> [!cite]- Metadata > 2025-08-17 18:18 > Status: #book > Tags: `Read Time:` The connected lives of ants, brains, cities, and software. 9 Foreword *Emergence* is a book about swarms, ant colonies, neighborhoods; a book about crowds and groups - and the intelligence those groups can possess, given the right circumstances. The strange truth about all emergent systems: they have a life of their own. 11 Introduction - Here Comes Everybody! In August of 2000, a Japanese scientist named Toshiyuki Nakagaki announced that he had trained an amoebalike organism called slime mold to find the shortest route through a maze. Despite its being an incredibly primitive organism (a close relative of ordinary fungi) with no centralized brain whatsoever; the slime mold managed to plot the most efficient route to the food, stretching its body through a maze so that it connected directly to the two food sources. Without any apparent cognitive resources, the slime mold had "solved the maze" puzzle. For scientists trying to understand systems that use relatively simple components to build higher-level intelligence, the slime mold may someday be seen as the equivalent of the finches and tortoises that Darwin observed on the Galapagos Islands 12 How did such a lowly organism come to play such an important scientific role? The story begins in the late sixties in New York City, with a scientist named Evelyn Fox Keller. A Harvard Ph.D. in physics, Keller had written her dissertation on molecular biology, and she had spent some time exploring the nascent field of "Non-equilibrium thermodynamics," which in later years would come to be associated with complexity theory. 13 The slime mold spends much of its life as thousands of distinct single-celled units, each moving separately from its other comrades. Under the right conditions, those myriad cells will coalesce again into a single, larger organism, which then begins its leisurely crawl across the garden floor, consuming rotting leaves and wood as it moves about. When the environment is less hospitable, the slime mold acts as a single organism; when the weather turns cooler and the mold enjoys a large food supply, "it" becomes a "they." The slime mold oscillates between being a single creature and a swarm. 14 One of Alan Turing's last published papers, before his death in 1954, had studied the riddle of "morphogenesis" - the capacity of all life-forms to develop ever more baroque bodies out of impossibly simple beginnings. It demonstrated using mathematical tools how a complex organism could assemble itself without any master planner calling the shots. 15 Much of the world around us can be explained in terms of command systems and hierarchies - why should it be any different for the slime mold? What if the community of slime mold cells were organizing themselves? What if there were no pacemakers? 16 Cells would begin following trails created by other cells, creating a positive feedback loop that encouraged more cells to join the cluster. If each solo cell was simply releasing acrasin on its own local assessment of of the general conditions, Keller and Segel argued in a paper published in 1969, then the larger slime mold community might be able to aggregate based on global changes in the environment - all without a pacemaker calling the shots. "It amazes me how difficult it is for people to think in terms of collective phenomenon," Keller says today. 17 Some of today's most popular computer games resemble slime mold cells because they are loosely based on the equations that Keller and Segel formulated by hand in the late sixties. Thirty years after Keller challenged the pacemaker hypothesis, students now take courses in "self-organization studies," and bottom-up software helps organize the Web's most lively virtual communities. But Keller's challenge did more than help trigger a series of intellectual trends. It also unearthed a secret history of decentralized thinking, a history that had been submerged for many years beneath the weight of the pacemaker hypothesis and the traditional boundaries of scientific research. 18 Indeed, some of the great minds of the last few centuries -- Adam Smith, Friedrich Engels, Charles Darwin, Alan Turing -- contributed to the unknown science of self-organization. They were wrestling with local issues, in clearly defined fields: how ant colonies learn to forage and build nests; why industrial neighborhoods form along class lines; how our minds learn to recognize faces. You can answer all these questions without resorting to the sciences of complexity and self-organization, but those answers all share a common pattern, as clear as the whorls on a fingerprint. But to see it as a pattern you needed to encounter it in several contexts. Keller and Segel saw it in the slime mold assemblages; Jane Jacobs saw it in the formation of city neighborhoods; Marvin Minsky in the distributed networks of the human brain. What features do all these systems share? In the simplest terms, they solve problems by drawing on masses of relatively stupid elements, rather than a single, intelligent "executive branch." They are bottom-up systems, not top-down. They get their smarts from below. In a more technical language, they are **complex adaptive systems** that display emergent behavior. In these systems, agents residing on one scale start producing behavior that lies one scale above them: ants create colonies; urbanites create neighborhoods; simple pattern-recognition software learns how to recommend new books. The movement from low-level rules to higher-level sophistication is what we call **emergence**. 