Turing’s paper on morphogenesis—literally, “the beginning of shape”—turned out to be one of his seminal works, ranking up their with his more publicized papers and speculations: his work on Gödel’s undecidability problem, the Turing Machine, the Turing Test—not to mention his contributions to the physical design of the modern digital computer. But the morphogenesis paper was only the beginning of a shape—a brilliant mind sensing the outlines of a new problem, but not fully grasping all its intricacies. If Turing had been granted another few decades to explore the powers of self-assembly—not to mention access to the number-crunching horsepower of non-vacuum-tube computers—it’s not hard to imagine his mind greatly enhancing our subsequent understanding of emergent behavior. But the work on morphogenesis was tragically cut short by his death in 1954.
Alan Turing was most likely a casualty of the brutally homophobic laws of postwar Britain, but his death also intersected with those discreet patterns of life on Manchester’s sidewalks. Turing had known about that stretch of Oxford Road since his arrival in Manchester; on occasion, he would drift down to the neighborhood, meeting other gay men—inviting some of them back to his flat for conversation, and presumably some sort of physical contact. In January of 1952, Turing met a young man named Arnold Murray on those streets, and the two embarked on a brief relationship that quickly turned sour. Murray—or a friend of Murray’s—broke into Turing’s house and stole a few items. Turing reported the theft to the police and, with his typical forthrightness, made no effort to conceal the affair with Murray when the police visited his flat. Homosexuality was a criminal offense according to British law, punishable by up to two years’ imprisonment, and so the police promptly charged both Turing and Murray with “gross indecency.”
On February 29, 1952, while the Manchester authorities were preparing their case against him, Turing finished the revisions to his morphogenesis paper, and he argued over its merits with Ilya Prigogine, the visiting Belgian chemist whose work on nonequilibrium thermodynamics would later win him a Nobel prize. In one day, Turing had completed the text that would help engender the discipline of biomathematics and inspire Keller and Segel’s slime mold discoveries fifteen years later, and he had enjoyed a spirited exchange with the man who would eventually achieve world fame for his research into self-organizing systems. On that winter day in 1952, there was no mind on the face of the earth better prepared to wrestle with the mysteries of emergence than Alan Turing’s. But the world outside that mind was conspiring to destroy it. That very morning, a local paper broke the story that the war-hero savant had been caught in an illicit affair with a nineteen-year-old boy.
Within a few months Turing had been convicted of the crime and placed on a humiliating estrogen treatment to “cure” him of his homosexuality. Hounded by the authorities and denied security clearance for the top-secret British computing projects he had been contributing to, Turing died two years later, an apparent suicide.
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Turing’s career had already collided several times with the developing web of emergence before those fateful years in Manchester. In the early forties, during the height of the war effort, he had spent several months at the legendary Bell Laboratories on Manhattan’s West Street, working on a number of encryption schemes, including an effort to transmit heavily encoded waveforms that could be decoded as human speech with the use of a special key. Early in his visit to Bell Labs, Turing hit upon the idea of using another Bell invention, the Vocoder—later used by rock musicians such as Peter Frampton to combine the sounds of a guitar and the human voice—as a way of encrypting speech. (By early 1943, Turing’s ideas had enabled the first secure voice transmission to cross the Atlantic, unintelligible to German eavesdroppers.) 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. Shannon had been particularly intrigued by the potential for machines to detect and amplify patterns of information in noisy communication channels—a line of inquiry that promised obvious value to a telephone company, but could also save thousands of lives in a war effort that relied so heavily on the sending and breaking of codes. Shannon and Turing immediately recognized that they had been working along parallel tracks: they were both code-breakers by profession at that point, and in their attempts to build automated machines that could recognize patterns in audio signals or numerical sequences, they had both glimpsed a future populated by even more intelligence machines. Shannon and Turing passed many an extended lunchtime at the Bell Labs, trading ideas on an “electronic brain” that might be capable of humanlike feats of pattern recognition.
