Complexity: The Emerging Science at the Edge of Order and Chaos by M. Mitchell Waldrop
This book was recommended by Michael Mauboussin on the first episode of Shane Parish's podcast the Knowledge Project. It's a challenging book since it has a decent amount of science terms but can be understood if you take the time to reread some paragraphs and pages. The overall premise of the book is that our economy isn't a system but is more like an ecosystem of living organisms since it is made up of a lot of complex variables that change and adapt.
It didn’t take very long for Arthur to realize that, when it came to real world complexities, the elegant equations and the fancy mathematics he’d spent so much time on in school were no more than tools – and limited tools at that. The crucial skill was insight, the ability to see connections. Drefyus, Arthur’s adviser, believed in getting to the heart of a problem. Instead of solving incredibly complicated equations, he taught me to keep simplifying the problem until you found something you could deal with. Looking for that made a problem tick. Look for the key factor, the key ingredient, the key solution. In particular, they tried to find out why rural families were still producing an average of seven children apiece, and even when modern birth control was made freely available – and even when the villagers seemed perfectly well aware of the country’s immense overpopulation and stagnant development. What we found was that the terribly predicament of Bangladesh was the outcome of a network of individual and group interest at the village level,” says Arthur. Since children could go to work at an early age, it was a net benefit to any individual family to have as many children as possible. Since a defenseless widow’s relatives, and neighbors might very welcome in and take everything she possessed, it was in a young wife’s interest to have as many sons as possible as quickly as possible, so that she would have grown sons to protect her in her old age. And so it went: “Patriarchs, women who were trying to hold onto their husbands, irrigation communities – all these interest combined to produce children and to stagnate development.”
Economics, as any historian or anthropologist could have told him instantly, was hopeless intertwined with politics and culture.
Everyone has a researcher style, says Arthur. If you think of a research problem as being like a medieval walled city, then a lot of people will attack it head on, like a battering ram. They will storm the gates and try to smash through the defenses with sheer intellectual power and brilliance. But Arthur has never felt that the battering ram approach was hi strength. “I like to take my time as I think,” he says. “So I just camp outside the city. I wait. And I think. Until one day – maybe after I’ve turned to a completely different problem – the drawbridge comes down and the defenders say, “We surrender.” The answer to the problem comes all at once.”
In every living cell there resides a long, helical DNA molecule: a chain of chemically encoded instructions, genes, that together constitute a blueprint for the cell.
At a molecular level, every living cell was astonishingly alike. The basic mechanisms were universal. And yet a tiny, almost undetectable mutation in the genetic blueprint might be enough to produce an enormous change in the organism as a whole. A few molecular shifts here and there might be enough to make the difference between brown eyes and blue, between a gymnast and a sumo wrestler, between good healthy and sickle cell anemia. A few more molecular shifts, accumulating over millions of years through natural selection, might make the difference between a human and a chimpanzee, between a fig tree and a cactus, between an amoeba and a whale. In the biological world, Arthur realized, small chance events are magnified, exploited, built upon. One tiny accident can change everything. Life develops. It has a history.
Arthur couldn’t imagine anything less like the real economy, where new products, technologies, and markets were constantly arising and old ones were constantly dying off. The real economy was not a machine but a kind of living system; with all the spontaneity and complexity that Judson was showing him in the world of molecular biology.
DNA residing in a cell’s nucleus was not just a blueprint for the cell – a catalog of how to make this protein or that protein. DNA was actually the foreman in charge of construction. In effect, DNA was a kind of molecular-scale computer that directed how the cell was to build itself and repair itself and interact with the outside world. Furthermore, Jacob and Monod’s discovery solved the long standing mystery of how one fertilized egg cell could divide and differentiate itself into muscle cells, brain cells, liver cells, and all the other kinds of cells that make up a newborn baby. Each different type of cell corresponded to a different pattern of activated genes.
