February 9, 2024
What is a "theory of everyone"? Do the social sciences currently have enough firm knowledge to synthesize such a theory? Have we been getting smarter as a species over the last few hundred years? Were great historical thinkers smarter than today's greatest minds? Why are governments so prone to corruption? What is the COMPASS framework? What is the "no hyphen" immigration model? What is the "umbrella" immigration model? How can governments change how they think and talk about immigration so that racism is less likely to find its way into immigration policy?
Michael Muthukrishna is an award-winning professor of economic psychology and affiliate in developmental economics and data science at the London School of Economics. His research has been featured in CNN, BBC, Wall Street Journal, The Guardian, The Economist, Scientific American, Time Magazine, Fortune Magazine, and many other news outlets. He is the author of A Theory of Everyone: The New Science of Who We Are, How We Got Here, and Where We're Going. Learn more about him at his website, follow him on Twitter at @mmuthukrishna; or read his writings on his Substack.
JOSH: Hello, and welcome to Clearer Thinking with Spencer Greenberg, the podcast about ideas that matter. I'm Josh Castle, the producer of the podcast, and I'm so glad you've joined us today. In this episode, Spencer speaks with Michael Muthukrishna about a "theory of everyone" and scales of learning.
SPENCER: Michael, welcome.
MICHAEL: Hi, thanks for having me on the show Spencer.
SPENCER: Today we're gonna talk about a theory of everyone, which is about how do we understand human nature, how do we understand human societies, what does science have to tell us about these things, and how can we use these ideas to improve society. So why don't we start with just the broadest level? When you're talking about a theory of everyone, what are you referring to?
MICHAEL: So, a theory of everyone is a play on a theory of everything from physics, which is this unifying theory that bridges, for example, quantum mechanics and general relativity. And the argument that I'm making is that the kind of revolution that turns a science from alchemy to chemistry, that leads to its maturity, has happened in the human and social sciences. So for the first time, we really do have a formal mathematical predictive theory of human behavior and societal change. And because of its natural ability to distinguish sense from nonsense, and unify the seemingly chaotic and confusing, it offers us true pathways from science to technology, in this case, policy. And so that's what the book is about. Part one of the book basically lays out what we now understand, that theory of everyone, as I describe it. And then part two says, "Okay, well assume that I'm correct. Hopefully, by now you're halfway through the book, you buy into it. What does that mean, for example, how we deal with polarization or fracturing societies, how do we develop better models of governance for the 21st century, how do we tackle and think about what inequality means, how do we trigger a creative explosion, how do we improve the internet, work with AI, and make ourselves brighter in the future?"
SPENCER: It's a fascinating idea, but I wonder if some of my audience members will be skeptical because they think of Psychology and Social Sciences as really fuzzy, not very hard sciences that don't have really crisp claims. So how would you respond to that kind of critique?
MICHAEL: Well, first thing I would say is that they should be skeptical, they should be critical. If you look at the Psychological Behavioral Sciences, somewhere between 50% to 75% of the literature does not replicate. So if you ever see a study, and you know that the evidence that's presented to you is just empirical data that comes out of these fields, you shouldn't just believe it at face value. In my previous work, I've argued that the real problem that we face in the Psychological Behavioral Sciences is not just that we're not using formal theories as they're used in other sciences, but that there is no attempt to really generalize findings. Everything that you discover are these isolated theories. But there's been an interesting advancement. It happened in the 1980s, where we began to extend the biological toolkit to explain the human animal. And so, the models were built, as I said, by people like Rob Boyd, Pete Richardson, Marcus Feldman, Cavalli-Sforza over at Stanford, and these models made some interesting predictions and people thought, "Okay, this is exciting. It describes how an animal can begin to rely on socially transmitted information and how cultural know-hows or tools for thinking technology can begin to evolve." But it was just mathematical modeling. I'll give you one example of the model and I'll show you how people got excited about it, and what really changed in the early 21st century. So every animal on earth, when it encounters a new environment, has to figure out how to survive in that environment. And there's two ways that you can do this, and every animal does this. One way is through genes. So genetic evolution, by things like natural selection, are one way that an animal can figure things out. So even for humans, if you look at something like human skin color, it varies across the globe and it's very well adapted to latitudes. So the amount of UV radiation: what human skin color is really optimizing is getting enough sunlight into your skin that you synthesize enough vitamin D to avoid cancers, but you're not getting so much that you get skin cancer. So when you're closer to the equator, you're more south, you get darker skin because it's protecting you from the sun. If you're further north, then you get lighter skin, because you need to get more of that very limited sunlight. It's a genetic solution. All animals do this. The other way that animals do this is just by figuring things out through trial and error. So they figure out that this is where you can go to get food, and that's where the predators are, so don't go over there but do go over here. And you can figure that out over the course of your lifetime. What was very interesting about these models is that they suggested that there was a kind of Goldilocks in between these two zones. When an animal wouldn't just figure things out on its own, and it wouldn't just rely on genetically hardwired instincts, but it would begin to rely on information from other members of its group. So it would engage in what we call social learning. And the zone that one would expect to find this is a zone where the environment is varying but it's not varying too much. So if the environment is very stable, like it is with sunlight, genes are the best solution. And if the environment is very unstable, like it's continuously changing — the water is here, the water is there; red berries are edible, blueberries are edible — then individual learning, trial and error learning, is the best. But if the environment changes only slightly — so think of something like a cyclical drought — then maybe your parents and grandparents actually have some knowledge worth paying attention to. So imagine that a drought happens and you've never experienced it and maybe your parents haven't either, but grandma remembers that when she was a child, there was a drought and they went left to the mountain past the forest, and so she led her tribe to safety. So everyone's like, "That's very exciting." If you get a moderate environmental variability, "Okay, that's wonderful." Then in the late 90s and early 21st century, for the first time, we began to get climate fluctuation data from ice cores that were being collected in the Arctic, for example. And we realized that the moment where humans emerged was exactly the moment that was described by these models. So the environment was first highly unstable, which would have selected big brains that were good at individual learning. And then this moderate stability that matched a generation length, such that one would expect an animal to begin to rely on socially transmitted information. So this was the beginning of a second line of inheritance or a third line of information, in addition to (if you like) millions of years of genetic evolution and a short lifetime of experience. There began to accumulate thousands of years of culturally transmitted knowledge. So how to process the local foods, how to evade the local predators, in fact, how to hunt them and where their prey, and humans began to specialize in this. So we marched across the globe around, let's say 60,000 to 70,000 years ago. We did it as hunter gatherers before we had science, before we had engineering, before he really understood how the world worked. And rather than do a lot of genetic changes, which we had some — like I mentioned skin color — we didn't kind of develop proteins to detoxify the local plants. We figured out how to cook the local plants or detoxify them in other ways. We didn't develop faster and bigger muscles (if you'd like) to outrun the predators; we learned how to develop weapons and how to hunt them and then wear their skin as our own to keep us warm. So the fact that we began to rely on this body of information was very interesting. But I want to give listeners kind of one clear example of the degree to which we rely on this. Our jaws are too weak, and our guts are too short to rely on anything other than cooked food. So if you're like a raw foodist, you actually require huge amounts of food and a lot of supplements in order to survive. But we don't have genetic instincts for cooking. We don't have genetic instincts even for fire. So what that means is we are a species that is reliant on transmitted information. Every generation has to kind of catch up with the last several thousand years of human history. And so from those earliest models, we began to branch-off new models. It's like, "Well, how do we learn from one another? What kinds of queues are we paying attention to?" We seem to be honing in on who's got the most valuable knowledge. We seem to be building mental models for thinking. "How does all of that work and what does that mean for our intelligence? What does that mean for the way that innovation happens at a population level?" And suddenly all of these insights started to emerge. And all of these otherwise chaotic and confusing things about our species suddenly started to make sense. And so, within various social sciences — from economics, political science, even law — they're beginning to realize, "Oh, well, if this is true, this changes so much about our understanding of ourselves and our world. And it changes how we should engage in policy." But at the same time, not enough people know about it and certainly not enough people in the general public know about it. And if they did, they'd be able to, I suppose, make better decisions and start to think about how to tackle some of the many problems that we face as a species.
