# Emergence  Author:: Steven Johnson ## AI-Generated Summary None ## Highlights > What if the community of slime mold cells were organizing themselves? What if there were no pacemakers? ([Location 140](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=140)) > 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. People had been thinking about emergent behavior in all its diverse guises for centuries, if not millennia, but all that thinking had consistently been ignored as a unified body of work—because there was nothing unified about its body. There were isolated cells pursuing the mysteries of emergence, but no aggregation. ([Location 167](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=167)) > 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. ([Location 181](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=181)) > 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. ([Location 202](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=202)) > 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. ([Location 228](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=228)) > 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. ([Location 230](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=230)) #### PART ONE ##### The Myth of the Ant Queen > the matriarch doesn’t train her servants to protect her, evolution does. ([Location 302](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=302)) > 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. ([Location 304](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=304)) > We know now that systems like ant colonies don’t have real leaders, that the very idea of an ant “queen” is misleading. ([Location 329](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=329)) > There is, first, the more conventional sense of complexity as sensory overload, ([Location 408](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=408)) > Bell Labs was the home base for another genius, Claude Shannon, who would go on to found the influential discipline of information theory, and whose work had explored the boundaries between noise and information. ([Location 490](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=490)) > Turing had imagined his thinking machine primarily in terms of its logical possibilities, its ability to execute an infinite variety of computational routines. But Shannon pushed him to think of the machine as something closer to an actual human brain, capable of recognizing more nuanced patterns. ([Location 498](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=498)) > First, the study of simple systems: two or three variable problems, such as the rotation of planets, or the connection between an electric current and its voltage and resistance. ([Location 522](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=522)) > Second, problems of “disorganized complexity”: problems characterized by millions or billions of variables that can only be approached by the methods of statistical mechanics and probability theory. ([Location 523](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=523)) > But there was a third phase to this progression, and we were only beginning to understand. ([Location 527](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=527)) > Much more important than the mere number of variables is the fact that these variables are all interrelated. . . . These problems, as contrasted with the disorganized situations with which statistics can cope, show the essential feature of organization. We will therefore refer to this group of problems as those of organized complexity. ([Location 532](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=532)) > A system of disorganized complexity would be that same table enlarged to include a million balls, colliding with one another millions of times a second. Making predictions about the behavior of any individual ball in that mix would be difficult, but you could make some accurate predictions about the overall behavior of the table. ([Location 537](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=537)) > problem of organized complexity, a problem that suddenly seemed omnipresent in nature once you started to look for it: What makes an evening primrose open when it does? Why does salt water fail to satisfy thirst? . . . What is the description of aging in biochemical terms? . . . ([Location 544](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=544)) > All these are certainly complex problems. But they are not problems of disorganized complexity, to which statistical methods hold the key. They are all problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole. ([Location 548](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=548)) > Tackling such problems required a new approach: ([Location 550](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=550)) > To solve the problems of organized complexity, you needed a machine capable of churning through thousands, if not millions, of calculations per second ([Location 556](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=556)) > The brilliance of 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,” he explains. “Lower-level demons shrieking to higher-level demons shrieking to higher ones.” ([Location 640](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=640)) > means, imagine a system with twenty-six individual demons, each trained to recognize a letter of the alphabet. ([Location 643](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=643)) > All the letter-recognizing demons would report to a master demon, who would tally up the votes for each letter and choose the demon that expressed the highest confidence. Then the software would move on to the next letter in the sequence, and the process would begin again. ([Location 