Basically, Prigogine was addressing the question, Why is there order and structure in the world? Where does it come from? This turns out to be a much tougher question than it might sound, especially when you consider the world’s general tendency toward decay. Iron rusts. Fallen logs rot. Bathwater cools to the temperature of its surroundings. Nature seems to be less interested in creating structures than in tearing structures apart and mixing things up into a kind of average. Indeed, the process of disorder and decay seems inexorable – so much so that nineteenth century physicists codified it as the second law of thermodynamics, which can be paraphrased as “You can’t unscramble an egg.” Left to themselves, says the second law, atoms will mix and randomize themselves as much as possible. That’s why iron rusts: atoms in the iron are forever trying to mingle with oxygen in the air to form iron oxide. And that’s why bathwater cools: fast moving molecules on the surface of the water collide with slower moving molecules in the air, and gradually transfer their energy. Yet for all of that, we do see plenty of order and structure around. Fallen logs rot – but trees also grow.
In fact, wrote Prigogine in one article, it’s conceivable that the economy is a self-organizing system, in which market structures are spontaneously organized by such things as the demand for labor and the demand for goods and services.
In fact, Arthur suddenly realized, that’s why you get patterns in any system: a rich mixture of positive and negative feedbacks can’t help producing patterns. Imagine spilling a little water onto the surface of a highly polished tray, he says; it beads up into a complex pattern of droplets. And it does so because two countervailing forces are at work. There is gravity, which tries to spread out the water to make a very thin, flat film across the whole surface. That’s the negative feedback. And there is surface tension, the attraction of one water molecule to another, which tries to pull the liquid together into compact globules and that's positive feedback. It’s the mix of the two forces that produces the complex pattern of beads.
Indeed, thought Arthur, that probably explains why history, in Winston Churchill’s phrase, is just one damn thing after another. Increasing returns can take a trivial happenstance – who bumped into whom in the hallway, where the wagon train happened to stop for the night, where trading posts happened to be set up, where Italian shoemakers happened to emigrate – an magnify it into something historically irreversible. Did a certain young actress become a superstar on the basis of pure talent? Hardly: the luck of being in a single hit movie sent her career into hyper drive on name recognition alone, while her equally talented contemporaries went nowhere. Did British colonists flock to cold stormy, rocky shores of Massachusetts bay because new England had the best land for farms? No: They came because Massachusetts Bay was where the Pilgrims got off the boat, and the Pilgrims got off the boat there because the Mayflower got lost looking for Virginia. Them that has gets – and once the colony was established, there was no turning back. Nobody was about to pick up Boston and move it someplace else.
the important thing is to observe the actual living economy out there,” he says. “It’s path-dependent, its complicated, its evolving, its open, and it’s organic.”
Look at a software product like Microsoft’s windows, he says. The company spent $50 million in research and development to get the first copy out the door. The second copy cost it – what, $10 in materials? It’s the same story in electronics, computers, and pharmaceuticals, even aerospace. (Cost for the first B2 bomber: $21 billion. Cost per copy: $500 million.) High technology could almost be defined as “congealed knowledge," says Arthur. “The marginal cost is next to zilch, which means that every copy you produce makes the product cheaper and cheaper.” More than that every copy offers a chance for learning: getting the yield up on microprocessor chips, and so on. So there’s a tremendous reward for increasing production – in short, the system is governed by increasing returns.
In the real world, outcomes don’t just happen, they build up gradually as small chance events become magnified by positive feedbacks.