SPENCER: So is it fair to describe these as three different learning processes that operate on different timescales? You have the evolutionary learning of genes that might, for example, affect our guts over a long period of time and influence what we can digest. Then you have the cultural learning, which operates at a faster timescale, which is passed down information, maybe the people in our tribe teaching us about which berries are okay to eat. And then finally, you have the super short timescale fast learning of the individual, where maybe from your own experiences, you learn, "Oh, if I eat that berry, I feel sick. And if I eat that berry, I feel good."
MICHAEL: Exactly right. I'm trained as an engineer, so the way I think about it is kind of like control systems or reinforcement learning systems. And these control systems or reinforcement learning systems just have different lags and delays on the learning. So the reason that human skin color is well adapted to UV radiation is because it's too long a lag from the amount of time you spend in the sun as a child, to when you might die of cancer from not enough vitamin D or too much sunlight as an adult. It's just too long a lag. So you can't figure that out over your own lifetime. And even culturally, it's difficult to kind of transmit that, so you end up with a genetic solution. Whereas, many other things that we use for thinking are actually culturally evolved solutions. So one of my favorite examples that I use in the book is counting. So you probably think humans can count, you probably think of it alongside the ability to reason and the ability to build kind of causal models as being things that are core to being a human being. But all of those things are actually delivered to us by our culture; they're delivered to us by the education system and by our society. So even today, many small scale societies count like this: 1, 2, 3, many. And our ancestors counted exactly in the same way. We began to overcome that kind of mental limitation by using stones or using body parts to count. So we use ten fingers these days, but it's not the only system that exists. Many different body parts have been used by different societies. And that gives you natural numbers. So the natural numbers are 1, 2, 3, 4, 5. It doesn't make obvious something like zero. So it takes centuries, even after we start counting with stones or putting notches on clay with those stones, to get to the concept of zero. And that's great, we get to zero. But negative numbers are still very confusing, like how do you represent negative numbers with stones? Or how do you represent them with body parts? You can't. So we needed a new mental model, a new analogy that we could transmit. And that emerged as late as in the 17th-18th century, when we went away from objects, like think of calcium, calculus. We went away from these stones to movement and position on a number line. And the number line makes obvious not only the natural numbers, but also real numbers, zero, and negative numbers to the point where you could now deliver that to young children, which is what we do. Similar kinds of things, even around things like logical reasoning, something you think that humans can do. So there's these great experiments, Spencer, that were conducted in the 1920s by this Russian psychologist Alexander Luria. He wanted to understand what was going on in Uzbekistan when an education revolution was happening. So this is actually one of the questions he asked. So he says, "Where it snows, the bears are white. In Novaya Zemlya, it snows. What color are the bears?" Now if I asked you that question, you immediately know the answer. If I asked my six year old that question, she'd immediately know the answer. And anyone with education in Uzbekistan also knows the answer is white. But when he asked people who hadn't been to school, they had all kinds of interesting answers. So they said things like, "I don't know, maybe brown. I've seen a brown bear once." And he's like, "Are you listening? Listen to me. Where it snows, the bears are white. Novaya Zemlya snows, what color is the bear?" And it's like, "I'm really not sure." Now, it seems like it's obvious that humans have the ability to do that because we can be taught to do that. But it's not like human proclivity. And we actually have some experiments down in Southern Africa where, again, there's an education natural experiment that's happening, and we find the same results. We asked people: "In another place, boats are made out of sand. I got a boat from this other place, what's it made out of?" And people without education will say things like, "I don't know. Wood?" And if you say, "Well, could it be made out of sand?" And they just laugh. So what I guess I'm trying to say is that, when you often think about what it means to be human, and you think about human intelligence, the focus is on the brain, the hardware. But just as you can't understand the power of pivot tables in Excel or ChatGPT by looking at the CPU or the GPU, it's in the software. Human intelligence is also in the software. But that's just one of many implications of this new, as I describe it, theory of everyone.
SPENCER: Those particular examples you gave like, "In this place, the boat is made out of sand. I got a boat from that place, what's it made of?" Do you think that that is about not accepting that original claim that the boats are made of sand, whereas, in, let's say, a modern educated society, people used to go to school where you have these kinds of funny, stilted questions that tell you something is true. And then you realize you're supposed to assume that that's true for the rest of the question. Whereas, if you've never seen something like that, you might just be, "Well, of course they are made of sand. Why are you telling me they're made of sand?"
MICHAEL: I think that's right, Spencer. I think what it is is the unwillingness or this unfamiliarity with dealing with hypotheticals that don't match your experience of the real world. And a lot of other data from our site, as well as Luria's data kind of reinforce this idea. He asks questions, "Okay, you've got two adults and a child, which two go together?" And they're like, "Oh, they all have to go together. They can't be separated. The child has to go, what would the child do?" It's not that these people are incapable of thinking this way. It is that you have to be trained to think this way. Do you know what I mean? It's not that you can't think like an engineer or an architect or anything, you have to be trained to think that way. And so what I'm saying is that many of the ways that we take for granted — our ability to count, our ability to reason — are delivered to us in our software. Obviously, the hardware can run it because we're doing it right now. But if you want to understand humans, you want to understand how we have become more intelligent, and you want to understand how we begin to innovate, it's not in our brains. It's the software that's changing. It's that culture, that socially transmitted information. Does that make sense?
SPENCER: Absolutely. And it reminds me of the way that in high school classes, you'll sometimes learn theories that were absolutely cutting-edge theories of their time that only the smartest people could understand. And now it's just a standard part of your high school math class. Just because so many ideas have been built on top of each other that now to understand the idea is vastly easier than it was at the time that it was created.
MICHAEL: Exactly right. And so, there's something called the Flynn effect, where when we look at IQ test scores, they've been rising over time. They've kind of plateaued in the developed world. In some cases, reverse, actually. But that rise has kind of been a mystery. It's too fast for genetic evolution. Some people have kind of attributed it to nutrition. I suspect nutrition might have some part to play but it's not clear in the data. What is most likely going on is that if you think of your ability to think as being something that your software is doing, then education (schools) are kind of a cultural download of that software. We're gonna efficiently say, "Sit down, kids. Let's go. Phonemes, numbers, algebra, calculus. Let's go, let's go, let's go. We're gonna download all of this, get you to a baseline, and then you can go learn whatever you want to learn." We've been packing in more, education has become more widespread, and as a result of that, the baseline expectations for people have advanced such that our entire societies have become more complex. So if you look at TV shows that our parents and grandparents watched, they're so much simpler than even the lowest browser TV today. So, I Love Lucy versus Rick and Morty, Wham Bam Batman versus The Dark Knight or any of the Marvel films. Even the lowest browser television has more plots, more characters, more convoluted connections than anything before. So, we've literally become cleverer as a result of this. But I think some of the barriers to why things have slowed down is that our schools these days are kind of trapped still in that factory, farmer-model (if you like), where it's a factory model for creating good factory workers with a baseline of knowledge. But it's difficult to get out of that, even though today, the entirety of the world's knowledge is at our literal fingertips. And it doesn't make sense to be delivering scientific formulas or memorizing a bunch of facts that anyone can pick up and use. It's like when I was in middle school, my middle school teacher said, "You better learn how to do mental math better; you're not going to be carrying a calculator in your pocket." But of course I do. He didn't see the advent of the iPhone.
