Nature, Nurture, and Identical Twins (with David Bessis)

To Econ Talk, Conversations for the Curious, part of the Library of Economics and Liberty. I'm your host, Russ Roberts of Shalem College in Jerusalem and Stanford University. Go to econtalk.org where you can subscribe, comment on this episode, and find links and other information related to today's conversation. You'll also find our archive. Our email address is mail at econalcks. We'd love to hear from you.

Today is december twenty second, twenty twenty five. Before introducing today's guest, I want to remind listeners to go to econtalk dot org and vote on your favorite episodes of twenty twenty five. And now for today's guest, mathematician and author David Bessis. He was last here in October of twenty twenty five discussing his marvelous book, Mathematica. Our topic for today is a provocative essay from his substack that we will link to.

Title of that essay Twins Reared Apart Do Not Exist The Shaky Science of Genetic Determinism. David, welcome back to Econ Talk. Hello Russ, delighted to be back. Now your book Mathematica takes what is for some people a controv controversial view of mathematics. Uh unlike uh Poincaré's quip that, quote, mathematicians are born, not made, you argue that almost anyone can do math if they approach it correctly, or more importantly, the great mathematicians are not uniquely

genius like they just have an approach that's very powerful that can be learned by normal human beings and it is that approach, that method, and their dogged focus and hard work that yields greatness. And you're talking in your book

about Descartes and Einstein and Grothedick. So you do not believe that everything is predetermined by genetics? So you were taken aback. Your essay was provoked by a claim that in studies of twins who were raised in different households who are quote separated at birth. Those studies find that genetics are destiny. And here's what you wrote as the takeaway. When you separate uh this is your summary of what is one view of this literature on twins separated at birth.

Quote When you separate two identical twins at birth, raise them in two different two random families. I'm gonna say that again, we'll edit that slip out. When you separate two identical twins at birth, raise them in two random families, and test their IQs in adulthood, the two results are barely more divergent than two different tests of the same person. So your IQ tested as a child, obviously you're the same person. You're gonna go through life and get older, your IQ is gonna get tested.

Twins raised in totally separate households with it seems totally different nurture, but their nature overwhelms it and they're as if they were a single person. Uh you're hardwired at birth to be smart or not smart or very smart. So talk about why this was disturbing to you. You confess in the piece, which I love, that you had a horse in this race that that that bothered you.

Yes, so I I think it's uh really important to to frame the debate in the correct turn because one thing I I I noticed uh since I started publicly discussing about math talent and its origins is People try to pigeonhole into one of the two extremes. One extreme would be everything is genetically determined and there is nothing you can do to change your destiny. And the other supposed uh opposed uh position would be the blank slates, like Everything is open and you can become whatever you want.

I do not believe in any of these extremes. And I think it's really important because I think that Uh many of your listeners uh hearing the first sentences of your introduction, we think, oh no, not another guy will pretend that genes don't matter at all. Of course they matter. I mean it's obvious. We we know that there are some genetic defects that prevent you.

from having uh normally functioning cognition. And that's just the extreme of the spectrum and it's clear that there is some variability and will not equal We know, for example, that, you know, uh brain size, the stupidest metric you can imagine, is somehow correlated with outcomes. So it's likely correlated also with math salon, but the question is so weak that it may not be meaningful.

And this is this thing about, you know, having genes being part of a determination versus being the entire determination that is the issue at stake.

Now, the uh studies you you mentioned are studies that were performed in the fifties, in the sixties, in the seventies, in the eighties and who have been widely publicised, especially on social media, and the specific post you you were mentioning is uh a visual that was uh reposted by someone I i respect as uh uh as a as an intellectual who is Paul Graham is not just an intellectual, he's a VCA and entrepreneur I respect his writings, I respect his thoughts, and I saw him

Uh posting that was three years ago I saw him posting that visual saying yeah, you take twins, you separate them at birth, you study the IQ. No difference from two different tests of the same person. That that looks like, you know, very compelling argument.

And it's sufficiently compelling for millions and millions of people to view that thing, to like it, to enjoy and to believe that it's correct. Now, if this is true If you really perform that experiment and it really yields that result, that there is no measurable difference.

between people that are raised completely separately, then of course it means something very deep about genetic determination. And these things seemed to contradict my own perception of or my own talent as a mathematician developed. And also what I understand about the way it works and what I've written about my book. So that was very annoying. I I I I saw that post when I was

um uh making the final corrections on on my book before the publication on on uh of the American edition and I was really disturbed. I mean if this post is correct then I am delusional, but not me. Because I'm quoting people like Einstein, like Descartes, like Rothenieck, and they all say, you know, I'm not s I was not born with something special, I just developed it by a certain practice. So are they delusional? Are we all delusional?

So, you know, I had to have a look at the science. And now the thing is When you look at the science, it's much more complicated than that. And actually that experiment, that perfect experiment that looks like it's going to be decisive, is just doesn't exist in real life. And that was a big surprise for me. I have to Make the observation that there is the possibility that Einstein, Descartes, and Grothedick are

False falsely modest. They're just trying to make a humble brag. And yourself as well. Uh you do know yourself pretty well, I suspect. But maybe you're quite gifted. It's interesting to me that That you wouldn't go the other way.

you know, most people want to say I'm incredibly gifted and I've done great things with it, then I'm not so gifted is i even if you've achieved a lot, somehow that seems uh a little bit demeaning. So you've you've you've embraced a s a very interesting uh Summary of your own

Yes, so so that's that's interesting because it raised many different questions about the motivations of people. So uh when my my book was published, I was interviewed by a quantum magazine who specialized on mathematics, and I was saying, you know I don't think genius is innate. I think it's a state that you develop. I I'm not saying that everybody can get there. I'm just saying that the people who got there.

