What are you thinking about right now? Because my words are being communicated to you via the one-way medium of the printed page, this is a difficult question for me to answer. But if I were presenting this argument while sitting across a table from you, I’d already have an answer, or at least an educated guess—even if you’d been silent the entire time. Your facial gestures, eye movements, body language, would all be sending a steady stream of information about your internal state—signals that I would intuitively pick up and interpret. I’d see your eyelids droop during the more contorted arguments, note the chuckle at one of my attempts at humor, register the way you sit upright in the chair when my words get your attention. I could no more prohibit my mind from making those assessments than you could stop your mind from interpreting my spoken words as language. (Assuming you’re an English speaker, of course.) We are both locked in a communicational dance of extraordinary depth—and yet, amazingly, we’re barely aware of the process at all.
Human beings are innate mind readers. Our skill at imagining other people’s mental states ranks up there with our knack for language and our opposable thumbs. It comes so naturally to us and has engendered so many corollary effects that it’s hard for us to think of it as a special skill at all. And yet most animals lack the mind-reading skills of a four-year-old child. We come into the world with a genetic aptitude for building “theories of other minds,” and adjusting those theories on the fly, in response to various forms of social feedback.
In the mideighties, the UK psychologists Simon Baron-Cohen, Alan Leslie, and Uta Frith conducted a landmark experiment to test the mind-reading skills of young children. They concealed a set of pencils within a box of Smarties, the British candy. They asked a series of four-year-olds to open the box and make the unhappy discovery of the pencils within. The researchers then closed the box up and ushered a grown-up into the room. The children were then asked what the grown-up was expecting to find within the Smarties box—not what they would find, mind you, but what they were expecting to find. Across the board, the four-year-olds gave the right answer: the clueless grown-up was expecting to find Smarties, not pencils. The children were able to separate their own knowledge about the contents of the Smarties box from the knowledge of another person. They grasped the distinction between the external world as they perceived it, and the world as perceived by others. The psychologists then conducted the same experiment with three-year-olds, and the exact opposite result came back. The children consistently assumed that the grown-up would expect to find pencils in the box, not candy. They had not yet developed the faculty for building models of other people’s mental states—they were trapped in a kind of infantile omniscience, where the knowledge you possess is shared by the entire world. The idea of two radically distinct mental states, each containing different information about the world, exceeded the faculties of the three-year-old mind, but it came naturally to the four-year-olds.
Our closest evolutionary cousins, the chimpanzees, share our aptitude for mind reading. The Dutch primatologist Frans de Waal tells a story of calculating sexual intrigue in his engaging, novel-like study, Chimpanzee Politics. A young, low-ranking male (named, appropriately enough, Dandy) decides to make a play for one of the females in the group. Being a chimpanzee, he opts for the usual chimpanzee method of expressing sexual attraction, which is to sit with your legs apart within eyeshot of your objet de désir and reveal your erection. (Try that approach in human society, of course, and you’ll usually end up with a restraining order.) During this particular frisky display, Luit, one of the high-ranking males, happens upon the “courtship” scene. Dandy deftly uses his hands to conceal his erection so that Luit can’t see it, but the female chimp can. It’s the chimp equivalent of the adulterer saying, “This is just our little secret, right?”
De Waal’s story—one of many comparable instances of primate intrigue—showcases our close cousins’ ability to model the mental states of other chimps. As in the Smarties study, Dandy is performing a complicated social calculus in his concealment strategy: he wants the female chimp to know that he’s enamored of her, but wants to hide that information from Luit. That kind of thinking seems natural to us (because it is!), but to think like that you have to be capable of modeling the contents of other primate minds. If Dandy could speak, his summary of the situation might read something like this: she knows what I’m thinking; he doesn’t know what I’m thinking; she knows that I don’t want him to know what I’m thinking. In that crude act of concealment, Dandy demonstrates that he possesses a gift for social imagination missing in 99.99 percent of the world’s living creatures. To make that gesture, he must somewhere be aware that the world is full of imperfectly shared information, and that other individuals may have a perspective on the world that differs from his. Most important (and most conniving), he’s capable of exploiting that difference for his own benefit. That exploitation—a furtive pass concealed from the alpha male—is only possible because he is capable of building theories of other minds.
Is it conceivable that this skill simply derives from a general increase in intelligence? Could it be that humans and their close cousins are just smarter than all those other species who flunk the mind-reading test? In other words, is there something specific to our social intelligence, something akin to a module hardwired into the brain’s CPU—or is the theory of minds just an idea that inevitably occurs to animals who reach a certain threshold of general intelligence? We are only now beginning to build useful maps of the brain’s functional topography, but already we see signs that “mind reading” is more than just a by-product of general intelligence. Several years ago, the Italian neuroscientist Giaccamo Rizzollati discovered a region of the brain that may well prove to be integral to the theory of other minds. Rizzollati was studying a section of the ventral premotor area of the monkey brain, a region of the frontal lobe usually associated with muscular control. Certain neurons in this field fired when the monkey performed specific activities, like reaching for an object or putting food in its mouth. Different neurons would fire in response to different activities. At first, this level of coordination suggested that these neurons were commanding the appropriate muscles to perform certain tasks. But then Rizzollati noticed a bizarre phenomenon. The same neurons would fire when the monkey observed another monkey performing the task. The pound-your-fist-on-the-floor neurons would fire every time the monkey saw his cellmate pounding his fist on the floor.
