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

Are your genes your destiny? Despite famous studies of identical twins that seem to answer in the affirmative, mathematician David Bessis says: Not so fast. He and EconTalk's Russ Roberts take a deep dive into the "twins reared apart" literature, showing how multiple flaws in those studies undercut their claims about heritability. Bessis demonstrates why the natural experiments are never perfect, and why differences across people in a particular time and place are no guarantee of what will happen to any one human being. They also discuss psychologist Eric Turkheimer's three laws of behavior genetics, emphasizing the role of unique experiences in shaping who we become.

• 0:02Speaker 00

  Welcome 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's Hoover Institution. Go to econTalk.org where you can subscribe, comment on this episode, and find links to other information related to today's conversation. You'll also find our archives with every episode we've done going back to 2006. Our email address is [email protected]. We'd love to hear from you. Today is December 22, 2025. Before introducing today's guest, I want to remind listeners

• 0:43Speaker 00

  to go to econTalk.org and vote on your favorite episodes of 2025. And now for today's guest, mathematician and author David Bessis. He was last here in October of 2025, 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.

• 1:15Speaker 01

  Hello, Russ. Delighted to be back.

• 1:18Speaker 00

  Now, your book Mathematica takes what is, for some people, a controversial view of mathematics. Unlike Juan Corre'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 Göthetik. So you do not believe that

• 1:58Speaker 00

  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 were, quote, "separated at birth,"

• 2:17Speaker 00

  Those studies find that genetics are destiny. And here's what you wrote as the takeaway. When you separate, 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.

• 3:02Speaker 00

  Twins raised in totally separate households with, it seems, totally different nurture, but their nature overwhelms it, and there is if they were a single person. 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 bothered you.

• 3:28Speaker 01

  Yes, so I think it's really important to frame the debate in the correct term, because one thing I noticed 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, opposed position would be the blank slate. 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 many of your listeners are hearing

• 4:13Speaker 01

  the first sentences of your introduction, and 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 know that there are some genetic defects that prevent you from having normally functioning cognition. And that's just the extreme of a spectrum, and it's clear that there is some variability, and we're not equal. We know, for example, that, you know, brain size, the stupidest metric you can imagine, is somehow correlated with outcomes. So it's likely correlated also with math. 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

• 4:55Speaker 01

  versus being the entire determination that is the issue at stake. Now, the studies you mentioned are studies that were performed in the 50s, in the 60s, in the 70s, in the 80s, and who have been widely publicized, especially on social media. And the specific post you were mentioning is a visual that was reposted by someone I respect as an intellectual, who is Paul Graham. He's not just an intellectual, he's a VC and entrepreneur. But I respect his writings, I respect his thought. And I saw him posting, that was two years ago, I saw him posting that visual saying,

• 5:39Speaker 01

  yeah, you take twins, you separate them at birth, you study their IQ, no difference from two different tests of a same person. 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 this thing seemed to contradict my own perception of, or my own talent as a mathematician

• 6:22Speaker 01

  developed, and also what I understand about the way it works and what I've written about my books. So that was very annoying. I saw that post when I was...

• 6:33Speaker 01

  Making the final corrections on my book before the publication 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 Rosenthal. And they all say, you know, I'm not, 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, the perfect experiment that looks like it's going to be decisive, it just doesn't exist in real life. And that was a big surprise for me.

• 7:17Speaker 00

  I have to make the observation that there is the possibility that Einstein, Descartes, and Rosenthal are falsely modest. They're just trying to make a humble brag. And yourself as well. You do know yourself pretty well, I suspect. But maybe you're quite gifted. It's interesting to me 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. Even if you've achieved a lot, somehow that seems a little bit demeaning. So you've embraced a very interesting summary of your own life.

• 7:56Speaker 01

  Yes, so that's interesting because he raised many different questions about the motivations of people. So when my book was published, I was interviewed by a quantum magazine who specialize in mathematics. And I was saying, you know, I don't think genius is innate. I think it's a state that you develop. 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 a quantified conjecture about the odds of becoming a one in a million genius. Let's put it like that. It's clear that someone like Einstein or Descartes or Rosenthal are one in a million.