Had to experience a very special journey. And without that journey, they would not be there. So let me maybe try to quantify that. Because I can actually phrase that into uh a quantified conjecture. But the odds of becoming a one in a million genius, let's put it like It's clear that someone like Einstein or Descartes or Grothendieck are one in a million, and if not less, actually may maybe closer to one in a billion. But still, let's take one in a million and that's something you can sample.

Uh the field medalists, you get the the the winners of the gold medal at the International Math Olympiads, you get the Grandmaster at Chase of whatever subpopulation that you characterize as cognitively one in a million. I'm not claiming that these guys are just random people with random genomes. Maybe they're gifted. Actually it's very likely that they're gifted from an internet perspective. What I'm saying is maybe they're just in the top one percent.

And to get into the one in a million, they need to experience a very unique journey. Uh in October when we discuss when we're discussing my my my book on mathematics, we're discussing this example of Bill Thurston. Bill Thurston was uh born with a uh with a squint and um He started his life not being able to view the world in three D because his eyes were looking at different regions of his space.

But he experienced a very unique childhood with a very loving mother who spent hours and hours with him. Training is perception of depth and through persistent effort you develop the ability to assemble two two dimensional images to re to reconstruct three D vision. And He continued playing that game and he learned to view the world, to think in 4D, 4 dimensions and then in 5 dimensions by using the same technique.

And when he entered primary school, he decided that he would practice visualisation every day. That guy. And it's documented. Maybe he was, you know, in the top one percent or one in a thousand from a cognitive standpoint. Maybe that's quite possible. But what happened to him? This very unique journey of trying to play a very special game with his imagination led him to develops a very unique geometric ability and he became one of the most famous geometers of the twentieth century.

Um he's uh he was instrumental to to solving the Poincare conjecture through his geometrization conjecture that generalizes it and he's he got the field metal, but he's an obvious genius in geometry. And it's quite obvious that something that's not innate happened to him. Uh uh w what would be Humber bragging in that situation? I don't know. I don't know if it's better for him to say I was born a g in a genius or I was I had a crazy unique life and I'm

It's hard to replicate in both cases really hard to replicate, but it's just maybe more honest to say that you went there through a special journey. And this journey is interesting because maybe People will not be able to replicate that, but maybe there's some lessons to learn from that that could apply. Maybe not at the same scale, but still maybe something to learn from everyone.

When I was in graduate school, I felt that many of my classmates liked to pretend that they never did any homework or cracked a book. Uh that somehow there was something more uh impressive and uh about just having this innate ability. But maybe that's an economist's problem. I don't know. But let's let's go to the um Let's talk a little bit before we get to the actual studies about

Numbers. So you give the example of thirty percent, fifty percent, and eighty percent as the possible correlation between some innate trait and its manifestation in in real life. And What I think is fascinating. And and these numbers get thrown around like they're scientific, you know, that that that IQ is eighty percent genetic. As if that means something really

Exact. It it doesn't on so in so many ways. It is not a precise statement because IQ is not a really precise measure of intelligence. And for a hundred reasons, that that might be wrong. But if we pretend for a minute that we could measure this with some precision. You make the case that thirty and fifty actually still leave a great deal of room for human uh

for choosing your own destiny, form making your own way in the world, despite your genetic uh gifts. And eighty percent starts to get a little closer to determinism about about unless of a blank state of any kind. You want to just talk about that for a minute? I think it's it's interesting. We can't give some nice pictures in the essay. I encourage people to look at'em. But uh without the pictures, go ahead.

Yeah, I I do think that you know being a mathematician helps you see through the noise because it's a very noisy conversation with many subliminal messages uh sent sent through the channel and and making everybody confused. When you say, for example Maybe, you know, IQ could be, let's say, fifty percent determined by your genes. What does it mean? What does it mean for you? Because you you you're not the world population, you're just one percent.

So one percent in that population. So uh i in my article I included three visualisation of what would look what it would look like. So it's it's a very simple uh graphic where you have, you know, uh the x axis is your polygenic score. And the y axis is the actual phenotype, your outcome, your IQ in that example. So the Fifty, thirty, eighty percent is what is called the heritability. That's basically the the regression coefficient where the deciding, you know.

That's one hundred percent all the points are on the line. It's just uh direct correlation direct causation between your your polygenic score and your IQ. Now fifty percent when you look at it, yeah, there's a trend, that's clear. But You know, fifty percent uh uh uh correlation coefficient is not a very strong correlation. It's it's still very weak.