Rizzollati called these unusual cells “mirror neurons,” and since his announcement of the discovery, the neuroscience community has been abuzz with speculation about the significance of the “monkey see, monkey do” phenomenon. It’s conceivable that mirror neurons exist for more subtle, introspective mental states—such as desire or rage or tedium—and that those neurons fire when we detect signs of those states in others. That synchronization may well be the neurological root of mind reading, which would mean that our skills were more than just an offshoot of general intelligence, but relied instead on our brains’ being wired a specific way. We know already that specific regions are devoted to visual processing, speech, and other cognitive skills. Rizzollati’s discovery suggests that we may also have a module for mind reading.
The modular theory is also supported by evidence of what happens when that wiring is damaged. Many neuroscientists now believe that autistics suffer from a specific neurological disorder that inhibits their ability to build theories of other minds—a notion that will instantly ring true for anyone who has experienced the strange emotional distance, the radical introversion, that one finds in interacting with an autistic person. Autism, the argument goes, stems from an inability to project outside one’s own head and imagine the mental life of others. And yet autistics regularly fare well on many tests of general intelligence and often display exceptional talents at math and pattern recognition. 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.
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Still, it can be hard to appreciate how rare a gift our mind reading truly is. For most of us, that we are aware of other minds seems at first blush like a relatively simple achievement—certainly not something you’d need a special cognitive tool for. I know what it’s like inside my head, after all—it’s only logical that I should imagine what’s inside someone else’s. If we’re already self-aware, how big a leap is it to start keeping track of other selves?
This is a legitimate question, and like almost any important question that has to do with human consciousness, the jury is still out on it. (To put it bluntly, the jury hasn’t even been convened yet.) But some recent research suggests that the question has it exactly backward—at least as far as the evolution of the brain goes. We’re conscious of our own thoughts, the argument suggests, only because we first evolved the capacity to imagine the thoughts of others. A mind that can’t imagine external mental states is like that of a three-year-old who projects his or her own knowledge onto everyone in the room: it’s all pencils, no Smarties. But as philosophers have long noted, to be self-aware means recognizing the limits of selfhood. You can’t step back and reflect on your own thoughts without recognizing that your thoughts are finite, and that other combinations of thoughts are possible. We know both that the pencils are in the box, and that newcomers will still expect Smarties. Without those limits, we’d certainly be aware of the world in some basic sense—it’s just that we wouldn’t be aware of ourselves, because there’d be nothing to compare ourselves to. The self and the world would be indistinguishable.
The notion of being aware of the world and yet not somehow self-aware seems like a logical impossibility. It feels as if our own selfhood would scream out at us after a while, “Hey, look at me! Forget about those Smarties—I’m thinking here! Pay attention to me!” But without any recognition of other thoughts to measure our own thoughts against, our own mental state wouldn’t even register as something to think about. It may well be that self-awareness only jumps out to us because we’re naturally inclined to project into the minds of others. But in a mind incapable of imagining the contents of other minds, that self-reflection wouldn’t be missed. It would be like being raised on a planet without satellites, and missing the moon.
We all have a region of the retina where the optic nerve connects the visual cortex to the back of the retina. No rods or cones are within this area, so the corresponding area of our visual field is incapable of registering light. While this blind spot has a surprisingly large diameter (about six degrees across), its effects are minimal because of our stereo vision: the blind spots in each eye don’t overlap, and so information from one eye fills in the information lacking in the other. But you can detect the existence of the blind spot by closing one eye and focusing the other on a specific word in this sentence. Place your index finger over the word, and then slowly move your finger to the right, while keeping your gaze locked on the word. After a few inches, you’ll notice that the tip of your finger fades from view. It’s an uncanny feeling, but what’s even more uncanny is that your visual field suffers from this strange disappearing act anytime you close one eye. And yet you don’t notice the absence at all—there’s no sense of information being lost, no dark patch, no blurriness. You have to do an elaborate trick with your finger to notice that something’s missing. It’s not the lack of visual information that should startle us; it’s that we have such a hard time noticing the lack.
The blind spot doesn’t jump out at us because the brain isn’t expecting information from that zone, and there’s no other signal struggling to fill in the blanks for us, or pointing out that there is a blank in the first place. As the philosopher Daniel Dennett describes it, there are no centers of the visual cortex “responsible for receiving reports from this area, so when no reports arrive, there is no one to complain. An absence of information is not the same as information about an absence.” We’re blind to our blindness.
Perhaps the same goes with the theory of other minds. Without that awareness of other mental states reminding us of our own limitations, we might well be aware of the world, yet unaware of our own mental life. The lack of self-awareness wouldn’t jump out at us for the same reason that the blind spot remains invisible: there’s no feedback mechanism to sound the alarm that something’s missing. Only when we begin to speculate on the mental life of others do we discover that we have a mental life ourselves.
If self-awareness is a by-product of our mind-reading skills, what propelled us to start building those theories of other minds in the first place? That answer comes more easily. The battle of nature-versus-nurture may have many skirmishes to come, but by now only the most blinkered anti-essentialist disagrees with the premise that we are social animals by nature. The great preponderance of human populations worldwide—both modern and “primitive”—live in extended bands and form complex social systems. Among the apes, we are an anomaly in this respect: only the chimps share our compulsive mixed-sex socializing. (Orangutans live mostly solitary lives; gibbons as isolated couples; gorillas travel in harems dominated by a single male.) That social complexity demands formidable mental skills: instead of outfoxing a single predator, or caring for a single infant, humans mentally track the behavior of dozens of individuals, altering their own behavior based on that information. Some evolutionary psychologists believe that the extraordinary expansion of brain size between Homo habilis and Homo sapiens (brain mass trebled over the 2-million-year period that separates the two species) was at least in part triggered by an arms race between Pleistocene-era extroverts. If successfully passing on your genes to another generation depended on a nuanced social intelligence that competed with other social intellects for reproductive privileges, then it’s not hard to imagine natural selection generating a Machiavellian mental toolbox in a surprisingly short period.
