. They were
1:19:30
Speaker 2: extremely pretty pretty dead on exactly.
1:19:32
Speaker 1: And so you see, you know, eight horses come out,
1:19:34
Speaker 1: they all look pretty similar. You know, the jockeys are
1:19:37
Speaker 1: all you know, the same size, and they're all pretty good.
1:19:39
Speaker 1: There's a lot of statistics you can see. But somehow
1:19:42
Speaker 1: the crowd has decided that number three is even money favorite,
1:19:47
Speaker 1: which is a fifty e d chance to win. A
1:19:49
Speaker 1: number six, who looks pretty good too, is like seventy
1:19:51
Speaker 1: to one, and they're mostly right. So you know, part
1:19:55
Speaker 1: of why I got into economics and psychology was thinking
1:19:58
Speaker 1: about episodes like that, how does the market put this
1:20:01
Speaker 1: information together? And are the mistakes? Like how do you
1:20:06
Speaker 1: beat the market?
1:20:07
Speaker 2: So Fama turns out to be more or less right about.
1:20:09
Speaker 1: You about twenty in Maryland. And there were other interesting
1:20:12
Speaker 1: lessons too, like so on the if you go with
1:20:14
Speaker 1: like around the third race. You know, I was I
1:20:16
Speaker 1: was a kid, so I just broke and my poor mom,
1:20:19
Speaker 1: my irish mom, was worried I was going to you know,
1:20:21
Speaker 1: lose too much money. I kept telling you, it's tuition, mom,
1:20:25
Speaker 1: it's tuition. But if you go in the third race,
1:20:30
Speaker 1: there were these people who would sell tip sheets for
1:20:31
Speaker 1: like five dollars, right.
1:20:33
Speaker 2: And you know, because because they know what's going to happen,
1:20:35
Speaker 2: they're selling the tip sheets, not making the bets.
1:20:37
Speaker 1: Exactly the customer's yachts exactly. But if you go like
1:20:40
Speaker 1: in the you know, the third or fourth race, they
1:20:43
Speaker 1: would quit selling them. They would just give them to
1:20:46
Speaker 1: you really well, like a lost leader. Maybe you'll you'll
1:20:49
Speaker 1: maybe next time you'll buy it. And so I'm sitting here,
1:20:52
Speaker 1: here's my little cynical twelve thirteen year old brain thinking,
1:20:56
Speaker 1: why are you giving away for free tips that you
1:20:59
Speaker 1: claim can make me money? Like this does not the
1:21:01
Speaker 1: math does not math. And I think that's a good lesson,
1:21:06
Speaker 1: like for markets. Right yeah, but you know, just just
1:21:10
Speaker 1: to clear away like the most naive, you know, immunize
1:21:13
Speaker 1: yourself to the most naive schemes.
1:21:16
Speaker 2: You know, you would think if the tips were valuable,
1:21:19
Speaker 2: rather than waste your time printing it up and selling them,
1:21:21
Speaker 2: you would just bet on the wing horses, especially in.
1:21:25
Speaker 1: A peramutual system, right because you know, the more the
1:21:30
Speaker 1: more your tip sheet buyers are betting on your horses.
1:21:33
Speaker 2: The lower guts right exactly.
1:21:35
Speaker 1: They're betting against.
1:21:38
Speaker 2: Our final question. Our final question, what do you know
1:21:40
Speaker 2: about the world of neuroeconomics today, might have been helpful
1:21:45
Speaker 2: when you were first getting started back in the nineteen eighties.
1:21:50
Speaker 1: You know, I'll answer that like a politicial answer a
1:21:53
Speaker 1: question I have a better answer for, which is about
1:21:54
Speaker 1: behavioral finance.
1:21:55
Speaker 2: Sure, so either or be fire or sure?
1:21:59
Speaker 1: I got it. So in your economics, I don't think
1:22:01
Speaker 1: we made too many mistakes. I think I wish we had.