And if you have which is the default assumption for everyone, if you have an average polygenic score, then you still have a chance to be eventually maybe in the top one percent. It's not bad. It's not a It's not a high chance, it's sure lower than if the correlation was weaker, but you still have a chance. Now the question is what is The remaining factor. And I think it's also an important confusion that you know many people have noticed in their life that

You get two kids from the same family with widely divergent outcomes. That's one of the things that Stephen Pink has mentioned in his book. You know, every every parent of two kids starts to think that things are genetic because they're so different. And I have two kids and I actually realize that very different but everything is very different around about them. Not just you know, not just the genes, but they were born at at a different moment.

in a different family because there was an an initial member when the second one was born. And the parents were a bit older and maybe at a different point in their career and maybe Having learnt from the mistakes they made with the first child and trying to do things a bit differently and then the dynamics of the interaction and then have You know, it's different dynamics and everybody is different. One thing that is true is

that the non genetic parameter is not that easily pigeonaled into very simple basic socio economic criteria. That is There is some socio economic dimension. It's clear that it's better to to grow up in a family that is better off and better educated with more books, but it doesn't decide everything. And there's still a massive amount.

Of idiosyncratic things that happen to you and only you and but which may be decisive. And the example of first one is actually very idiosyncratic and I do believe that it's super important. Um there is uh uh it's actually the the notion that's uh um that Stephen Picker used at the beginning of uh chapter nineteen in the blank slate. The chapter is called the children, it's about

Genetic determinism and causation. Is it start with the three laws of Turkheimer and I think that's very important to you. Are you familiar with these three laws? No. I saw one of them in your essay. Who is Turkheimer? And then tell us his rules.

Eric Torkheimer is is a is a behavioral uh geneticist, you know, studying the influence of genome on uh behavioral traits and Uh I think it was in maybe two two thousand that he published that very famous paper about the three laws of behavioral genetics and what they mean. And it's the first thing that that uh Stephen Pinker cites at the beginning of his chapter on that. And I think it's good to to go back to these three laws because they have clarified a lot.

The law number one is that everything is heritable to a certain degree. And, you know, maybe it was bold and new when it was written and it was great to write that, but I think everybody n agrees on that. You know, it's it's clear that there is some genetic No but uh it's very hard to find s someone who truly believes in the blank slate here. You know, I certainly not me, certainly not you. So there is a genetic determination, that's obvious.

The second law says that the family you're born in usually matters less than your genome. So this is kind of sobering for the blank slatist viewpoint, saying, you know, yes, there is social determinism, but it's may not be as important as genetic determinism. But the really beautiful one is the third law. The third law says there is a huge amount of a variability that is neither coming from your genome.

And this is your life, you know, who you are, what happens in your brain, what happens in your life, your friends, your chance encounters, the ideas, your things you the games you play, and everything like that. Now When you um when you read the paper and so I after after I I st got you know stuck in that rabbit hole, I spent like three months reading everything I could on on the subject. It's very interesting that

Torkheimer makes an interesting comment about this third law. He says maybe What matters? from your family is something that is unique to you. Like, you know, it's like Heraclit, you know, and you no nobody swims twice in the same river. Nobody grows up twice in the same family. You know, it doesn't happen like that. So maybe that's an interesting perspective to say that it's not because it doesn't apply equally to all children in the family that parents do not matter.

And this nuance that sounds like a bit like nitpicking is actually, I think, very important and people underestimate that. Say it again. So, you know, what is proven why when you study siblings, The correlation between siblings is not at high. And it's a fact. It's an experimental fact. The correlation between identical twins is raised together, it's super high.

The correlation between fraternal twins raised together is is much lower, and that's a fact. This is undisputable. And the correlation between siblings who are not twins is even weaker than that. Now You have many ways to look at that. One is to say family don't matter that much because two siblings can have widely divergent outcomes. Another way to look at that is to say there is an influence from your background, but it's not deterministic. It depends on the dynamics.

of who you are, in which situation. You know, everybody knows that parents behave differently from one parent to another. One child to another, yeah. And when you when yeah, one child to another. Uh and and if you think about it from a Developmental perspective, when you think that the brain is a very complex thing, very nonlinear machine that develops over decades.

You have to realize that this is a very unstable, dynamic, multidimensional problem. So it's the kind of thing that tends to be stochastic. So maybe actually your outcome is not that deterministic, in fact. Yeah, I think um it also I don't really like rule number two. Law number two, the the quantification. It it might be true for things that are easily quantified. Um like height.

Uh, height is is genetic and it's also determined by your environment, your nutrition, both in the womb and after birth and Uh it is it is not destiny. There's regression to the mean, there's a random component that that is at play. You want to say something? Yeah, but height isn't

uh the one example of of a very very socially important trait that is actually massively determined by by your genes. Right. That's what I'm saying. And and the case is even though you know you still have to t to get decent nutrition, but if you If you if you get good nutrition and good health care, then you're going to probably grow to your Potential height, you know the one you you you can do.

This results in in modern society is in a very strong correlation between your genome and your height. Exactly. I didn't mean to suggest that because there's regression of the mean it's not genetic or Th there's a random component, but there's obviously uh a s very strong genetic component in how how tall you are, the color of your hair, a thousand traits that we have as human beings. Uh how happy you are, how friendly you are.

How shy you are, how smart you are. These are words that we use to capture certain aspects of the human experience, but they're very difficult to quantify. Um but so I just wanna I just wanted to make that point. Let's let's move on to the

I'll call it the science, but I I will say that I'm not a expert on this um this field. I I have some interest in it as as listeners will know. Um I did ask Claude to summarize uh excuse me, yeah, it was Claude, to summarize the studies on this question of of twins raised in different homes.

