Shownotes Transcript
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Welcome to the You Are Not So Smart Podcast, episode 312. Welcome to the You Are Not So Smart Podcast, episode 312.
In 2023, the movie Jurassic Park turned 30 years old. Yes. And 30 years ago, Steven Spielberg's Jurassic Park wasn't just the biggest movie of the year. It felt like it was the biggest movie of all time.
Today, it's still the highest grossing movie of Spielberg's career, which says a lot. His career includes Raiders of the Lost Ark and Jaws and E.T., all of which were blockbusters in their day. But the cultural impact of Jurassic Park was immense and long-lasting, and it's still with us. It's still making money, too. It cost about $63 million to make. And thanks in part to the re-release in 2013, it has now grossed more than $1.1 billion, with a B,
Dollars worldwide. When I first saw Jurassic Park, my favorite character, by far, was Dr. Ian Malcolm, a scampi, flirty, self-amused, playful, shades-and-all-black-wearing rock star mathematician portrayed by...
Yes, Jeff Goldblum, who really dialed up his overall Goldbluminess for the role that he was playing as an expert on chaos theory. Based on that expertise, Ian Malcolm, the character,
was very skeptical and pessimistic about the idea of bringing dinosaurs back to life. Throughout the first third of the film, he keeps telling people that based on what we know in the realm of complexity science, life as a complex system is
is dangerously unpredictable, even when you know a great deal about the starting conditions, a great deal about all of the bits and pieces of the thing you're about to do. When you include life in such a thing, he laments the hubris required to assume a small number of human beings could control nature to the point that they could run a theme park featuring long extinct creatures and
where the public would be invited to interact with them. And as he famously said in the film, no matter what controls and safety protocols you put in place in such a scenario, "I'm simply saying that life finds a way." Jurassic Park was and still is a massive cultural touchstone and thanks to its immense success,
Not only did it introduce and popularize the idea that birds evolved from dinosaurs, it also introduced to the general public and popularized chaos theory and complexity science. And there's a great scene in which Ian Malcolm introduces this side of mathematics to Dr. Ellie Sattler, a paleobotanist played by Laura Dern,
He does so by dipping his finger into a cup of water and asking her, before he drops it on her hand, to predict how the drop will behave as it drips to the floor of the Jeep in which they are riding across the park. Will it drop the exact same way or differently? She predicts the same way. He drops it on her knuckle. It goes one way. He drops it on her knuckle again. It goes a completely different way. And then he says, Why?
Because tiny variations, the orientation of the hairs on your hands... Hey, Alan, look at this. The amount of blood distending your vessels, imperfections in the skin... Imperfections in the skin? Microscopic, microscopic. And never repeat and vastly affect the outcome. That's what... Unpredictability. And it turns out that this scene is actually a really great way to introduce chaos theory, which is all about trillions upon trillions of variables that, taken together...
become the initial conditions in a system. And in very complex systems, very tiny changes to any of the variables can lead to very complex outcomes. As Malcolm explains in that scene earlier, a butterfly flapping its wings in Beijing could lead to a rainy day in New York City.
But flapping its wings in a different place on Earth, it could lead to a sunny day in New York City. And that's chaos theory, as he says. And in very complex systems like organisms, in ecosystems, on planets, in solar systems, even if you can account for all the bits and pieces and forces acting upon them, once you allow that system to play out over time...
Those conditions interact and become ever more complex emergent properties that will lead to ever more difficult to predict outcomes. Someone somewhere doesn't go to a party or before that, a streetlight doesn't change in time or before that, a hurricane doesn't form in the ocean because a butterfly doesn't flap its wings and you end up in a world without this podcast or whatever
Jeff Goldblum himself.
Ha ha!
In just one or two turns, you will find yourself reading books like Godel, Escher, and Bach, and listening to bootleg recordings of spiritual celebrities like Alan Watts and Terence McKenna, or wandering into early internet saloons where people shared thoughts on things like Conway's Game of Life and cellular automaton.
We'll talk about all of that in a second. It's all very deliciously woo-adjacent, plenty of bigger-than-you-aw-and-one-with-everything realizations, but still strongly rooted in the hardest of the hard sciences. Pure math, numbers, and graphs, and formulae. That was my experience. But no matter your introduction, once you go down that hole, it's difficult not to become obsessed, at least for a while.
