(Unedited) Podcast Transcript 365: A City is Not a Computer

January 13, 2022

This week we’re joined by Shannon Mattern, professor of Anthropology at the New School for Social Research. Shannon talks with us about her new book A City is Not a Computer: Other Urban Intelligences.  We discuss the ideas of smartness versus wisdom, the idea of maintenance as a way of absorbing information, and the city as a processing machine, just not in the ways you might automatically think.

You can listen to this episode at Streetsblog USA or the Libsyn Archive.

Jeff Wood (44s):
Well, Shannon Mattern, welcome to the Talking Headways Podcast.

Shannon Mattern (1m 22s):
Thanks so much for having me. I’m glad to be here.

Jeff Wood (1m 24s):
Thanks for coming back. We had you on episode 182 to chat about your book code, clay, data and dirt, while you’ve been up to since then,

Shannon Mattern (1m 32s):
What I’ve been up to since then? Well, I wrote a new book that came out in August, a, a bunch of articles. I moved to a different department in my university. I was in media studies now in anthropology, a whole bunch of life things happened. So yeah, lots of professional and personal activities.

Jeff Wood (1m 48s):
That’s awesome. So what’s it like going from media studies to anthropology? I mean, I imagine you study the same things, but is there a difference? Does your department head tell you different things? Like what’s the difference between the two?

Shannon Mattern (1m 58s):
So they’re rather different disciplines in terms of their awareness of themselves as disciplines, media studies is relatively new. It hadn’t been especially more traditional universities. Hadn’t been regarded as a real discipline for quite a while, and it still tends to be not very high on the hierarchy in terms of prestige anthropology, I have learned by contrast really takes itself seriously as a discipline and always ask questions. If anyone’s proposing a research project, like what is anthropological about this? Or where is the ethnographic sensibility here? So just the awareness of, and I think even the protection of disciplinary boundaries is quite different in both fields. And one of the reasons I moved is that after 15 years in media studies, I needed a bit of a change.

Shannon Mattern (2m 40s):
And also the anthropology department wanted to start a new program in anthropology and design. And because I had used some ethnographic methods and some of my previous projects, and I was a known quantity, I was familiar with all the faculty. They moved me over to do the different division, different department to start that new graduate minor.

Jeff Wood (2m 57s):
I feel like I’ve seen a lot more people, urban anthropologists, you know, as a, as kind of a field, have you seen a growth in that too? Just over the last, you know, five, 10 years or so?

Shannon Mattern (3m 7s):
I don’t know that I necessarily have like the quantitative data or Google data to talk about how, how widely or how much it’s grown. But I do think that there is an increasing interest in ethnographic methods and you can put scare quotes around that kind of ethnography means many different things to different people and some design disciplines in particular, it means kind of going and hanging out in a park or on a job site for awhile, asking some questions to people in the neighborhood and walleye ethnography. Whereas if you were to ask one of those more kind of anthropologists I was talking about before they would want something that’s typically like spending a year in the field, going through a lot of reflexivity, there are a lot of kind of protocols, all for good reason that are attached to, to the methodology. So I definitely see an increase of interest in ethnography.

Shannon Mattern (3m 49s):
However, you want to define it in multiple fields, including design fields, as you mentioned. But also I think that as we have recognized the limitations of kind of purely data driven approaches, despite however much insight that big data approaches do provide for us, there’s this attempt to combine the big data with what some people call thick data. So realizing that for example, mapping and large data sets can tell us where large scale trends are, but then they can identify things we might, we might want to dig into with greater kind of qualitative depth. And that’s where kind of anthropological or ethnographic methods can become useful. So I think they’ve maybe grown in prevalence because they are seen as a useful parallel or a useful kind of companion to data driven approaches.

Jeff Wood (4m 31s):
And that brings us to your book. A City is Not a Computer, Other Urban Intelligences published by places. What kind of got you to after the previous books and articles and everything you’ve written, what got you to put together another volume?