19 Such a system would define the most elemental form of complex behavior: a system with multiple agents dynamically interacting in multiple ways, following local rules and oblivious to any higher-level instructions. But it wouldn't truly be considered emergent until those local interactions resulted in some kind of discernible macro behavior. That would mark the beginnings of emergence, a higher-level pattern arising out of parallel complex interactions between local agents. Out of those low-level routines, a coherent shape emerges. Does that make our mechanized billiard table adaptive? Not really, because a table divided between two clusters of balls is not terribly useful, either to the billiard balls themselves or to anyone else in the pool hall. But, like the proverbial Hamlet -writing monkeys, if we had an infinite number of tables in our pool hall, each following a different set of rules, one of those tables might randomly hit upon a rule set that would arrange all the balls in a perfect triangle, leaving the cue ball across the table ready for the break. That would be adaptive behavior in the larger ecosystem of the pool hall, assuming that it was in the interest of our billiards system to attract players. The system would use local rules between interacting agents to create higher-level behavior well suited to its environment. 20 **Emergent complexity without adaptation** is like the intricate crystals formed by a snowflake: it's a beautiful pattern, but it has no function. The forms of emergent behavior that we'll examine in this book show the distinctive quality of *growing smarter over time*, and of responding to the specific and changing needs of their environment. In that sense, most of the systems we'll look at are more **dynamic** than our adaptive billiards table: they rarely settle in on a single, frozen shape; they form patterns in time as well as space. A better example might be a table that self-organizes into a billiards-based timing device: with the cue ball bouncing off the eight ball sixty times a minute, and the remaining balls shifting from one side of the table to another every hour on the hour. That might sound like an unlikely system to emerge out of local interactions between individual balls, but your body contains numerous organic clocks built out of simple cells that function in remarkably similar ways. An infinite number of cellular or billiard-ball configurations will not produce a working clock, and only a tiny number will. So the question becomes, how do you push your emergent system toward clocklike behavior, if that's your goal? *How do you make a self-organizing system more adaptive?* The history of emergence has entered a new phase in the past few years, one that should prove to be more revolutionary than the two phases before it. In the first phase, inquiring minds struggled to understand the forces of self-organization without realizing what they were up against. In the second, certain sectors of the scientific community began to see self-organization as a problem that transcended local disciplines and set out to solve that problem, partially by comparing behavior in one area to behavior in another. By watching the slime mold cells next to the ant colonies, you could see the shared behavior in ways that would have been unimaginable watching either on its own. 21 Self-organization became an object of study in its own right, leading to the creation of celebrated research centers such as the Santa Fe Institute, which devoted itself to the study of complexity in all its diverse forms. But in the third phase -- the one that began sometime in the past decade, the one that lies at the very heart of this book -- we stopped analyzing emergence and started creating it. We began building self-organizing systems into our software applications, our video games, our art, our music. In recent years our day-to-day life has become overrun with *artificial emergence*: systems built with a conscious understanding of what emergence is, systems designed to exploit those laws the same way our nuclear reactors exploit the laws of atomic physics. *What unites these different phenomena is a recurring pattern and shape*: a network of self-organization, of disparate agents that unwittingly create a higher-level order. At each scale, you can see the imprint of those slime mold cells converging; at each scale, the laws of emergence hold true. 22 This book roughly follows the chronology of the three historical phases. The first section introduces one of the emergent world's crowning achievements -- the colony behavior of social insects such as ants and termites -- and then goes back to trace part of the history of the decentralized mind-set, from Engels on the streets of Manchester to the new forms of emergent software being developed today. The second section is an overview of emergence as we currently understand it; each of the four chapters in the section explores one of the field's core principles: neighbor interaction, pattern recognition, feedback, and indirect control. The final section looks to the future of artificial emergence and speculates on what will happen when our media experiences and political movements are largely shaped by bottom-up forces, and not top-down ones. Certain shapes and patterns hover over different moments in time, haunting and inspiring the individuals living through those periods. 