Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life, in Organisms, Cities, Economies, and Companies

by Geoffrey West

We as primates are one of those. For instance, we take longer to mature than we “should” given our body weight. This is the result of our rapid evolution from being purely biological to becoming sophisticated socioeconomic creatures. Our effective metabolic rate is now one hundred times greater than what it was when we were truly “biological” animals, and this has had huge consequences for our recent life history. We take longer to mature, we have fewer offspring, and we live longer, all in qualitative agreement with having an effectively larger metabolic rate arising from socioeconomic activity. I shall return to this fascinating development in our history when discussing how these ideas apply to cities.

sublinear scaling and the associated economies of scale arising from optimizing network performance lead to bounded

growth and the systematic slowing of the pace of life.

Thus at a deep level, birth, growth, and death are all governed by the same underlying dynamics driven by metabolic rate and encapsulated in the dynamics and structure of networks.

“I’m not afraid of death, I just don’t want to be there when it happens.”

An intriguing way of quantifying these is to ask what the gain in life expectancy would be if each of these specific causes was eliminated. A sampling of these is shown in Table 4 taken from data analyzed by the U.S. Centers for Disease Control (CDC) and the World Health Organization (WHO). You can see, for instance, that if all heart and cardiovascular disease were cured, life expectancy at birth would increase by only about six years. Perhaps more surprising is to learn that if all cancer were cured, life expectancy at birth would increase by only about three years—and, at age sixty-five, only by a little less than two years.

At the time perhaps no one appreciated this more than the writer and futurist thinker Stewart Brand, who passionately felt that the image of the whole Earth from space would be a powerful symbol, evoking a sense of shared destiny among all people living on the planet. He relentlessly lobbied NASA to release the first images in 1967, which he then used on the cover of his highly influential Whole Earth Catalog, one of the great icons of the 1960s and ’70s.

To quote the American architect and urban planner Andrés Duany: “In 1860, the capital city of Washington, with a population of 60,000, had unlighted streets, open sewers, and pigs roaming about its principal avenues. This condition was worse than the worst of our current cities. There is hope.”

ex nihilo nihil fit—nothing comes from nothing.

energy delivered by the sun to the Earth is approximately a million trillion (1018) kilowatt-hours a year, compared with our “measly” needs (on this scale) of 150 trillion (1.5 × 1014) kilowatt-hours we collectively use each year. So on the scale of what the Earth receives from the sun, our energy use represents only about 0.015 percent of what is in principle actually available to us. To put it another way: more energy is delivered by the sun in just one hour than is used by the entire world in a single year. Indeed, the scale of solar energy is so vast that in one year it is about twice as much as will ever be obtained from all of the Earth’s nonrenewable resources of coal, oil, natural gas, and uranium combined. So from this point of view, there is no energy problem—at least in principle.

Just to give a sense of the sorts of comparisons we have to make, consider the following. We are surprisingly tolerant of death and destruction arising from “unnatural, man-made” causes when they occur on a continual and regular basis, but are extremely intolerant when they occur suddenly as discrete events even though the numbers involved are much smaller. For instance, each year more than a million and a quarter people die from car accidents worldwide, which is comparable to the number who die from lung cancer, the most common cause of cancer death.

Stevenage was like living in a fancy country resort. And that was its problem. Just as Jacobs sarcastically remarked a few years later: it was a “really very nice town if you were docile and had no plans of your own and did not mind spending your life with others with no plans of their own.” Although this is pretty harsh, it does capture that sense of boredom, routine, isolation, and benign “niceness” hiding and suppressing inner passions that later became associated with suburbia.

This savings leads to a significant decrease in the production of emissions and pollution. Consequently, the greater efficiency that comes with size has the nonintuitive but very important consequence that on average the bigger the city, the greener it is and the smaller its per capita carbon footprint.

Yet despite appearances, the interstate system is in fact a quintessential fractal when viewed through the lens of the actual traffic flowing on it, rather than when viewed simply as a physical road network.

This is another dimension of “the good, the bad, and the ugly” consequences of increased connectivity with its resulting superlinear scaling as city size increases. Systematically having more per capita not only means higher wages, more patents, and more restaurants, greater opportunity, more social activity, and a greater buzz, but also more crime and disease—and living with greater stress, anxiety, and fear, and with less trust and

Putting all of this together we are led to the outrageous speculation that cities are effectively a scaled representation of the structure of the human brain. It’s a pretty wild conjecture, but it graphically incorporates the idea that there is a universal character to cities. In a nutshell: cities are a representation of how people interact with one another

and this is encoded in our neural networks and therefore in the structure and organization of our brains. In a curious way, this is perhaps more than just metaphor and would mean that a map of a city representing its physical and socioeconomic flows is a nonlinear representation of the geometry and flows in the neural network of our brains.

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all . . . faced with massive data, this approach to science—hypothesize, model, test—is becoming obsolete. . . . Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. . . . Today companies like Google, which have grown up in an era

This sems rather odd and shortsighted. Massive amounts of "knowing" most likely tells you nothing at all.

In the last few years almost every university has opened up a well-funded center or institute devoted to big data while at the same time paying due obeisance to the other buzzword, interdisciplinary.

A contrary view to this trend was forcibly expressed by the Nobel Prize–winning geneticist Sydney Brenner, whom I quoted in chapter 3 and who was coincidentally director of the famous institute in Cambridge founded by Max Perutz that I mentioned earlier: “Biological research is in crisis. . . . Technology gives us the tools to analyse organisms at all scales, but we are drowning in a sea of data and thirsting for some theoretical framework with which to understand it.

There it is, we are not gaining greater understanding at a rapid clip, just gathereing an incredible amount of mostly uncorrelated data.

history tells us that ‘least is best.’

“a theory that you can’t explain to a bartender is probably no damn good.”

One is the development of a quantitative theory of sleep, showing, for example, why whales sleep for just a couple of hours, mice for fifteen, and we for about eight. Together with Van’s bright young student Junyu Cao we recently extended this to understand sleep patterns in babies and children and showed how this framework provides important insights into early brain development.