Shannon Mattern (4m 43s):
It was published by Princeton university press. It’s part of the places book series through Princeton university press. And yes, it was through places. I had my calmness four places journal, which is an open access public scholarship venue for writing about architecture, urbanism, and landscape. And I’ve been writing with them for about a decade really, and that has been a really fruitful and rewarding relationship for me and places. As I mentioned, has a relationship with Princeton university press, where they take some of their long-term authors and encourage them to select a few of their existing pieces of remix them added in some new material to make a short form book. These are meant to be short kind of pocket size, a little bit larger than pocket-sized books. So that’s what this is part of that series.

Shannon Mattern (5m 24s):
And I, my editor and the editor at Princeton places and Princeton editors asked me a couple of years ago, if I wanted to do this, we talked about a couple possible packagings or ways to remix some of the material I had done because I’ve, for places I’ve written about things ranging from libraries to urban data science, to hardware stores, to design for dementia care. And Alzheimer’s a whole bunch of different topics, but some of the pieces that had done the best and had the greatest reach were about the urban data science and kind of smart city critique type stuff. So I decided which pieces I should put together, update them, added some new material. So it was really a prompt from the editors and a sense of desire among certain readers, I guess I would like to think.

Shannon Mattern (6m 5s):
So that’s where the book came from.

Jeff Wood (6m 7s):
I was really drawn at the start to this idea of a tree versus a lattice structure in cities. And I’m wondering, you know, what do you think created the original semi loudest construct of cities that you discussed in the book

Shannon Mattern (6m 18s):
We are referring to Christopher Alexander’s idea. So I wrote a piece for places maybe five or six or maybe more years ago. I don’t remember exactly when it came out called A City is Not a Computer. And that article came about because I was invited by another group of editors to contribute a book chapter where I was tasked with writing about how a city is an information processing machine. And as I was researching and I realized like, yes, there are lots of kinds of computational dimensions that happen in cities that it runs on data that accountancy and logistics are integral parts for cities to function, but reducing a city to an information processing machine, kind of really brackets out a lot of the other, really more important epistemological functions and knowledge and wisdom that is inherent in a city.

Shannon Mattern (7m 1s):
So essentially submitted to them the opposite of what they asked for. So I wrote about how a city is like an information processing machine, but it’s more, hence the article A City is Not a Computer which came out in places. And then somebody reminded me after the fact that Christopher Alexander, who, as you probably know, is a, an architect whose work has been very influential to computer programmers and designers across the ARIDE array, wrote a piece called a C is not a tree in that piece. He contrast the idea of a city as a tree form, which is a kind of a symmetrical, heavily designed kind of very regimented type of master planning versus the cities of semi lattice, which is more organic, really tightly interwoven. So it’s more a formal contrast, but it also implies a different way of designing cities, a different way of understanding how cities function.

Shannon Mattern (7m 49s):
So those are where those two ideas came from the city versus a tree versus a semi lattice. I think that if you think about kind of organic organization before the age of master planning, why should I can’t say that master planning, we tend to associate with the mid 20th century, but if we look at kind of a lot of prehistoric and historic cities where you have a ruler or a cosmology that’s defining the way cities are kind of buildings are laid out there because that’s kind of a prodo master plan in a way itself. But the more the, the medieval cities with the kind of the intersecting streets and picturesque alleyways that we tend to fetishize in some cases, those tend to be those that are more emblematic of the semi lattice. So I’d say that throughout history, we’ve had multiple examples of both tree like planning processes and semi lattices that have emerged more organically.

Jeff Wood (8m 36s):
It’s really interesting talking about the two, because I had this discussion when I was working at a think tank called reconnecting America. And we did transparent development policy and worked a lot with HUD and FDA and others. And my colleague was always against kind of this kind of typology, if a equals B, then B equals C kind of, you know, this goes into this, which is kind of, I imagine like the tree thinking and I was more formatted towards the tree thinking. So I think, you know, how do our brains kind of respond to this? I think as humans, we kind of want to put things into those diagrams and we want to put them into simpler forms, but in the book you even mentioned, you know, our mind can’t really see the complexity and diagrams or the complexity and cities through a diagram. And I was curious by that too, because my thinking and previous trying to make sense of the world or make sense of cities kind of went more towards that tree thinking rather than that semi lattice, the organic, the unknowable to a certain extent.