27 Cities have no central planning commissions that solve the problem of purchasing and distributing supplies… How do these cities avoid devastating swings between shortage and glut, year after year, decade after decade? The mystery deepens when we observe the kaleidoscopic nature of large cities. Buyers, sellers, administrations, streets, bridges, and buildings are always changing, so that a city’s coherence is somehow imposed on a perpetual flux of people and structures. Like the standing wave in front of a rock in a fast-moving stream, a city is a pattern in time. — John Holland 29 “Why don’t we start with me showing you the ants that we have here, and then we can talk about what it all means.” 30 Gordon’s work focuses on the connection between the microbehavior of individual ants and the overall behavior of the colonies themselves, and part of the research involves tracking the life cycles of individual colonies, following them year after year as they scour the desert floor for food, competing with other colonies for territory, and — once a year — mating with them. She is a student, in other words, of a particular kind of emergent, self-organizing system. Dig up a colony of native harvester ants and you’ll almost invariably find that the queen is missing. To track down the colony’s matriarch, you need to examine the bottom of the hole you’ve just dug to excavate the colony: you’ll find a narrow, almost invisible passageway that leads another two feet underground, to a tiny vestibule burrowed out of the earth. There you will find the queen. She will have been secreted there by a handful of ladies-in-waiting at the first sign of disturbance. That passageway, in other words, is an emergency escape hatch, not unlike a fallout shelter buried deep below the West Wing. 31 But despite the Secret Service-like behavior, and the regal nomenclature, there’s nothing hierarchical about the way an ant colony does its thinking. It would be physically impossible for the queen to direct every worker’s decision about which task to perform and when. The harvester ants that carry the queen off to her escape hatch do so not because they’ve been ordered to by their leader; they do it because the queen ant is responsible for giving birth to all the members of the colony, and so it’s in the colony’s best interest - and the colony’s gene pool - to keep the queen safe. In other words, the matriarch doesn’t train her servants to protect her, evolution does. Popular culture trades in Stalinist ant stereotypes — witness the authoritarian colony regime in the animated film Antz — but in fact, colonies are the exact opposite of command economies. 32 The colonies that Gordon studies display some of nature’s most mesmerizing decentralized behavior: intelligence and personality and learning that emerges from the bottom up. “That’s the midden,” she says. “It’s the town garbage dump.” “These ants are in midden duty: they take the trash that’s left over from the food they’ve collected — in this case, the seeds from stalk grass — and deposit it in the midden pile.” "And this is the cemetery." I look again startled. Hundreds of ant carcasses are piled atop one another, all carefully wedged against the table's corner. It looks brutal, and yet also strangely methodical. 33 "Look at what actually happened here: they've built the cemetery at exactly the point that's furthest away from the colony. And the midden is even more interesting: they've put it at precisely the point that maximizes its distance from both the colony *and* the cemetery. It's like there's a rule they're following: put the dead ants as far away as possible, and put the midden as far away as possible without putting it near the dead ants." - We know now that systems like ant colonies don't have real leaders, that the very idea of an ant "queen" is misleading. But the desire to find pacemakers in such systems been powerful - in both the group behavior of the social insects, and in the collective human behavior that creates a living city. 34 Manchester In a real sense, the city grew too fast for the authorities to keep up with it. For five hundred years, Manchester had technically been considered a "manor," which meant, in the eyes of the law, it was run like a feudal estate, with no local government to speak of - no city planners, police, or public health authorities. 35 This constitutes one of the great ironies of the industrial revolution, and it captures just how dramatic the rate of change really was: the city that most defined the future of urban life for the first half of the nineteenth century didn't legally become a city until the great explosion had run its course. - The result of that discontinuity was arguably the least planned and most chaotic city in the six-thousand-year history of urban settlements. 