SPENCER: Yeah, that's a very ironic statement. One thing that confuses me about this is, absolutely, it seems like society is getting so much more complex in many ways, and we're constantly able to access information and learn so many things that would have been difficult to learn in the past. People used to have to literally go to a physical library to get information, and now we can get it so quickly, which means we can learn so much. And yet, at the same time, if I look back in history at people, like Alexander Hamilton or Ben Franklin, they just seem so much smarter than your typical politician today or even the peak politician today. That just kind of baffles my mind. I find that sort of seeming contradiction confusing.
MICHAEL: I don't think that's actually true. I don't think that they were smarter than your typical politician today, in the sense that I think you just take it for granted that the kind of complex thoughts building on everything that has happened before that. Any top lawyer, let alone politician, is probably greater than what Alexander Hamilton had, given his cultural corpus and what he had available to him. Or, let me say it another way. If the US Constitution were rewritten today, the barrier is not intelligence, the barrier is our ability to coordinate with one another in a more diverse society where demands are different. Twenty five people in a room don't get to dictate what the rest of the country is going to be doing in the same way that the Founding Fathers faced. This is actually related to something I talked about in the book. And it's a question that sometimes people ask me, since I work on intelligence and innovation, is, "Where have all the geniuses gone? It seems to me that in the past, there were these great geniuses — the Einsteins, the Newton's of the world — where have they all gone?" And I think the answer to that is actually that those individuals are deified in some ways, despite not necessarily being all that bright. So let me put it another way. It is very unlikely that Sir Isaac Newton was the person with the most amount of potential in Britain at a time when literacy rates were so low. Or even Einstein at a time when he was sitting in a patent office reading patents about the electrical devices for the synchronization of time, he was kind of putting these pieces together. And we have little bits of evidence to suggest that this is the case. One bit of evidence is that something like calculus was invented at the same time by both Newton and Leibniz. And that's weird. You have all of this history, no calculus, and then suddenly, in one moment in time, two guys come up with calculus at the same time. That's very interesting. And what it suggests is that these guys are reading the same material, and the pieces are kind of coming together in their heads. Now, it's not the case that anyone off the street came up with calculus; it was still to these two guys. But I think the better way to think about innovation in that way is that they are ideas flowing through our social networks and recombining in people's heads to the point where an innovation doesn't really require a specific innovator, any more than your thoughts require a specific neuron. They're really a product of crowd computing, like a collective brain, as I describe it.
SPENCER: I do find that claim of Einstein really surprising. I think I disagree, because I think Einstein was actually ridiculously smarter than almost everyone that's ever lived. For example, in one year, when he was 26 years old, he published the Photoelectric effect, Brownian motion, Theory of Special Relativity, and also the =MC2 paper. Those were the four papers published in a year. I don't know, I don't know what to say [laughs].
MICHAEL: So let me try the claim in another way. Given the current population size, massive amounts of selection into elite colleges and very difficult jobs, if I were to take a big tech superstar, or if I were to take a quantum superstar, if I were to take an Ivy League physics professor from the present day, and I would have put that person with their cognitive abilities in Einstein specific social position — so given access to the same information, working in the same patent office on things that were relevant in Bern, staring at his clock every day — or in Sir Isaac Newton's position, I would say that they would not only do more than Einstein and do more than Newton did. Well, they would have achieved the same thing, but they would probably do more.
SPENCER: Wow, we have just incredibly different intuitions about that, because I think almost none of them would produce a fraction of what Einstein produced.
MICHAEL: So Spencer, my question to you is why do you think that? And I would say the answer, because of his output, because of what he managed to achieve in such a short space at such a young age.
SPENCER: Yes. And I think a lot of other people at the time, in theory, could have produced those papers. If the information was out there, it wasn't like he had access to some secret information nobody else had. He had information that lots of people had, and in one year, he was able to put four pieces of output out, that incorporated that information in a way that nobody had ever figured out how to do.
MICHAEL: I'm not trying to say that there are no super geniuses, there aren't like Terrence Taos or John von Neumann's or whatever — (and John Von Neumann, to be honest, was probably brighter than Einstein was) what I'm saying is that that wasn't what made Einstein so successful. It was, in fact, his specific position at a time when information did not flow. You mentioned we have to go to libraries to access things. At a time when information didn't flow as freely as it does today, when he had access to cutting edge science, by virtue of working in a patent office on the very things that he ends up writing down theories about, he did actually have access to special information. He happened to be in Bern, where he was, clock city (if you like). All of these things are stewing in his head. I'm not saying that if I took anybody and I put them in that position, they would do the same thing. I'm saying that if you imagine there's a distribution of intelligence that is a product of genes and nutrition, and having a good prenatal environment, and all of those other things, what's happening in terms of human innovation — and the reason I think this is because this is what we see across the kind of innovation literature — it's really driven by a population level process. So what the theory of everyone says is that we live in a world where we have reached a point very early on (much earlier than we are now) where the world was more complicated than even the smartest among us could recreate. And what we did was we moved the cognition — the figuring out of solutions — out of our own heads and into the population. Because of our tendency to kind of seek out relevant information, the best, most successful pieces — and we're constantly doing this — our societies are actually acting as brains. They're doing a kind of computation, where they're filtering the good stuff and getting rid of the bad stuff. And so as a result of this, you find more innovations in places with bigger populations, more interconnections, places that have better education or means of transmitting information between people, places where a greater diversity of people come such as big cities or centers of knowledge, things like that. And that's why you find things like simultaneous invention. So I mentioned Newton and Leibniz as kind of one example of simultaneous invention. There's lots of these: Darwinian theory. So Darwin comes up with this at the same time as Alfred Wallace does. Crazy. In that case, we have very good knowledge of what they were exposed to. They had both read Thomas Malthus. They had both read Vestiges of the Natural History of Creation, nothing could change. They both traveled to the island archipelago, where they could see some kind of evolution in action. And so kind of putting these pieces together, they're like, "Oh, this is what's going on." And what I'm saying is that a similar process happened for Einstein. It's just that he did actually have this kind of unique experience (if you like). He was at a particular point in the social network that gave him access to that. It wasn't guaranteed who would come up with it. But it wasn't about him, particularly. I know it's a bold claim, but read the book, and you'll see why I say that.
SPENCER: Yeah. I guess my view on this is that on average, people have gotten smarter. And I think we see a lot of lines of evidence for that. We see things like the Flynn effect. We also just know that education is much more widely available now. Information is much easier to access. But it's less obvious to me that peak intelligence has improved. That the greatest geniuses of our generation are smarter than the greatest geniuses 100 years ago or 200 years ago. I think that's much less clear.