648](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=648)) > 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. ([Location 674](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=674)) > With DNA-based organisms, natural selection works by creating a massive pool of genetic variation, then evaluating the success rate of the assorted behaviors unleashed by all those genes. ([Location 698](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=698)) > programs can reproduce themselves. Except that they usually reproduce themselves exactly. But I recognized that if there was a way to have them reproduce imperfectly, and if you had not just one program but a whole population of them, then you could simulate evolution with the software instead of organisms.” ([Location 719](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=719)) > Those more successful ants would be allowed to mate and reproduce, creating a new generation of sixteen thousand ants ready to tackle the trail. ([Location 736](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=736)) > Eventually we saw a perfect score, after only about a hundred generations. It was mind-blowing.” The software had evolved an entire population of expert trail-followers, despite the fact that Jefferson and Taylor had endowed their first generation of ants with no skills whatsoever. ([Location 749](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=749)) > Rather than engineer a solution to the trail-following problem, the two UCLA professors had evolved a solution; ([Location 751](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=751)) > they had created a random pool of possible programs, then built a feedback mechanism that allowed more successful programs to emerge. ([Location 752](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=752)) Note: The feedback is the key here. How did they use it to improve future generations? > the virtual ants evolved a strategy for survival that was uniquely adapted to their environment. ([Location 756](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=756)) > 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 library and the world is immediately transformed; or the “paradigm shift” theory, where the occupants of the halls of science awake to 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. ([Location 774](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=774)) > With only a few minds exploring a given problem, the cells remain disconnected, ([Location 781](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=781)) > But plug 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. ([Location 783](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=783)) Note: This is why [[Learning in public]] is essential to further communal knowledge. #### PART TWO ##### Street Level > While there’s no single key to the success of the social insects, the collective intelligence of the colony system certainly played an essential role. Call it swarm logic: ([Location 839](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=839)) > Local turns out to be the key term in understanding the power of swarm logic. We see emergent behavior in systems like ant colonies when the individual agents in the system pay attention to their immediate neighbors rather than wait for orders from above. They think locally and act locally, but their collective action produces global behavior. ([Location 847](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=847)) Note: Like virtual users that only know what's happening on the load generator, not the global test. > This local feedback may well prove to be the secret to the ant world’s decentralized planning. Individual ants have no way of knowing how many foragers or nest-builders or trash collectors are on duty at any given time, but they can keep track of how many members of each group they’ve stumbled across in their daily travels. Based on that information—both the pheromone signal itself, and its frequency over time—they can adjust their own behavior accordingly. ([Location 886](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=886)) > If you’re building a system designed to learn from the ground level, a system where macrointelligence and adaptability derive from local knowledge, there are five fundamental principles you need to follow. Gordon’s harvester ants showcase all of them at work: ([Location 896](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=896)) > More is different. ([Location 898](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=898)) > the statistical nature of ant interaction demands that there be a critical mass of ants for the colony to make intelligent assessments of its global state. ([Location 899](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=899)) Note: Sample size > “More is different” also applies to the distinction between micromotives and macrobehavior: individual ants don’t “know” that they’re prioritizing pathways between different food sources when they lay down a pheromone gradient near a pile of nutritious seeds. ([Location 901](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=901)) > Ignorance is useful. ([Location 906](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=906)) Note: Load generators don't communicate with each other. > Emergent systems can grow unwieldy when their component parts become excessively complicated. Better to build a densely interconnected system with simple elements, and let the more sophisticated behavior trickle up. ([Location 907](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=907)) Note: Atomic notes. > Having individual agents capable of directly assessing the overall state of the system can be a real liability in swarm logic, ([Location 909](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=909)) > Encourage random encounters. ([Location 