• 8:39Speaker 01

  And if not less, actually, maybe closer to one in a billion. But still, let's take one in a million. And that's something you can sample. You take the field medalists, you get the winners of the gold medal at the International Math Olympiad, you get the grand master at chess of whatever subpopulation that you characterize as cognitively one in a million.

• 8:58Speaker 01

  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 innate perspective. What I'm saying is, maybe they're just in the top 1%, and to get into the 1 in a million, they need to experience a very unique journey. In October, when we were discussing my book on mathematics, we were discussing this example of Bill Thurston. Bill Thurston was born with a squint, and he started his life not being able to view the world in 3D because his eyes were looking at different regions of his face. But he experienced a very unique childhood with a very loving mother,

• 9:43Speaker 01

  who spent hours and hours with him, training his perception of depth, and through persistent effort, he developed the ability to assemble two-dimensional images to reconstruct 3D vision. And he continued playing that game, and he learned to view the world in, to think in 4D, four dimensions, and then in five dimensions by using the same technique. And when he entered primary school, he decided that he would practice visualization every day. That guy, and it's documented, maybe he was, you know, in the top 1% or 1 in 1,000 from the community standpoint. Maybe, that's quite possible, but what happened to him?

• 10:25Speaker 01

  This very unique journey of trying to play a very special game with his imagination led him to develop a very unique geometric ability, and he became one of the most famous geometern of the 20th century. He was instrumental to solving the Poincaré conjecture through his geometrization conjecture that generalizes it, and he got the field medal. He's an obvious genius in geometry. And it's quite obvious that something that's not innate happened to him. Now, what would be Humbert bragging in that situation? I don't know. I don't know if it's better for him to say, "I was born in a genius,"

• 11:09Speaker 01

  or "I had a crazy, unique life," and it's hard to replicate. In both cases, it's 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 are some lessons to learn from that that could apply. Maybe not at the same scale, but still, maybe something to learn for everyone.

• 11:36Speaker 00

  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, that somehow there was something more impressive about just having this innate ability. But maybe that's an economist's problem. I don't know. But let's go to the... Let's talk a little bit before we get to the actual studies about numbers. So you give the example of 30%, 50%, 80% as the possible correlation between some innate

• 12:20Speaker 00

  trait and its manifestation in real life. And what I think is fascinating, and these numbers get thrown around like they're scientific, you know, that IQ is 80% genetic, as if that means something really exact. It doesn't in so many ways. It is not a precise statement because IQ is not a really precise measure of intelligence. And for 100 reasons, that might be wrong. But if we pretend for a minute that we could measure this with some precision, you make the case that 30 and 50 actually still leave a great deal of room for human, for choosing

• 13:03Speaker 00

  your own destiny, for making your own way in the world, despite your genetic gifts. And 80% starts to get a little closer to determinism and less of a blank slate of any kind. You want to just talk about that for a minute? I think it's interesting. We can't...

• 13:20Speaker 01

  Yes.

• 13:21Speaker 00

  There's some nice pictures in the essay. I encourage people to look at them. But without the pictures, go ahead.

• 13:26Speaker 01

  Yeah, 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 sent through the channel and making everybody confused. When you say, for example, maybe, you know, IQ could be, let's say, 50% determined by your genes. What does it mean? What does it mean for you? Because you're not the whole population, it's just 1%. So 1% in that population. So in my article, I included three visualization of what it would look like. So it's a very simple graphic where you have, you know, the x-axis is your polygenic score and the y-axis

• 14:07Speaker 01

  is the actual phenotype, your outcome, your IQ in that example. So the 50, 30, 80% is what is called the heritability. That's basically the regression coefficient, deciding, you know, at 100%, all the points are on the line. It's just a direct correlation between your polygenic score and your IQ. Now, 50%, when you look at it, yeah, there's a trend, that's clear. But, you know, 50% correlation coefficient is not a very strong correlation. 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