MICHAEL: Just think, statistically. Just think of it like a normal distribution which is an exponential function at the tails. And just think about the world population of, I don't know what it was in Einstein's time, was that 2 billion or something like that? And think about that compared to 8 billion people. Just by the law of averages, those tails have stretched. The reason that there aren't any geniuses today is because we live in a world of geniuses. Not only is it the case that the average has stretched — actually I think the bottom end has also stretched by the way, just because more people are alive today without needing to understand most of the world — but the top end has almost certainly stretched to the point where you just can't distinguish anymore. Everyone who gets into Harvard has maxed out, we have to come up with all kinds of other criteria.
SPENCER: Well, I think that's a very good argument and a very compelling argument like a priori. Absolutely, if the average goes up and you think about a bell curve, you should expect that to be way more people with upper tail. The main thing that makes me think it's not true is just looking at the people who are alive and comparing them to the people that used to exist. But I agree a priori, I would completely think that was the case. But if you look at Alexander Hamilton and the 50 plus essays he wrote about the Constitution, and you compare that to almost any politician alive today, I just think they just seem way smarter. And so yeah, I don't know what to make of that, but...
MICHAEL: If you look at the top end of political theorists, or the top end of physicists, or the top end of any field, I would say they're far in excess. And it's just exceedingly unlikely that, at this time, there happened to be a group of super geniuses. For multiple reasons, that's very, very unlikely. I will also say this. I think one of the reasons that people naturally think the way that you're describing is because we have a psychology that is designed to find people to learn from. In the same way that some societies, if you ask them, "Who gave you fire?" It's like, "Prometheus! Mātariśvan! It was these great ancestors that invented these great things. That's how we ended up with these things." We have that psychology, too, in the West. If you ask people, "Who invented the light bulb?" Who invented the light bulb?
SPENCER: I don't even know. I know Thomas Edison helped perfect it but I don't know who invented it.
MICHAEL: Yeah, exactly. Maybe that's not a good story for you because you know that Thomas Edison, at the same time as Joseph Swan in the UK, and of course, there were like 22 other patents. And so they were just kind of working on it, perfecting it, and they reached commercial success. This is the story of the world over for just about everything. And we've had major advancements. It's just that we live in a world of wonders. It's not like a world where there's a few wonders and then, these big leaps seem so big because it's not a world of wonders, and not that many people are well-educated, not that many people have access to books and reading and whatever. We live in a world where so many have access to so much that we take it for granted. We don't realize that we truly live in a world of wonders and mega-geniuses that have become every day.
SPENCER: Some part of me wonders whether something about society has compressed the standard deviation, so that as the mean goes up, the standard deviation goes down, because that's the only way I can make sense of my observation of the...clearly you and I disagree about what we observe about the smartest people today, but that's fine. So stepping back, let's go back to a theory of everyone at a broader scale. We talked about this idea of these different learning rates of genetics versus culture versus the individual. What are some other elements of this theory? And I'd really love to go into a specific example of talking about how to use this in practice.
MICHAEL: Sure. So, some of the things I've already said. Just to revise, that is intelligence should be really thought about as our software and not our hardware. Innovation should really be thought about as happening at a collective level, and that's where the levers are. If you want to make a brighter, more innovative and creative society or company, you should be thinking about these levers. I think one of the most compelling examples is how our evolutionary history shows us some of the interesting... The existence of grandmothers, the asymmetry between sexual norms between men and women. So why body count, for example. I don't know if you're following this discourse around female body count versus male body count.
SPENCER: Basically, how many sexual partners people have.
MICHAEL: Yeah. Why do women get a lot more flak for it than guys do? That and long childhoods, all of these things are connected to each other. So this is some of my work on what's called the cultural brain hypothesis, but other people have fleshed out some of the pieces. When you ask people what it is that separates humans from other animals, the assumption that people make is, "Look, we've got these giant brains." That's what we did. You told the story 60-70,000 years ago, an African ape stood up and walked across the world. And why did they do it? Because they have big brains, they just figure things out, that's what makes us so clever, that makes us human. And if they want to go a little further, they'll say, "Well, it's language. Look at language, that's what we have that other animals don't have." That's not an explanation at all, actually, for a couple of reasons. The big brain thing, big brains are actually calorificly costly. So brain tissue is about 20 times as energy expensive as muscle tissue. And so most animals prefer to get brawn rather than brain. What an animal really wants is the smallest brain that lets it evade predators, find prey, and compete with other members of its group for mates. Because the bigger your brain is, the more you have to spend time finding calories to feed the damn thing. So, if you think big brains are doing the work, then you should wonder why other animals don't also have giant brains the way we do. It doesn't make sense. And language, too, doesn't make a lot of sense as an explanation because language has a startup problem. If I'm the only one with language, it's kind of pointless, because it's just gibberish to everyone else, like talking to an animal. You've got an issue. Language can't evolve so easily, because it's not clear what's going on. If I'm speaking to somebody, they have to also understand that. They also have to have the cognitive machinery to understand and produce language. Does that make sense?
SPENCER: Absolutely. And I've heard different theories to try to address those but I'm interested to see where you're going with us.
MICHAEL: So, what the theory of everyone that's kind of cohesive theory says is, "Look, what happens is that initially, there is this fair, wildly variable amount of variation such that you get these very smart, asocial individual trial and error learners. They learn kind of the way that chimps do today. There's this really nice experiment that really reveals the difference between humans and chimps the way they learn. There's a black box, and the box has a hole in the top and the hole on the side. And what a demonstrator does is he takes a stick, pokes the hole through the top, pokes the hole through the side, and then they hand it to the chimp. Inside, there's a piece of fruit for the chimp. And so the chimp watches, they're clever, they take the stick poker through the top poker through the side, and they get their fruit. And then the experimenter does the same thing with the human child. So poke the hole through the top, poke the hole to the side, and hand it to the child. Child pokes the hole through the top, pokes to the side, and they get a sticker, which children love. So then what the experimenter does is they use the same box, but now it's clear. And so you can see into the box and you realize that that first action of poking the stick through the top hole actually doesn't do anything at all. All the actions happening on the side hole. So again, the experimenter pokes a hole through the top, pokes a hole through the side, and hands it to the chimp. Chimps are smart, they learn in a very different way we do. If you've ever seen chimps through Instagram doing a working memory task, they're very clever. So they ignore that first action, they go straight to the second action, where they retrieve the fruit right away. When the adult does it again, pokes a hole through the top, pokes holes inside and hands it to the child, what does the child do? Pokes a hole through the top and pokes a hole through the side, copying exactly. So what this ends up doing is it gives us the three ingredients for any evolutionary system: variation, because people do all kinds of things for all kinds of reasons; transmission, because what you actually have is a head full of recipes, beliefs, and behaviors and ways of thinking that most of what you don't really understand; you're shielded from it with what's called the illusion of explanatory depth. You believe you understand the world more than you actually do. But when you learn, you do it selectively. So that's the third ingredient, some kind of selection. So what you end up having is a cultural evolutionary system. So, if I were to ask you, "Why do you brush your teeth, Spencer?"
SPENCER: Mainly because I think it will reduce the number of cavities I get.
MICHAEL: Yeah, exactly right. But if I ask you, "Well, What's going on with cavities? How does that happen?"
SPENCER: No idea. Just trusting that dentistry.