Shannon Mattern (9m 26s):
Yeah. So there is a presumption that if you have more legible forms and a tree has historically been a very legible kind of typology way of organizing spaces and information and genealogies and all kinds of complex systems, social, cultural, political, economic, spacial, et cetera. So there is an assumption that by organizing things and more kind of regimented masterplan ways, it’ll make it a more legible form for people, but there’s also all the critique there’s been decades of critique of master planning. And when you impose an order on people that it really creates an artificial sense of order on modes of living and forms of social practice that are a lot more productively, messy than that. And in many cases, as with the case with Brasilia, for instance, you have people who were Belle against it, or that the situation is to push against the grid and push against the master plan.

Shannon Mattern (10m 12s):
So there are presumptions that again, having a more kind of clearly defined order will make things easier, but it’s also a form of social and spatial engineering as well. So there are both kind of maybe more nefarious purposes for imposing a quote-unquote tree structure for the purposes of social control, but then they’re also kind of maybe more benevolent or seemingly benevolent purposes, and that it’s creating a space that makes more sense to people, or at least that’s the assumption.

Jeff Wood (10m 38s):
Another thing that came to mind when I was reading the book was, you know, there’s a lot of talk about autonomous vehicles. We had Peter Norton on to talk about his book and kind of the critique of the autonomous vehicle in the, in the idea of the salesmanship of it all. But I’m wondering if you know, that tree thinking is the reason why autonomous vehicles are just always going to be a fantasy because of, you know, things are messy. Things are organic. They’re not necessarily going to go into the structure that you want them to go in. And so it makes it harder to fit something that is very structured onto an organic form.

Shannon Mattern (11m 8s):
I think that’s part of it. Yes. But then they’re also like the agents that have to fit within that tree structure. Like the children that you can anticipate are going to, you know, run into the street. Or I remember reading a couple of years ago that in Australia, they were having trouble with kind of all the sensing devices on cars, reading kangaroos, because they moved in a pattern that the, you know, the algorithm wasn’t trained to see there was like kind of a biomechanics that all the LIDAR and sonar and cameras really, weren’t kind of trained to recognize as an avoidable obstacle. I mean, I was just talking to one of my cousins over the holiday. Who’s a civil engineer in DC and he was telling me about how his office or affirm he used to work for has opposites in DC and New York.

Shannon Mattern (11m 49s):
And he was always surprised how in New York, they would start by modeling pedestrian activity, all of the, kind of, much more hard to predict things and smaller individual moving bodies and all of the accoutrements that they bring with them like walkers and scooters and motorized kind of scooters for the elderly, for instance, whereas at his office in DC, they started with cars, both moving cars and parked cars. So there, we just see that the sensibility or the place that organic objects, organic entities play in the planning process has different degrees of salients and different ways of thinking about planning for transportation or kind of urban planning, writ large.

Jeff Wood (12m 26s):
I like the idea of thinking of children as agents as if they were like covert operatives, trying to, trying to upend the status quo. Why is the word smart, such a hard word to avoid?

Shannon Mattern (12m 40s):
Well, it’s ubiquitous, it’s a marketing term for one thing. And lots of folks, including, you know, a colleague of mine. Jonathan, Sadowski wrote a book called to smart a couple of years ago where people are critiquing just the ubiquity of the term smart and how it’s applied, been applied for a long time. Now, actually to the point where it’s actually quite annoying in multiple sectors, we have smart can be a pre-fixed for pretty much anything, smart trash cans, smart street lights, smart cars, smart coffee machines, smart, toothbrushes, all kinds of things. And really it doesn’t just connote, but it denotes a form of kind of data capture and prediction typically in kind of corporate structures. It raises lots of questions about privacy, about agency, going back to your comment early about what does it mean to be have agency or to be an agent to put your question was what are the problems of having smartness applied to everything?