• 14:52Speaker 01

  maybe in the top 1%. It's not bad. It's not a high chance. It's show 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 Steven Pinker has mentioned in his book, you know, every parent of two kids starts to think that things are genetically because they're so different. And I have two kids and I actually realized they're very different, but everything is very different about them. Not just, you know, not just the genes, but

• 15:33Speaker 01

  they were born at a different moment in a different family because there was 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 learned from the mistakes they made with the first child and trying to do things a bit differently and the dynamics of the interaction and they have

• 15:53Speaker 01

  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 pigeonholed into very simple, basic, social, economic criteria. That is, there is some social, economic dimension. It's clear that it's better 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, which may be decisive. And the example of first on is actually very idiosyncratic, and I do believe that it's super important.

• 16:38Speaker 01

  There is, it's actually the notion that Stephen Pika used at the beginning of the chapter 19 in the Blank Slate. The chapter is called "Children," it's about genetic determinism and causation. It starts with the three laws of Turkheimer, and I think they're very important. Are you familiar with his three laws?

• 16:59Speaker 00

  No, I saw one of them in your essay. Who is Turkheimer? And then tell us his rules.

• 17:04Speaker 01

  Eric Turkheimer is a behavioral geneticist, you know, studying the influence of genome on behavioral traits. And I think it was in maybe 2000 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 Stephen Pika cites at the beginning of his chapter on that. And I think it's good to go back to these three laws because they have clarified a lot. The law number one is that everything is irritable 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 agrees on that.

• 17:49Speaker 01

  You know, it's clear that there is some genetic... It's very hard to find someone who truly believes in the Blank Slate here, you know. 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.

• 18:13Speaker 01

  So this is kind of sobering for the blank slate is viewpoints and you know, yes, there is social determinism, but it 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 variability that is neither coming from your genome nor your family. 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, your games you play and everything like that.

• 18:56Speaker 01

  Now, when you, um, when you read the paper and so I, after, after I got stuck in that rabbit hole, I spent like three months reading everything I could on the subject. It's very interesting that Turkhaman, Turkhaman 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 Heraklit, you know, and you know, 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

• 19:38Speaker 01

  equally to all children in the family that parents do not matter. And this nuance that sound like a bit like nitpicking is actually, I think, very important and people underestimate that.

• 19:51Speaker 00

  Say it again.

• 19:52Speaker 01

  So, you know, what is proven, when, when you study siblings, the correlation between siblings is not that high. It's a fact. It's an experimental fact. The correlation between identical twins is raised together is super high. The creation between fraternal twins raised together is, is much lower and that's a fact. This is undisputable. And the creation between siblings who are not twins is even weaker than that. Now,

• 20:23Speaker 01

  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.

• 20:53Speaker 00

  One child to another, yeah.

• 20:54Speaker 01

  And when, when, wait, one child to another. 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, multi-dimensional problem. So it's the kind of thing that tends to be, you know, stochastic. So maybe actually your outcome is not that deterministic, in fact.

• 21:25Speaker 00

  Yeah, I think it also, I don't really like rule number two, law number two, the quantification. It might be true for things that are easily quantified, like height. Height is genetic and it's also determined by your environment, your nutrition, both in the womb and after birth. And it is, it is not destiny. There's regression to the mean, there's a random component that is a play. You want to say something?

• 21:55Speaker 01

  Yeah, but height is the one example of a very socially important trait that is actually massively determined by your genes.

• 22:03Speaker 00

  Right, that's what I'm saying.

• 22:04Speaker 01

  And the case is, even though, you know, you still have 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 genes determine. And these results in modern societies in a very strong correlation between your genome and your height.

• 22:27Speaker 00

  Exactly.

• 22:28Speaker 01

  Intelligence is not either.

• 22:30Speaker 00

  I didn't mean to suggest that because there's regression of the mean, it's not genetic, or there's a random component, but there's obviously a very strong genetic component in how old you are, color of your hair, a thousand traits that we have as human beings, 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. So I just want to, I just wanted to make that point. Let's, let's move on to the, I'll call it the science, but I will say that I'm not an expert on this, this field. I have some interest in it, as listeners will know.