MICHAEL: Yeah, exactly. Like diet and exercise. Like Trump's generation. The guy thinks that exercise is gonna hurt you. These days, we think it's because of too much movement or something. Nutrition is one of my favorite examples of this. We used to think that fat made you fat. And it makes a lot of sense. Eat fat, get fat. What's confusing about that? And then we were told, actually, it's not fat, it's sugar. And suddenly, that also makes sense. You're like, "Oh, yeah, totally fat. You keep this with the sugar that's stored as fat. Oh, that totally makes sense." And now you're like, "Maybe it's calories overall." You've got explanations for things but you don't really have true causal models. You've just accepted it and you accept all kinds of things by the way. You will swear up and down that we are on a spheroid, rotating around a star, one many stars is in the Milky Way, denying your everyday experience of flat Earth and the Sun tracing the sky from east to west. You believe that you get sick because of invisible animals that we call germs, despite never having seen this. You live in a world that is given to you in your software by the people, most people think, what the smartest people think and so on. Does this make sense so far?
SPENCER: Uh huh.
MICHAEL: Okay, so this process of accepting and learning beyond our cognitive capabilities, and without having to question everything allowed us to surpass a lifetime of experience. And so the knowledge begins to grow. And as the knowledge begins to grow, you have to be able to store and manage more of this information. So this creates a selection pressure for a couple of things. One is a bigger brain, so that you can store and manage that information. And two, is the ability to communicate that. Now the pathway to language was probably something like this. So we were bipedal prior to transmitting and going on this kind of pathway I'm describing. And by being bipedal, not only does it cheapen the cost of making tools. If you're a quadrupedal animal like a chimp, you don't want to spend too much time napping a stone because you gotta carry this big ass rock everywhere. If you're bipedal, you can do that, you can spend time really sharpening that axe and you can carry it to the next place and use it. I'm talking to you in audio, but I'm actually waving my hands around. What I'm doing is that I'm supplementing my guttural utterances with hand gestures. And deaf children, for example, babble with their hands. So this produces another pathway. So you begin to go down what's called a Path of Baldwinian evolution. So Baldwinian evolution describes when something can be learned, with a lot of difficulty, but if genes can make you learn that faster, they'll be selected. So we began to speak with our hands. You can imagine, "There's a leopard," and you go waving in a particular way. If you have cognitive changes that allow you to pick that up much faster, then you can learn more things. Eventually, there were all these changes that happened in our throat, FOXP2, and whatever that actually made us more susceptible to choking. And that's very interesting. So we accepted a mutation that made us literally die more often, in order to be able to communicate with one another. And that only made sense because we had something worth communicating. This big body of cultural knowledge, including fire, because we now require it to feed the brains, because it makes food more bioavailable. But also, things like rocks and probably how to process food, how to evade predators, and so on. This big body of knowledge, these tools and so on. They're growing. So the brain starts to grow. Eventually, the brain grows so big that you can't birth the thing; it becomes painful. It's like we ate from the tree of knowledge. And you had the curse of Eve: a painful and difficult birth that was dangerous for the child and mother. And by the way, if you look in the medical literature, even today, the biggest predictor of emergency cesareans and instrumental births is a big head. Once you get to about the 85th percentile, the thing just like hockey sticks upward. So now you have bigger brains but you've still got this growing body of knowledge, thanks to cultural evolution. So what do we do? Well, we extend our childhood. We have a very long child, we begin to give birth to our babies prematurely. This extended childhood, by the way, has continued. So, not only do we now have an extended childhood, but we also have a cultural adolescence. So the period from when you can reproduce to when you actually do is growing and growing and growing, alongside our growing cultural corpus. We have to learn a bunch more things before we can settle down and have a family, which now creates a new selection pressure not for the ability to give birth to a big head which is relaxed by cesareans, but the ability to give birth at an older age. But the big thing that really shifted our societies was giving birth to premature babies. Not to mean some babies are premature. I mean, relative to chimps, or relative to a gazelle that's ready to walk, our babies are floppy, useless messes. Because we now have these premature babies, we have a new problem: Mom needs help. I suspect that we had a cooperative breeding situation early on. There's some evidence for this, it would also solve the problem of how we got good at who to learn from. So chimps learn from their mothers but if you're not in a cooperative breeding situation, then you can develop strategies for, "Martha thinks this, and Aunt May thinks this other thing. But Aunt May is more successful, so we can learn from her." But it also meant the dads should get involved. And this is unusual. So for most great apes, there's no doting dads, you just kind of do the deed, and then that's the extent of your involvement for the most part. But we need a dad to get involved because mom has been on the line for a very long time with this floppy, useless mess. I got three of them. So I don't mind saying that they're floppy, useless messes for maybe 18 years, I don't know, at least 18 months while they're learning to walk and they're breastfeeding. So what does dad want to know if he wants to get involved in this? Dad wants to know that it is his. What this means is that different societies around the world have created this solution that you might call the 'shaming control solution.' So you shame females for their sexuality to ensure the dad can notice and you control male resources. So even today, the vast majority of child support is paid by males, and females do have more scrutiny on things like body count. But it's not the only solution. Every society has to come up with a solution but not all. So many societies have polygamy. A few have polyandry, where it's one woman and many husbands. This is typically the case when resources are limited, and you require more males who are typically brothers to provision the child. The other thing that this does is it opens the space for grandmothers. Grandmothers are very unusual. Normally evolution doesn't allow you to continue living after you've stopped giving birth because you're kind of not helpful. But because we had this big body of knowledge that had to be transmitted and parents didn't necessarily have that lifetime of knowledge, you have these kinds of professors of the past, these Wikipedias of their time, where grandmothers could sit down and teach a bunch of kids. And so they stabilize the whole solution. And where we do find grandmothers like with Orca, killer whales, they tend to be cultural species with a lot of transmitted information. So, the other thing we started to do is divide up the knowledge. This is the beginning of the collective brain. So initially, the division of labor was between males and females. But eventually, as long as our populations grow large enough, we can rely on half of the population learning some stuff and us learning some other stuff. And as the population grows, despite a limited brain, as a collective, we can become cleverer. This is the Einstein story. So imagine that you have a 10-sized brain and you've got 10 things to learn, then you can only reach scale unit one. But if you live in a larger society, you can reach scale unit 10 by focusing on only one of those things, and everyone else handles the other stuff. And if you're a doctor in New York, you can focus on one tiny part of the renal system, whereas if you're a doctor in a small town, you have to do a lot more medicine. In the same way, Einstein could not worry about a bunch of stuff and use his (no doubt) amazing brain on a very small sliver and be stupid at everything else. And so that division of labor led to a whole bunch of other things that led to new possibilities for creativity through things like intellectual arbitrage where we're recombining across these disciplines.
SPENCER: So I'm just curious, so that seems like quite a plausible story for what happened but there are some alternative theories. I'm just curious what you think about, for example, the theory that sexual selection drove our rapid brain size development. The idea being that it was actually competition for mates that led to runaway intelligence, kind of the way peacock feathers get bigger and bigger despite peacock feathers being a great way to get hunted by predators.