The group element may even explain the explosion in sheer cranial size: social complexity is a problem that scales well—build a module that can analyze one person’s mind, and all you need to do is throw more resources at the problem, and you can analyze a dozen minds with the same tools. The brain didn’t need to invent any complicated new routines once it figured out how to read a single mind—it just needed to devote more processing power. That power came in the form of brain mass: more neurons to model the behavior of other brains, which themselves contained more neurons, for the same reason. It’s a classic case of positive feedback, only it seems to have run into a ceiling of 150 people, according to the latest anthropological studies. We have a natural gift for building theories of other minds, so long as there aren’t too many of them.
Perhaps if human evolution had continued on for another million years or so, we’d all be mentally modeling the behavior of entire cities. But for whatever reason, we stopped short at 150, and that’s where we remained—until the new technologies of urban living pushed our collectivities beyond the magic number. Those oversize communities appeared too quickly for our minds to adapt to them using the tools of natural selection, and so we hit upon another solution, one engineered by the community itself, and not by its genes. We started building neighborhoods, groups within groups. When our lived communities extended beyond the ceiling of human comprehension, we started building new floors.
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Mirror neurons and mind reading have an immense amount to teach us about our talents and limitations as a species, and there’s no doubt we’ll be untangling the “theory of other minds” for years to come. Whatever the underlying mechanism turns out to be, the faculty of mind reading—and its close relation, self-awareness—is clearly an emergent property of the brain’s neural networks. We don’t know precisely how that higher-level behavior comes into being, but we do know that it is conjured up by the local, feedback-heavy interactions of unwitting agents, by the complex adaptive system that we call the human mind. No individual neuron is sentient, and yet somehow the union of billions of neurons creates self-awareness. It may turn out that the brain gets to that self-awareness by first predicting the behavior of neurons residing in other brains—the way, for instance, our brains are hardwired to predict the behavior of light particles and sound waves. But whichever one came first—the extroverted chicken or the self-aware egg—those faculties are prime examples of emergence at work. You wouldn’t be able to read these words, or speculate about the inner workings of your mind, were it not for the protean force of emergence.
But there are limits to that force, and to its handiwork. Natural selection endowed us with cognitive tools uniquely equipped to handle the social complexity of Stone Age groups on the savannas of Africa, but once the agricultural revolution introduced the first cities along the banks of the Tigris-Euphrates valley, the Homo sapiens mind naturally recoiled from the sheer scale of those populations. A mind designed to handle the maneuverings of less than two hundred individuals suddenly found itself immersed in a community of ten or twenty thousand. To solve that problem, we once again leaned on the powers of emergence, although the solution resided one level up from the individual human brain: instead of looking to swarms of neurons to deal with social complexity, we looked to swarms of individual humans. Instead of reverberating neuronal circuits, neighborhoods emerged out of traffic patterns. By following the footprints, and learning from their behavior, we built another ceiling on top of the one imposed on us by our frontal lobes. Managing complexity became a problem to be solved on the level of the city itself.
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. At first glance, these three solutions—brains, cities, and software—would seem to belong to completely different orders of experience. But as we have seen over the preceding pages, they are all instances of self-organization at work, local interactions leading to global order. They exist on a continuum of sorts. The materials change as you jump from the scale of a hundred humans to a million to 100 million. But the system remains the same.
Amazingly, this process has come full circle. Hundreds of thousands—if not millions—of years ago, our brains developed a feedback mechanism that enabled them to construct theories of other minds. Today, we are beginning to create software applications that are capable of developing a theory of our minds. All those fluid, self-organizing programs tracking our tastes and interests, and measuring them against the behavior of larger populations—these programs are the beginning of a progression that will, in a matter of years, lead to a world where we regularly interact with media that seems to know us in some fundamental way. Software will recognize our habits, anticipate our needs, adapt to our changing moods. The first generation of emergent software—programs like SimCity and StarLogo—displayed a captivatingly organic quality; they seemed more like life-forms than the sterile instruction sets and command lines of early code. The next generation will take that organic feel one step further: the new software will use the tools of self-organization to build models of our own mental states. These programs won’t be self-aware, and they won’t pass any Turing tests, but they will make the media experiences we’ve grown accustomed to seem autistic in comparison. They will be mind readers.
From a certain angle, this is an old story. The great software revolution of the seventies and eighties—the invention of the graphic interface—was itself predicated on a theory of other minds. The design principles behind the graphic interface were based on predictions about the general faculties of the human perceptual and cognitive systems. Our spatial memory, for instance, is more powerful than our textual memory, so graphic interfaces emphasize icons over commands. We have a natural gift for associative thinking, thanks to the formidable pattern-matching skills of the brain’s distributed network, so the graphic interface borrowed visual metaphors from the real world: desktops, folders, trash cans. 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. If the ants had been the first species to invent personal computers, they would have no doubt built pheromone interfaces, but because we inherited the exceptional visual skills of the primate family, we have adopted spatial metaphors on our computer screens.
To be sure, the graphic interface’s mind-reading talents are ruthlessly generic. Scrolling windows and desktop metaphors are based on predictions about a human mind, not your mind. They’re one-size-fits-all theories, and they lack any real feedback mechanism to grow more familiar with your particular aptitudes. What’s more, their predictions are decidedly the product of top-down engineering. The software didn’t learn on its own that we’re a visual species; researchers at Xerox-PARC and MIT already knew about our visual memory, and they used that knowledge to create the first generation of spatial metaphors. But these limitations will soon go the way of vacuum tubes and punch cards. Our software will develop nuanced and evolving models of our individual mental states, and that learning will emerge out of a bottom-up system. And while this software will deliver information tailored to our interests and appetites, its mind-reading skills will be far less insular than today’s critics would have us believe. You may read something like the Daily Me in the near future, but that digital newspaper will be compiled by tracking the interests and reading habits of millions of other humans. 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. We will have more powerful personalization tools than we ever thought possible—but those tools will be created by massive groups scattered all across the world. When Patti Maes first began developing recommendation software at MIT in the early nineties, she called it collaborative filtering. The term has only grown more resonant. In the next few years, we will have personalized filters beyond our wildest dreams. But we will also be collaborating on a scale rivaled only by the cities we first started building six thousand years ago.