• 23:15Speaker 00

  I did ask Claude to summarize, excuse me, yeah, it was Claude, to summarize the studies on this question of twins raised in different homes, especially with respect to IQ. And then I asked it for critique. And 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, 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.

• 23:54Speaker 00

  So there are five studies that were looked at in the post that you were talking, that you were talking about, but it turns out there really, there's problems with a number of them that are kind of obvious, like one of them has apparently fraud, and others have very tiny sample sizes, populations in their, in their study. But two are relatively large, but 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, and I never thought about that, even if that's the only thing listeners get out of this.

• 24:38Speaker 00

  Because people say, oh, well, you know, they've studied twins in different houses. And it turns out, and it's like, well, how many? Not many, it turns out. So the most famous study, which is Bouchard. Uh, I think it was 81 pairs of identical twins.

• 24:55Speaker 01

  So I think it's uh 56 or 58 in in the 1990s science paper. It's like 56 or 58. I'm not sure in the 50s.

• 25:07Speaker 00

  Now, it's not tiny. It's not tiny, but it's not big.

• 25:13Speaker 01

  And it's global. It's not a local study. So they they went on, you know, they they were big articles in the New York Times. And some of the twins where you reunited for for this study were coming from different continents.

• 25:26Speaker 00

  Uh huh.

• 25:27Speaker 01

  So you you don't find even 15 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 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.

• 26:11Speaker 01

  And and the fact that it's special is actually very important in the story. Because you try to extrapolate.

• 26:23Speaker 01

  General laws about human cognition that apply to billions of people. And you try to learn that from fifty something pairs of people in a very unusual circumstance. And just from that, there's an issue. Now, it's a very seducing idea. And I admit that the first time you're exposed to that, you find it beautiful. Like, it's what people dream of, you know, economists, they love that, like, a natural experiment. You don't have to do anything, just measure it because nature has done the experiment for you. And in my article, I have included this very interesting interview of Thomas Bouchard,

• 27:07Speaker 01

  who conducted the study, and he described, oh, he fell in love with the idea. And honestly, I completely understand him. The first time I heard it, I fell in love with it. It's a beautiful idea. 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.

• 27:33Speaker 00

  And it's not just the beauty of the experiment. We human beings, as, I think, listeners know, 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 triplets who are separated. And of course, they all look exactly alike, which is already fascinating, that three human beings raised apart.

• 28:17Speaker 00

  We know they're going to look exactly alike because they are identical triplets. But then it turns out, they have all kinds of hap- when they reunited later in life, they have all these things that they have in common. And I'm not going to get these right because it doesn't matter. But this is the kind of thing we love. They all married 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 the list just gets longer and longer. And it gives me a little bit of goosebumps just telling this story I'm making up because it's, there's something magical and mysterious. And even though we might have dramatic and strong feelings about nature versus nurture,

• 29:00Speaker 00

  this story is so juicy. It's so delicious.

• 29:05Speaker 01

  And it really started like that. So you blended two stories, the three identical triplets and the Jim twins. The Jim twins are the seminal case of a Bouchard study. Because these two twins, I think they were born in 1940 and reunited in 1979. And they had the same name, the first and first name, both of them Jim. Both of them, you know, having married Linda and then Betty, or if it's the baby first Betty and Linda. But actually, they had married twice with women who have the same name. They named their son with the same name. And the two of them grew up in families where they have another adopted brother

• 29:50Speaker 01

  whose name was Larry. Now, this is off the Larry thing because everything at this point could be involving some of their own agency. But the name of their adopted brother, like it's, Jims are not supposed to have a telepathic influence on your parents. It's that they would name your brother with the same name. Now, 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.

• 30:27Speaker 01

  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. And it looks like they were living in a very conformist world in the 1940s, 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 still a fact. And so when you read that story and they ask kid, the two of them had a dog named Toy.