MICHAEL: So the problem with these other theories is, show me the model, show me the mathematical model, show me the bits of evidence that you're putting together, and tell me why it hasn't happened in other animals in the same way. In order to have a general theory, it needs to kind of put all these pieces together. You're right, you've got these kinds of mini theories, but they break down. They don't put the pieces together. So when I opened the book, I kind of talked about the process that other more mature sciences went through. We used to think that the weather was created by Thor banging his hammer and capricious gods, and then Newton, Einstein, and Maxwell, they write down equations. Weather is still difficult to predict, but we know how it works. Newton is not a dumb guy, but he's trying to turn lead into gold. And that's because he doesn't know that the world is made of elements. And yes, you can make gunpowder, and yes, you can do certain kinds of chemistry, but you can't turn one element into another. But once we had a periodic table, we could say, "Oh, this makes sense. And this other thing doesn't make sense." And you can really begin to do chemistry. And in the same way with evolutionary biology, we used to be, "Well, why does the peacock have a giant tail?" as you said. "And the pea hen is a drab brown? And why are some animals laying eggs and others have live births?" None of this makes sense, and then Darwin comes along and eventually the modern synthesis where we formalize it mathematically and we have a cohesive theory for the evolution of life. Still hard to predict species, still hard to predict ecologies, but we have general theories that hang together, putting all the pieces together. That is what has happened in the human and social sciences. So you're right, there are these kinds of mini explanations, but — language did it, big brain sizes did it, sexual selection did it — they don't make sense because they can't explain a variety of phenomena. They're just, I don't know, many hypotheses, I guess.
SPENCER: I want to make sure we get into sort of the applications of this way of thinking. What do you think is one of the best examples of how to apply this? What area of society?
MICHAEL: One is our understanding of — I haven't even gotten into this — how we cooperate with one another. So there are various mechanisms for cooperation. I know your listeners might be familiar with some from past guests. Kin selection, inclusive fitness: genes that can identify and favor copies of themselves will spread at the expense of those that don't. Direct reciprocity or reciprocal altruism: "You scratch my back, I scratch yours. An eye for an eye, a tooth for tooth." Reputation: "I don't know who Spencer Greenberg is, but I've heard about him and so I'll work with him based on his reputation." Institution: "I don't actually go after people typically when they commit a crime, I pay my taxes and I expect a police force and judiciary to handle that all for me." This is great. So we now understand all of these mechanisms, but we also understand that they exist at the same time. And so what we call corruption in society is better described as one scale of cooperation undermining another. Or to say in another way, people often like, "How do we explain why some countries are corrupt?" You don't have to explain why some countries are corrupt, you need to explain why some countries are not corrupt because favoring your family is natural. It's found across the animal kingdom. When a lion comes in, it might kill the cubs of another lion, but it won't kill its own cubs. Favoring your friends is, again, very natural. When a President gives a contract to his son, we call that nepotism. But it's also inclusive fitness undermining our institutions. Or when a manager gives a job to a friend or a friend of a friend, that is direct reciprocity or reputation undermining our meritocracy. And so we now know if you look at all of the corruption literature, how you fight corruption is by undermining those lower scales of cooperation. So for Europe as a whole, my collaborator, Joseph Henrich and his colleagues, Jonathan Schulz, Jonathan P. Beauchamp, Duman Bahrami-Rad have found that the Catholic Church's banning of cousin marriage in Europe destroyed the European tribes and led to more individualism and greater democracy. And the earlier that was instituted, the more successful countries are today. How did it work? By undermining inclusive fitness. You couldn't scale up with kin where suddenly your uncle wasn't just your uncle, but connected to you by many, many relationships and many, many obligations. If you want to undermine corruption in other places, what you do is you create cooling off periods. You prevent the revolving door. Like in Botswana, you prevent people from working for their own tribes. You undermine lower skills of cooperation. So that's one example. Another example is how we innovate. So if you think about innovation the way that you were thinking, you're like, "Okay, we just got to find the super geniuses. That's all we need, we just need the one Einstein." That's one way of thinking about innovation. The other is: innovation is a product of ideas flowing through our social networks. What we want to do is to maximize the probability of those ideas flowing, which means making sure people are cooperating with one another. And so then what you can do is you can arrange your society in, for example, the way that the United States is arranged, the way that Microsoft under Nadella was arranged, the way that Estonia (which is now the highest ranking country on student performance for mathematics, science, and reading) is arranged in terms of educational system. How does it work? What you do is you structure diversity in the way that every state, as Justice Brandeis calls it, is a laboratory for democracy. You create a company where each unit is kind of a mini startup. So if it fails, it fails at a low level. And if it succeeds, it bubbles the solution to the top. That's what Estonia does, as well. So each of its schools and municipalities have a lot of autonomy and a lot of sharing of information. That's why Silicon Valley is so successful. They don't have non-compete laws and so, you can just grab entire teams. People think of Silicon Valley as this kind of bastion of success. It's not; it's a graveyard of failure. But, it's structured in such a way that there's a lot of sharing of information. So, the few successes lead to Apples, Amazons, Alphabets and so on. So it's a very different way to think about the process of innovation or how to make a society or a company more innovative, how to increase our intelligence, and so on.
SPENCER: Yeah, on the innovation point, I think that what you said makes a lot of sense that you can have information flows that kind of provoke-promote innovation versus ones that it kind of holds innovation back. I guess my view is not just that innovation comes from lone geniuses, sometimes does, I think, but I think it is a combination of factors from individual geniuses adding pieces to the puzzle, also the way society in general is educated, also the way that incentives work and culture works. Like in the US, we seem to have both an incentive structure and a cultural system that pushes innovation more than some cultures, and we ended up having a lot of start ups as a result. So I think it's a kind of combination of many of these factors.
MICHAEL: I do go into that. I describe it as a kind of the compass framework. So just to draw on one of the points you made, the US has this go-big-or-go-home, you-be-you in a large marketplace, such that many entrepreneurs would have been better off taking a salary job because they fail. But the few successes leave America, and actually the world, better off. That's why it's so innovative. Whereas the Asian, think of Japan, that strategy of incremental improvement, don't fail — it's like the tiger mom-type parenting — leads to a lot of incremental improvement, but not those radical successes. But my point is, the levers that you find are also at an individual level because, of course, people embody a lot of these things. If you have a better education system, if you have exposure to more ideas and also genes and whatever other factors, you can have 10x people, 10x engineers, and if you get them working together in the right way, you get 100x. But you can also have 1x people working as a 10x team.
SPENCER: Can you describe the compass framework that you mentioned?