1:22:04
Speaker 1: You know, we got a lot of grand support. Caltech
1:22:07
Speaker 1: was very supportive. I got to know a lot of
1:22:10
Speaker 1: interesting people who are generous with their time, who were
1:22:12
Speaker 1: kind of my tutors on neuroscience. I never took any formal,
1:22:16
Speaker 1: you know, coursework on it. It was came way, way
1:22:18
Speaker 1: way after my original rad training. So thank you everyone.
1:22:23
Speaker 1: I wish we had. We have not had much impact
1:22:25
Speaker 1: in academic economics particularly, and that's something we're kind of
1:22:29
Speaker 1: working on. Maybe we can do better behavioral finance. I
1:22:32
Speaker 1: think I started graduate school in the late seventies. In
1:22:35
Speaker 1: nineteen seventy eight, Mike Jensen published a very influential paper.
1:22:40
Speaker 1: It was an intruction to a special issue, and one
1:22:42
Speaker 1: of the first sentences is the market efficiency apothesis is
1:22:46
Speaker 1: one of the most well established empirical regularities in economics.
1:22:51
Speaker 1: But but that was like the high water mark, and
1:22:55
Speaker 1: the special issue was about there's some things that are anomalists,
1:22:58
Speaker 1: like earnings drift. He got a weird earnings announcement. The
1:23:02
Speaker 1: market reacts, but then the market reaction drifts up for it.
1:23:05
Speaker 1: It takes a couple of weeks, almost like food for
1:23:07
Speaker 1: the market so so absorbed it should not take a
1:23:10
Speaker 1: couple of weeks, right, right, There were other things where
1:23:13
Speaker 1: we see, you know, like one within one hour markets
1:23:16
Speaker 1: are repricing really well. But despite this Jensen article, the
1:23:24
Speaker 1: hostility to ba Heybalk finance was ferocious.
1:23:28
Speaker 2: That's a big word at that time. It was that
1:23:30
Speaker 2: so late seventies, early late.
1:23:32
Speaker 1: Seventies, early eighties, and so that's when I was kind
1:23:34
Speaker 1: of deciding do I want to stay in finance or
1:23:35
Speaker 1: mix it with and I remember having a discussion. I
1:23:38
Speaker 1: don't know if Jeane remembers it the same way with
1:23:41
Speaker 1: I had to write a paper for Eugene Fama's course,
1:23:43
Speaker 1: who was also kind of a mentor in this sense.
1:23:45
Speaker 1: Even though I didn't end up doing work that was close,
1:23:48
Speaker 1: you know, he was he was really relentless and very
1:23:51
Speaker 1: empirically driven, and he had a really good idea. When
1:23:54
Speaker 1: he started, people were thought he was crazy because there
1:23:57
Speaker 1: was all this stuff on, you know, there was even
1:24:00
Speaker 1: he wrote some papers on dividends, like well, the Optimal
1:24:02
Speaker 1: Dividend Payment Policy, and of course Miller and him would like,
1:24:06
Speaker 1: what be dividends at all? You just like take money
1:24:08
Speaker 1: from one bucket and put it in the other.
1:24:11
Speaker 2: Well, back in the early days of widows and orphan stocks,
1:24:14
Speaker 2: you people lived on.
1:24:15
Speaker 1: Their digiti Yeah, exactly because of the liquidity.
1:24:17
Speaker 2: Right, you don't want to sell do you want to
1:24:19
Speaker 2: hold on to it?
1:24:20
Speaker 1: And then the dividends, you know, it is enough to
1:24:22
Speaker 1: live on.
1:24:22
Speaker 2: Now the theory has shifted towards uh, it's more efficient
1:24:27
Speaker 2: return of capital to shareholders doing buybox than dividends. But
1:24:32
Speaker 2: that's only total return. If you're looking for that income stream,
1:24:36
Speaker 2: buybacks don't necessarily help you.