Those collaborations will build more than just music-recommendation tools and personalized newspapers. Our new ability to capture the power of emergence in code will be closer to the revolution unleashed when we figured out how to distribute electricity a century ago. Almost every region of our cultural life was transformed by the power grid; the power of self-organization—coupled with the connective technology of the Internet—will usher in a revolution every bit as significant. Applied emergence will go far beyond simply building more user-friendly applications. It will transform our very definition of a media experience and challenge many of our habitual assumptions about the separation between public and private life. A few decades from now, the forces unleashed by the bottom-up revolution may well dictate that we redefine intelligence itself, as computers begin to convincingly simulate the human capacity for open-ended learning. But in the next five years alone, we’ll have plenty of changes to keep us busy. Our computers and television sets and refrigerators won’t be thinking themselves, but they’ll have a pretty good idea what we’re thinking about.
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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—it was true for the printing press, for the VCR, for Web-based broadband. But video games are challenging that old adage. Because part of their appeal lies in their promise of new experiences, and because their audience is willing to scale formidable learning curves in pursuit of those new experiences, games often showcase cutting-edge technology before the tech makes its way over to the red-light district. Certainly that has been the case with emergent software. 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.
In fact, video-game culture is the one arena today where you can see the “theory of other minds” integrated into a genuinely engaging media experience. Play any advanced first-person-shooter such as Quake or Unreal against computer opponents and you’ll witness astonishingly lifelike behavior from the simulated gunslingers battling against you. They’ll learn to anticipate your idiosyncrasies as a player; they’ll form complicated flocking patterns with other computer “bots”; they’ll grow familiar with new environments as they explore them. There’s not much art to these talents, since they are mostly in service of blowing things up, but there is an undeniable intelligence to those computer opponents—an intelligence that is only indirectly controlled by the games’ original programmers.
Will Wright’s games have historically been the first to embrace bottom-up routines, but even an advanced game like The Sims falls short of his own ambitions in this arena. The residents of Simsville may display remarkably lifelike personality and behavioral traits, but they are unlikely to spontaneously develop a new skill that Wright didn’t program into the game originally. You’ll see them fall in love or zone out in front of the television, but you won’t see one start yodeling or become a serial killer unless a human programmer has specifically added that behavior to the system. But Wright’s dream is to have Sims that do develop unique behavior on their own, Sims that exceed the imagination of their creators. “I’ve been fascinated with adaptive computing for some time now,” he says. “There are some rather hard problems to overcome, however. Some of the most promising technologies seem to also be the most parallel, like genetic algorithms, neural networks. These systems tend to learn by accumulating experience over a wide number of individual cases.” Think here of Danny Hillis’s number-sorting program. Hillis did manage to coax an ingenious and unplanned solution from the software, but it took thousands of iterations (not to mention a Connection Machine supercomputer). No gameplayer wants to sit around waiting for his on-screen characters to finish their simulated evolution before they start acting naturally. “In a game like The Sims,” Wright says, “learning in ‘user time’ might best be accomplished by giving the characters a form of hypothetical modeling. In other words they might constantly be running ‘microsimulations’ in their little heads—simulating a subset of the main simulation—to find ways of improving their decision-making.”
But that learning need not be limited to the fictional universe of the game itself. “Another possibility would be to give the game some sense of how much the user is engaged and having fun,” Wright speculates. “If we could measure this in some way—perhaps by analyzing the input stream and comparing it to a historical user profile—then we could design the game to learn what you like and enjoy. Each copy of the game would learn and evolve to fit each individual player. Maybe you’re getting bored with the original gameplay; the game would detect this and try adding new elements to the game, getting more radical each time, until it hits on something you like. It would then take this and continue to evolve and refine it in directions that you find entertaining.” Introduce real feedback into the equation—beyond the simple input of joysticks and trackballs—and suddenly the genre grows more flexible, more other-minded. The game becomes much more like a live performer, adapting to its audience, punching up certain routines while toning others down. Wright’s vision is a significant step beyond the “choose your own path” vision of hypertext fiction championed in the early nineties. The “author” isn’t presenting the “reader” with a selection of prefab threads to follow; the reader’s interests and inclinations generate entirely novel threads, to the extent that the rules of the game vary from player to player. The first-generation interactive narratives were finally all about choosing one of several sanctioned links, picking one path over the others. The future that Wright envisions will be about creating a new path—or eliminating paths altogether.
Could such a model be applied to television? Not in the sense of growing your own sitcom, or choosing the ending of ER—but rather in the sense of growing your own network of programming. In the summer of 2000, a national ad campaign began running on the major networks, starring, for probably the first time in TV history, an office full of television programmers. “Look at these guys,” the voice-over says contemptuously as the camera swoops through a workspace bustling with suits, casually canceling sitcoms and flirting with their personal assistants. “Network TV programmers. They decide what we watch and when we watch it.” The camera tracks through an office door and homes in on an executive leaning back at his desk, contemplating the view from his corner office. Suddenly, two burly guys in black T-shirts appear at the corners of the screen. They pull the head programmer out of his chair and unceremoniously toss him out the window while the voice-over notes, “Who needs ’em?”