36 Friedrich Engles The first book that Engles eventually wrote, *The Condition of the Working Class in England,* remains to this day one of the classic tracts of urban history and stands as the definitive account of nineteenth-century Manchester life in all its tumult and dynamism. 37 "The town itself is peculiarly built, so that a person may live in it for years, and go in and out daily without coming into contact with a working-people's quarter or even with workers, that is, so long as he confines himself to his business or to pleasure walks. This arises chiefly from the fact, that by unconscious tacit agreement, as well as with outspoken conscious determination, the working-people's quarters are sharply separated from the sections of the city reserved for the middle- class;" - I know very well that this hypocritical plan is more or less common to all great cities; I know, too, that the retail dealers are forced by the nature of their business to take possession of the great highways; I know that there are more good buildings than bad ones upon such streets everywhere, and that the value of land is greater near them than in remoter districts; but at the same time I have never seen so systematic a shutting out of the working-class from the thoroughfares, so tender a concealment of everything which might affront the eye and the nerves of the bourgeoisie, as in Manchester. And yet, in other respects, Manchester is less built according to a plan, after officials regulations, is more an outgrowth of accident than any other city; and when I consider in this connection the eager assurances of the middle-class, that the working-class is doing famously, I cannot help feeling that the Liberal manufacturers, the "Big Wigs" of Manchester, are not so innocent after all, in the matter of this shameful method of construction. 38 "The point to be taken is that this astonishing and outrageous arrangement cannot fully be understood as the result of a plot, or even a deliberate design, although those in whose interests it works also control it. It is indeed too huge and too complex a state of organized affairs to ever have been *thought up* in advance, to have preexisted as an idea" - That mix of order and anarchy is what we now call emergent behavior. 39 Complexity is not solely a matter of sensory overload. There is also a the sense of complexity as a self-organizing system. This sort of complexity lives up one level: it describes the system of the city itself, and not its experiential reception by the city dweller. The city is complex because it overwhelms, yes, but also because it has a coherent personality, a personality that self-organizes out of millions of individual decisions, a global order built out of local interactions. - This is the "systematic" complexity that Engels glimpsed on the boulevards of Manchester: not the overload and anarchy he documented elsewhere, but instead a strange kind of order, a pattern in the streets that furthered the political values of Manchester's elite without being deliberately planned by them 40 We know now from computer models and sociological studies - that larger patterns can emerge our of uncoordinated local actions. The city appeared to have a life of its own. - When we see repeated shapes and structure emerging out of apparent chaos, we can't help looking for pacemakers. - Understood in the most abstract sense, what Engels observed are *patterns* in the urban landscape, visible because they have a repeated structure that distinguishes them from the pure noise you might naturally associate with an unplanned city. They are patterns of human movement and decision-making that have been etched into the texture of city blocks, patterns that are then fed back to the Manchester residents themselves, altering their subsequent decisions. (In that sense, they are the very opposite of the traditional sense of urban complexity - they are signals emerging where you would otherwise expect only noise.) 41 You don't need regulations and city planners deliberately creating these structures. All you need are thousands of individuals and a few simple rules of interaction. - The history of urbanism is also the story of more muted signs, built by the collective behavior of smaller groups and rarely detected by outsiders. 42 It was in Manchester that Alan Turing began to think about the problem of biological development in mathematical terms, leading the way to the "Morphogenesis" paper, in 1952, that Evelyn Fox Keller would rediscover more than a decade later. - How does a seed know how to build a flower? - 48 Organized Complexity What makes an evening primrose open when it does? Why does saltwater fail to satisfy thirst? 49 Warren Weaver had seen early on the promise of digital computing, and he knew that the mysteries of organized complexity time. For millennia, humans had used their skills at observation and classification to document the subtle anatomy of flowers, but for the first time they were perched on the brink of answering a more fundamental question, a question that had more to do with patterns developing over time that with static structure: Why does an evening primrose open when it does? And how does a simple seed know to make a primrose in the first place? 