It may be a A cliché — I think I have used it myself — to say that scientists and philosophers’ explanations of how the brain works tend to metaphorically follow the most advanced technology of their time. Greek writers believed that brains worked like hydraulic clocks. European writers of the Middle Ages suggested that thoughts operated through gear-like mechanisms. In the 19th century, the brain was like a telegraph; a few decades later, it was more of a telephone network. Soon afterwards, unsurprisingly, people thought that the brain was functioning like a digital computer, and that they could perhaps build computers that function like the brain, or talk to him. Not easy, since, metaphors apart, no one knows How does the brain work. Science can be exciting like that.
The absence of a good metaphor did not prevent anyone from studying brain, of course. But sometimes they mistake the map for the terrain, mistaking a good metaphor for a workable theory. This is easy to do when it comes to complex systems that interact at scales too large or too small for us to observe them in their entirety. That’s true for the brain, a piece of thought meat that generates an individual mind from, according to the researchers, around 86 billion individual cells woven into a network of electrochemical jelly. And this is true for a city, the dense network in which millions of these individual spirits come together to form a community. People who write about cities—I made it myself–also tend to fumble around to organize metaphors in current science. A city is a machine, a city is an animal, a city is an ecosystem. Or maybe a city is like a computer. For urban planner and media studies writer Shannon Mattern, this is the most dangerous.
Mattern’s new book comes out August 10; this is a collection (with revisions and updates) of some of his very clever work for Place diary called A city is not a computer: other urban intelligences. In it, Mattern grapples with the ways in which this particular metaphor marred the design, planning, and habitat of cities in the 20th century. It happens at all scales, from monitoring individual people as if they were bits, to monitoring the big screen data needed to run a city for the good of its people. Of all the ways that information can flow through an urban network, says Mattern, it would probably be better if public libraries were the nodes rather than the centralized panopticon-style dashboards that many cities are trying to build. The problem is, the metrics people choose to follow become targets to hit. They become their own kind of metaphors, and they’re usually wrong.
The first two essays were the ones that had the most oomph when they were first published – and still do. “City Console” is a crazy story of information dashboards and control rooms designed to be panopticon for city data. These information centers collect data on the proper functioning of municipal systems, crime policing, children’s education, etc. Mission control, but for highways and sewers. My favorite example from Mattern’s book is the 1970s effort by Salvador Allende, then the leader of Chile, to build something called the Cybersyn Project, with an “operations room” full of button chairs that would have made the captain Proud Kirk, plus a large wall-screen with flashing red lights. Of course, since no city had real-time data to fill these screens, they instead displayed hand-drawn slides. It’s wacky, but there’s a direct line between Cybersyn and how many US cities now collect and display law enforcement and other city data in CompStat programs. They are supposed to hold government accountable, but they often justify worthless arrests or highlight misleading numbers – on-time transit trips instead of the number of people carried, say.
In the titular’s next essay, Mattern warns of the ambitions of big Silicon Valley companies to build “smart cities.” When the essay first appeared, Amazon was still ready to build a city-sized headquarters in New York City, and Google was pushing to do much the same in Toronto. (The Google Project, from a sister company called Sidewalk Labs, would have presented wooden skyscrapers, sidewalks that used lights to reconfigure their uses on the fly, self-driving cars, and underground garbage tubes.) Now, of course, most big smart and tech city projects have either failed or been scaled back. Hudson Yards in New York City hasn’t rolled out with the level of detection and surveillance technology its developers promised (or perhaps threatened). Cities come together and still share all kinds of data, but they are not exactly “smart”.
In a conversation last month, I asked Mattern why tech companies seem to have failed to improve any city, at least so far. She thinks it’s because they missed the most important parts of building the city. “A lot of ways of thinking about cities that are more based on computation and data give a false sense of omniscience,” says Mattern. City officials think they are getting the raw truth when in fact the filters they choose determine what they see. “When it’s all computer science, or when we can operationalize even the most poetic and evanescent aspects of a city into a data point,” says Mattern, “it makes us ignore that this is a metaphor.”