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. One day over lunch at the lab, Turing exclaimed playfully to his colleagues, “Shannon wants to feed not just data to a brain, but cultural things! He wants to play music to it!” Musical notes were patterns too, Shannon recognized, and if you could train an electronic brain to understand and respond to logical patterns of zeros and ones, then perhaps sometime in the future we could train our machines to appreciate the equivalent patterns of minor chord progressions and arpeggios. The idea seemed fanciful at the time—it was hard enough getting a machine to perform long division, much less savor Beethoven’s Ninth. But the pattern recognition that Turing and Shannon envisioned for digital computers has, in recent years, become a central part of our cultural life, with machines both generating music for our entertainment and recommending new artists for us to enjoy. The connection between musical patterns and our neurological wiring would play a central role in one of the founding texts of modern artificial intelligence, Douglas Hofstadter’s Gödel, Escher, Bach. Our computers still haven’t developed a genuine ear for music, but if they ever do, their skill will date back to those lunchtime conversations between Shannon and Turing at Bell Labs. And that learning too will be a kind of emergence, a higher-level order forming out of relatively simple component parts.
Five years after his interactions with Turing, Shannon published a long essay in the Bell System Technical Journal that was quickly repackaged as a book called The Mathematical Theory of Communication. Dense with equations and arcane chapter titles such as “Discrete Noiseless Systems,” the book managed to become something of a cult classic, and the discipline it spawned—information theory—had a profound impact on scientific and technological research that followed, on both a theoretical and practical level. The Mathematical Theory of Communication contained an elegant, layman’s introduction to Shannon’s theory, penned by the esteemed scientist Warren Weaver, who had early on grasped the significance of Shannon’s work. Weaver had played a leading role in the Natural Sciences division of the Rockefeller Foundation since 1932, and when he retired in the late fifties, he composed a long report for the foundation, looking back at the scientific progress that had been achieved over the preceding quarter century. The occasion suggested a reflective look backward, but the document that Weaver produced (based loosely on a paper he had written for American Scientist) was far more prescient, more forward-looking. In many respects, it deserves to be thought of as the founding text of complexity theory—the point at which the study of complex systems began to think of itself as a unified field. Drawing upon research in molecular biology, genetics, physics, computer science, and Shannon’s information theory, Weaver divided the last few centuries of scientific inquiry into three broad camps. 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. 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 probab
ility theory. These tools helped explain not only the behavior of molecules in a gas, or the patterns of heredity in a gene pool, but also helped life insurance companies turn a profit despite their limited knowledge about any individual human’s future health. Thanks to Claude Shannon’s work, the statistical approach also helped phone companies deliver more reliable and intelligible longdistance service.
But there was a third phase to this progression, and we were only beginning to understand. “This statistical method of dealing with disorganized complexity, so powerful an advance over the earlier two-variable methods, leaves a great field untouched,” Weaver wrote. There was a middle region between two-variable equations and problems that involved billions of variables. Conventionally, this region involved a “moderate” number of variables, but the size of the system was in fact a secondary characteristic:
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.
Think of these three categories of problems in terms of our billiards table analogy from the introduction. A two-or three-variable problem would be an ordinary billiards table, with balls bouncing off one another following simple rules: their velocities, the friction of the table. That would be an example of a “simple system”—and indeed, billiard balls are often used to illustrate basic laws of physics in high school textbooks. 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. Assuming there’s enough energy in the system at the outset, the balls will spread to fill the entire table, like gas molecules in a container. It’s complex because there are many interacting agents, but it’s disorganized because they don’t create any higher-level behavior other than broad statistical trends. Organized complexity, on the other hand, is like our motorized billiards table, where the balls follow specific rules and through their various interactions create a distinct macrobehavior, arranging themselves in a specific shape, or forming a specific pattern over time. That sort of behavior, for Weaver, suggested a 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? … What is a gene, and how does the original genetic constitution of a living organism express itself in the developed characteristics of the adult?
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.
Tackling such problems required a new approach: “The great central concerns of the biologist … are now being approached not only from above, with the broad view of the natural philosopher who scans the whole living world, but also from underneath, by the quantitative analyst who measures the underlying facts.” This was a genuine shift in the paradigm of research, to use Thomas Kuhn’s language—a revolution not so much in the interpretations that science built in its attempt to explain the world, but rather in the types of questions it asked. The paradigm shift was more than just a new mind-set, Weaver recognized; it was also a by-product of new tools that were appearing on the horizon. To solve the problems of organized complexity, you needed a machine capable of churning through thousands, if not millions, of calculations per second—a rate that would have been unimaginable for individual brains running the numbers with the limited calculating machines of the past few centuries. Because of his connection to the Bell Labs group, Weaver had seen early on the promise of digital computing, and he knew that the mysteries of organized complexity would be much easier to tackle once you could model the behavior in close-to-real time. For millennia, humans had used their skills at observation and classification to document the subtle anatomy of flowers, but for the first time they were perched on the brink of answering a more fundamental question, a question that had more to do with patterns developing over time than with static structure: Why does an evening primrose open when it does? And how does a simple seed know how to make a primrose in the first place?