51 Jane Jacobs Under the seeming disorder of the old city, wherever the old city is working successfully, is a marvelous order for maintaining the safety of the streets and the freedom of the city. It is a complex order. Its essence is intimacy of sidewalk use, bringing with it a constant succession of eyes. This order is all composed of movement and change, and although it is life, not art, we may fancifully call it the art from of the city and liken it to the dance - not to a simple-minded precision dance with everyone kicking up at the same time, twirling in unison and bowing off en masse, but to an intricate ballet in which the individual dancers and ensembles all have distinctive parts which miraculously reinforce each other and compose an orderly whole. - In parts of the city which are working well in some respects and badly in others (as is often the case), we cannot even analyze the virtues and the faults, diagnose the trouble or consider the helpful changes, without going at them as problems of organized complexity," she wrote 52 Vital cities have marvelous innate abilities for understanding, communicating, contriving and inventing what is required to combat their difficulties," she wrote. They get their order from below; they are learning machines, pattern recognizers - even when the patterns they respond to are unhealthy ones. 54 Pandemonium The brilliance of Oliver Selfridge's new paradigm lay in the fact that it relied on a distributed, bottom-up intelligence, and not a unified, top-down one. Rather than build a single smart program, Selfridge created a swarm of limited miniprograms, which he called demons. "The idea was, we have a bunch of these demons shrieking up the hierarchy. Lower-level demons shrieking up to higher level demons shrieking up to higher ones. 55 How do you teach a machine to recognize letters - or vowel sounds, minor chords, fingerprints - in the first place? The answer involved adding another layer of demons, and a feedback mechanism whereby the various demon guesses could be graded. This lower level was populated by even less sophisticated miniprograms, trained only to recognize raw physical shapes. - Some demons recognized parallel lines, others perpendicular ones. Some demons looked for circles, others for dots. None of these shapes were associated with any particular letter; these bottom-dwelling demons were like two-year-old-children -- capable of reporting on the shapes they witnesses, but not perceiving them as letters or words. 56 The results are close to random at first, but if you repeat the process a thousand times, or ten thousand, the system learns to associate specific assembles of shape-recognizers with specific letters and soon enough is capable of translating entire sentences with remarkable accuracy. The system doesn't come with any predefined conceptions about the shapes of letters - you train the system to associate letters with specific shapes in the grading phase. 57 The system Selfridge described - with its bottom-up learning and its evaluating feedback loops - belongs in the history books as the first practical description of an emergent software program. The world now swarms with millions of his demons. 57 In the sixties, after graduating as the first computer science Ph.D. in the country, John Holland began a line of inquiry that would dominate his work for the rest of his life. Holland's great breakthrough was to harness the forces of another bottom-up, open-ended system: natural selection. 58 Holland took the logic of Darwinian evolution and built it into code. He called his new creation the genetic algorithm. - A traditional software program is a series of instructions that tells the computer what to do. Usually those instructions are encoded as a series of branching paths. The art of programming lay in figuring out how to construct the most efficient sequence of instructions, the sequence that would get the most done with the shortest amount of code - and with the least likelihood of a crash. - But Holland imagined another approach : set up a gene pool of possible software and let successful programs *evolve* out of the soup. 59 Software already has a genotype and a phenotype, Holland recognized; there's the code itself, and then there's what the code actually does. - Natural selection relies on a brilliantly simple, but somewhat tautological, criterion for evaluating success: your genes get to pass on to the next generation if you survive long enough to produce a next generation. Holland decided to make that evaluation step more precise: his programs would be admitted to the next generation if they did a better job of accomplishing a specific task - doing simple math, say, or recognizing patterns in visual images. - Holland developed his ideas in the sixties and seventies using mostly paper and pencil - even the more advanced technology of that era was far too slow to churn through thousandfold generations of evolutionary time. - It was a program called Tracker, designed in the mid eighties by two UCLA professors, David Jefferson and Chuck Taylor. "I got the idea from reading Richard Dawkins first book, *The Selfish Gene* Jefferson says today. That book really transformed me." 60 He makes the point that in order to watch the Darwinian evolution in action, all you need are objects that are capable of reproducing themselves, and reproducing imperfectly, and having some resource limitation so that there's competition. And nothing else matters - it's a very tiny, abstract