This strangely hostile spot is part of an extended campaign for a “personal digital TV recorder” called TiVo. While the ad itself belongs to the gonzo marketing tradition usually associated with day-trading outfits, its message may be more profound—and prophetic—than its homicidal-slacker demeanor suggests. At first glance, TiVo (and its main competitor, Replay) looks like the ultimate in Daily Me–style personalization: the device is primarily a large hard disk that records television programming based on your requests. In this respect, you can think of TiVo as a VCR that has a really good user interface, and that doesn’t bother with the clutter and inconvenience of tapes. Because TiVo and Replay can analyze the program listings that you’ll find on your cable provider’s channel guide, the device can create automated filters that make your old VCR’s scheduling features look as sophisticated as a Mr. Coffee unit. You can tell TiVo to record every episode of NewsRadio that appears on any channel anytime, or to record any Steve McQueen movies playing this week. Because it’s permanently recording the last thirty minutes of television you’ve watched, you can actually pause live events or rewind them. The Einsteins at TiVo still haven’t figured out how to jump ahead two minutes into the future, but if you’re watching a program that’s previously been recorded, you can zap through the ads with a click of the remote.
The upshot of all this gadgetry is that when you sit down at your television set, the question becomes less “What’s on right now?” and more “What’s on my hard drive right now?” This is where TiVo proposes to chuck the network programmers out the proverbial window. If the suits at Rockefeller Center decide that Frasier should move to Tuesday nights, and Will and Grace should move to Thursday, what do you care anymore? All your TiVo needs to know is that you’re a Frasier fan, and you can watch the show anytime you want. You create your own prime time; you decide what you watch, and when you watch it.
This is a genuinely useful innovation, and while it bears some similarity to the original features of the VCR, the improvements in instant access and navigation make it a different beast altogether. And yet, it’s still a transfer of control that looks more like the original vision of interactivity: instead of the network programmers calling the shots, you call the shots. There’s a transfer of power in that change, but there’s nothing emergent about it. The TiVo device “knows” what you want to watch, and thus in some relatively limited way, it possesses a theory of your mind. But it only knows what you want to watch because you programmed it yourself.
But TiVo and Replay—and their descendants—will also fall under the sway of self-organization. In five years, not only will every television set come with a digital hard drive—all those devices will also be connected via the Web to elaborate, Slashdot-style filtered communities. Every program broadcast on any channel will be rated by hundreds of thousands of users, and the TiVo device will look for interesting overlap between your ratings and those of the larger community of television watchers worldwide. You’ll be able to build a personalized network without even consulting the channel guide. And this network won’t necessarily follow the ultrapersonalization model of the Daily Me. Using self-organizing filters like the ones already on display at Amazon or Epinions, clusters of like-minded TV watchers will appear online. You might find yourself joining several different clusters, sorted by different categories: retirement-home senior citizens; West Village residents; GenXers; lacrosse fanatics. Visit the channel guide for each cluster, and you’ll find a full lineup of programming, stitched together out of all the offerings available across the spectrum.
Despite the prevailing conventional wisdom, the death of the network programmer does not augur the death of communal media experiences. If anything, our media communities will grow stronger because they will have been built from below. Instead of a closed-door decision on West Fifty-seventh Street rebranding CBS as the “Tiffany Network,” a cluster of senior citizens will form organically, and its constituents will participate far more directly in deciding what gets top billing on the network home page. To be sure, our media communities will grow smaller than they were in the days of All in the Family and Mary Tyler Moore—but they’ll be real communities, and not artificial ones conjured up by the network programmers. There will still be a demand for entertaining television content—perhaps even more of a demand than there is today. But it will be distributed over a wider pool of shows, and the networks won’t be able to force that demand on us by positioning shows in prime-time spots. The shows themselves will remain top-down affairs—the clusters won’t be choosing the ending of this week’s Frasier by popular vote—but the networks those shows find themselves aligned with will come from below. They’ll be created by footprints, not fiat.
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In a world where mass entertainment is delivered to us on our timetable, and according to our personal desires, how can advertising possibly hope to find a place at the table? If my TiVo device is already smart enough to detect my Audrey Hepburn obsession and my penchant for old episodes of the Ben Stiller Show, surely it’s smart enough to know that I have zero interest in Colgate ads. Already, there are software applications that strip out banner ads from Web sites. What’s stopping our digital filters from eliminating advertising entirely from our screens?
The answer, in a word, is nothing. If you thought the recording industry was frightened by Napster, imagine the terror on Madison Avenue. At least with Napster, consumers are still listening to music, even if they’re not paying for it. With devices like TiVo and the banner blockers appearing on the market, there’s a real chance that the public will stop tolerating ads altogether.
What are the salesman of Madison Avenue to do? There are three primary routes they can pursue. The first is to continue down the illustrious path that began with E.T.’s Reese’s Pieces: product placement. If consumers start zapping past your thirty-second spots to watch the real content, then build your ads into the content. This approach has the risk of triggering a kind of marketing arms race between advertisers and consumers: future sitcom stars may well look like Formula One race cars, decked out in a dozen corporate logos, while the software wizards dream up new dynamic filters to block those logos from view. Alternately, the advertising industry can borrow a page from the MTV playbook and strive to make the ads as engaging and as indistinguishable from traditional content as possible. Imagine a future where every day is like Super Sunday: TV watchers program their TiVos to skip the tedious game itself and capture only the ads.
There is a third way, however—one that doesn’t chip away at the wall between advertising and content, and that provides a genuine service to the consumer. And that is to make advertising smarter by tapping into the same feedback routines and self-organizing clusters that the content providers use. If advertising too starts to be rated and dynamically linked like any other unit in the mediasphere’s emergent system, then specific ads will naturally find their way to consumers likely to respond well to their message. Already, I receive automated messages from Amazon alerting me to new releases that match my user profile. At first glance, these e-mails look utterly indistinguishable from the worst kind of spam, cluttering my in-box with yet another “special deal.” But because I have a long and informative purchase history with Amazon, and because patterns in that history are generating the alert, I find the messages that Amazon sends me completely useful, and I often find myself buying items that they recommend. Having gotten a taste of this personalized advertising, I actually find it frustrating that other vendors that I’ve purchased goods from don’t use the same system.