For Neil Theis, a professor of pathology at the NYU Grossman School of Medicine who changed the world a few decades ago with research into stem cell plasticity, his introduction led to an obsession that has lasted more than 20 years, an obsession that resulted in him becoming a leading communicator on the topic of complexity science. Lots of lectures, lots of videos on lots of platforms, and most recently, a book titled Notes on Complexity.
And since I was thrilled when my research into genius led me back into this topic, and I'm eager to learn more about it for the sake of the book I'm currently working on, I emailed Thies asking if I could ask him some questions and if he'd be okay with me sharing some of that audio on this podcast. He said yes, and well, here we are.
So if you always wondered if there was anything to Ian Malcolm's serious monologues and playful demonstrations in Jurassic Park, if life really does find a way, or if you've already gone a bit deeper and would like to hear about scientists waking up
surrounded by simulated life forms, or how all of this helps us understand, predict, and treat cancer, or understand, predict, and prepare for weather systems, or how ants and liver pathology are all part of a giant system of systems in which everything is influenced by everything else and it all shares the same starting conditions if you run back the cosmic clock, stay tuned. Music
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Check all of this out at kitted.shop or just click the link in the show notes. And now we return to our program. My name is David McCraney. This is the You Are Not So Smart podcast. And in this episode, we are discussing complexity science with Neil Theis, the author of a book on the subject titled Notes on Complexity.
And this whole thing, complexity science, it's a fascination I often find myself returning to. One that began, whose initial conditions were much the same for myself as they were for Neil. Much the same for everyone who ends up reading or writing books about this. Much the same for researchers in this field, for a lot of people. This way in, this way into complexity science. It's
usually either directly or via a descendant of an early computer program called Conway's Game of Life. The creation of Conway's Game of Life is itself an example of complexity theory, and like everything else in this episode, it's difficult to pinpoint the initial conditions that led to it.
You could start with the asteroid that killed the dinosaurs if you wanted, or go back to the star that exploded and led to that asteroid. But you could also start with Norbert Weiner, an engineer, mathematician, and philosopher who worked on U.S. anti-aircraft control systems in World War II. Yes, Weiner. He researched how to improve the accuracy of these anti-aircraft guns.
by developing these feedback mechanisms that could predict the trajectory of fast-moving enemy aircraft and continuously adjust the weapons firing systems to account for the angles at play in this dynamic system. And he noted how there seemed to be parallels here to things that happen in living systems, mechanisms of feedback and adjustment to changing conditions.
And he set out to work out as much of the math as he could. You could start there. Or you could start with John von Neumann, a mathematician who worked on the creation of the atomic bomb during World War II. As part of that work, and later after it was all done, he and Stanislaw Ullum, another mathematician working on the project, worked out the math for predicting and simulating natural phenomena like hydrodynamics.
Von Neumann was fascinated with the idea of a future in which robots could build more robots, basically copies of themselves. And Ulam was fascinated with the math of how crystals grew, which featured large outcomes from simple conditions. Ulam suggested that von Neumann create a very simple mathematical model of his big idea, his idea of making robots who could make robots,
And out of all this, von Neumann returned to his idea of liquids, seeing them as a collection of individual bits whose motion was based on the influence of other neighboring bits. And that led him to develop these two-dimensional grids of bits that could indeed simulate biological systems of self-replication. And those would later become known as cellular automaton. ♪
Or you could start with the work of Edward Lorenz, a mathematician from Connecticut who was fascinated with weather patterns. In 1961, he used what we today would consider a primitive computer to set up 12 variables, stuff like temperature and wind and speed and so on,
and then ran a simulation over time of weather patterns that might emerge as those conditions interacted. To his surprise, if he ran the simulation again, but from the middle instead of the beginning, the end of the simulation, the outcome, would be completely different. He discovered the reason for this was because he used a printout of all the conditions in the middle of the run and took those variables and plugged that back into the machine.
And when it had printed out the variables, it rounded the digits behind the decimal to three digits down from six. So even though that difference was extremely tiny, seven, eight, and nine decimals behind a zero, the resulting weather pattern was completely different. And he called this deterministic non-periodic flow, now known as deterministic chaos. And from that, chaos theory was born.