Jeff Wood (13m 26s):
Yeah. Like smart cities, smart growth, it’s such a hard word to nail down, and it’s hard to refute, you know, smart versus dumb, those, those types of like binary ideas. And

Shannon Mattern (13m 37s):
Yeah. So smart. Does it going back to the connotation smart does have a positive connotation. I’m an under that too many people who don’t want to be smart, although there are kind of some other kind of terms like to smart means to be rude or to be sassy in some, in some cultural contexts. But in this case, I think that promotes people would prefer smartness over dumbness. So smartness is in a sentence and epistemological claim by that mean, smartness implies having a certain kind of wealth of data or information, and I’m using these terms, but they’re not really interchangeable. I mean, this is a kind of a classic classical or canonical model that you might see in information studies and information science, the data information, knowledge, and wisdom triangle, where there’s a proliferation of data. And then once you kind of package that and process it, it becomes information when you process and filter information and put it in context, it becomes knowledge.

Shannon Mattern (14m 24s):
And then when you live that knowledge over the context of a lifetime in relationship to other people in your community, then the hardest thing to acquire and the, probably the, the most revered form of epistemological farmers’ wisdom. So smartness tends to imply having a lot of data, which doesn’t always imply that smartness is applied to wisely or knowledgeably. So I think that when we think about smartness as the telos, the end goal, for all kinds of product development, urban planning and everything, it really kind of limits us in terms of thinking about what types of intelligence is. And knowledge is really matter. Lots of questions aren’t asked, like, what are the risks of being smart?

Shannon Mattern (15m 4s):
What types of understandings and experiences are bracketed out that don’t lend themselves to smartification in the way smartness is operationalized in a toothbrush or a car, for instance. So these are just some of the limitations of that, that marketing term or that brand, that branding concept.

Jeff Wood (15m 21s):
That’s an interesting connection. I mean, the idea of time or age and wisdom as compared to kind of a smartness and how you can’t really program wisdom into materials or things. It’s just kind of, I’m thinking of like an old man sitting on the corner who probably knows way more than me, or, or for example, my grandma just celebrated her a hundred ninth birthday and she’ll obviously know, you know, 109 years worth of stuff more than my 41. And it’s, it’s, it’s an amazing idea about what wisdom she’s gathered over time, but a tool or a thing can’t be smart just when you turn on the switch.

Shannon Mattern (15m 55s):
Great. And there is kind of a fetishization of efficiency of now and as a prediction, always looking forward based on kind of data that are harvested in the moment, of course there are kind of historical data. So you’re drawing on historical data as well, but there’s a very different sense of temporality when you think about smartness versus wisdom. And one of the other ways of thinking about that, the temporal dimension of knowing that I talk a bit about it in the book and that other people have talked about much more eloquently and knowledgeably than I have is indigenous wisdom. So they’re in my class, I taught at anthropology and design class this past semester, and we had the indigenous artist and designer, Suzanne kite join us who has done a lot of work on indigenous AI, artificial intelligence. And she reminds us in many indigenous communities, which is again, multiple generations of accumulated knowledge.

Shannon Mattern (16m 40s):
A lot of which is, is distributed. You know, it’s not all collected in a data set, some of which is conveyed through oral culture through performance, through food ways, all different types of ways of thinking about how knowledge is embodied in the world in different ways. But one of her things that she advocates for is thinking about seven generations. So when you make a design decision, you’re just not thinking about increasing efficiency for the current residents or occupants of a place. You’re also thinking about the long-term impacts seven generations from now, maybe from several generations back as well. This requires us to think about ecological impacts of what we’re doing. I know there’s been a lot of research recently about the fact that we’re relying on automated vehicles, all types of smart technologies rely on rare earth minerals and mining in many conflict zones in the world to what are the long-term seven generations worth of impact of not only ecological, but also kind of terms of cultural values.

Shannon Mattern (17m 34s):
So that’s a very different way of thinking about the temporality of design and wisdom than it is thinking about the numbness of smartness.