Progressive critics invariably find something sinister in the notion of smart advertising: all those corporations tracking our interests, and serving up ads custom-tailored to our needs, based on some devious mind-reading algorithm. Once you look beyond the admittedly significant privacy issues involved, the resistance to bottom-up, smart advertising strikes me as being absurdly reactionary and shortsighted. Imagine, for the sake of argument, that the development of advertising technology had proceeded in the opposite direction, and we had lived through most of the twentieth century with personalized ads like the Amazon e-mail alerts, and only now, at the beginning of a new era, did the technology arise that enabled mass advertising. All the social critics now up in arms over smart ads would be even more appalled by the blank, impersonal fire hose of mass advertising being directed at them. “These ads don’t have any idea what we’re interested in as individuals! All they know about us is that we’re part of some age demographic, or that we’re in a specific income bracket. In the old days, the ads at least used to know something about me personally. But these new mass ads are an affront to our individuality.” Of course, I wish there were fewer billboards in the world, and less spam in my in-box. But if we’re going to live in a world with advertising—and particularly if that world expects its entertainment to be partially subsidized by advertising—then I’d much prefer to see smart ads than stupid ones.
What’s preventing charlatan mind readers from spamming my in-box with fake personalized ads, or tweaking the hidden algorithm so that my interests happen to align with merchandise they need to move? It’s a legitimate problem, but it can be solved, presuming that two things are in place. First, we need strong anti-spam regulation that ensures that you can get off any mailing list at any time with a single e-mail request. Second—and most important—smart advertising systems should themselves be rated by the user community, at consumer sites like Epinions. You get book recommendations based on your ratings of other books; you sign up for a book recommendation service based on the service’s ratings. A huckster who rejiggered his filtering software so that the most expensive products were promoted would see his ratings decline, as consumers discovered that the smart ads weren’t all that smart after all. Just as you can adjust the quality threshold for posts on Slashdot, you could do the same with personalized advertising: sign me up for the most highly rated services and ignore the rest. Who knows? In a few decades, we might not have a need for the Consumer Protection Agency anymore—not because the corporations finally won out over the state, but because the consumers learned to regulate themselves.
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Most attempts to speculate on the state of the Web five years out focus on the endlessly scrutinized dream of “convergence”: the holy trinity of video, audio, and text at long last ushered into your living room via the same delivery mechanism, at speeds sufficient to convey the latest Lucas epic or Eminem release at the quality we’ve come to expect from CDs and DVDs. Once we hit that threshold, the critics like to tell us, the traditional media universe will no longer obey its previous laws of gravity, and a new order will emerge. What that new order will look like is a matter of great dispute. Some see a “one nation under AOL Time Warner” scenario; others envision a Hobbesian melee where everyone who sells zeros and ones is suddenly in the entertainment business. While the analysts disagree on the specifics of life after the revolution, there’s a general consensus that the rise of convergence will finally trigger that last storming of the network media barricades.
No one who has spent any time contemplating the tsunami discharged by Napster can say with a straight face that the arrival of genuine convergence won’t transform the media landscape. Two years ago, music downloaded from the Web was basically unlistenable for anyone who’d experienced post-Victrola audio quality; as I write, Napster’s worse-than-CD-quality MP3 downloads have the recording industry transfixed with abject fear. But convergence is not the entire story. From a consumer’s perspective, convergence will mean that the ordered universe of media offerings—prime-time sitcoms, top-forty radio, summer blockbusters—will be shattered into a million options, conveyed by a thousand providers. Turning on your television will be like logging on to the Web today: an infinite collection of links will beckon you—if not through the front door, then at least a few doors down the chain. This decentralized process is already under way: today’s breakaway Nielsen hits have half the audience reach of The Cosby Show or M*A*S*H—and only two-thirds of Americans are even wired for cable. Imagine what happens when there are a million channels; imagine what happens when channel isn’t the right word anymore, and we’re simply surfing a giant hard drive of every song, movie, or television show that’s ever been recorded. That is the inevitable future that awaits us at the other end of convergence.
It’s a future that looks a lot like the present. We’ve lived through a comparable period of information expansion since the midnineties. The information available online doubles every six months, and we all know the vertiginous climb that exponential growth produces. The information overload presented by the billion or so HTML pages has forced us to reach for new tools to manage that glut, tools that eliminate the need for centralized archivists or editors, tools that lean on the entire community of surfers for their problem-solving. The same crisis will confront the media providers of 2005: the crisis of overload. For fifty years, the television industry—and all its tributaries—has been predicated on the idea that running a show on a network at 8 P.M. on Thursday nights guarantees a massive audience. But in a future where everyone is running the equivalent of a million TiVos off their desktop home entertainment device, what use is 8 P.M. Thursday night? You watch what you want to watch when you want to watch it, remember? And what use is a network in an age of infinite connectivity—when every program ever made is only a few clicks away? Why bother pledging your allegiance to NBC’s “must-see TV” when it’s just as easy to find that Buffalo Bill rerun anytime your heart desires a little Dabney Coleman?