Especially with respect to IQ. And then I asked it for critique. And uh I I can say safely that it is pretty clear to me that you know as much as Claude does about this, and maybe even more, which is not a small s not a small thing. So you've gone very deeply into it, and it is a, unfortunately, just one more example of the phrase studies show not being that reliable. Um so there are

five studies that were looked at in the post that you were talk that you were talking about. But it turns out they're really There's problems with a number of them that are kind of obvious, like one of them has apparently fraud, uh and others have very tiny uh sample sizes, populations in their in their study. But two are qu are relatively large, but i it is worth saying right at the start, as you do that.

They're all small because there aren't that many twins reared in separate homes, just by uh and I never thought about that. Even if that's the only thing listeners get out of this. Because people say, Oh, well, you know, they've studied twins in different houses. And it turns out and I it's like, well, how many? Not many, it turns out. So the most famous study, which is uh Bouchard. Uh I think it was eighty one pairs of identical twins. I think it it's uh

Oh fifty eight. In in the nineteen nineties science paper it's like fifty six or fifty eight. Okay. I'm not sure. In the fifties. Yeah. Uh I pair the item between. It's not tiny, but it's not big. And it's global. It's not a local study. So they they went on you know, they there were big articles in the New York Times and some of the twins were you r reunited for for this study. W were coming from different continents. Uhhuh. So you you don't find even fifty in the US, you know, it's it's a very

unusual circumstance to be to have an identical twin. First of all, it's not the normal situation to have an identical twin. Then to get adopted is another rare event. So if you correlate the two you get a very small sample. Now to get Adopted by different families is very unique. And actually, for um in many regions and in many periods, you would not do that. You would not separate

identical twins because it's often considered I I'm not an expert on that, but it's often considered that it's not a good idea to do that. So you end up with a very Special situation, and and the fact that it's special is actually very important in the story because you try to extrapolate uh General laws about human cognition that apply to billions of people, and you try to learn that from 50 something pairs of people in a very unusual circumstance. And just from that, there's an issue.

Now it's a very seducing ID and I admit that the first time you're exposed to that. You find it beautiful. Like it's what peop people dream of, you know, economists they love that. Like a natural experiment. You don't have to do anything, you just measure it because nature has done the experiment for you. And in my um article I I I

um included this very interesting interview of Thomas Bouchard who conducted the study and he described how he fell in love with the idea. And honestly I completely understand. The first time I heard it, I fell in love with it. It's a beautiful ID. It it looks like it's going to give you a complete, precise and definite answer to the question of nature versus nurture. And, you know, how can you resist that? It's so tempting.

And it it's not just the um beauty of the experiment. We human beings as uh As uh I think listeners know, we we like patterns. We love patterns. And some of them are not representing what we think they are, but we like them anyway. So we'll maybe talk about this later as well, but there's a documentary which I recommend with a little bit of hesitation because it's I think a little bit deceptive, but it's very dramatic.

It's called Three Identical Strangers and it's about thr uh triplets who are separated. And of course they all look exactly alike, which is already fascinating that that three human beings raised apart, we know they're gonna look exactly alike because they are identical triplets. But then it turns out they have all kinds of hap when they were reunited at later in life. They have all these things that they have in common.

And I'm not gonna get these right'cause it doesn't matter, but this is the kind of thing we love. They all married the a woman with the same name. They all smoke, or they don't smoke. They all like basketball.

They all had a pet dog, a small dog, too. And and the list just gets longer and longer. And and it gives me a little bit of goosebumps just telling the Story I'm making up because it's there's something magical and mysterious and we've even though we might have dramatic and strong feelings about nature versus nurture. This this story is is so juicy. It's so delicious.

And it's really started like that. So so so you you you you you you blended two stories, the three identical stro uh triplets and the gym twins. The gym twins are the seminal case. of a Bouchard study because uh these two twins, uh I think they were born in nineteen forty and re reunited in nineteen seventy nine. And they have the same name, the first name, first name, both of them Jim.

Both of them, you know, um having married uh Linda and then a Betty or if it's the maybe first a Betty and a Linda, but actually they're married twice with women who have the same name. They named their their son with the same name. And the two of them grew up in families where they have another adopted brother whose name was Larry. It's this is off, the Larry thing, because everything at this point could be involving some of their own agency. But the name of their adoptive brother? Like it's

Gets are not supposed to have a telepathic influence on your parents. That's why they would name your brother with the same name. Now, what you when you take a step back and realize that what this means, it just means that the two of them were placed. into distinct families both in suburban Ohio In probably, you know, people who adopt kids. They're not random people. They tend to be family oriented. They tend to have certain values.

Uh, and it looked like they were living in a very conformist world in the nineteen forties where, you know, people would always pick the same name for their children. And I never dated any Linda, but it's not because of my genes, it might be because I'm French, you know. So that's a simple fact. And so when you read that story and they all as kids they the two of them had had a dog named Toy.