In part because of their computer simulations, and in part because of new mathematic insights, physicists had begun to realize by the early 1980's that a lot of messy, complicated systems could be described by a powerful theory known as “nonlinear dynamics.” And in the process, they had been forced to face up to a disconcerting fact: the whole really can be greater than the sum of its parts. (The name linear refers to the fact that if you plot such an equation on graph paper, the plot is a straight line.) Besides, an awful lot of nature does seem to work that way. Sound is a linear system, which is why we can hear an oboe playing over its string accompaniment and recognize them both. The sound waves intermingle and yet retain their separate identities. Light is also a linear system, which is why you can still see the walk/don’t walk sign across the street even on a sunny day: the light rays bouncing from the sign to your eyes are not smashed to the ground by sunlight streaming down from above. The various light rays operate independently, passing right through each other as if nothing were there. In some ways even the economy is a linear system, in the sense that small economic agents can act independently. When someone buys a newspaper at the corner drugstore, for example, it has no effect on your decision to buy a tube of toothpaste at the super market. Our brains certainly aren’t linear: even though the sound of an oboe and the sound of as string section may be independent when they enter your ear, they emotional impact of both sounds together may be very much greater than either one alone. Nor is the economy really linear. Millions of individual decisions to buy or not to buy can reinforce each other, creating a boor or a rescission. And then economic climate can then feedback to shape the very buying decisions that produced it. Indeed, except for the very simplest physical systems, virtually everything and everybody in the world is caught up in vast, nonlinear web of incentives and constraints and connections. The slightest change in one place causes tremors everywhere else. We can’t help but disturb the universe, as t.s Eliot almost said. The whole is almost always equal to a good deal more than the sum of its parts.
To believe in natural law is to believe that the universe is ultimately comprehensible – that the same forces that determine the destiny of a galaxy can also determine the fall of an apple here on Earth; that the same atoms that refract the light passing through a diamond can also form the stuff of a living cell; that the same electrons, neutrons, and protons that emerged from the big bang can now give rise to the human brain, mind, and soul. To believe in natural law is to believe in the unity of nature at the deepest possible level.
Take water, for example. There’s nothing very complicated about a water molecule: it’s just one big oxygen atom with two little hydrogen atoms stuck to it like Mickey Mouse ears. Its behavior is governed by well-understood equations of atomic physics. But now put a few zillion of these molecules together in the same pot. Suddenly you’ve got a substance that shimmers and gurgles and sloshes. Those zillions of molecules have collectively acquired a property, liquidity, that none of them possess alone. In fact, unless you know precisely where and how to look for it, there’s nothing in those well-understood equations of atomic physics that even hints at such a property. The liquidity is “emergent.”
Life is an emergent property, the product of DNA molecules and protein molecules and myriad other kinds of molecules, all obeying the laws of chemistry. The mind is an emergent property, the product of several billion neurons obeying the biological laws of the living cell. In fact, as Anderson pointed out in the 1972 paper, you can think of the universe as forming a kind of hierarchy: “At each level of complexity, entirely new properties appear. [And] at each state, entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one. Psychology is not applied biology, nor is biology applied chemistry.
In particular, the founding workshops made it clear that every topic of interest and at its heart a system composed of many, many “agents.” These agents might be molecules or neurons or species or consumers or even corporations. But whatever their nature, the agents were constantly organizing and reorganizing themselves into larger structures through the clash of mutual accommodation and mutual rivalry. Thus, molecules would form cells, neurons would form brains, species would form ecosystems, consumers and corporations would form economies, and so on. At each level, new emergent structures would form and engage in new emergent behaviors. Complexity, in other words, was really a science of emergence.
Charles Darwin was absolutely right: human beings and all other living things are undoubtedly the heirs of four billion years of random mutation, random catastrophes, and random struggles for survival; we are not here as the result of divine intervention, or even space aliens. But, he would emphasize, neither was Darwinian natural selection the whole story. Darwin didn’t know about self-organization – matter’s incessant attempts to organize itself into ever more complex structures, even in the face of the incessant forces of dissolution described by the second law of thermodynamics. Nor did Darwin know that the forces of order and self-organization apply to the creation of living systems just as surely as they do to the formation of snowflakes or the appearance of concoction cells in a simmering pot of soup. So the story of life is, the story of accident and happenstance, indeed declared Kauffman. But it is also the story of order: a kind of deep, inner creativity that is woven into the very fabric of nature.
He was thunderstruck. Here was this absolutely stunning phenomenonlogy,” he says. “Here you start with a fertilized egg, and the damn thing unfolds, and it gives rise to an ordered newborn and adult.” Somehow, that single egg cell manages to divide and differentiate into nerve cells and muscle cells and liver cells – hundreds of different kinds. And it does so with the most astonishing precision. The strange thing isn’t that birth defects happen, as tragic as they are; the strange thing is that most babies are born perfect and whole. “This still stands as one of the most beautiful mysteries in biology,” says Kauffman. “Well, I became absolutely enthralled with the problem of cellular differentiation, and set straight away to thinking hard about it.”