911](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=911)) > Their encounters with other ants are individually arbitrary, but because there are so many individuals in the system, those encounters eventually allow the individuals to gauge and alter the macrostate of the system itself. Without those haphazard encounters, the colony wouldn’t be capable of stumbling across new food sources or of adapting to new environmental conditions. ([Location 912](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=912)) > Look for patterns in the signs. ([Location 915](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=915)) > Pay attention to your neighbors. ([Location 920](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=920)) > “Local information can lead to global wisdom.” ([Location 921](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=921)) > The primary mechanism of swarm logic is the interaction between neighboring ants in the field: ([Location 921](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=921)) Note: How can virtual users stumble upon each other? > Adding ants to the overall system will generate more interactions between neighbors and will consequently enable the colony itself to solve problems and regulate itself more effectively. Without neighboring ants stumbling across one another, colonies would be just a senseless assemblage of individual organisms—a swarm without logic. ([Location 923](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=923)) > The persistence of the whole over time—the global behavior that outlasts any of its component parts—is one of the defining characteristics of complex systems. ([Location 959](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=959)) > Cells self-organize into more complicated structures by learning from their neighbors. Each cell in your body contains an intricate set of tools for detecting the state of surrounding cells, and for communicating to those cells using various chemical messengers. ([Location 998](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=998)) > This is the secret of self-assembly: cell collectives emerge because each cell looks to its neighbors for cues about how to behave. ([Location 1016](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1016)) > Sim-City was one of the first games to exploit the uncanny, bottom-up powers of emergence. ([Location 1039](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1039)) > Users grow their virtual cities, but the cities evolve in unpredictable ways, and control over the city’s eventual shape is always indirect. ([Location 1043](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1043)) > The algorithms themselves are relatively simple—look at your neighbors’ state, and change your state accordingly—but the magic of the simulation occurs because the computer makes thousands of these calculations per second. Because each cell is influencing the behavior of other cells, changes appear to ripple through the entire system with a fluidity and definition that can only be described as lifelike. ([Location 1053](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1053)) > weltanschauung. ([Location 1097](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1097)) > Sidewalks allow relatively high-bandwidth communication between total strangers, and they mix large numbers of individuals in random configurations. ([Location 1138](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1138)) > Sidewalks work because they permit local interactions to create global order. ([Location 1165](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1165)) > Encountering diversity does nothing for the global system of the city unless that encounter has a chance of altering your behavior. There has to be feedback between agents, cells that change in response to the changes in other cells. ([Location 1171](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1171)) > the intelligence of the colony actually relies on the stupidity of its component parts: an ant that suddenly started to make conscious decisions about, say, the number of ants on midden duty would be disastrous for the overall group. ([Location 1182](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1182)) > We contribute to that emergent intelligence, but it is almost impossible for us to perceive that contribution, because our lives unfold on the wrong scale. The next chapter is an attempt to see our way around that blind spot. ([Location 1221](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1221)) ##### The Pattern Match > learning is not always contingent on consciousness. Our immune systems learn throughout our lifetimes, ([Location 1248](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1248)) > Like any emergent system, a city is a pattern in time. ([Location 1275](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1275)) > The neighborhood system of the city functions as a kind of user interface for the same reason that traditional computer interfaces do: there are limits to how much information our brains can handle at any given time. We need visual interfaces on our desktop computers because the sheer quantity of information stored on our hard drives—not to mention on the Net itself—greatly exceeds the carrying capacity of the human mind. ([Location 1331](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1331)) > is the Web learning as well? ([Location 1401](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1401)) > my last book, Interface Culture. ([Location 1404](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1404)) > Is the Web itself becoming a giant brain? I still think the answer is no. But now I think it’s worth asking why not. ([Location 1423](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1423)) > Individual human minds have coalesced