How will we find our way through that kind of a mediated anarchy? Maybe we’ll simply grow accustomed to the noise and learn to live with a remote control that feels more like a slot machine than a traditional guide. Maybe one opportunistic company will swoop in and become our primary conduit to the media frontier, as AOL partially did with the Web. But I suspect the overall media system will end up reaching a different equilibrium point, somewhere between Roman ultracentralization and the scattered chaos of the Dark Ages. Out of the turbulence of media convergence, the hill towns will appear. They’ll be built out of patterns of local behavior, and they’ll be in continuous flux. But they will give shape to what would otherwise be an epic expanse of shapelessness. The entertainment world will self-organize into clusters of shared interest, created by software that tracks usage patterns and collates consumer ratings. These clusters will be the television networks and the record labels of the twenty-first century. The HBOs and Interscopes will continue to make entertainment products and profit from them, but when consumers tune in to the 2005 equivalent of The Sopranos, they won’t be tuning in to HBO to see what’s on. They’ll be tuning in to the “Mafia stories” cluster, or the “suburban drama” cluster, or even the “James Gandolfini fan club” cluster. All these groups—and countless others—will point back to The Sopranos episode, and HBO will profit from creating as large an audience as possible. But the prominence of HBO itself will diminish: the network that actually serves up the content will become increasingly like the production companies that create the shows—a behind-the-scenes entity, familiar enough to media insiders, but not a recognized consumer brand. You’ll enjoy HBO’s programming, but you’ll feel like you belong to your clusters. And you’ll be right to feel that way, because you’ll have played an important role in making them a reality.
Think of the media world as a StarLogo simulation. It begins with a perfectly ordered grid, like an aerial view of Kansas farmland: each network has its lineup in place, each radio station has its playlist. And then the convergence wave washes across that world and eliminates all the borders. Suddenly, every miniseries, every dance remix, every thriller, every music video ever made, is available from anywhere, anytime. The grid shatters into a million free-floating agents, roaming aimlessly across the landscape like those original slime mold cells. All chaos, no order. And then, slowly, clusters begin to form, shapes emerging out of the shapelessness. Some clusters grow into larger entities—perhaps the size of small cable networks—and last for many years. Other clusters are more idiosyncratic, and fleeting. Some map onto the physical world (“inner-city residents”); some are built out of demographic categories (“senior citizens”); many appear based on patterns in our cultural tastes that we never knew existed, because we lacked the tools to perceive them (“Asian-American Carroll O’Connor fans”). These new shapes will be like the aggregations of slime mold cells that we first encountered at the very beginning of this book; they will be like the towns blossoming across Europe eight hundred years ago; they will be like the neighborhoods of Paris or New York City. They will be like those other shapes because they will be generated by the same underlying processes: pattern-matching, negative feedback, ordered randomness, distributed intelligence. The only difference is the materials they are made of: swarm cells, sidewalks, zeros and ones.
In the end, the most significant role for the Web in all of this will not involve its capacity to stream high-quality video images or booming surround sound; indeed, it’s quite possible that the actual content of the convergence revolution will arrive via some other transmission platform. 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, and instead of those patterns being restricted to the invisible gaze of Madison Avenue and TRW, consumers will be able to tap into that pool themselves to create communal maps of all the entertainment and data available online. You might actually have the bits for The Big Sleep sent to you via some other conduit, but you’ll decide to watch it because the “Raymond Chandler fans” cluster recommended the film to you, based on your past ratings, and the ratings of millions of like-minded folks. 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.
That promises a genuine revolution in what it means to be a media consumer, but it also demands a comparable revolution in the way businesses work. No company has more thoroughly explored the commercial possibilities of clusters and bottom-up organization than the celebrated auction site eBay. Since its launch in 1995, the site has been a virtual laboratory for experiments with clusters and self-regulating feedback. The “news” on eBay is almost entirely generated by the users of the service, and by the collective behavior of specific groups of users. The top auctions, the highly rated buyers-and-sellers lists, the user feedback, the communities formed around specific categories like Stamp Collecting or Consumer Electronics, the regional filters, the lists of new offerings from people you’ve bought from before—all of these are attempts to make patterns of group behavior transparent to individual users, the way a city neighborhood makes comparable patterns visible to its residents. EBay’s founder, Pierre Omidyar, originally created the site to enable his wife to trade Pez Dispensers with other Pez fanatics worldwide; six years later the site harbors thousands of similar microcommunities, united by shared interests. If eBay had restricted itself to showcasing the collector’s items that happened to be in vogue that month—Beanie Babies or PlayStation 2—the results wouldn’t have looked all that different from your traditional shopping mall. But they wisely allowed their site to splinter into thousands of smaller clusters, like little eddies in the group current of their customer activity. Skeptics used to argue that online auctions would never become a mainstream activity because the electronic medium would make it easy for scam artists to sell bogus merchandise. Those critics wildly underestimated the extent to which software can create self-regulating systems, systems that separate the scoundrels from the honest dealers, the way Slashdot’s quality filters separated quality from crap. Every seller on eBay has a public history of past deals; scam one buyer with a fake or broken item, and your reputation can be ruined forever. Like the public safety of Jacob’s sidewalks, the eBay population polices itself with almost unbelievable efficiency, which is why the site now attracts more than 30 million users. And unlike almost any other Web-based commerce site, eBay has been consistently profitable from its early days. The history of self-organizing clusters includes the silk and fine linens bought and sold on Florence’s Por Santa Maria or London’s Savile Row. But don’t underestimate the significance of Pez.