But if you mush all of these together into the major influences of a British mathematician named John Horton Conway, you'll arrive at a moment in 1970 when he developed what would later be called Conway's Game of Life. He wanted to create a simple game with a very simple set of rules that anyone could play that would then demonstrate all of these things.
One that would show how complex behaviors could arise from just a few on/off starting positions. You start with what looks like a crossword puzzle, a grid of squares, and you can turn on or off each of these squares. Make them either black or white, alive or dead.
Then you hit start to see what happens. And with just four rules, you get incredible outcomes like self-replicating patterns, patterns that produce streams of simpler offspring. And if you give yourself a very large grid, a vast, humongous grid, these enormous systems of interaction can simulate computers themselves.
And the rules, by the way, are very simple, and there are only four of them. One, any live cell with fewer than three neighbors dies. Two, any live cell with two or three live neighbors lives on to the next generation. Three, any live cell with more than three live neighbors dies. And four, any dead cell with exactly three live neighbors becomes a live cell.
So you just run that one frame at a time, and as it goes, frame by frame, sped up into a big animation, it appears to be alive. And this is a pretty good starting point for how complexity science, as we know it today...
Because a computer scientist named Christopher Langton began playing with this game and developing his own ideas from it. And well, here is Neil Fees, the author of Notes on Complexity, with the story. This beautiful origin story for complexity is this computer geek trying to avoid the draft in the early 70s working in a computer lab at MIT.
and he's up late at night doing computer geek types of stuff, but he set the game of life running on a computer, because people like to play with it. It's beautiful, it's fascinating, like you said. So you have an open-ended grid if you go off the screen. I mean, technically it's an infinite grid of squares, like a checkerboard, and cells turn on or off, i.e. alive or dead, depending on how many cells around them are alive or dead.
And John Conway, who developed this, looked for a set of rules that would give rise to something interesting. And the set of rules he came up with resulted in things, you know, you start off with an initial pattern, which is the game. You set what the initial pattern is and you see how it evolves over time. So again, this idea that adding the time element. And most things you might set up
just run a few things and then they all die. But some of them will become stable. Either, let's say, a 3x3 block of cells
Every turn of the game will look like a 3x3 block of cells. It just keeps renewing itself exactly the same. Or what they call blinkers, like if you have a row of three cells across horizontally, the next turn of the game becomes three cells, three squares vertically, and then back to horizontal and back to vertical, and just blinks on forever, back and forth like that.
But what they discovered was sometimes you just get these elaborate displays that... Well, first you get things like you mentioned called gliders, where you have a solid pattern and then it just moves off the screen. It's gliding across the screen and you know that if you could follow to infinity, it would just keep gliding. So it's not stable, but it is predictable. You know where it's headed. And that turned out to be a form of chaos.
But there was this other thing that sometimes happened. These elaborate things, they often will actually look organic in their shape, like plants unfurling, for example. And there's this origin story
where Langton talks about being up late at night and felt the hairs on the back of his neck go up and he thought someone had snuck into the lab and he turned around thinking he'd find one of his co-workers who decided to be working late at night but he turned around and there was no one in the lab and then he saw the screen and realized it was the game of life that was giving him that feeling that someone was in the room
that it was something more and then he turned around and looked out the window and it was a cold winter night in Boston and he saw the lights of the city and the cars moving along the roadways in the distance and and lights moving down the rivers and Lights turning on and off is when in people's buildings and he suddenly had the sense that the life of the city was the same as the life on the screen and
And he and another researcher named David Packard independently but completely in parallel realized that there was a new kind of order arriving at that border between order and chaos. And Packard coined the phrase, "Complexity lies at the edge of chaos." That life is at the edge of chaos. And what complexity describes are living things.
That's Neil Theis, and he writes and speaks and thinks a lot about complexity science. He's often referred to as a complexity theorist, but that's not where he started, academically speaking. His initial conditions were the sort that led to medical school and the sort that led him to become a world-renowned pathologist, a medical doctor who, as he puts it, quote, "...found sustained pleasure in sitting at a microscope hour after hour..."
looking at diagnostic pathology specimens, pieces of people, end quote. Those pieces of people under a microscope often look like abstract paintings or kaleidoscopic images or psychedelic desktop computer backgrounds. So to make sense of what you're looking at, to understand how it all relates and connects to the organism from which it was taken, takes a lot of education and a lot of practice.