into “group brains” many times in modern history, most powerfully in the communal gatherings of cities. ([Location 1443](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1443)) > the Web is a tremendously disorganized space, a system where the disorder grows right alongside the overall volume. ([Location 1460](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1460)) > Yahoo and Google function, in a way, as man-made antidotes to the Web’s natural chaos—an engineered attempt to restore structure to a system that is incapable of generating structure on its own. ([Location 1461](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1461)) > These patterns may be self-organizing, but they are not adaptive in any way. ([Location 1490](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1490)) > Relationships in these systems are mutual: you influence your neighbors, and your neighbors influence you. All emergent systems are built out of this kind of feedback, the two-way connections that foster higher-level learning. ([Location 1505](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1505)) > Ironically, it is precisely this feedback that the Web lacks, because HTML-based links are one-directional. ([Location 1507](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1507)) Note: Obsidian backlinks! > Two months after starting work on Alexa, Kahle added a new button to his toolbar, with the simple but provocative tag “What’s Next?” Click on the button while visiting a Marilyn Monroe tribute site, and you’ll find a set of links to other Marilyn shrines online; ([Location 1544](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1544)) Note: Generating links to related topics automatically. > the associations are not the work of an individual consciousness, but rather the sum total of thousands and thousands of individual decisions, a guide to the Web created by following an unimaginable number of footprints. ([Location 1549](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1549)) > In fact, the “intelligence” of Alexa is really the aggregated wisdom of the thousands—or millions—of people who use the system. ([Location 1562](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1562)) > An emergent software program that tracks associations between Web sites or audio CDs doesn’t listen to music; it follows purchase patterns or listening habits that we supply and lets us deal with the air guitar and the off-key warbling. On some basic human level, that feels like a difference worth preserving. And maybe even one that we won’t ever be able to transcend, a hundred years from now or more. But is it truly a difference in kind, or is it just a difference in degree? This is the question that has haunted the artificial intelligence community for decades now, and it hits close to home in any serious discussion of emergent software. ([Location 1622](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1622)) ##### Listening to Feedback > You can normally spot one of these feedback loops as it nears its denouement, since it almost invariably triggers a surge of self-loathing that washes through the entire commentariat. These self-critical waters seem to rise on something like an annual cycle: think of the debate about the paparazzi and Princess Di’s death, or the permanent midnight of “Why Do We Care So Much About O.J.?” ([Location 1678](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1678)) Note: How can load tests be self-critical? > Why do these feedback loops and reverberating circuits happen? They come into being because the neural networks of the brain are densely interconnected: each individual neuron contains links—in the form of axons and synapses—to as many as a thousand other neurons. ([Location 1695](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1695)) > The likelihood of a feedback loop correlates directly to the general interconnectedness of the system. ([Location 1700](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1700)) > The Flowers affair is a great example of why emergent systems aren’t intrinsically good. Tornadoes and hurricanes are feedback-heavy systems too, but that doesn’t mean you want to build one in your backyard. Depending on their component parts, and the way they’re put together, emergent systems can work toward many different types of goals: some of them admirable, some more destructive. ([Location 1736](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1736)) > The system uses negative feedback to home in on the proper conditions—and for that reason it can handle random changes in the environment. Negative feedback, then, is a way of reaching an equilibrium point despite unpredictable—and changing—external conditions. The “negativity” keeps the system in check, just as “positive feedback” propels other systems onward. ([Location 1758](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1758)) > A thermostat with no feedback simply pumps seventy-two-degree air into a room, regardless of the room’s temperature. An imaginary thermostat driven by positive feedback might evaluate the change in room temperature and follow that lead: if the thermostat noted that the room had grown warmer, it would start pumping hotter air, causing the room to grow even warmer, causing the device to pump hotter air. Next thing you know, the water in the goldfish bowl is boiling. Negative feedback, on the other hand, lets the system find the right balance, even in a changing environment. A cold front comes in, a window is opened, someone lights a fire—any of these things can happen, and yet the temperature remains constant. Instead of amplifying its own signal, the system regulates itself. ([Location 1761](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1761)) > negative feedback is not solely a software issue, or a device for your home