• 31:02Speaker 01

  When you read that with a little bit of, you know, training in data science 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. How are you in the 1940s? Which is interesting in itself, but it's not what you want to prove. Now, that's, that's interesting because it's actually on a more philosophical, epistemological perspective, the whole story about twins re-older part is very typical of the pre-replication crisis psychology, where people were moving into quantitative science,

• 31:44Speaker 01

  but coming from, I would say, a more literary perspective on things, where stories were more important. And I think that the lack of statistical training, advanced statistical training

• 31:58Speaker 01

  of most scientists of that time, you know, they accepted p-value 5% as a viable criterion for truth, which is not, as we know now,

• 32:08Speaker 01

  is probably very important in the way the story developed and becomes serious. Because when Thomas Bouchard saw interviews of his twin 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.

• 32:35Speaker 00

  Well, I think it's interesting that you attribute the 60s and 70s, 50s, 60s and 70s is the dark ages of statistical sophistication. Obviously, in modern times, when people are very well-trained in the techniques, at least, maybe not the interpretation, but the techniques, they still manage to have challenges, which is what the replication crisis is representing. But let's talk about the actual study, the Bouchard study and some of the others, because the things that are wrong, often in these kind of stories, data challenges

• 33:11Speaker 00

  and generalization challenges, that the issues are quite complex. Something is missed about 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?

• 33:30Speaker 01

  So, on paper, it looks like the twins reorder part is going to be a perfect natural experiment that tells nature apart. The thing is, the twins share nine months of life in the womb. And if you've been around a pregnant woman, you know that pregnant 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 that. Now, 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 average, it was like five months, which is not nothing.

• 34:12Speaker 01

  We can say family are not that important, but the first five months 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 especially dysfunctional families. And some joint event happened that caused them to get separated. What was it? Was it social services taking the kids out because the mother was putting booze in the baby formula? 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 that may or may not

• 34:57Speaker 01

  be that important, but you need to control for that. And what's interesting, and it's something that, you know, troubled me when I read that, because I was thinking, okay, how do they control that? That's kind of instinctive to any, I would say, any modern scientist. You have some bias somewhere, that's fine. You can live with it if you control it. Now, there's a technique for that, which actually traced back to a textbook from 1960. So everything was already known in 1960. There is that book by Falconer with a geneticist and statistician who created the famous Falconer formula that is used in twin-reared apart and twin-reared together studies. That says, it looks like it's a natural experiment, but it's not.

• 35:41Speaker 01

  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 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 1% but 50% of the DNA. And from that, you cannot be 100% sure, but you can have a first order approximation by, you know, looking at the correlation between the identical twins, the correlation between the fraternal twins. You subtract one from your value, multiply by two,

• 36:26Speaker 01

  that gives you a gross approximation of what you want, the irreliability. 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 that. So in the same interview I mentioned about, where Thomas Bouchard explained, oh, he's falling in love, or he fell in love with the study. This interview is from the beginning of his study. I think he was a couple of years into it, and 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.

• 37:07Speaker 01

  Now, in the 1990 paper in Science, they mentioned that they have this control group of fraternal twins, and they say, "Due to space conference, we decided not to publish it", which is really bizarre, honestly, really, really bizarre.

• 37:28Speaker 00

  So that, um, I've always loved that phenomenon. It does raise a red flag, of course. It's not a good excuse. You know, it, it's a implausible excuse. It's an, it's an excuse that no referee, a good referee of a paper in reviewing it would say, "No, I think I'd like to see that at least." It would be okay to say, to tell us the results and then say, "But we can't put the full table up," or "It's really complicated, so we didn't have room to show you everything, but here's

• 38:10Speaker 00

  what we found, and the data are available in requests from the authors." But to say, "We're not going to share it because we don't have room," is, uh, is the "dog ate my homework." It's not a good, good answer.

• 38:22Speaker 01

  Yeah, so 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 30 against 56, 30 fraternal twins against 56 identical twins, but it's not very compelling because, you know, the margin of error is one of a square root of the sample size. So having close to 2X in sample size actually is like, close to 40% difference in precision, which is not that big. I mean, sure, it's, it's not as good because it's smaller, but you cannot decide, one

• 39:05Speaker 01

  is gold standard, I'm going to keep it, and the other one is not even good for publication. It is something, it's not believable. Now, the other one about the space constraint, it, so, of course, I made that joke 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 include it. Which is, you know, in mathematics, it got everybody busy for 350 years to try to recover the proof, which probably was wrong, actually. Everybody thinks that Fermat did not really have a proof.