And Thies got so good at this that in the early 2000s, he and his colleagues published papers and conducted research into adult stem cell plasticity that appeared in journals like Science, Nature, and Cell. Very prestigious. And he helped make the discovery that bone marrow stem cells could grow into new liver, lung, gastrointestinal, and skin cells, which revolutionized how we treat related diseases.
It was a huge finding that led to a lot of attention, both scientific and political, thanks to the fact that at the time there was a lot of controversy surrounding harvesting stem cells from destroyed three- to five-day-old embryos, which led to President George W. Bush signing an executive order banning federal funding for stem cell research and therapy based on George W. Bush's religious beliefs.
an order that later was partially revoked by President Barack Obama. And all that attention, all that interest, led to an artist friend of Thies who thought the visual nature of Thies' work was fascinating, whose abstract art slices of the body would be interesting to other artists, to introduce him to an artist friend named Jane Proffitt. So he did. He introduced the two. And at the time, Proffitt was very into complexity theory.
She had created a video game art project back in 1995 with programmer Gordon Selle called Technosphere.
And it featured thousands of independent creatures, little digital life forms competing in a virtual environment where they would adapt and evolve over time. Each player created their own creature choosing its basic characteristics and behaviors and whether or not it would be a carnivore or a herbivore. And then the creature would go through that world and periodically email the player to let the player know how and what it was doing in that game world.
But the astonishing outcome of all of this was how the creatures would often arrive at behaviors and outcomes that Prophet and Selly had not programmed into the game on purpose. Especially the carnivores, which would develop strategies like waiting near entrances to the grazing areas where the herbivores were gathering and then picking them off as they attempted to exit.
When Neil told Jane about his work, she told him the way that stem cells adhere to heuristics and algorithms as they move through the body seemed extremely similar to the systems and rules of her evolving evolution game. And then she told him all about how there were a lot of those sorts of parallels all throughout complexity science. As we just mentioned, systems that seemed spookily similar to the way life worked on both cell-by-cell and ecosystem-wide scales.
and that was it. Thies was instantly obsessed. She was explaining to me her approach to art and I was explaining to her my approach to science. We were brought together in order to do that, to look at how an artist and scientist could communicate with each other about what they do. And she told me about complexity theory. She said, "Wow, the way you're talking about stem cells,
is very much like what I hear people saying ant colonies do, and that's how she introduced me to complexity. And here we are, 20 years into that obsession, and after decades of lecturing about this, providing videos about this, teaching about this,
He's written a book about it called Notes on Complexity. And a whole lot of that book is about life, about trying to predict outcomes in life. And it reminded me of Ian Malcolm right away. We have...
chaotic systems in our bodies. The beat of a heart is a chaotic pattern of electrical waves, for example. We have the branching of blood vessels in our body looks like the branches of river systems seen from a satellite, the same fractal structures. But overall, we also have crystalline order in our bodies and bones, for example. So our living bodies contain all these things, but they're more than these things.
And the more is complexity. And partly the more there is that there is a very...
discrete level of unpredictability or randomness in complex systems that you don't have in chaotic systems and you don't have in perfect order. And that zone of randomness is what allows for the creativity and adaptation that's the hallmark of living things.
As I was reading your book, I was like, oh, wait, this is Jeff Goldblum from Jurassic Park. This is the Ian Malcolm character because this is the chaos theory, complexity, life finds a way person that steps in and says, have you considered that you actually can't control these outcomes? I have the math to prove it. Has anybody ever told you that? No, no, no, no, but I'll take that. That's okay. If you have a good leather jacket, wear it for all your future interviews. All right, sure. Yeah.
There was some Alan Watts lecture way back that I listened to where he talked about imagine a human being, imagine yourself as your entity like a whirlpool in a river. Like there's a rock somewhere in the river that causes a whirlpool. And you can't put the whirlpool in a box. You can't pin it to a board like a butterfly. Right.
It's a stable, a temporarily stable pattern within a larger temporarily stable pattern within a larger temporarily stable pattern and on and on it goes. And the whirlpool is the river. That's you. And I was like, wait, that makes sense in some way. When I sober up, I'm going to write some notes about this. Yeah.
