Maybe it’s a cliché – I think I used it myself – to say that the explanations of scientists and philosophers about the way the brain works metaphorically follow the most advanced technology of their time. Greek writers thought that the brain works like a hydraulic water clock. European writers in the Middle Ages suggested that thoughts act through gear-like mechanisms. In the 19th century, the brain was like a telegraph; a few decades later, it was more like a telephone network. Soon after, not surprisingly, people thought that the brain works like a digital computer, and that they might be able to build computers that work like the brain, or talk to it. It’s not easy, because metaphors aside, actually no one he knows how the brain works. Science can be exciting like that.
The absence of a good metaphor did not stop anyone studying brain, of course. But sometimes they confuse the field map, confusing good metaphor with feasible theory. It is easy to do this when it comes to complex systems that interact at too high or too low a level to be observed as a whole. This is true for the brain, a pile of thought flesh that creates an individual mind, according to researchers, about 86 billion individual cells woven into an electrochemical jelly network. And that is true for the city, a dense network in which millions of these individual minds gather to form a community. People who write about cities –I did it myself–also they tend to seek the organization of metaphors in current science. The city is a machine, the city is animals, the city is an ecosystem. Or maybe the city is like a computer. For urbanist and writer Shannon Mattern, this is dangerous.
Mattern’s new book comes out Aug. 10; it is a collection (with revisions and updates) of some of her very clever works Places Journal called The city is not a computer: other urban intelligences. In it, Mattern struggles with the ways in which a particular metaphor tricked the design, planning, and housing of cities in the 20th century. It happens on all scales, from monitoring individual people as if they were fragments to monitoring the wide-screen data needed to make a city function for the benefit of its residents. Of all the ways information can travel through an urban network, Mattern says, it would probably be better for public libraries to be hubs than centralized panopticon-like dashboards that many cities are trying to build. The problem is that the metrics that people choose to follow become goals to be achieved. They become their kind of metaphor and usually make mistakes.
The first two essays were the ones that had the most success when they were first published – and they still have them. The “City Console” is a wild history of information dashboards and control rooms designed to be panopticons for urban data. These information centers collect data on how municipal systems work, crime is controlled, children are educated, and so on. Mission control, but for highways and sewers. My favorite example from Mattern’s book is Salvador Allende’s then-Chilean leader’s attempt, in the 1970s, to build something called Project Cybersyn, with an “operating room” full of chairs with buttons that Captain Kirk would be proud of, plus wall-sized screens with flashing red lights. Of course, since no city had real-time data to fill in those screens, they displayed hand-drawn slides instead. It’s silly, but there’s a direct line from Cybersyn to the ways many U.S. cities now collect and display law enforcement data and other urban data in CompStat programs. We should make the government accountable, but often justify worthless arrests or point out misleading figures — on time transit travel instead of the number of people, say.
In the next, headline essay, Mattern warns of the ambitions of large Silicon Valley companies to build “smart cities.” When the essay first appeared, Amazon was still building a city-sized headquarters in New York City, and Google was trying to do the same in Toronto. (Google project, from a sister company called Sidewalk Labs, it would be highlighted wooden skyscrapers, sidewalks that used lights to reconfigure their use on the go, self-driving cars, and underground waste pipes.) Now, of course, most large smart cities, technologically skilled projects, have failed or shrunk. The Hudson Yards in New York didn’t even come close to the level of sensors and surveillance technology that its developers had promised (or perhaps threatened). Cities continue to come together and divide all types of data, but they are not very “smart”.
In an interview last month, I asked Mattern why it seems that technology companies have failed to smarten up any city, at least so far. She thinks it’s because they missed the most important parts of creating the city. “A lot more computer and thinking ways of thinking about cities give a false sense of omniscience,” Mattern says. The people who run the cities think they are getting the raw truth, and in fact the filters they choose determine what they see. “When everything is computational or when we can operationalize even more poetic and less important aspects of the city at the data point,” says Mattern, “it makes us unaware that it’s a metaphor.”