• 39:39Speaker 00

  Of course not. But if he did, it's almost certainly true that the margin was too small, so.

• 39:45Speaker 01

  Oh, yes, but I don't think, you know, if Andrew Wise struggled so much and got the first proof that was initially wrong.

• 39:54Speaker 00

  Right.

• 39:54Speaker 01

  Leveraging 20th century mathematics, it's quite probable that Thirma made a mistake somewhere. Nobody can prove it, but it's kind of likely. Now, in mathematics, at least, you can do your homework, you can try to recover a proof. In experimental science, this is just unacceptable. Like, they spent like 10 years collecting identical 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 to... Because if you have that, this is the silver bullet.

• 40:38Speaker 01

  That's 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 what you're not supposed to do in data-driven science. They try to talk themselves out of having to show the control group, which is very awkward. Like, they say, okay, yes, it's true that they shared a womb 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, but it's not clear they're that important.

• 41:22Speaker 01

  Which is really, honestly, I think there's something about the paper being published in 1990, because for our eyes right now, it's just egregious. It's just impossible that the referee could accept that. You know, this is a very important debate, with very important ramifications. You know, people understand society and education and all that, and you just cannot have that kind of scopic criteria in a paper or in science. And I actually think that science should ask the data, actually, even today. I mean, 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.

• 42:04Speaker 01

  Is there a correlation or not? You know, it's fairly important. You cannot just leave that thing confidential. It's not acceptable.

• 42:11Speaker 00

  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 path, the tortuous path that we just went down. So, in a perfect world, where you had thousands of identical twins reared apart, what would you do with the fraternal, if you then had thousands, again, forgetting, we've got 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

• 42:53Speaker 00

  data to make sure that what you found with the identical twins was reliable?

• 43:00Speaker 01

  Yeah, so it's interesting because 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 fraternal twins that are raised together. And your question is about how do the studies of twins reared together work? Because this is actually what they do. So, 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 irritability, but

• 43:40Speaker 01

  at least you have a functioning model that is a first order approximation of. So how does it work? So the idea is, identical twins share 1% of the DNA. They are literally clones. So 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 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 exactly correct, but at least you can start from that. So they say you have the genetic variance

• 44:23Speaker 01

  and you have the family environment variance, which is, the second one is the same for fraternal twin, except that the first one, the genetic variance, is only 50% for fraternal twins. So what you do is you make the difference. You take the correlation coefficients for identical twins. You subtract the correlation coefficient from fraternal twins and you multiply that by 2 to extrapolate what would be the full contribution of the genome. Because with fraternal twins, you have 50% of the genomic contribution. So let me give you an example, which is, actually, the twins we are together studies give something like that.

• 45:03Speaker 01

  Let's say you have 80% correlations between the IQs of 1,000 pairs of identical twins. So you look at the within pair correlation. You, X is twin 1, Y is twin 2. You make a scatter plot of that and you take the regression line. What is the correlation coefficient? It's something like 80%, okay? If you do the same thing for fraternal twin, you may get maybe 50%. So the difference is 30%. And using that simplification formula that dates back to Falconer in 1960, you get 60% irritability. So this is an estimate and it's debatable because, you know, genome is not linear

• 45:48Speaker 01

  and there are some interactions between the different variables. They're not completely separated. And the fact that you have an identical twin is kind of changes your life experience. So 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.

• 46:10Speaker 01

  Thank you. Go ahead to edit because I just punched my microphone.

• 46:16Speaker 01

  Yeah, 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 correlation, you should have an estimate.