Alan Turing had played an essential role in creating both the hardware and the software that powered this first digital revolution, and his work on morphogenesis had been one of the first systematic attempts to imagine development as a problem of organized complexity. It is one of the great tragedies of this story that Turing didn’t live to see—much less participate in—the extraordinary intellectual flowering that took place when those two paths intersected.
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Ironically, Warren Weaver’s call to action generated the first major breakthrough in a work that had nothing to do with digital computers—a work that belonged to a field not usually considered part of the hard sciences. In the years after the war, urban planners and government officials had been tackling the problem of inner-city slums with a decidedly top-down approach: razing entire neighborhoods and building bleak high-rise housing projects, ringed by soon-to-be-derelict gardens and playgrounds. The projects effectively tried to deal with the problem of dangerous city streets by eliminating streets altogether, and while the apartments in these new high-rises usually marked an improvement in living space and infrastructure, the overall environment of the projects quickly descended into an anonymous war zone that managed to both increase the crime rate in the area and destroy the neighborhood feel that had preceded them.
In October of 1961, the New York City Planning Commission announced its findings that a large portion of the historic West Village was “characterized by blight, and suitable for clearance, replanning, reconstruction, or rehabilitation.” The Village community—a lively mix of artists, writers, Puerto Rican immigrants, and working-class Italian-Americans—responded with outrage, and at the center of the protests was an impassioned urban critic named Jane Jacobs. Jacobs had just spearheaded a successful campaign to block urban-development kingpin Robert Moses’s plan to build a superhighway through the heart of SoHo, and she was now turning her attention to the madness of the projects. (The proposed “rehabilitation” included Jacobs’s own residence on Hudson Street.) In her valiant and ultimately triumphant bid to block the razing of the West Village, Jacobs argued that the way to improve city streets and restore the dynamic civility of urban life was not to bulldoze the problem zones, but rather to look at city streets that did work and learn from them. Sometime in the writing of what would become The Death and Life of the Great American Cities—published shortly after the Village showdown—Jacobs read Warren Weaver’s Rockefeller Foundation essay, and she immediately recognized her own agenda in his call for exploring problems of organized complexity.
Under the seeming disorder of the old city, wherever the old city is working successfully, is a marvelous order for maintaining the safety of the streets and the freedom of the city. It is a complex order. Its essence is intimacy of sidewalk use, bringing with it a constant succession of eyes. This order is all composed of movement and change, and although it is life, not art, we may fancifully call it the art form of the city and liken it to the dance—not to a simple-minded precision dance with everyone kicking up at the same time, twirling in unison and bowing off en masse, but to an intricate ballet in which the individual dancers and ensembles all have distinctive parts which miraculously reinforce each other and compose an orderly whole.
Jacobs gave Death and Life’s closing chapter the memorable title “The Kind of Problem a City Is,” and she began it by quoting extensively from Weav
er’s essay. Understanding how a city works, Jacobs argued, demanded that you approach it as a problem from the street level up. “In parts of cities which are working well in some respects and badly in others (as is often the case), we cannot even analyze the virtues and the faults, diagnose the trouble or consider helpful changes, without going at them as problems of organized complexity,” she wrote. “We may wish for easier, all-purpose analyses, and for simpler, magical, all-purpose cures, but wishing cannot change these problems into simpler matters than organized complexity, no matter how much we try to evade the realities and to handle them as something different.” To understand the city’s complex order, you needed to understand that ever-changing ballet; where city streets had lost their equilibrium, you couldn’t simply approach the problem by fiat and bulldoze entire neighborhoods out of existence.
Jacobs’s book would revolutionize the way we imagined cities. Drawing on Weaver’s insights, she conveyed a vision of the city as far more than the sum of its residents—closer to a living organism, capable of adaptive change. “Vital cities have marvelous innate abilities for understanding, communicating, contriving and inventing what is required to combat their difficulties,” she wrote. They get their order from below; they are learning machines, pattern recognizers—even when the patterns they respond to are unhealthy ones. A century after Engels glimpsed the systematic disappearing act of Manchester’s urban poor, the self-organizing city had finally come into focus.
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