MICHAEL: Yeah. So when I kind of worked with companies — I guess most recently, we did this with Uber — you kind of distill the insights from this theory of everyone into this idea of where the levers are. So compass is an acronym for the C is collective brain thinking. So thinking about what are the collective features of this company that are aiding or harming innovation. One example of that is something like what I call Enron effects. So Enron is famous for (not the only company but it was famous for) having rank and yank, where they would rank everybody, then they would chop the bottom off. And that came because Jeffrey Skilling's (the CEO) favorite book was Dawkins' 'The Selfish Gene.' What he didn't realize, of course, is that evolution isn't just about competition, it's about cooperation. And he was undermining people's tendency to work together and share knowledge. So this is one example. The O is about how you kind of move off the beaten track. So we get trapped in sometimes sub-optimal equilibria. So you talked about Hamilton. Hamilton existed in a big evolutionary landscape. Other countries are trying all kinds of other things but the United States didn't necessarily need to succeed, but it did. It ended up being a good solution. But again, we forget all the failures, in the same way that we do with any company or any innovation. So how do you get off the beaten track? How do you rethink assumptions from first principles to say, "Well, what's holding us back?" Because today, we can't rewrite the Constitution even though it may not be the most amazing document or maybe even the most amazing document of the time, it's pretty good and it would be very difficult to try to redo that, because we're stuck that way. Or schools, as I mentioned earlier, have come about a certain way and it would be hard to retrain all the teachers. So how do you innovate by going off the beaten track? You can do things like what Nadella did with Microsoft: restructuring. You can create startup cities, as I described in the book. The M stands for magpies strategy with a prepared mind. So this idea of intellectual arbitrage, if you think that creativity is driven by serendipity and incremental innovation and particularly recombination, then what you should be doing is, like a magpie, looking for shiny things to bring back to your nest. So in other words, you have to have a prepared mind that really understands the problem because its solution is spread across the heads of many people. And if you can piece those pieces together, you will create something brand new. It's about exposing yourself to the smartest people whose ideas you disagree with. For example, maybe the things I'm saying for listeners on your show, they'd be like, "That doesn't ring true." Well, you should ask yourself, "Why doesn't it ring true? And what would be the implications if it did? How would you adjust it?" That's how we are creative. The A is looking for adjacent possibilities. So some things are easier to get to for your company as a person, as a country. If you are trained in discrete math, maybe game theory is easier for you. If you're trained in something else, then there are other adjacent possibilities. So looking to see, "What can I borrow from an industry that's close enough that I can begin to implement it right away?" So the two S's. One is key sharing, the second is being social often beats being smart. Because in a world where everyone is smart enough, one mistake I find that many grad students make is that they're so used to being the top of their class and focusing on intelligence, they don't realize that once you get to grad school, everyone is kind of smart enough. And what differentiates people are other factors: how hard you can work, how much you can focus, which conferences you go to, where you're gathering information to get those new ideas and hit those breakthroughs. So with Uber, we turned it into what was called the five s strategy, which is, no company can just do something from scratch, you have to integrate with the way people are currently working. So this is a way to kind of graft the strategy on to the way the companies are currently working. But that's what I described in that section.
SPENCER: So stepping back for a moment, you mentioned three applications of this way of thinking. The first application we talked about here was corruption. The second was innovation. Why don't we jump into the third one now?
MICHAEL: Yeah. So another one that I think is quite topical right now is how do we handle diversity and immigration. Humans are a migratory species. We have always moved around. No population is completely stagnant. When we meet each other, we share information. But, we also fight, we cooperate, we do all of that. Since the age of mass migration, when we had big ships and now airplanes, it was easier to move. And so suddenly, people from very different parts of the world. And if you buy this idea that why humans managed to succeed in different parts of the world is through adapted cultural software, that literally changes even things like perception. Then people running different operating systems even are now living side-by-side and trying to form a society. And that is a challenge. So, I describe this challenge as the paradox of diversity, which is that immigrants are the lifeblood of a society. They are America's super serum. This is a great quote from the founder of Singapore, Lee Kuan Yew, where he says to political scientist, Joseph Nye, "China could draw on a talent pool of 1.3 billion people, but the United States could draw on the world 7 billion people and recombine them in a diverse culture that exudes creativity in a way that ethnic Han nationalism cannot." That is the power, it's that recombinatorial power to create new foods like the Hawaiian pizza, and new inventions, and so on. But diversity is also divisive by definition. If you and I didn't speak the same language, this would be a difficult podcast. And if listeners don't speak the language that we're speaking, they're not going to get all of that rich information that they can recombine with whatever it is that they do to make themselves more successful. And so, different countries have different ways of creating what I call this kind of optimal acculturation, where you're leaving alone the diversity that drives this creativity and allows people to think of things in different ways, but you are assimilating people along key lines that would otherwise harm communication and coordination. So it could be language, obviously. It could be subtle norms about risk, hierarchy, egalitarianism, respect for women or men, or even being on time (like Germans versus Italian society). So I describe the major multicultural models as the No Hyphen Model. Do you know the no hyphen model?
SPENCER: I don't know.
MICHAEL: So I don't know if you remember, Trevor Noah of The Daily Show got into this fight with the French Ambassador Gérard Araud after France won the World Cup. Do you remember this?
SPENCER: No, I didn't.
MICHAEL: And so in 2018, France wins the World Cup. France is like a canonical no-hyphen country. And out of 23 members of the football team, 14 players are of African ancestry. And so Noah goes on the show and said, "Yeah, look at those guys. You don't get that tan by hanging out of the south of France. Africa won the World Cup." And so Gérard Araud gets super angry about this. He's like, "You are feeding into the ideology which claims whiteness is the only definition of being French." And he says, "France is indeed a cosmopolitan country, but every citizen is part of the French identity. And together, they belong to the nation of France." He contrasted this with the United States. "Unlike in the United States, France does not refer to its citizens based on race, religion, or origin. To us, there is no hyphenated identity; roots are an individual reality. So there's no Algerian-French, German-French, Muslim-French, Christian-French. There's just French. And if you have some ancestry, that's your business." That's how they think about it. Now, of course, in reality, it doesn't work that way. You can't assimilate people unless they come in small enough numbers. They actually want to assimilate. The host country is actually very welcoming. There's all kinds of things that need to be in place for that to work. And so in practice, it just doesn't. Then you've got the Canadian model, which is sometimes called the salad bowl or the mosaic model where, "We're not gonna even try to simulate. You live in your own communities. It's fine. Those communities are just satellites to their countries of origin, and we'll just find a way to work together." And Canada seems to make it work. But what I argue in the book is that mosaics are more fragile than a single pane of glass when put under pressure. So if there were resource stress, then you do see these kinds of conflagrations in Canada between ethnic groups, or between English speaking Ontario, and French speaking Quebec. Then you've got kind of the melting pot model that's associated with America, where the idea is that everybody comes in, everyone's melted into this big pot, and they are all part of the American identity. And it mostly works, except some flavors dominate more than others. And you've also got the more Ankh-Morpork problem, which is this fictional city in Terry Pratchett's fantasy novels where he describes, "Ankh-Morpork is the melting pot of the world, which occasionally runs foul of lumps that don't melt." So what I argue is that all of these models are fine, but they miss out on the importance of a couple of things. One is your strategy for what immigrants are coming in and under what conditions, and resources. So kind of the Enron effect that I mentioned earlier, if the world is limited — people are struggling to get jobs, places in hospitals, houses or places in schools — it creates destructive competition between people. So an analogy I use is to imagine economic growth like buses coming along and they're coming every five minutes, and everybody's standing in a queue. People are going to be upset with the 1% who have special passes that get them to the front of the line, and they're going to be upset with some ethnic groups coordinating with one another to kind of help their co-ethnics or whatever. But they're going to put up with it as long as buses are coming every five minutes, because they're gonna get a seat. But if the rate of buses slows down, like one every hour or one every day, suddenly, all of those fractures that always existed come apart. So this kind of reveals the importance of infrastructure. So if you're going to have a lot of people come in, you have to have pathways, you have to invest in infrastructure, you have to make sure that there are actually jobs and there are actually houses, and there are actually things that you're not creating more stresses that lead to fractions. And the problem that I think we face and the reason we see a rise of the right wing and polarization is because — I haven't even gotten to the Spencer — economic growth has slowed down because energy returns have fallen. And so the way around this is what I described as the umbrella model, which if you think about an umbrella corporation, the goal is sustainably managed migration, where you're thinking about immigrants coming in as almost hiring for your company. If other employees recognize that you need a particular skill set, they're not going to be upset when you bring people from wherever to fill those skills. There's a place for them and you have onboarding. Australia, for example, has what's called the Australian Cultural Orientation. It's a five day program that refugees go through so that they know what's coming, they know the resources available, they understand a little bit about Australian culture, and it helps transition them. And then there are further resources available to them to come into the country. So that's one side of it is the resources. The second is, culture does matter. And when you have greater cultural distances, it can be difficult for people to understand one another and get along, or it can take a very long time. That works if resources are plentiful. But when resources are strained, it can be very difficult. And so, if you think about cultures that are not homogenous — it's not like in China, all people in China are a certain way, and all people in the United States are a certain way. You're a lifelong New Yorker. New York is different to even Boston. There's a cultural difference between the east coast and west coast, but even New York and Boston, and even within New York, the different parts of the city have different cultures associated with them. — So when you have an immigration strategy, what you're really doing is sampling from those countries. If you have refugees, it's not an economic framing that's appropriate, it is more humanitarian, but you are sampling from the entire population. And so, whatever cultural traits — good and bad — that exist in that culture are coming in those proportions. If you have a more selective migration policy, based on age, education, or whatever, then you're sampling in a different way. And so every country has to work out how it wants to sample, in order to build a cohesive society that balances, I suppose, fiscal responsibility against social cohesion.
