The Myth of Progress: Toward a Sustainable Future

1 | The Myth of Control: Complex versus Linear Systems

13 1 THE MYTH OF CONTROL Complex versus Linear Systems “The control of nature is a phrase conceived in arrogance, born of the Neanderthal age of biology and philosophy . . .” —Rachel Carson1 Chaos My first exposure to computers based on microchip technology was in 1 980. Jimmy Car ter had just lost the presidency to Ronald Reagan. An e vening ne ws r eport showed Carter at home writing his memoirs on a“word processor ,” which if I r emember correctly looked like an early Apple computer. Just eight years earlier I had been r unning punch cards through a transistor–r un IBM 3 60 mainframe computer that took the space of a small room at the University of Ne w Hampshire’s computer center. At that time the future of computers appeared to be in large mainframes lik e this one, which ser viced a host of pr ogrammers; computers would be the domain of the highly trained. Who could have imagined that in less than a decade ne w technology would change the field so dramatically? As I watched Carter typing away on his personal computer, I realized that in eight shor t years all of my computer training had become obsolete.Yet THE MYTH OF CONTROL 14 the now-antiquated mainframe computer did generate some startling discoveries, possibly the most important being chaos theory. It was on a Royal McBee,a vacuum tube–run computer in the early 1960s, that Edward Lorenz, a research meteorologist at MIT, inadvertently stumbled upon a finding that shook the very paradigmatic foundations of Western science. Lorenz’s Royal McBee would look like a prehistoric dinosaur next to today’s computers—a huge mass of tubes and wires that rattled loudly while operating. Although more than a hundred times as large as a per sonal computer, it had thousands of times less“brain power.” Yet it could do something that people couldn’t—it could ex ecute millions of calculations in a relatively short span of hours. Lorenz had been attracted to w eather as a child and fol lowed this interest to one of the premier research institutions in the w orld. Unlike astronomy—a physical science that could make fairly accurate long-term predictions regarding eclipses or the r eturn of comets —meteorology had pr ogressed little through the twentieth century in terms of accurately projecting the weather just a fe w days hence. Lorenz hoped to change that. Within his Ro yal McBee he cr eated a vir tual weather system. Through the coupling of tw elve equations that related such things as pressure to wind direction or temperature to pressure, he produced a computerized system that mimicked the weather.2He hoped that he w ould be able to glean repeated patterns from his virtual system that could be applied to improving real weather forecasting. During the winter of 1961 he stopped the computer in the midst of one of its runs to double check a weather sequence in his virtual world. He typed in the numbers from his printout at the point wher e he wanted to restart the run and left Complex versus Linear Systems 15 his office to let the McBee rattle away. Upon his r eturn he was shocked to find that the new run, after just a few cycles, had totally diverged from the original run. Because each run started at the same point and followed the same laws as prescribed by his programmed equations, both runs should have been identical. Lorenz double-checked the n umber he inputted to star t the second run with those on the first printout. It was this comparison that led Lorenz to a starling discovery. For the second r un he had enter ed the n umber 0.506, a r ounded down version of the pr intout’s number—0.506127.The two numbers differed by only 0.000127—a little more than one ten-thousandth.3Based on the well-established scientific notion —proximate knowledge of initial conditions —such a small change shouldn’t have affected the outcome of the run. It was known in the scientific community that absolutely accurate measurements of an ything were not possib le. But ha ving a close measurement of initial conditions —proximate knowledge —was fine for making future predictions due to convergence , a situation where minor perturbations in a system tend to cancel each other out, allowing...