• 46:32Speaker 00

  But I just want to say it a little, a little different. If, if genetics was important, there's this high correlation between IQs, say, for two twins raised in different households. So if we found that, if we found that twins raised in different households are frequently, 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, they're not assigned randomly, obviously, they, they're often were assigned, we discover later, I

• 47:17Speaker 00

  know, you mentioned this in your paper, I saw this from Claude, some of them are assigned to families, the same family, just not the same house. You know, it's like an uncle and a, two brothers, at two different houses in nearby neighborhoods. So 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, of having fraternal twins as the control would be that if they were raised in separate households, they don't have the same genetics.

• 48:02Speaker 00

  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 genetics is really important, you'd want it, 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 finding. Now,

• 48:37Speaker 00

  I don't know if, there may be something that Claude knows that you don't know. According to Claude, Jay Joseph, a person who tends toward blank slatism evidently, was very critical of the Bouchard study, he claimed to have access to their data. Is that correct, do you know?

• 48:55Speaker 01

  So I mentioned that in the footnote, yeah. So 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 meaningful signal, which is, to be honest...

• 49:11Speaker 00

  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.

• 49:22Speaker 01

  Yeah, which is not surprising when you look at the sample size. You have like 56 on one hand, 13 on the other end, so the error bounds would be pretty wide.

• 49:33Speaker 00

  Yeah.

• 49:34Speaker 01

  So 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 raised apart. 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 influence. 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.

• 50:16Speaker 01

  So after my article was published, it triggered many, many reactions. And of course, some hardcore hereditary critics me for requesting that control group data. And they mentioned that there was some subsequent samples from the same study published years after much, which is already a bit... Because when you do a control group study, you have, it's basically having a placebo arm. You know, you do a direct, you're supposed to get the same vintage from the data. You cannot say the control group in 1990 was, was not good, but I'm going to use the one from 1995 because I like it better. It's not the way it works.

• 51:00Speaker 01

  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 values. And they say, they were mentioning, 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. I just say that the study doesn't work. That's, that's not that IQ is negatively created. 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.

• 51:37Speaker 00

  So let's, 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, 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, it's pretty straightforward and the visuals about the importance of a 30 or 50 or 80% number of quite helpful.

• 52:05Speaker 00

  I will just mention as an aside, it's one of my frustrations in economics that people can actually publish papers where there's 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 a circle of 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.

• 52:43Speaker 00

  Parenting is really important to me. 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 the idea that it's irrelevant, that I had no impact on how my children's curiosity, happiness, maturity, kindness, et cetera. The idea that the, the times I tormented my children by making them say, "Thank you,"

• 53:25Speaker 00

  or "I'm sorry," were actually irrelevant because parenting isn't 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 is not so important. And there's, there's a definite literature in parenting that says, "You don't have much control over how your kids turn out. It's an illusion." So, you know, you wonder, if you're an analytical person at all, "Did I make any difference? Even negative, did I make any difference?" And so that's one of the reasons I think this debate is important. Why else is it important?

• 54:08Speaker 01

  Oh, it's, this first point is already quite important. I mean, I have two young kids and 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, your control is very, 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, it's beyond doubt, that drinking during pregnancy causes...

• 54:50Speaker 01

  Damage to your kid, especially to cognition. This is proven. This is parenting.

• 54:55Speaker 00

  Keep going, David. I'm going to critique that a little bit. Go ahead.

• 54:59Speaker 01

  It depends. Okay. 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. Can we agree on that?

• 55:07Speaker 00

  Yes. I just, listeners can go back to the Emily Oster episodes on parenting. And I, I, I think alcoholism is really bad for, for a child in the womb. Whether a glass of wine is, is I think more complicated.

• 55:20Speaker 01

  But, okay. I can, I can agree with that. Now, what you have 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 scare. Usually you have a very neat pilot with an expert who invents a new way of making teaching better. And when you try to scare it, like it vanishes away. Okay. This is sad. But does it mean that teachers don't matter and education doesn't matter? That's, that's a tough sell, 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?

• 56:02Speaker 01

  And this is where I think people are stuck with deterministic thinking. And I think this is one of the, again, you know, my, my theme about, you know, the ability to grasp mathematical subtleties in these issues is actually very important and goes a long way to explaining all the confusions in the political debate. The third law of Turkheimer says, things are going to be noisy. Whatever quantified variables you have to describe in a few dimensions, your environment, like the income of your family, whatever, you know, this is just going to be.