Jeff Wood (17m 42s):
I think of how hard it is. I do something simple, maybe like take out the trash or, you know, fix something in the house or it is. And I’m thinking to myself sometimes like, how would I ever pass this knowledge on, or when you’re young or when you’re a baby and you trying to learn all these things, how do you absorb all of these pieces of information? It seems daunting at times to think about passing along or even just, you know, conveying something that you’ve learned over so many years, but, you know, if you’re a kid or a child or younger, how do you pass that, you know, wisdom or knowledge or how to do something on? I mean, now we have YouTube videos and all this stuff about how to learn things, but that can’t replace kind of that lived experience, I guess.

Jeff Wood (18m 22s):

Shannon Mattern (18m 22s):
Well this gets it to the chapters in the book. So one thing that maybe I hoping is making my book a bit different than some of the other books about smart technologies and data driven, urban planning is that I included a chapter at the end about maintenance, because I want to say that that is one of those types of embodied, ways of knowledge, the type of stuff that you learned that maybe you even have to be an apprentice, you have to watch people do it. You have to put yourself in certain kind of messy situational context and talk to an expert and ask, you know, how would I handle this into this situation? It’s not something you can read a textbook to learn how to do. It’s not something in an app out of enterprise software is going to tell you how to fix something in many cases. So yes, there are certain aspects of maintenance that have been automated, certain things where data is very helpful, kind of diagnosing problems and testing the efficient kind of effective operation of systems.

Shannon Mattern (19m 9s):
But there’s an actual, a lot of care work, repair work and maintenance that involve a very different type of knowledge than what we can find in kind of the world of smartness. Another chapter in the book of the penultimate, one is about libraries and I have written about public libraries and, and kind of community knowledge infrastructures for that’s probably the longest read in my career. My dissertation was about that 20 years ago. My first book was about public library buildings. And there, I acknowledged that libraries can be really valuable data hubs in their cities. They can be the repositories of like public urban data sets that can help people to develop critical data literacy, to empower citizens and residents, to learn how to use data for their going to their own benefit.

Shannon Mattern (19m 51s):
They can help people to get access to digital technologies, moving over the digital divide. They can help people to develop their own data or create their own kind of media content, but they also remind us that not everything is reduceable to the digital, that there are multiple ways of knowing one example that I use a lot and talking about this book as a project that opened up in Greenpoint library last year, the Greenpoint public library and environmental education center. And there it’s a library that’s built in a neighborhood in Brooklyn. That’s the home of several Superfund sites and in a lawsuit with, I think it was Exxon or one of the big energy companies to kind of atone for partly a tone for the damage they had done the community decided to put that fund, that money into a public trust or a community trust.

Shannon Mattern (20m 34s):
And one of the things they chose to do with it was to build a public library where they want to really grapple with all of the ways of knowing about environmental justice and injustice. So yes, they’re building kind of a community data sets about environmental and environmental conditions, but they’re also engaging with indigenous knowledge recognizing kind of traditions, ways of engaging with the land that proceeded all the development that is now currently Brooklyn. They’re also building an oral history, kind of an environmental justice oral history collection. So there, I feel like libraries are really great at reminding us that when we do a doc digital data-driven technologies, we have to do so critically aware of injustices or kind of inequities of the risks that they pose to people, how to use them thoughtfully, and then also supplement them with other equally valid ways of knowing, including things like, as I mentioned, oral history, embodied ways of knowing books still as well, too.

Shannon Mattern (21m 28s):
I mean, those are still imminently relevant. So it’s just the variety of different epistemologies that are embodied in a library is what I think is a good way to remember the richness of how we can go beyond the reductiveness of smartness.

Jeff Wood (21m 42s):
I often say on the show that I wish I could live a thousand years, so I could live in all the cities and process all the information in my own mind. Do you ever wish you could read all the books?