That idea kept coming back in different formats. Like I eventually found the Godel, Escher, and Bach book. And that's a book that every time you read it, you don't understand it in a new way. Well said. I remember when it came out, I was working in a bookstore and I was, all my jobs early on were bookstore jobs. And I remember, I think I was 17 when it came out and I still have the copy. And I'm excited that,
I finally, 40 to 50, some 45 years later, I now know why I don't agree with him. Oh, that's great. Let's see. That's how long it takes. Yeah. Yeah. I'm just, wow. I understand enough of what his book is about to know that I disagree. That's great. I still don't, couldn't tell you what his book is about in detail, but I know I think he's wrong.
I'd like to start with your very bold statement, which is that nothing is more complex than life. To make sense of that, let's just tell people, what is this thing called complexity science? What are we even talking about? Just for people who've never really, they've heard the term maybe, but they haven't gone much farther than that. Sure. The simplest thing I can say is that we have this notion that parts that interact with each other assemble themselves into a whole. So,
In straightforward things, the gears and casing and wheels of a clock, we put them together and they build a clock. And we can take a clock apart and there it's pieces. And if we put them back together again, we will get exactly the same clock. But that's the idea that parts assemble into a whole. Now, the parts of a clock don't self-assemble. We have to do that.
Though a really smart engineer type can look at a bunch of parts laid out on a desk, and without doing the assembly, they can say, oh, that's going to be a clock. Oh, that's going to be a can opener. That's going to be a model airplane, just by looking at what the parts are. But most of us would have to try and put it together and see what we get. Things in nature do that, too. And one...
cluster of those that are probably well known to your audience represented by chaos theory. And in chaotic systems you get parts that assemble into holes and those holes are characterized by fractal geometries and fractal mathematics. And so this was something
that was investigated in the 60s to 70s. Popularized by the 90s, thanks to Jurassic Park. Right, right. James Glick's book on chaos really sort of, you know, it's still a bestseller. And I read that book at the time. And so we see chaotic systems all around us in the world. We see the fractal structures of trees, that the trunk goes up and divides into branches, each of which looks like a small trunk. And those divide into branches, each of which looks like a small trunk.
and those divide. And so a hallmark of these is their self-similarity across scales. No matter how close you zoom in, you get the same sort of structure. And if your listeners are familiar with fractals, these are the most precise mathematical forms of these kind of structures. Benoit Mandelbrot discovered them. And you can only generate them, unlike most geometry where you have a simple equation that describes the shape.
with fractals and chaotic systems, you have to have a computer system that can run a program over time and the shapes, the fractal geometries emerge. And so this brought into our ability to understand systems
as a dynamic thing that happen over time. They're not just static sorts of things. And we see this in the puffiness of clouds, for example, or the shapes of coastlines. The closer in you get, it looks like there's more ins and outs and ins and outs, and you get closer and there's more. So you can't ever quite get to...
the end of it. Now we know that in a tree twigs come to an end with leaves and those can have fractal structures inside them but mathematically these go on into infinity. And so we have these two kinds of things
Two kinds of systems in the world that are well identified. One, perfect order where things are very simple and you can assemble them into machines like a clock. Or you have chaotic systems. The most important version of those probably for us are weather systems.
And thanks to the fractal geometries and chaotic systems theory, we can model weather fairly well. So we now have apps on our phones that accurately predict what are the next few hours going to be like.
in my zone. I know when I was growing up in the 60s, there was no way that weather predictions from TV or the news, the radio, were going to be accurate whatsoever. But chaotic systems became studied, weather is a chaotic system, and this became better and better and better. And now we can tell, you know, hurricane season. We can watch for the hurricane before it happens and be able to predict what its ultimate trajectory will be pretty strongly.
So these are the two things. So through the 60s, people were recognizing these kinds of order in the world, machine-like order in which things are very predictable, and then chaotic systems, which are very strange. I mean, it's a cornerstone of Western thought that individuals are, you know, individuals. I look at a plant, and I'm discussing this very categorically defined plant.
amongst other things. It's...
At best, it's a contained phenomenon. To come from another kind of thinking that eventually we get to with complexity science, to see the plant as a continuous system between the gigantic thermonuclear orb in space and the substrate of the material that collected thanks to gravitational forces and from which it springs, and it's all one thing.