Still, if eBay is a model for the way bottom-up systems can transform the relationship between buyer and seller, can the principles of emergence be usefully applied to the internal structure of organizations? Is it possible to build corporate systems that are more like ant colonies than command economies? Marketplaces—even those dominated by global megacorporations—tend to work in decentralized ways, but the internal structures of most corporations today rely on org charts that look more like feudal states than slime molds. The market may be bottom-up, but it is populated by chronically top-heavy agents. Decentralized production and development have done wonders for the world of Open Source software, where certain fundamental rights of ownership have been disavowed, but it remains a real question whether the more proprietary wing of late capitalism can model its internal organization after ant farms or neural nets. For one, the unpredictability of emergent systems makes them an ideal platform for book recommending or gameplaying, but no one wants a business that might spontaneously fire a phalanx of middle managers for no discernible reason. Controlled randomness is a brilliant recipe for city life and ant foraging, but it’s harder to imagine selling shareholders on it as a replacement for the CEO. Software designers like Danny Hillis or Oliver Selfridge leaned on evolutionary techniques to rein in their systems and to force them toward specific goals. But evolution requires many parallel generations to do its handiwork; no investor wants to wait around while her investment breeds a long-term strategy out of a million random business plans.
Still, emergent systems can be brilliant innovators, and they tend to be more adaptable to sudden change than more rigid hierarchical models. Those qualities make the principles of bottom-up intelligence tantalizing ones for businesses struggling to keep up with the twenty-first-century rate of change. A number of companies, concentrated mostly in the high-tech industry, have experimented with neural-net-like organizational structures, breaking up the traditional system of insular and hierarchical departments and building a more cellular, distributed network of small units, usually about a dozen people in size. Units can assemble into larger clusters if they need to, and those clusters have the power to set their own objectives. The role of traditional senior management grows less important in these models—less concerned with establishing a direction for the company, and more involved with encouraging the clusters that generate the best ideas. 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. The Australian software company TCG, the Taiwanese Acer Group, and Sun Microsystems have all implemented cellular techniques with positive results. There’s even a management-theory journal devoted to these developing models. It is called, appropriately enough, Emergence.
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If decentralized intelligence can transform the way businesses work, what can it do for politics? That many New Economy companies have been so quick to embrace the emergent worldview—in both their products and their internal structure—can sometimes make it seem as though emergence belongs squarely to the libertarian camp. Certainly the emphasis on local control and the resistance to command systems resonates with the Gingrichian call for anti-big-government devolution. But the politics of emergence are not so readily classified. The intelligence of ant colonies may be the animal kingdom’s most compelling argument for the power of the collective, and you can think of “local knowedge” as another way of talking about grassroots struggle. The libertarian right likes to rail against the centralized authority of the state, but at least most politicians in the world today are democratically elected, unlike the executives of most multinational corporations. The public sector has no monopoly on top-down systems, and there’s no reason why progressives shouldn’t also embrace decentralized strategies, even if those same strategies are being explored by right-wing think tanks and dot-coms. In fact, the needs of most progressive movements are uniquely suited to adaptive, self-organizing systems: both have a keen ear for collective wisdom; both are naturally hostile to excessive concentrations of power; and both are friendly to change. For any movement that aims to be truly global in scope, making it almost impossible to rely on centralized power, adaptive self-organization may well be the only road available.
Nowhere are the progressive possibilities of emergence more readily apparent than in the anti-WTO protest movements, which have explicitly modeled themselves after the distributed, cellular structures of self-organizing systems. The Seattle protests of 1999 were characterized by an extraordinary form of distributed organization: smaller affinity groups representing specific causes—anti-Nike critics, anarchists, radical environmentalists, labor unions—would operate independently for much of the time, only coming together for occasional “spokescouncil” meetings, where each group would elect a single member to represent their interests. As Naomi Klein reported in The Nation, “At some rallies activists carry actual cloth webs to symbolize their movement. When it’s time for a meeting, they lay the web on the ground, call out ‘All spokes on the web,’ and the structure becomes a street-level boardroom.” To some older progressives, steeped in the more hierarchical tradition of past labor movements, those diverse “affinity groups” seemed hopelessly scattered and unfocused, with no common language or ideology uniting them. It’s almost impossible to think of another political movement that generated as much public attention without producing a genuine leader—a Jesse Jackson or Cesar Chavez—if only for the benefit of the television cameras. The images that we associate with the antiglobalization protests are never those of an adoring crowd raising their fists in solidarity with an impassioned speaker on a podium. That is the iconography of an earlier model of protest. What we see again and again with the new wave are images of disparate groups: satirical puppets, black-clad anarchists, sit-ins and performance art—but no leaders. To old-school progressives, the Seattle protesters appeared to be headless, out of control, a swarm of small causes with no organizing principle—and to a certain extent they’re right in their assessment. What they fail to recognize is that there can be power and intelligence in a swarm, and if you’re trying to do battle against a distributed network like global capitalism, you’re better off becoming a distributed network yourself.
That is not a reason to embrace pure anarchy, of course. Ant colonies do not have leaders in any real sense, but they do rely heavily on rules: how to read patterns in the pheromone trail, when to change from foraging to nest-building, how to respond to other ants, and so on. An ant colony without local rules has no chance of creating a higher-level order, no chance of creating a collective intelligence. The antiglobalization movements are only beginning to figure out the proper rules for engagement between different cells. The spokescouncils of Seattle were a promising start, but learning how to cluster takes time. Klein writes, “What emerged on the streets of Seattle and Washington was an activist model that mirrors the organic, interlinked pathways of the Internet.” But as we’ve seen countless times over the preceding pages, even the Web itself—the largest and most advanced man-made self-organizing system on the planet—is only now becoming capable of true collective intelligence. By any measure, the Web’s mind-reading skills are embryonic at best, because we are still tweaking the rules of the system, still fiddling with how adaptive and intelligent clusters can prosper online. And if the Web’s collective intelligence is still in its infancy, think of how much room the new protest movements must have to grow. But thus far, their instincts have been sound ones. Beneath the window-smashing and the Rage Against the Machine concerts, the anti-WTO activists are doing something profound, even in these early days of their movement. They are thinking like a swarm.