Shannon Mattern (21m 53s):
No, I don’t think I do. I mean, this is a part of this longstanding kind of feudal desire and it actually, this is kind of cultivated some Imperial motivations over the years. You know, we have a history of colonialism of colonizers coming into an area, colonizing an area and decided that not only do we want to control the existing knowledge or race, a lot of the existing knowledge and replace it with our own and then control the production of new knowledge. So I feel like this desire to have a totalizing complete library, the complete data set is something you see often in data science, where people are wondering like what methodologies can we use to get all the potential extractable information about somebody that will allow us to have the most perfect predictable kind of modeling system.

Shannon Mattern (22m 37s):
There’s kind of a natural human desire to want to know as much as possible, but these are also really kind of dangerously hubristic tendencies have been integral elements of colonialism and white supremacy and other kinds of bad things humans have done throughout their history. Yeah.

Jeff Wood (22m 53s):
Kind of went dark there. I was thinking from Logista like, I’d like to know, like I’m a, I’m a viewer from the outside, not taking all the data to be the emperor or anything like that. It’s I want to see what happens at the end of the, it’s almost the doctor who thing, what happens at the end of the timeline? Just a curiosity, almost, not necessarily trying to control everything

Shannon Mattern (23m 11s):
I’ve heard of it too. That is definitely part of it too. But I think I’ve just been in so many different kinds of smart city, like conferences and contexts, where people have talked about wanting to harvest like a complete quote unquote data set or this remind, and then being somebody who studies libraries always hearing about this desire for the total library. I mean, sure. I have it too. I’m surrounded by a couple of thousand books right now, but also there can be something really paralyzing. This is something I’m dealing with with students often, you know, how do you know when you’ve read enough that you can actually start contributing when you can write about it? So this idea that you have to reach some state of completion or totality can be paralyzing. So it’s both exciting and a great motivating factor and curiosity is wonderful. And I fully supportive of all of those things, but I think we also have to recognize the potentially paralyzing and deleterious aspects of this desire to know, and to see everything to

Jeff Wood (24m 1s):
Book. There’s also a discussion about how we shouldn’t expect advertising companies such as like Google or Facebook to act like public knowledge basis. But how do you create a public alternative without kind of the advertising or the fundraising baggage that goes along with all of that mess?

Shannon Mattern (24m 15s):
I think in that section that you’re referring to Ember drawing on the work of be a noble. I’m not sure if you’ve ever had Sophia on your program, but she’s really a fantastic kind of critical data studies and looking at kind of critical race studies scholar. She wrote a book called algorithms of oppression several years ago. That’s looking at the biases of our search terms and the inequities that are built into different types of algorithms. So there, this is an aspirational goal, obviously because we have allowed ourselves to kind of build infrastructures that are so much built on private enterprise or public private partnerships. It’s really hard for us to imagine how things could have been designed otherwise. But if we were to start over, we might be able to imagine that our internet might look completely different.

Shannon Mattern (24m 56s):
Our public institutions might look completely different if they were fully supported by public funding and multiple levels of kind of government involvement. So I think part of our inability to imagine what a public infrastructure would look like or a public interest technology look like is in part, because we have been trained for decades, if not centuries, to think that all of the most ambitious planning, all the funding has to come from the corporate world, that all the innovation happens in the corporate world. And to some degree that is true. It’s incentivized. People are incentivized to move into that world because that’s where the money is. That’s where the resources are. But if our society weren’t designed, quote unquote, otherwise we could be placing a lot more priority and prestige and resources into the public sector.

Jeff Wood (25m 40s):
I want to go back to maintenance a little bit. What’s the first thing that you think of when I use the word fixer,

Shannon Mattern (25m 45s):
First thing that comes to mind is we’ll fix her. I’m kind of thinking about like a rogue, public figures who tend to do kind of messy things. Then you have the fixer come in and you kind of, you clamp the mess that they left behind them. So I’m thinking about the fixer as a role, a person plays an informal job. Somebody plays to clean up the mess in a, in a disastrous person’s wake. That’s what I think of when I hear fixer,