Like, I can draw the box around a much bigger system than just the plant. And, you know, from the science point of view, there's a paper I wrote with my collaborator, Manas Kefatos. The bulk of my academic work on complexity theory and the nature of existence has been with him. He's a quantum physicist and cosmologist. But one of our first papers together...
We had these really lovely illustrations, I think, by this artist Jill Gregory. She provided one of the images for the book, actually, in which...
You know, I have this entomologist and it sort of looks like a 1960s school book for elementary school kids in the style of drawing, which I also kind of like. And so you have this picture of an entomologist in the field studying this ant colony. And the reason an ant colony, because we'll probably get to using ants as an example of a complex system soon enough, the entomologist believes they are being objective about this ant colony.
But then the next image is this ecologist coming into the scene to observe the ecology, and there's this entomologist in the middle of the scene! Like, what are you doing there? You're messing up my field of investigation here, literally.
And so the thing is that you're always inside a system that contains you. There is no, you can, it's a useful fiction to say, I can be separate from my ant colony.
But you're mucking up the ecology that's around you. And when the ecologist comes up, they're going to have issues with this. And the farther out you go, you discover there's always another way to contain the system as something that's interlocked.
So, as I said, when I first learned about complexity, Jane mentioned ant colonies. And that's a nice place to start because there isn't anyone who didn't spend time looking at ants when they were kids. Ants are everywhere on the planet. Every kid spent time looking at these ants. So we're all familiar with them. And it's also nice because when I give talks and I start to talk about ant colonies, I can see the audience relax,
because they're suddenly in their four or five year old brain for just, you know, part of it. So the four rules out of which complex things arise are really simple. You know, this is the common complexity is not so complex. Those four rules are the first one as I arrange them, and they don't really follow in any particular order. But the first one is
The more interacting parts or individuals within a system, the more complexity you can get. And the greater the diversity of interactions between them, the more complexity you get. So numbers matter.
And so if you have a colony of 25 ants, which is your standard mail order ant colony that you can get online, we used to, when I was a kid, we'd get them ordered from the back of a comic book. And you have ants that will dig tunnels, and you have food lines, and you have a cemetery where dead ants get placed.
And as the ants die off slowly over time, you get less and less complexity. And by the time you have three or four ants, there's no collective tunnel building. There are no food lines. And an ant lies where it dies. There's no cemetery. So you need a certain level of numbers of interacting individuals to get complexity, meaning...
The ants interacting at a local level are giving rise to global structures from the bottom up. So global structures, I mean the food lines, the tunnels, the cemetery.
Now, if you have an ant colony of 250 ants, you get more complexity. You get greater complexity of those structures. You get new structures sort of emerging. And if you have 2,500 ants or 25,000 ants, still more complexity. And one of the lovely things about complexity is that however complex
complicated the computational mathematics may be of modeling these in a computer, the same rules apply to whatever you're talking about. So what I just said for ant colonies is true of humans. A village is not a city, is not a megalopolis. They share similarities, but the complexity is greater the more people you have and the more interactions you have.
And what I went on to show in my early work in this area, that cells do the same thing. You need a certain number of cells to create a tissue or an organ or a body. And the more cells you have and the greater diversity of interactions they have, the greater complexity you get. So the more cells, the bigger a multicellular organism, the more you get diversification into organ systems and organs and then systems of organs. Mm-hmm.
And it's all the same. Or you look at an economy, a small market economy of a bunch of local villages is not as complex as the global economy now, but it all displays the same properties. It's just more complex because there are
are more of them. So that's number one. You need a certain number of interacting individuals to create a complex system, and the more you have and the greater the diversity of interactions, the more complexity you have. And by complexity I mean these global scale structures that are arising from the bottom up. They look like they are planned from the top down, but they're not.
The second rule I go into is the nature of those interactions between the individuals. So we have what are called negative feedback loops and positive feedback loops.
A negative feedback loop is like an air conditioner. The temperature of a room goes up until it hits a set point where the air conditioner turns on, the temperature of the room cools down until it hits a bottom set point and the air conditioner turns off. And so the air conditioner keeps the temperature of our room comfortable for our living bodies and this is referred to as homeostasis.