furnace. It is a way of indirectly pushing a fluid, changeable system toward a goal. It is, in other words, a way of transforming a complex system into a complex adaptive system. ([Location 1778](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1778)) > At its most schematic, negative feedback entails comparing the current state of a system to the desired state, and pushing the system in a direction that minimizes the difference between the two states. ([Location 1781](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1781)) Note: The reverse fo a chaos test perhaps. > Once again, we return to the fundamental laws of emergence: the behavior of individual agents is less important than the overall system. ([Location 1864](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1864)) > But for the borderline case, a subtle but powerful mechanism is at work in any face-to-face group conversation: if an individual is holding a conversation hostage with an irrelevant obsession, groups can naturally establish a consensus—using words, body language, facial expressions, even a show of hands—making it clear that the majority of the group feels their time is being wasted. The face-to-face world is populated by countless impromptu polls that take the group’s collective pulse. Most of them happen so quickly that we don’t even know that we’re participating in them, and that transparency is one reason why they’re as powerful as they are. In the face-to-face world, we are all social thermostats: reading the group temperature and adjusting our behavior accordingly. ([Location 1934](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1934)) > In a public discussion thread, not all the participants are visible. A given conversation may have five or six active contributors and several dozen “lurkers” who read through the posts but don’t chime in with their own words. This creates a fundamental imbalance in the system of threaded discussion and gives the crank an opportunity to dominate the space in a way that would be much more difficult off-line. ([Location 1942](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1942)) > Group conversations in the real world have an uncanny aptitude for reaching a certain kind of homeostasis: the conversation moves toward a zone that pleases as much of the group as possible and drowns out voices that offend. ([Location 1953](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=1953)) > Slashdot is only the beginning. In the past two years, user ratings have become the kudzu of the Web, draping themselves across pages everywhere you look. ([Location 2034](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2034)) ##### Control Artist > local interactions between large numbers of agents, governed by simple rules of mutual feedback. ([Location 2171](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2171)) > Programming used to be thought of as a domain of pure control: you told the computer what to do, and the computer had no choice but to obey your orders. ([Location 2203](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2203)) > It’s no accident that Norbert Wiener derived the term cybernetics from the Greek word for “steersman”: the art of software has from the beginning been about control systems and how best to drive them. But that control paradigm is slowly giving way to a more oblique form of programming: software that you “grow” instead of engineer, software that learns to solve problems autonomously, the way Oliver Selfridge envisioned with his Pandemonium model. The new paradigm borrows heavily from the playbook of natural selection, breeding new programs out of a varied gene pool. The first few decades of software were essentially creationist in philosophy—an almighty power wills the program into being. But the next generation is profoundly Darwinian. ([Location 2206](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2206)) > Once the software climbs all the way to the top of a ridge, there’s no reward in descending and looking for another, higher peak, because a less successful program—one that drops down a notch on the fitness landscape—would instantly be eliminated from the gene pool. ([Location 2243](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2243)) > Hillis’s stroke of genius was to force his miniprograms out of the ridges by introducing predators into the mix. ([Location 2246](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2246)) > I think we lose something important in stressing how talented this generation is with their joysticks. I think they have developed another skill, one that almost looks like patience: they are more tolerant of being out of control, more tolerant of that exploratory phase where the rules don’t all make sense, and where few goals have been clearly defined. In other words, they are uniquely equipped to embrace the more oblique control system of emergent software. The hard work of tomorrow’s interactive design will be exploring the tolerance—that suspension of control—in ways that enlighten us, in ways that move beyond the insulting residue of princesses and magic spells. ([Location 2312](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2312)) > Emergent behaviors, like games, are all about living within the boundaries defined by rules, but also using that space to create something greater than the sum of its parts. ([Location 2382](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2382)) #### PART THREE > No instructions come from events of the world to the system on which selection occurs. Moreover, events occurring in an environment or a world are unpredictable. How then do we simulate events and their effects on selection? One way is as follows: 1. Simulate the organ or the animal as described above, making provision for the fact that, as a selective system, it contains a generator of diversity—mutations, alterations in neural wiring, or synaptic changes that are unpredictable. 