Jeff Wood (26m 9s):
I was just curious. Cause the maintenance chapter kinda got me thinking about the idea of a fixer. I mean, obviously we have political fixers and you have fixers and movies and film, you know, do you have a director that goes through a whole movie and make something and then the studio doesn’t like it. And so they have somebody come in to make the happy ending or whatever else, even if, even if it’s a mess, it’s just a curious term, almost a, a fixer, something to think about

Shannon Mattern (26m 30s):
It is. And that’s something I talk a little bit about in that chapter. And then I’ve had other discussions with people about is, you know, maintenance care repair, fixing mending as another one, healing. These are all terms that are kind of within this larger umbrella of amelioration or remediation, I guess you could say, but they all have different connotations. They call to mind different. Imaginaries like maintenance. We think of like guys fixing bridges, whereas caring. We think of like women working with babies. So there are gender and class dimensions. So yeah, these terms have different, again, both denotations and connotations and they seem to circulate within different realms.

Jeff Wood (27m 6s):
I kind of liked that idea of caring for a bridge or caring for a, I don’t know, a train line or something, you know, it seems like something we should do.

Shannon Mattern (27m 15s):
And this is something that Justin Garrett Moore who’s at the Mellon foundation and Alexander Lang who writes for curbs and a bunch of other publications. She wrote a piece, I think it was just last month drawing on Justin Garretts. Moore’s work calling for a department of care saying that a city should, you know, we should institutionalize it. And I think that think of like caring for roads and bridges as much to be cared for for bodies and families.

Jeff Wood (27m 36s):
One of the things you mentioned too, is that, you know, we do think a lot about maintenance from a, an infrastructure standpoint of physical infrastructure, like roads and bridges and transit and all those things. But then there’s this other thing that’s coming up about the care of, or the maintenance of municipal software, which is really a fascinating topic too, because of first off, because of all the ransomware issues that are appearing these days, but just also how you by word 95 and then word 95 seems to stay part of the part of the city until there’s a budget to replace it or somebody who takes care of it. And then it’s an interesting thought, you know, you have all these layers of infrastructure and the software part of it just hasn’t been discussed as much.

Jeff Wood (28m 17s):
It’s not part of that infrastructure bill discussion, right?

Shannon Mattern (28m 20s):
Well, part of it is it because the power to the knowledge required to maintain these different things exists in different disciplines, different professions. So the people who are maintaining the bridges are very different than the ones who are maintaining the software or cleaning the data for instance. But I think it is, and this is what I tried to do in that chapter in the book is identifier talk about how all of these different scales or layers are integrated. And if you want to think about maintaining a bridge or a transit system, transit systems do rely on a lot of data, especially if we’re moving more towards automated vehicles. I’m not sure when that day will happen if it ever does. But if that is part of some people’s future vision or you can’t not think about maintaining a transit system at the same time that you’re not thinking about cleaning data and maintaining software.

Shannon Mattern (29m 2s):
So I feel like there has to be much more integration of realizing that maintenance has to happen across these scales. And then furthermore care is not a separate realm too. You also have to care for the workers who are doing this work and the people who are putting themselves at risk. You know, the pandemic has definitely caught a lot of attention to the precarity, the risks that frontline workers and maintenance workers and healthcare workers put themselves in. So caring for the bodies and all the effect of stuff that is a part of these systems is an integral part of maintaining the larger system to

Jeff Wood (29m 32s):
Do you think the pandemic has kind of recast the discussion about maintenance and care?

Shannon Mattern (29m 36s):
I think so. Yeah. First of all, there’s just been a lot more people talking about it. We have see the rise of kind of mutual care networks. The fact that that is a, a term that is part of general parlance and many circles that we recognize the value of health. You know, not that as if we’ve been oblivious to this, but I think it was really put into stark relief, the need to care for care workers. And we see the breakdown of things like supply chains over the past several months as well. So yeah, I think just the, snafoos the broken systems, the appreciation for those who are putting themselves out for others, the rise of kind of community networks to fill in where social infrastructures fall short or other infrastructures fall short. Yeah. I think it’s like care as a part of discussion, but it’s also many people are practicing in different ways during the pandemic.