And it's not a steady temperature. It oscillates between a higher and a lower value. And that kind of oscillation is the hallmark of a negative feedback loop. So there are constraints on the system. It can't wander off in too many directions. And so when you have a predominance of homeostatic negative feedback loops in a group of interacting individuals like humans, cells, or ants, or birds in a flock,
then they keep each other within a healthy realm for the system so that all the birds, all the cells, all the humans are sort of, for the most part, functioning within a healthy realm, a creative realm, and giving rise to these larger structures like, in the case of humans, city neighborhoods, economies, etc.,
Positive feedback loops, you can get self-organization, but it is not creative and it is not adaptive. So imagine a room where the hotter it gets, the higher the heater turns on. And so the room just gets hotter and hotter and hotter. You can have that participating in a complex system. Think about how our bodies get temperatures when you have an infection.
and the increasing body temperature increases the metabolic rate of the body, which helps the immune system rev up in order to fight off the infection. But if
the body isn't able to fight off the infection. If the fever can't be turned off, you die of a fever. I mean, you know, if you get too hot, you dehydrate and that's the end of you. So when an infection is fought off, you need the body to come back in with superseding negative feedback loops to bring the body temperature back into normal range. So negative feedback loops always have to predominate. If they don't, then you wind up with
self-organization of the elements of the system, but instead of being creative and self-sustaining and adaptive, they rapidly expand, rapid growth, think an economic bubble, and then collapse, the recession that follows an economic bubble. In cellular terms, this would be like a cancer. Cells that have outstripped
The feedbacks of neighboring cells to tell each cell, don't grow too much, don't move around, stay put, do your job. As the negative controls get turned off by mutations, for example, in cancer, or if a mutation arises so that a cell learns to ignore its neighbors, no matter how much they try to signal it to stay put.
then you get cancer and you get a tumor and you get very rapid expansion, but that leads to ultimate collapse and demise. So economic systems are a cancer arising in the complex system of an economy, cancer cells, the same thing in human body. So negative and positive feedback loops, negative feedback loops have to predominate.
The third rule: where these global structures arise are not because someone's planning the structure of the ant colony. They're arising from the local interactions of ants. And so people who know a tiny bit about ant colonies know there's a queen ant. And well, isn't the queen ant organizing the colony, deciding where the food lines go and such things? But no, the queen is just serving a reproductive function. In fact, she has very little option for creative movement. She just produces more ants.
So...
Things can appear from the top down as being well-controlled and designed, but it's always a bottom-up kind of thing. And so for the ant colonies, how does a food line develop? So you have an ant wandering around, and it's laying down a pheromone ascent trail for itself that does not degrade over time or does so very slowly. So at any moment, if it wants to turn around, it can follow its own path back to the food
the colony, it bumps into a sugar cube and goes, ah, food. There's probably some chemical signaling with the ant perceives this is nutrition, takes a part of it. And when it does that, turns around and follows its trail back to the colony. But now, because it's carrying food, it puts down a different scent trail, one that rapidly degrades.
So that another ant coming along and picking up that new food-bearing scent trail...
reads it and goes, "Some ant nearby" - doesn't need to know who it was - "Some ant nearby has got some food. I better go get that food too because there could be more of it." And it knows which way to turn because the direction that the scent trail is becoming, is fading away, points in the direction of the sugar cube. The second ant crosses that "I've got food" scent trail,
turns in the direction that it's fading away, and if it walks in that direction, sure enough, it's going to bump into that sugar cube. It now gets a bit of food, turns around, starts laying the same kind of fading scent trails as it follows the other ant back to the colony. More ants come along and crisscross that path and get the same signal, and now you have a food line. And so no ant had to figure out where's the food, where's the colony, what's the best route to find a food line.
They just do it. Now, you come upon that food line when you're a kid and you see this line of ants going back and forth. If you lean down to look at it more closely, there's always a few ants that aren't following the trail. And when I saw that, I always kind of felt sorry for those ants because I thought, well, these ants don't have it quite together. You know, they're kind of lost and
They're not part of the group, and I felt sorry for them. And if one of them wound up in my mother's kitchen, I knew I better get that out of there because my mother would see it and call the exterminator and kill the ant, and I'd feel sorry for it. So I'd get it onto a piece of paper, and I would carry it outside. But my mother had the right idea. Those ants that aren't following the food line
So 2% to 4% of the ants are the ants that if you step your foot in the middle of the food line, it's not the ants in the food line that are going to find a root around your foot. It's those ants that aren't part of the food line.