When you read that with a little bit of you know training in data science and and spurious correlation, you start to realize that there's something off with the experiment. It looks like it was not completely randomized. So that your natural experiment is just

Something about the social background of suburban who are you in the nineteen forties. Which is interesting in itself, but it's not what you want to prove. Now that's that's interesting because it's actually Uh on a more uh philosophical, epistemological perspective, the whole story about twins real depart is very typical of the pre replication crisis psychology. where people were moving into

quantitative science, but coming from I would say a more literary perspective on things, where stories were more important. And I think the the lack of statistical training, advanced statistical training, uh of most scientists of that time. You know, they accepted P value five percent as a viable criterion for truth, which is not as we know now. um is probably very important in the way the story developed and becomes serious because when Thomas Bouchard saw

interviews of his gene twins, he thought, okay, I'm going to do a big study on that. And then he started working for 10 years on that, mobilizing huge resources and having a very ambitious project. And once you start running a project like that, you have to find something, whether or not it's there. Well I think it's interesting that you attribute the

The the sixties and seven fifties, sixties and seventies is the dark ages of statistical sophistication. Obviously in modern times, when people are very well trained in the techniques at least, if maybe not the interpretation, but the techniques. They still manage to have challenges, which is what the replication crisis is is, you know, representing. Um but let's talk about the actual stu study, the Bouchard study and and some of the others, because

The things that are wrong often in these kind of stories, data challenges and generalization challenges, that you know, the the issues are quite complex. Um, you know, there's something is missed about

uh the way that the sample was created. These are not complicated. These are kind of straightforward. So what's wrong? What was in the design of these studies, what were some of the problems? So uh On paper it looks like the twin three other part is going to be a perfect natural experiment that tears nature apart. The thing is, the twins share nine months of life in the womb.

And you know, if you've been around a pregnant woman, you know that pregnant w women tend to be careful about what they're doing because this is a critical period actually for many things, especially for IQ. Everybody knows. Uh this is just one of the first bias. There is more than that. There is the fact that they were not really separated at birth. I think on a on average it was like five months, which is not nothing.

We can say family as are not that important, but the first five months of the seem to matter at least a little bit, especially in families where you're going to have a reason to get an adoption after that. So maybe that was like especially dysfunctional families. And some joint event happened that caused them to to get separated. What was it? Was it, you know, social services taking the kids out because the mother was putting booze in the baby formula? You didn't you don't know. But

It looks like the odds of something really bad happening to them is higher than normal. So you have a bunch of correlations between those twins. And what's interesting and it's something that, you know, uh troubled me when I read that, because w I was thinking, okay, how do they control for that? That's kind of instinctive to to any I would say any

Modern scientists, you you have some bias somewhere, that's fine. You can live with it if you control. Now, there's a... A technique for that which actually I traced back to to a textbook from nineteen sixty. So everything was already known in nineteen sixty. There is that book by Falconer, who is a geneticist and statistician who created the famous Falconer formula that is used

In twins reared apart and twins reared together studies. That says it looks like it's a natural experiment, but it's not, it's biased, and the way to de bias it is to take a control group of fraternal twins. Which is normal, so you say you have identical twins. Not separated at birth, but separated after f five months. So living through nine months together in the womb, five months together after that.

Being separated for the same reason. You do the same thing for fraternal twins, who share not one hundred percent but fifty percent of the DNA. And from that you cannot be one hundred percent sure but you can have a first order approximation. by you know looking at at the correlation between the the identical twins, the correlation between the fraternal twins, you sub you subtract one from the other, you multiply by two, but gives you a gross approximation of

what you want, the irritability. So this is what you're supposed to do. And what's interesting is that the study started with the idea that they were going to do So in the same interview I I I mentioned about uh where Thomas Bouchard explained or he's falling in love or he fell in love with a study. This interview is from the the beginning of his study, I think it he was a couple of years into it.

And he he he was mentioning that he was collecting a control group of fraternal twins, which is a normal thing to do, which is a wise thing to do. Now in in the 1990 paper in science. They mentioned that they have this control group of Protono twins and they say due to space conference we decided not to publish it, which is really bizarre, honestly, really, really bizarre. So that um I've always loved that phenomenon. Um, it does raise a red flag, of course. Um It's not a good excuse.

Uh you know, i it it it's a uh implausible excuse. It it's an imp it's a it's an excuse that no referee, uh good referee of a paper uh in reviewing it would say, No, I think I'd like to see that at least. It it would be okay to say To tell us the results and then say, but we can't put the full table up or we c it's really complicated, so we didn't have room to show you everything. But here's what we found and the data available in requests from the authors.

But to say we're not gonna share it because we don't have room is uh is the dog ate my homework. It's not a good good answer.

Yeah, so they they give two excuses and none of them are believable. The first one is there is no space. And the second one is the sample is too small. So it is true that the sample is smaller. Um I think it's like thirty against fifty six. thirty fraternal twins against fifty six uh identical twins, but it's not very com compelling because you know uh the margin of error is one of the square root of the sample size. So having close to two X in support size actually is like

close to forty percent difference in precision, which is not that big. I mean Sure it's i it's not as good because it's smaller. You cannot decide one is gold standard, I'm going to pick keep it, and the other one is not even good for publication. So uh of course I I made that joke i in the paper because you know, being a mathematician, it looks like Pierre de Fermat mentioning he has a very beautiful proof, but the margin is too small for him to meant to to include it.

Which is, you know, in mathematics it it got everybody busy for three hundred and and fifty years to to tr to try to recover the proof, which probably was wrong actually. We everybody thinks that Thema did not really have a proof. Of course not. But if he did, it's almost certainly true that the margin was too small. So Oh yes, but I don't think you know if and rule wise uh st struggled uh so much and and got a first proof that was initially wrong. Right.

uh leveraging twentieth century mathematics, it's quite s probable that Firma made a mistake somewhere. Nobody can prove it, but it's kind of likely. Now in mathematics at least you can do the You can do your homework, you can try to recover the proof. In experimental science, this is just unacceptable. Like they

They spent like ten years collecting uh identicals and and fraternal twins from across the world and they are the only ones sitting on the data and they just don't want to share it. I mean, this is unacceptable. And it's not believable that the one line that it would take you to just mention the correlation coefficient between these pairs takes too much because if you have

This is the silver bullet. That's the the argument that's going to be more compelling than everything you have written in your paper. So you can throw all the arguments away and just publish that. So there's no reason not to publish it. So what they do instead. Is they go through Uh what you're not supposed to do in data driven science, they they try to talk themselves out of having to show the control code.