1:24:37
Speaker 1: Right, right exactly. So that's and that's also where the
1:24:40
Speaker 1: hero economic comes in with you know, why can't you
1:24:42
Speaker 1: just like create whatever income stream you want by borrowing
1:24:46
Speaker 1: and selling, right, that's right? And if you know, if
1:24:49
Speaker 1: you're really liquidity constrained or credit constrained, you can't. But
1:24:51
Speaker 1: for most people that's not a big deal anyway. So
1:24:55
Speaker 1: if I had known behavioral finance, would it didn't take
1:24:59
Speaker 1: off quickly. From nineteen seventy eight, which is Jensen nineteen
1:25:02
Speaker 1: eighty one, I graduated nineteen eighty five was the failure
1:25:05
Speaker 1: in DeMont paper about January effects. And even that was
1:25:10
Speaker 1: published as a It was in the Proceedings issue, which
1:25:15
Speaker 1: meant that the President of the of the AFA could
1:25:19
Speaker 1: pan pick papers. So the preceding issue had the most
1:25:22
Speaker 1: radical papers that were the foundation of aper economics. Fisher
1:25:26
Speaker 1: Black wrote a paper called Noise Traders. I thot it
1:25:30
Speaker 1: might have just been called noise. And then Dick Roll
1:25:33
Speaker 1: wrote a paper got R Squared, and he said, you know,
1:25:36
Speaker 1: if only news moves the market, right, then the R
1:25:39
Speaker 1: squared On days with no news, you know, you shouldn't
1:25:42
Speaker 1: have any volatility, And of course days with big news
1:25:45
Speaker 1: and small news similar to the story you were telling
1:25:50
Speaker 1: you in the beginning, Days with big news, big obvious
1:25:53
Speaker 1: news and hardly any news move about the same.
1:25:57
Speaker 2: The assumption being by the time it's in the front
1:26:00
Speaker 2: age of the new York Times, it's already reflected the.
1:26:03
Speaker 1: Markets, right, But also there may be things that are
1:26:05
Speaker 1: not newsy at all, Like in October eighty seven crash,
1:26:08
Speaker 1: you know, the Bundesbank moved rates by a quarter of
1:26:11
Speaker 1: a point or something.
1:26:13
Speaker 2: Who cares? That was the big news, right, but you know,
1:26:16
Speaker 2: you never know when that last straw breaks the camels correct.
1:26:19
Speaker 1: But but so all those ideas now that that we
1:26:22
Speaker 1: we you know, we feel like we have an understanding
1:26:24
Speaker 1: and examples. There was a lot of hostility to that.
1:26:27
Speaker 1: So I remember asking Gene, I'd like to study market psychology,
1:26:33
Speaker 1: like what do you know about market psychology? And he said,
1:26:37
Speaker 1: what's that, Mike? And psychology is Boston accent? You know.
1:26:42
Speaker 1: I think it's just a word they use on the news,
1:26:45
Speaker 1: like in Bloomberg. It's just a word they use on
1:26:47
Speaker 1: the news when the market moved.
1:26:48
Speaker 2: They don't know why, right, Well, no one wants to
1:26:51
Speaker 2: admit it's fairly random day to day. We're very humans
1:26:55
Speaker 2: are very I know that humans are very uncomfortable.
1:26:58
Speaker 1: And we're good at pattern right.
1:27:01
Speaker 2: We make up patterns. We come up with a narrative
1:27:03
Speaker 2: to explain it. I recall Dick Thaylor quoting maybe it
1:27:10
Speaker 2: was Max Planck, who's talking about physics scientis one funeral
1:27:15
Speaker 2: at a time. Taylor said the same thing about behavioral finance,
1:27:19
Speaker 2: and he also said, I'm bypassing the current generation and
1:27:22
Speaker 2: going right to the kids so they'll adapt a wholesale
1:27:25
Speaker 2: and literally he said, I'm teaching grads and undergrads this
1:27:30
Speaker 2: so we don't even have to wait for the funeral.
1:27:32
Speaker 2: And it seems to have worked. Oh yeah, no, absolutely, Colin,
1:27:36
Speaker 2: thank you so much for being so generous with your time.
1:27:39
Speaker 2: This has been absolutely fascinating. I'm glad we finally managed
1:27:44
Speaker 2: to do this. We have been speaking with Professor Colin
1:27:47
Speaker 2: Camera of California Institute of Technology. If you enjoy this conversation,
1:27:53
Speaker 2: well check out any of the five hundred previous interviews
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