• 56:46Speaker 01

  A few correlations with the outcome, but the actual thing that matters 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 thing that 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 most G-loaded activities you can imagine.

• 57:14Speaker 00

  G meaning, G-loaded meaning related to your general intelligence?

• 57:18Speaker 01

  Yeah. It's supposed to be, it looks like, you know, mathematics looks like a giant IQ test in many ways. But what I realized in my studies and then in my career as a pure mathematician, and 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, 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.

• 57:59Speaker 01

  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 kind of, we cancel that conversation. And that's a big issue.

• 58:29Speaker 00

  Yeah. I want to close with a related point. Get your thoughts.

• 58:40Speaker 00

  It really shouldn't be a controversial idea that the world is complicated, which is another way of saying, third law, you know, the thing that's happening in your head, your inner journey, your inner narrative, your inner experiences is never going to be in the data set, at least in our lifetime, maybe someday down the road, but I suspect not. And so there's always, you know, the fancy name for this in statistics is there are 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, an important point. But the second thing that I find fascinating, related to this, is

• 59:23Speaker 00

  that people, people really care. And it's not obvious that they should. This is not important. Nobody lives their life, in a certain dimension, it's not important. Nobody lives their life saying, well, if it's only 50%, I'm going to do X, but if it's 80%, 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

• 1:00:04Speaker 00

  fascinating. It's not, in some sense, it's irrelevant. It's meta meta. It's one more, once more removed from the reality, but it's not irrelevant. It's important to us to understand who we are, and we get in 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 idea 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

• 1:00:47Speaker 00

  the hereditary and say. But anyway, just react to that.

• 1:00:53Speaker 01

  Yeah, I think you're spot on. There is something very personal behind these controversies. And actually, I don't think I have very good control on my destiny. I just think that it's non-zero. I just think that.

• 1:01:09Speaker 01

  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 the most important discovery of my life. But if you look at anecdotes about how people frame their perception of the nature versus nurture debate. There is a very interesting post called "The Parable of Talents" by Scott Alexander in the old Schleswig-Schallberg collection. And he starts by saying, you know, "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.

• 1:01:48Speaker 01

  And I tried hard and I was really 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, have the same activity. And we don't really know what causes that. And because we don't really know that, there are certain tendencies to put the blame or assign an act of God somewhere that controls that. So some people put the blame on society and their blank slate is saying, okay, it's because they were coming from bad parents or whatever, bad family or bad social background. Some people put the blame on their genes, you know, saying, okay,

• 1:02:32Speaker 01

  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 seventeen, I started, you know, some kind of advanced program for kids who were really good at math. And I was confronted with people who were unbelievably, unbelievingly smarter than me. One of the kids in my class went on to win a gold medal at the International Marathon Olympics. And it was, that was mesmerizing. I could not understand how he was doing it. It was magic. There was not a single problem I could... I could answer that he could not answer, but there are many problems that he could answer that I could not answer.

• 1:03:17Speaker 01

  And it was instant and he was sleeping during classes. 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, not at all, quite the opposite. So I completely understand where Harry Dittarian is coming from. I was coming from a same perspective. Now, we're in 2025 and we start to understand a few things about the brain and we start to understand a few things about, you know, oh, you can create a machine that emulates some brain functions. So it's kind of interesting to try to maybe unpack that mystery and there's something

• 1:04:02Speaker 01

  to learn. And so that's why I really think that we should really go beyond ideology on that because ideology is really, in that instance, it's really censorship.

• 1:04:18Speaker 00

  My guest today has been David Bessis. David, thanks for being part of Econ Talk.

• 1:04:23Speaker 01

  Thank you very much.

• 1:04:31Speaker 00

  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 today's podcast and find links and readings related to today's conversation. The sound engineer for Econ Talk is Rich Goyette. I'm your host, Russ Roberts. Thanks for listening. Talk to you on Monday.