SPENCER: Could you tie this back to a theory of everyone? Where's the connection there? I'm not sure I'm seeing that.
MICHAEL: So the connection is that an assumption that you get out of, I guess, an evolutionary approach to humans that doesn't consider culture is that humans everywhere are kind of the same. This was called the WEIRD people problem. Psychologists used to be like, "Look, if we just study our undergrads, that's humans." What they didn't realize is that the psychology you find in Western educated, industrialized, rich and democratic nations looks very different. And in fact, the psychology you find in many different parts of the world is completely different. So, when America tried to go to Afghanistan to bring democratic institutions, that was nearly impossible because it's a completely different culture. If you don't have the idea that we are ruled by principles and not people, that we're ruled by laws and not lords, then it doesn't matter what the Constitution says. It's not going to work. Liberia has America's institutions but it's not successful because it doesn't have the invisible normative pillars that uphold it. Does it make sense what I'm saying?
SPENCER: Yep, that makes sense.
MICHAEL: And so then the flip side is that when people come in, they bring with them cultural norms, as well. And they are more and less compatible with what is already there, or the institutions that exist. And so you need some pathway to ensuring that you get a simulation on all of the right metrics. So I'll give you an example of a dataset. You're driving along in a car with a friend of yours, and the friend hits a kid on the road. And the lawyer for your friend says, "If you can lie and say that your friend was going under the speed limit, your friend will get off the crime." And so they asked people around the world, "Would you do this? Would you lie for your friend? Should your friend expect you to lie?" If you ask Germans or Scandinavians, they are like, "Absolutely, no way." If you ask Latin Americans, that'd be, "Yeah, of course. I would. Who cares about it. Of course, I would lie for my friend." Very, very, very different psychology. And so, a lot of the rhetoric around immigration doesn't even make sense. It doesn't make sense to talk about immigrants as a single category, any more than it makes sense to talk about citizens. How can you talk about our immigrants being successful versus our citizens being successful? There's huge differences in the outcomes of different immigrant groups based on education levels, the selectivity of migration, their countries of origin, what age they came in at and the economic conditions they came in at. And you really have to disaggregate that. And that makes sense in light of the theory of everyone because so much of our cognition, so much of what makes us people is this cultural stuff that is transmitted.
SPENCER: So is the idea here that you think countries should be thinking about what sort of people do they want in the country and then kind of be selecting based on some set of laid out criteria, rather than just thinking of it as "more immigration or less immigration?"
MICHAEL: Well, that's definitely part of it but it's very easy for some of this stuff to slip into racist, I suppose. The kind of criteria that come in is like the wrong immigrant versus the right immigrant. I think what you need is, rather than thinking about immigrants as a whole, you want to be thinking about categories and criteria that are predictive of success for both people coming into the country, as well as the local populations because, often, immigration is a zero sum. It's not like borders are completely open. You want to select the best people. And quite often, things like brain drain are actually not a thing. They create links back to their countries of origin and they raise standards in those countries as they're competing over these places. So to give you an example, Canada's recently implemented this, the UK is trying to implement it, Australia has had it for years. It's a points-based immigration system. Under the Australian system, they prioritize people who are aged 25 to 36, for example. Why? Because that's that sweet spot. You've had an education but you can begin to contribute. They continually change the occupations that are prioritized based on what the country is missing. So if you need more engineers, doctors, care workers, whatever you need, you can adjust that and look for immigrants that meet those skills. It is kind of taking it almost like we're building a company. Here, we're building a country together. What we need is to find people to fill these things. And then there's less resentment, if you do it in that way. So you're looking for these kinds of objective fair criteria, that are trying to ensure that when immigrants come in, they have the best chance of success. And if they have the best chance of success, then so too will the country and so too will the local populations will be less resentful.
SPENCER: So we've talked about a lot of different applications of this way of thinking. This idea that you call a theory of everyone. Why did you end up writing this book? What was your ultimate motivation?
MICHAEL: Yeah, so this book was kind of bursting out of me. And the reason for that is once you have a kind of theory like this, a cohesive theory that connects a bunch of things, it allows you to really move from science to technology, it allows you to make better decisions, it allows you to do chemistry. You have a periodic table, it's time to move from alchemy to chemistry. And one thing that concerns me a lot is — I didn't even get into this, Spencer — that our societies run on excess energy, and that excess energy is falling. I'll give you one statistic to end on. This is oil discovery rates. In 1919 one barrel of oil found you at least another thousand. In 1950, one barrel of oil found you another one hundred. And by 2010, one barrel found you another five. The leap in every metric of human progress that we saw in the Industrial Revolution was the result of burning stored sunlight in the ground in the form of peat-turned-to black-rock coal, and zooplankton and algae turned to oil and natural gas. And we burn that to literally shoot the human rocket upward, cooperate on a scale we'd never seen before, and create the world of wonders that we live in today. But because that excess energy is falling, and we haven't made the transition to something like nuclear, we are in deep trouble. And so I think it was critical to me that more people understood this science at this particular moment in history because a small group of people with an easily understood idea and the right tools, like Alexander Hamilton's of the world, can really effect change. "We hold these truths to be self-evident that all men are created equal," was evoked by Abraham Lincoln in his Gettysburg Address, was evoked by women's rights activists, and the Declaration of Sentiments was evoked by Martin Luther King Jr. And it was an expansion of all that we were capable of. And so, I hope that this book gives people those similar kinds of rules, these ideas that they can latch on to go, "Okay, this is how I can apply it in my life. And this is how I can apply it to make sure that I and my kids and all Homo sapiens to follow have a future."
SPENCER: Michael, thanks so much for coming on.
MICHAEL: Thanks for having me, Spencer.
JOSH: [A listener asks:] "What do you want to make sure you do achieve or experience before you're gone?"
SPENCER: The big thing that comes to mind for me is that I have this life goal of massively improving people's lives. And that is really what I would like to do before I'm gone: have some way of very positively impacting a very large number of people, where they're happier and they suffer less because of my efforts.
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