2. Independently simulate a world or environment constrained by known physical principles, but allow for the occurrence of unpredictable events. 3. Let the simulated organ or animal interact with the simulated world or the real world without prior information transfer, so that selection can take place. 4. See what happens. —GERALD EDELMAN ([Location 2509](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2509)) ##### The Mind Readers > Their disorder is not a disorder of lowered intellect. Rather, autistics lack a particular skill, the way others lack the faculty of sight or hearing. They are mind blind. ([Location 2580](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2580)) > Over the last decade we have run up against another ceiling. We are now connected to hundreds of millions of people via the vast labyrinth of the World Wide Web. A community of that scale requires a new solution, one beyond our brains or our sidewalks, but once again we look to self-organization for the tools, this time built out of the instruction sets of software: Alexa, Slashdot, Epinions, Everything2, Freenet. Our brains first helped us navigate larger groups of fellow humans by allowing us to peer into the minds of other individuals and to recognize patterns in their behavior. The city allowed us to see patterns of group behavior by recording and displaying those patterns in the form of neighborhoods. Now the latest software scours the Web for patterns of online activity, using feedback and pattern-matching tools to find neighbors in an impossibly oversize population. ([Location 2658](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2658)) > Just as certain drugs are designed specifically as keys to unlock the neurochemistry of our gray matter, the graphic interface was designed to exploit the innate talents of the human mind and to rely as little as possible on our shortcomings. ([Location 2682](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2682)) > because we inherited the exceptional visual skills of the primate family, we have adopted spatial metaphors on our computer screens. ([Location 2684](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2684)) > we’re a visual species; ([Location 2689](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2689)) > Interacting with emergent software is already more like growing a garden than driving a car or reading a book. In the near future, though, you’ll be working alongside a million other gardeners. ([Location 2694](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2694)) > Technology analysts never tire of reminding us that pornography is the ultimate early adopter. New technologies, in other words, are assimilated by the sex industries more quickly than by the mainstream ([Location 2708](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2708)) > Gamers have been experimenting with self-organizing systems at least since SimCity’s release in 1990, but the digital porn world remains, as it were, a top-down affair—despite the hype about putatively “interactive” DVDs. ([Location 2713](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2713)) > Instead, the Web will contribute the metadata that enables these clusters to self-organize. It will be the central warehouse and marketplace for all our patterns of mediated behavior, ([Location 2890](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2890)) > The cluster will build a theory of your mind, and that theory will be a group project, assembled via the Web out of an unthinkable number of isolated decisions. Each theory and each cluster will be more specialized than anything we’ve ever experienced in the top-down world of mass media. These mind-reading skills will emerge because for the first time our patterns of behavior will be exposed—like the sidewalks we began with—to the shared public space of the Web itself. ([Location 2895](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2895)) > Imagine a corporate system structured like the Slashdot quality filters: in a traditional company, the CEO composes the posts himself; in a Slashdot-style company, he’s merely tweaking the algorithm that promotes or demotes posts based on their quality. The vision for the company’s future comes from below, out of the ever-shifting alliances of smaller groups. Senior management simply provides the feedback mechanism—in the form of bonuses, options, or increased resources—ensuring that the most productive clusters thrive. CEOs still have a place in even the most distributed corporate structure, but they’re no longer allowed to be pacemakers. ([Location 2935](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2935)) ##### See What Happens > Dorigo’s insight was to solve the traveling salesmen problem the way an ant colony would: send out an army of virtual salesman to explore all possible routes on the map. ([Location 2997](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=2997)) > Selfridge wants to attack the problem of traffic the way Danny Hillis attacked the problem of number sorting: by giving the network the general goal of minimizing delays, but letting the overall system figure out the details, using the tools of feedback, neighbor interaction, and pattern recognition that are the hallmark of all self-organizing systems. ([Location 3037](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=3037)) > Make the traffic lights smart—by connecting them and feeding them information about backups or accidents—and you have a solution that can actually manage the immense and constantly changing problem of urban movement. You can conquer gridlock by making the grid itself smart. ([Location 3041](https://readwise.io/to_kindle?action=open&asin=B008TRUBLY&location=3041))