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