And so a change in the environment, this low-level randomness is what allows for adaptation to the changed environment. Or these are the ants that are going to find another bit of food out there somewhere while these ants are busy schlepping the food they've already figured out. What about when that runs out? Don't you want some ants already looking for the next one? And so this low-level randomness in the system
emerges from the third rule to give us the fourth rule, which is that there has to be a low level of randomness. Too much randomness and you don't get any organization at all. You just get, you know, not mathematical chaos, you just get complete disorder. And there's no formation of food lines, there's no organization of the ant colony, you've got anarchy.
But if there's too little randomness in the system, then there's no way for the colony to figure out a new way to organize in response to a changing environment, in response to the foot interrupting the food line, or in response to this food source running out. And so you need this low-level randomness, and that allows for the creativity and adaptability of a living system, of a complex system.
Where that takes you immediately is that the universe isn't a machine. It's not an infinite array of possibilities, which would just be total disorder. It's a constrained array of possibilities. And Stuart Kaufman, one of the founders of the Santa Fe Institute, and to me, probably the leader in terms of how
complexity applies to questions of biology, how life arises, how it persists, etc. He talked about these as the adjacent possibles. And in every moment of a complex system, you have an array of adjacent possibles of what might happen in the next moment. And then in the next moment, one of those will be selected, and suddenly all the other adjacent possibles collapse, and the system is now in a slightly new state.
and continues with a new array of possibles around it. This comes back to that idea that complex systems lie at the edge of chaos. Because you can think of changing states of order as a boundary. So you think of how you heat water and at a certain temperature suddenly it becomes steam.
or at a certain humidity and drop in temperature, fog starts condensing into liquid water on your clothes, or sunlight hitting a block of ice, the ice turns directly into vapor. You can depict those mathematically as a line, and you step off, depending on the temperature and the pressure, there's a distinct line between those states of order.
But the line between order and chaos, where complexity lies, is actually shaped like a fractal. And so you might think you can mathematically plot any complex system at a specific point in that zone.
But because of the limited randomness, it isn't a point in that zone, it isn't a single location, it wanders around. And because fractals are infinitely filigreed, you can't predict on the basis of where you think you're plotting it, what its next step is going to be. And over time, you're wandering around in that information-rich, life-like zone, or living zone, because this is, we're talking about living things for real. Given enough time,
you're inevitably going to stumble off into either chaos or order, and the system will die. And so the mathematics that describes how living systems are creative and can adapt and sustain themselves in a homeostatic realm that allows for continuing life, necessarily, given enough time, will stumble out of that and die.
And so, I get choked up talking about this. So, right there, hardcore mathematics of systems theory takes us right into the heart of life versus death. That there's no such thing as eternal life. There's no such thing as a fountain of youth. Given enough time, every system will die. Every living system will die, us included, our culture included, our ecosystems included.
And the thing that allows us to be creative and adaptive and alive is the very thing that guarantees that it will not be forever.
I really enjoyed my conversation with Neil Theis. We don't agree on everything. I'm more from the sort of Carl Sagan school of things when it comes to this particular topic. And as such, Neil has opinions about consciousness and the meaning of all of this that I don't share. And he has perspectives on what complexity science suggests that differ from mine. But none of that took away from our comparing of notes.
He was very open to answering all sorts of questions I had, if he could, and pointed me in the direction of others who might be able when he could not. And so all that being said, I highly recommend checking out his book, Notes on Complexity, if any of this piqued your interest. In my ongoing research for this book, I'm writing on genius.
I've spent time with the people at the Santa Fe Institute, where complexity science is their main focus. So I have plenty more to share about all of this, and I will do that in future episodes, and especially in the book. And as I continue to conduct research on this topic, I'll share more and more of my interviews right here. But for now, that is it for this episode of the You're Not So Smart podcast.
For links to everything that we talked about, head to youarenotsosmart.com or check out the show notes right there inside your podcast player.
You can find my book, How Minds Change, wherever they put books on shelves and ship them in trucks. Details about all that are over at davidmccraney.com. That's M-C-R-A-N-E-Y.com. And I'll have all that in the show notes as well, right there in your podcast player. On my homepage, you can find a roundtable video with a group of persuasion experts featured in the book, and you can read a sample chapter, download a discussion guide, sign up for the newsletter, read reviews, and more. And
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