Which is very awkward. Like they they say, okay, yes, it's true that they share the warm for nine months and maybe, you know, there are some factors like fetal alcohol syndrome. That could cause correlations, you know, between the IQs of the twins. Really I honestly, uh I think there's something about the paper being published in nineteen ninety because for for our eyes right now it's just

It great impossible that the referee could accept that. You don't you know, this is a very important debate. Like they with very important ramification, you know people understand society and education and all that, and you just cannot have That kind of slopy criteria in a paper in science and I actually think that

Science should should ask the data actually, even today. I mean I I'm not saying the paper is fraudulent, I'm just saying that the way it's written is just unacceptable. And we want to see the data. Is there a correlation or not? You know, it's it it's fairly important. You cannot just leave that thing confidential, it's not acceptable.

Now, I want you to restate the argument about why fraternal twins are a good control group for people who may have gotten lost in the uh the path the the torturous tortuous path that um that we just went down. So in a perfect world Or you had thousands of uh identical twins reared apart.

Uh what would what would you do with the fraternal if you then had thousands again, forgetting forget the small sample size. I just want listeners to understand the intuition. If you had these fraternal twins, that were raised apart, what would you do with their data to make sure that what you found with the identical twins was reliable?

Yeah. So uh it's interesting because it it it it bridges with another kind of studies that is the twins reared together that actually are exactly what you described. We have thousands of pairs. of identical twins that are raised together and of paternal twins that are raised together. And the your question is about how do the studies of twins reared together work? Because this is actually what they do.

So um they operate under a number of simplifying assumptions that are debatable and are probably not exactly true. So that means that there is a debate about how you model The actual heritability, but at least you have a functioning model that is a first order approximation. So how does it work? So the idea is uh identical twins share one hundred percent of a DNA. They are literally clones.

So um when you look at the correlation between, say, their IQ, there is the genetic correlation and if you look, you know, in terms of variance, the the the variance is good because if variables are not interacting with each other, you can sum the variance of the different components. Which is a simplifying assumption that is not not exactly correct, but at least you can start from that. So they say you have the genetic variance and you have the

family environment variance, which is the second one is the same for fraternal twins, except that the f but the first one, the genetic variance, is only fifty percent for fraternal twins. So what you do is you make the difference, you take the Uh correlation coefficients for identical twins. You subtract the correlation coefficient from fraternal twins.

And you multiply that by two to a set to to extrapolate what would be the full contribution of the genome, because with fraternotwins you have fifty percent of the genomic contribution. So let me give you an example, which is Um actually the the twins read together studies give something like that. Let's say you have eighty percent correlations between the IQs. Oh yeah. One thousand pairs.

of identical twins. So you look at the within pair correlation, you X is twin one, Y is twin two, you make a scatter plot of that and you take the regulation line. What is the correlation coefficient? It's something like eighty percent. Okay? If you do the same thing for Triton or Twin, you may get maybe fifty percent. So the difference is thirty percent and using that s simplification formula that dates back to Falconer in nineteen sixty, you get sixty percent irritability.

So this is an estimate and it's debatable because you know uh genome is not linear and there are some interactions between the different variables, they're not completely separated, and the fact that you have an idiot twin is kind of changes your life experience. There is an active conversation about whether or not this approximation is correct, but at least you have one. And this is what Bouchard set up Thank you. We're going to edit because I just punched my microphone. Um

So this is what Bouchard set out to study when he collected data for the fraternal twins and identical twins. So based on the two correlations, you should have an estimate. But I just want to say it a little d a little different. If if Genetics was important. There's this high correlation between IQ say for two

twins raised in different households. So if we found that, if we found that twins raised in different households are frequently uh their IQs are very similar We'd want to say we might want to conclude, well therefore, again, forgetting all the things you already mentioned about the womb and the not they're not assigned randomly, obviously they

They're often were assigned we we discover later. I don't know if you mentioned this in your paper. I saw this from Claude. Some of them were assigned to families

The same family, just not the same house. You know, it's like an uncle and uh uh two brothers at two different houses in nearby neighborhoods. So the the actual nurture part of this is extremely similar. So we would expect them Putting Turkheimer's third law aside for a moment, we'd expect them to have very similar IQs, not just because of the genetics, but because their environment was very similar. But the idea of having fraternal twins as the control would be that if

They uh were raised in separate households. They don't have the same genetics. So how far apart are their IQs? You know, they're also probably going to be raised in similar homes, maybe by assignment. It's not random, not a random house in America. So you'd want to compare if if genetics is really important, you'd want to you'd want to find that the correlation for the identical twins was much bigger than it is for the fraternal twins. And Bouchard in theory had that data.