When you look at economic history, as opposed to economic theory, he told Kauffman, technology isn’t really like a commodity at all. It is much more like an evolving ecosystem. “In particular, innovations rarely happen in a vacuum. They are usually made possible by other innovations being already in place. For example, a laser printer is basically a Xerox machine with a laser and a little computer circuitry to tell the laser where to etch on the Xerox drum for printing. So a laser printer is possible when you have computer technology, laser technology, and a Xerox reproducing technology. But it is also only possible because people need fancy, high-speed printing.” In short, technologies form a highly interconnected web – or in Kauffman’s language, a network.
Technology A, B, and C might make possible technology D, and so on,” says Arthur. “So there’d be a network of possible technologies, all interconnected and growing as more things became possible. And therefore the economy could become more complex.” Moreover, these technological webs can undergo bursts of evolutionary creativity and massive extinction events, just like biological ecosystems. Say a new technology like the automobile comes in and replaces an older technology, the horse. Along with the horse go the smithy, the phony express, the watering troughs, the stables, and the people who curried horses, and so on. The whole sub network of technologies that depended upon the horse suddenly collapses in what the economist Joseph Schumpeter once called “a gale of destruction.” But along with the car come paved roads, gas stations, fast-food restaurants, motels, traffic courts and traffic cops, and traffic lights. A whole new network of goods and services beings to grow, each one filling a niche opened up by the goods and services that came before it.
Within minutes Kauffman was off, explaining to Arthur why the process of technological change is exactly like the origin of life.
So how could such a thing [such as life] form all by itself in a pond? Lots of people had tried to calculate the odds of that happening, and their answers always came out pretty much the same: if the formation were truly random, you would have to wait far longer than the lifetime of the universe to produce even one useful protein molecule, much less all the myriads of proteins and sugars and lipids and nucleic acids that you need to make a fully functioning cell. Even if you assumed that all the trillions of stars in all the millions of galaxies in the observable universe had planets like Earth, with warm oceans and an atmosphere, the probability that any of them would bring forth life would still be – infinitesimal. If the origin of life had really been a random event, then it had really been a miracle.
In other words, it was just like his genetic networks: if the primordial soup passed a certain threshold of complexity, then it would undergo that funny phase transition. The autocatalytic set would indeed be almost inevitable. In a rich enough primordial soup, it would have to form – and life would “crystallize” out of the soup spontaneously…. I believe very strongly that this is how life began.
Most obviously, they agreed, an autocatalytic set was a web of transformations among molecules in precisely the same way that an economy is a web of transformations among goods and services. In a very real sense, in fact, an autocatalytic set was an economy – a submicroscopic economy that extracted raw materials (the primordial “food” molecules) and converted them into useful products (more molecules in the set). Moreover an autocatalytic set can bootstrap its own evolution in precisely the same way that an economy can, by growing more and more complex over time.
Arthur remembers someone asking him during his talk that first day, “Isn’t economics a good deal simpler than physics?” Well Arthur replied, in one sense it is. We call our particles agents – banks, firms, consumers, governments. And those agents react to other agents, just as particles react to other particles. Only we don’t usually consider the spatial dimension in economics much, so that makes economics a lot simpler.” However, he added, there is one big difference: “Our particles in economics are smart, whereas yours in physics are dumb.” In physics, an elementary particle has no past, no experience, no goals, no hopes or fears about the future. It just is. That’s why physicists can talk so freely about “universal laws”: their particles respond to forces blindly, with absolute obedience. But in economics, said Arthur, “Our particles have to think ahead, and try to figure out how other particles might react if they were to undertake certain actions. Our particles have to act on the basis of expectations and strategies. And regardless of how you model that, that’s what makes economics truly different.
The only problem, of course, is that real human beings are neither perfectly rational nor perfectly predictable – as the physicists pointed out at great length. Furthermore, as several of them also pointed out, there are real theoretical pitfalls in assuming perfect predictions, even if you do assume that people are perfectly rational. In nonlinear systems – and the economy is most certainly nonlinear – chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions will often grow inexorably. After a while, your predictions are nonsense.