axiom that is required to make evolution work. - After a few small-scale experiments, Jefferson and Taylor decided to simulate the behavior of ants learning to follow a pheromone trail. "Ants were on my mind - I was looking for simple creatures, and E.O. Wilson's opus on ants had just come out" - "What we were really looking for was a simple task that simple creatures perform where it wasn't obvious how to make a program do it. Somehow we came up with the idea of following a trail - and not just a clean trail, a noisy trail, a broken trail." - The two scientists created a virtual grid of squares, drawing a meandering path of eighty-two squares across it. Their goal was to evolve a simple program, a virtual ant, that could navigate the length of the path in a finite amount of time, using only limited information about the path's twists and turns. At each cycle, an ant had the option of "sniffing" the square ahead of him, advancing forward one square, or turning right or left ninety degrees. - Jefferson and Taylor gave their ants one hundred cycles to navigate the path; once an ant used up his hundred cycles, the software tallied up the number of squares on the trail he had successfully landed on and gave him a score. An ant that lost his way after square one would be graded 1; an ant that successfully completed the trail before the hundred cycles were up would get a perfect score, 82. 61 The scoring system allowed Jefferson and Taylor to create fitness criteria that determined which ants were allowed to reproduce. Tracker began simulating sixteen thousand ants - one for each of the Connection Machine's process - with sixteen thousand more or less random strategies for trail navigation. - Those more successful ants would be allowed to mate and reproduce, creating a new generation of sixteen thousand ants ready to tackle the trail. 62 "To our wonderment and utter joy," Jefferson recalls, "it succeeded the first time. We were sitting there watching these numbers come in: one generation would produce twenty-five, then twenty-five, and then it would be twenty-seven, and then thirty. Eventually we saw a perfect score, after only about a hundred generations." - Rather than Engineer a solution to the trail following problem, the two UCLA professors had evolved a solution; they had created a random pool of possible programs, then built a feedback mechanism that allowed more successful programs to emerge. - By any measure, Tracker was a genuine breakthrough. Finally the tools of modern computing had advanced to the point where you could simulate emergent intelligence, watch it unfold on the screen in real time, as Turing and Selfridge and Shannon dreamed of doing years before. 63 In Mitch Resnick's computer simulation of slime mold behavior, there are two key variables, two elements that you can alter in your interaction with the simulation. The first is the number of slime mold cells in the system; the second is the physical and temporal length of pheromone trail left behind by each cell as it crawls across the screen. - Keep the trails short and the cells few, and the slime mold will steadfastly refuse to come together. The screen will look like a busy galaxy of shooting stars, with no larger shapes emerging. But turn up the duration of the trails, and the number of agents, and at a certainly clearly defined point, a cluster of cells will suddenly form. This is not gradual but sudden, as though a switch had been flipped. But there are no switch-flippers, no pacemakers - just a swarm of isolated cells colliding with one another, and leaving behind their pheromone footprints. 64 Histories of intellectual development - the origin and spread of new ideas - usually come in two types of packages: either the "great man" theory, where a single genius has a eureka moment in the lab or the "paradigm shift" theory, where the occupants of the halls of science awake and find an entirely new floor has been built on top of them, and within a few years, everyone is working out of the new offices. - Both theories are inadequate - I suspect Mitch Resnick's slime mold simulation may be a better metaphor for the way idea revolutions come about: think of those slime mold cells as investigators in the field; think of those trails as a kind of institutional memory. With only a few minds exploring a given problem, the cells remain disconnected, meandering across the screen as isolated units, each pursuing its own desultory course. Like an essay published in a journal that sits unread on a library shelf for years. - Plug in more minds into the system and give their work a longer, more durable trail - by publishing their ideas in best-selling books, or founding research centers to explore those ideas - and before long the system arrives at a phase transition: isolated hunches and private obsessions coalesce into a new way of looking at the world, shared by thousands of individuals. 65 In 1969, Marvin Minsky and Seymour Papert published "Perceptrons," which built on Selfridge's Pandemonium device for distributed pattern recognition, leading the way for Minsky's bottom-up *Society of Mind* theory developed over the following decade. --- ### **References** [The Great Towns - Friedrich Engels](https://www.marxists.org/archive/marx/works/1845/condition-working-class/ch04.htm)