And he did not share it. Now he just had the data. He did the the finding. Now

I don't know if there may be something that Claude knows that you don't know. Uh according to Claude Jay Joseph, uh, a person who tends toward blank slatism, evidently, uh, was very critical of the Bouchard study. He cl he claimed to have access to their data. Is that correct, you know? So I mentioned that in a footnote, yeah. So um he claims to have located the data in a subsequent paper by the team, by the same team.

That's basically show that there is no um meaningful signal, which is no statistically significant difference. between the correlation of the fraternal twins raised apart and the identical twins raised apart, which means that the identical part can't be decisive. Yeah, which is not surprising when you look at the sample size. You have like fifty six in one on one thirty in the other end, so the airbouts will be pretty wide. Yeah.

So it's it's kind of normal that the two confidence interval between fraternal twins and identical twins overlap. It's not surprising. It's a fact of life. You don't get that many twins rear the path. I'm not saying that there's no genetic correction. Of course, there is one. The question is: how much can you quantify it? And this protocol. probably was unable to compute a decisive

confident value for that share of genetic inference. And that's a fact of life. It's a normal fact of statistics. The study is too small to prove anything. But this is obfuscated in the paper. So uh after after the the the my article was published uh it it triggered uh many, many reactions. I uh uh and of course some hardcore hereditarians.

criticise me for requesting bad control group data. And they mentioned that there was some subsequent uh samples from the same study published years after that, which is already a bit off because you when you do a control group study, you have it's basically having a placebo arm, you know. You do the directory you're supposed to get the same vintage from the data. You cannot say

The control group in nineteen ninety was was not good, but I'm going to use the one from nineteen ninety five because I like it better. But they were mentioning that if you take that later data and you put it in the model. Then you get a confidence interval for the irritability that includes negative value. And they say they they were mentioning uh a as if it was a good argument. Look, they could not publish something where the irritability could be negative because

That's meaningless. Of course that's meaningless. Let's just say that the study doesn't work. That's that's not that IQ is negatively correlated. We we it's it's not that. It just it doesn't work. That's a fact. Why do you hide it? It's

So let let's talk about some of the let's readers can go to your essay, we'll put a link up to it. You can it's um we've done our best in this conversation to make it accessible without visuals and and and statistics. It's it's not a hard essay to read. It's uh it's pretty straightforward and the visuals about th the importance of a thirty or fifty or eighty percent number quite helpful. Um I will just mention as an aside, as one of my frustrations in economics

that people can actually publish papers where there's a a scatter plot of the data points and it's a cloud and it's a circle. You can look at it, you go, that looks like like a circle. Uh a randomly Done points. But if you put a line through it, sometimes you can get a significant result and they trumpet that as if it's an important finding. It just it's it's depressing to me. It's the nature of our of our game. But

I want to talk about the significance of the of this, not the statistical significance, the human significance. So um Parenting is really important to me. Um I have four kids, so I have four data points, and of course they have some similarities because of genetics and They all grew up in the same with the same parents, but they did grow up at different times. We learned things. We also probably made mistakes trying to correct things that we that we thought we'd done wrong with the earlier kids.

And uh the idea that it's irrelevant, that I had no impact on how my children Curiosity, happiness, maturity, uh kindness, etcetera. The idea that the the times I tormented my children by making them say thank you or I'm sorry were actually uh irrelevant because

Parenting is important and listeners can go back and listen to Brian Kaplan's episode on parenting here at Econ Talk from a long time ago. And Brian argues that parenting's not so important and there's a there's a definite Literature and parenting that says You don't have much control over how your kids turn out. It it's an illusion. So I'll you know, you wonder if you're an analytical person at all, did I make any difference? Um even negative, did I make any difference?

Um and so that's one of the reasons I think this debate is important. Uh why else is it important? Oh it's this first point is already quite important. I mean I have two young kids and um just these debates, what is under your control and what is not, I think is completely biased when you start from saying, you know, you have no control at all. Of course you control is very

uh very diffuse, very not direct, but still, you know, you don't drink during pregnancy when you're a pregnant woman. You should not do that. And this is parenting, you know, there are these labels on the wine. Bottles and they're there for a reason. And actually it's proven this beyond doubt that drinking during pregnancy cause damage to your kid and especially to cognition. This is proven. This is parenting. Keep going, David. I w I'm gonna criti critique that a little bit, but go ahead.

Uh it depends. Okay. I I don't want to be completely paranoid, but you should not drink a bottle of day when you're pregnant, that's for sure. Okay. Can we agree on that? Yes. Uh I just listeners can go back to the Emily Auster episodes on parenting and I I I think

Alcoholism is really bad for for uh a child to the womb. Uh whether a glass of wine is, is I think more complicated. But carry on out. I can I can agree with that. Now what you have with with with parenting, you can transport that to education.

Of course we we all know that story and it's a sad one about the fact that intervention in education rarely scares. Like usually you have a very neat pilot with an expert who invents a new way of making teaching better and when you try to scale it like it vanishes away. Okay, this is sad, but does it mean that teachers don't matter and education doesn't matter? I

Uh that's that's a tough set, you know. Maybe maybe there is a good point to say that it doesn't matter as much as we would like it to matter, but that doesn't say it doesn't matter, you know. And

This is where I think people are stuck with deterministic thinking. And I think this is one of the again, you know, uh my my my theme about, you know, uh The ability to grasp mathematical subtleties in these issues is actually very important and goes a long way to explain all the confusions in the public debate. The third law of Turkheimer says things are going to be noisy. Whatever

uh quantified variables you have to describe in a few dimensions your environment like the income of your family, uh whatever you know. This is just going to be a few correlations with the outcome, but the actual thing that matter is what's happening in your head, your journey. And this is actually the whole topic of, you know, the conversation we had about mathematics. And that's the whole thing.