Holland started by pointing out that the economy is an example par excellence of what the Santa Fe Institute had come to call “complex adaptive systems.” In the natural world such systems included brains, immune systems, ecologies, cells, developing embryos, and ant colonies. In the human world they included cultural and social systems such as political parties or scientific communities. Once you learned how to recognize them, in fact, these systems were everywhere. But where you found them, said Holland, they all seemed to share certain crucial properties. First, he said, each of these systems is a network of many “agents” acting in parallel. In a brain the agents are nerve cells, in an ecology the agents are species, in a cell the agents are organelles such as the nucleus and the mitochondria, in an embryo the agents are cells, and so on. In an economy, the agents might be individuals or households. Or if you were looking at business cycles, the agents might be firms. And if you were looking at international trade, the agents might even be whole nations. But regardless of how you define them, each agent finds itself in an environment produced by its interactions with the other agents in the system. It is constantly acting and reacting to what the other agents are doing. And because of that, essentially nothing in its environment is fixed.
Complex adaptive systems are constantly revising and rearranging their building blocks as they gain experience. Succeeding generations of organisms will modify and rearrange their tissues through the process of evolution. The brain will continually strengthen or weaken myriad connections between its neurons as an individual learns from his or her encounters with the world. A firm will promote individuals who do well and (more rarely) will reshuffle its organizational chart for greater efficiency. Countries will make new trading agreements or realign themselves into whole new alliances.
To Holand, evolution and learning seemed much more like – well, a game. In both cases, he thought, you have an agent playing against its environment, trying to win enough of what it needed to keep going. In evolution that payoff is literally survival, and a chance for the agent to pass its genes on to the next generation. In learning, the payoff is a reward of some kind, such as food, a pleasant sensation or emotional fulfillment. But either way, the payoff (or lack of it) gives agents the feedback they need to improve their performance: if they’re going to be adaptive at all, they somehow have to keep the strategies that pay off well, and let the others die out.
Large creatures such as whales and redwoods are made of trillions of tiny cells because the cells came first; when large plants and animals first appeared on Earth some 570 million years ago, it was obviously easier for natural selection to bring together the single celled creatures that already existed than to build biog new blogs of protoplasm from scratch. General motors is organized into several zillion divisions and subdivisions because the CEO doesn’t want to have half a million employees reporting to him directly; there aren’t enough hours in the day. In fact, as Herbert Simon had pointed out in the 1940's and 1950's in his studies of business organizations, a well designed (emphasize well-designed) hierarchy is an excellent way of getting some work done without any one person being overwhelmed by meetings and memos. As Holland thought about it, however, he became convinced that the most important reason lay deeper still, in the fact that a hierarchical, building block structure utterly transforms a systems’ ability to learn, evolve, and adapt. Think of our cognitive building blocks, which include such concepts as red, car, and road.
Prediction is what helps you see an opportunity or avoid getting suckered into a trap. An agent that can think ahead has an obvious advantage over one that can’t.
Very often, moreover, the models are literally inside our head, as when a shopper tries to imagine how a new couch might look in the living room, or when a timid employee tries to imagine the consequences of telling off his boss. We use these “mental models” so often, in fact, that many psychologists are convinced they are the basis of all conscious thought.
All complex, adaptive systems – economies, minds, organisms – build models that allow them to anticipate the world,” he declares. Yes, even bacteria. As it turns out, says Holland, many bacteria have special enzyme systems that cause them to swim toward stronger concentrations of glucose. Implicitly, those enzymes model a crucial aspect of the bacterium’s world,: that chemicals diffuse outward from their source, growing less and less concentrated with distance. And the enzymes simultaneously encode an implicit prediction: If you swim toward higher concentrations, then you’re likely to find something nutritious.
After all, standard operating procedures are often taught by rote, without a lot of whys and wherefores. And if the company has been around for a while, there may not be anyone left who even remembers why things are done a certain way. Nonetheless, as the standard operating procedure collectively unfolds, the company as a whole will behave as if it is understood that model perfectly.
Show a textbook exercise to an experienced physics teacher and he wont’ waste any time scribbling every formula in sight, the way a movie will; his mental procedures will almost always show him a path to the solution instantly: “Aha! That’s a conservation of energy problem.” Lob a tennis ball across the net to Chris Evert and she won’t spend any time debating how to respond: after years of experience and practice and coaching, her mental procedures will allow her to slam the ball back down your throat instinctively. Holland’s favorite example of implicit expertise is the skill of the medieval architects who created the great gothic cathedrals. They had no way to calculate forces or load tolerances, or anything else that a modern architect might do. Modern physics and structure analysis didn’t exist in the twelfth century. Instead, they build those high, vaulted ceilings and massive flying buttresses using standard operating procedures passed down from master to apprentice – rules of thumb that gave them a sense of which structures would stand up and which would collapse. Their model of physics was completely implicit and intuitive. And yet, these medieval craftsmen were able to create structures that are still standing nearly a thousand years later.
It simply has to try the models out, see how well their predictions work in the real world, and – if it survives the experience – adjust the models to do better the net time. In biology, of course, the agents are individual organisms, the feedback is provided by natural selection, and the steady improvement of the models is called evolution… Either way, says Holland, an adaptive agent has to be able to take advantage of what its world is trying to tell it.
As biologists have been pointing out for more than a century, one of the most striking characteristics of any living organism is the distinction between its genotype – the genetic blueprint encoded in its DNA – and its phenotype – the structure that is created from those instructions. In practice, of course, the actual operating of a living cell is incredibly complicated, with each gene serving as a blueprint for a single type of protein molecule, and with myriad proteins interacting in the body of the cell in myriad ways. But in effect, said Langton, you can think of the genotype as a collection of little computer programs executing in parallel, one program per gene. When activated, each of these programs enters into the logical fray by competing and cooperating with all the other active programs. And collectively these interacting programs carry out an overall computation that is the phenotype: the structure that unfolds during an organism’s development.
Organisms cooperate and compete in a dance of coevolution, thereby becoming an exquisitely turned ecosystem. Atoms search for a minimum energy state by forming chemical bonds with each other, thereby becoming the emergent structures known as molecules. Human beings try to satisfy their material needs by buying, selling, and trading with each other, thereby creating an emergent structure known as a market. Humans likewise interact with each other to satisfy less quantifiable goals, thereby forming families, religions, and cultures. Somehow, by constantly seeking mutual accommodation and self-consistency, groups of agents manage to transcend themselves and become something more.
But at heart, he says, science is about the telling of stories – stories that explain what the world is like, and how the world came to be as it is . And like older explanations, such as creation myths, epic legends, and fairy tales, the stories that science tells help us understand something about who we are as human beings, and how we relate to the universe. There is the story of how the universe came into existence some 16 billion years ago at the instant of the big bang; the story of how quarks electrons, neutrinos, and all the rest came flying out of the big bang as an indescribably hot plasma; the story of how those particles gradually condensed into the matter we see around us today in the galaxies, the stars, and the planets, the story of how the sun is a star like other stars, and how earth is a planet like other planets; the story of how life arose on this earth and evolved over 4 billion years of geological time; the story of how the human species first rose on the African Savannah some 3 million years ago and slowly acquired tools, culture, and language.
Witness the collapse of communism in the former Soviet Union and its Easter European satellites, she says: the whole situation seems all too reminiscent of the power law distribution of stability and upheaval at the edge of chaos. “When you think of it,” he says, “the cold War was one of these long periods where not much has changed. And although we can find fault with the US and Soviet governments for holding a gun to the world’s head – the only thing that kept it from blowing up as Mutual Assured Destruction – there was a lot of stability. But now that period of stability is ending.
It’s good to have evolution progress. If single celled things had found a way to stop evolution to maintain themselves as dominant life forms, then we wouldn’t be here. So you don’t want to stop it. On the other hand, maybe you want to understand how it can keep going without the massacres and the extinctions.