But is from my perspective the most important discovery I made in my life, which is to become better at math, which is one of the supposedly more st loaded activity you can imagine.

G meaning G loaded meaning related to your general intelligence. Yeah. It's supposed to be it looks like your mathematics looks like a giant IQ test in many ways. But What I realised in my studies and then in my career as a pure mathematician, and I it's something that most mathematicians agree on, is that what's really important is how you process things in your head. your invisible metacognitive approach.

To interacting with mathematical objects, playing with your imagination, trying to hone your intuition, being patient, being persistent. uh not succumbing to the fear that, you know, this thing is going to be too difficult for you and you should run away from it. Like there is it's very subtle, it's invisible, it's something that is very personal. Therefore your journey will be very idiosyncratic.

And this is super important. And yes, it doesn't scale easily, but it's one of the most important debates we can have. And if we say everything is decided on the day you were born, we we kind of uh we cancel that conversation. And that's a big issue.

Yeah. Um I I want to close with a a related point and get your thoughts. It really shouldn't be a controversial idea that the world is complicated. Uh, which I s w another way of saying Turkheimer's third law. You know, the the thing that's happening in your head, your inner journey, your inner narrative, your inner experiences. is never gonna be in the data set, at least in our lifetime, maybe maybe someday down the road, but I suspect not.

And so there's always, you know, the fancy name for this in in statistics is there are omitted omitted variables. And sometimes they're incredibly important, and that's just a reality, but we don't like that reality. So that's one I think that's a great uh an important point. But the second thing that I find fascinating related to this is that people

And it's not obvious that they should. This is not important. No nobody lives their life in a certain dimension it's not important. Nobody lives their life saying, Well, if it's only fifty percent I'm gonna do X, but if it's eighty percent, oh my gosh, then I won't I'd never bother with doing that. We live our lives, we understand the world's complicated, we do the best we can. The idea that we are so passionate

about this issue of nature versus nurture strikes at the very heart of how we see our humanity and ourselves. And I think that is a piece of this debate that is utterly fascinating. It's not in some sense it's irrelevant. It's it's it's meta meta. It's it's a it's one more once more removed from from the reality. But it's not irrelevant. It's it's important to us.

to understand who we are and we get a position that we care about on this issue and we don't like having it just like you admitted at the beginning of this, the the idea that I that that my notion of my own journey might be an illusion, is unbearable.

So you've turned out okay. You found out that it turns out you don't have to give up your illusion. You can actually believe that you're in control of your fate as a mathematician more than than the hereditarians say. But anyway, I re f just react to that. Yeah, I think you y y you you spot on there is something very personal. uh behind these controversies. And actually I I I don't think I have very good control on my destiny. I just think that it's non-zero. I just think that. Okay. Uh

And and actually one reason I don't think that is because I know that my own ability fluctuated a lot depending on my emotional state. That's that's the most important discovery of my life, you know. But if you if you look at Анекдотс або пипоф Their perception of the nature versus nature debate. There is a very interesting post called The Parable of Talents by Scott Alexander.

um in the old Slate Star codex. And um it he starts by saying, you know, it's it's been obvious to me that things are innate to a certain degree because when I was a kid I was really bad at math. And I tried hard and I was very bad at that, but I was very good at English and I was not trying hard.

So that's interesting because here you see something that, you know, is coming from childhood and you have this shocking thing that some people appear gifted and some people appear handicapped, had the same activity and we don't really know what causes. And because we don't really know that, there are certain tendencies to uh put the blame Or sign an act of God somewhere that controls that. So

Some people put the blame on society and they're blank slate is saying, Okay, it's because they were coming from they had bad parents or whatever, bad family or bad social background. Some people put the blame on the genes, you know, saying, Okay I don't understand what made me bad at math, so I'm going to assume that it was genetic. And that's understandable. Actually when I was um when I was seventeen I started, you know, some a kind of advanced programme for

kids were really good at math and I was confronted with people who were unbelievably uh unbelievably smarter than me. Uh one of the kids in my class went on to win a gold medal at the International Mass Olympias and it was That was mesmerizing. I could not understand how he was doing it. It was magic. There was not a single problem like

I could answer that he could not answer, but ev there are many problems that he could answer that I could not answer. And it was instant and he was sleeping during classes. But it looked like magic. And at that point. I was pretty convinced that this thing was genetic because I could not imagine a non-genetic explanation. This guy was not coming from a fancy family, you know. Not at all. Quite the opposite.

So I completely understand where hereditarians are coming from. I was coming from the same perspective. Now Uh we're in twenty twenty five. And we start to understand a few things about the brain. And we start to understand a few things about, you know, how you can create machine that emulates some brain function. So it's kind of interesting to to try to maybe unpack that mystery. And there's something to learn.

So that's why I I really think that we should really go beyond ideology on that, because ideology is really in that instance, it's really censorship. My guest today has been David Bessis. David, thanks for being part of Econ Talk. Thank you very much. This is Econ Talk, part of the Library of Economics and Liberty. For more Econ Talk, go to econtalk.org where you can also comment on. FreeCon Talk is Rich Goyette. I'm your host, Russ Roberts. Talking about it.