Jeff Wood (30m 23s):
Another interesting thing from the book that you mentioned slightly is the idea that Lewis Mumford had as kind of the city as a container. And I kind of connect that to what you’re talking about is city X or X or Y as a platform or the idea of this is a platform to do all these other things. I’m curious what you think of this kind of idea of X or Y as a platform or the city as a container, as a whole curious what you thought about that

Shannon Mattern (30m 46s):
Part of this is also about metaphors. I mean, I’ve read something someone recently said like this book is full of mixed metaphors. If that’s a bad thing, I’m like, yeah, it’s a part about the next metaphor about cities and how these metaphors arise again in different professional contexts. And how, if we have kind of civic engineers who were thinking about cities as kind of, I don’t know, like circulation systems and ecologists were thinking about cities as biophysical bodies, maybe there’s some interesting connections that aren’t happening because they’re using different metaphors and different language, which implies different ways of designing and intervening and administering and maintaining. So I think it’s important to pay attention to the existing mixed metaphors that we use in different professions to talk about cities, urban planning, architecture, and yes, platform is one of them.

Shannon Mattern (31m 29s):
I mean, that’s something that is again, arisen out of, I don’t know the entire genealogy of it. I’m sure I looked at it when I was researching the book, but I’m forgetting it right now, but just the proliferation of platforms in the tech world and then how that’s then imported over to larger scales. Like the city itself has a platform. There was also a period of several years ago where people were talking about libraries in different civic institutions as platforms where there’s a lot of kind of empowering rhetoric or assumptions that are built into that metaphor because the platform is essentially lifting you up here. We’re going to provide you with the tools are going to give you all the stuff you need and you can build things on top of it. So that can be very empowering to kind of a citizenry or a community to realize that there is a, so there’s something out there that’s going to lift them up, provide them with the stuff they need to build stuff with it.

Shannon Mattern (32m 15s):
There’s also some risks to platform and that it doesn’t really prompt us to ask about like what’s underneath, who’s holding it up. And then he cases, especially with tech platforms, it’s kind of black box technologies and profit different corporations who are building platforms who are then extracting the data of the people who are working on top of them. So just, I don’t mean to be kind of obnoxiously reading too much into these metaphors, but they do imply a different kind of politics and a different form of agency where if we think of the city as an infrastructure versus the city as a body, the city is a machine. The city is a platform. They do really imply different ways of understanding what our roles are in them who holds the power in them and how they operate and what possible changes could be made.

Jeff Wood (32m 56s):
The first thing that comes to my mind is thinking about a platform like an offshore platform, which is very extractive, right? Well, you mentioned earlier about my, my idea about learning everything in the world. It’s maybe colonialistic or extractive in a certain way more than maybe the idea about libraries. It’s just an interesting connection. There’s also a part of the book about dashboards. And I was fascinated to read about this because it seems like everybody in the past, at least from the fifties on or so, or even maybe before that, wanting to have a dashboard of some sort to tell you everything, which goes back to that kind of every knowledge you could possibly have, but it’s not possible to have every knowledge you could possibly have on a dashboard. The dashboard discussion reminded me a lot of these kind of best of lists like the economist, most livable city or, or those types of things.

Jeff Wood (33m 41s):
And I’m curious how the dashboard plays into cities need to be recognized with some sort of a data scoring system.

Shannon Mattern (33m 47s):
Well, there, there had been a lots of critiques from multiple fields about the fact that we’re living in an audit culture, that it’s all about rankings, that we have to quantify everything that it’s, we live in a competitive culture where prestige and capital and investment come to those who are ranked higher on whatever the criteria might be. So that’s a larger cultural critique about whether Y Y we tend to regard everything is competitive as a zero sum game as kind of a fixed quantities that we all have to compete over. And I think an interesting parallel to that, going back to our tree discussion is the resonance that Susan, some ARDS work and a lot of indigenous thinkers work has had, and realizing that forest, for instance, they’re kind of more co-operative ecologies rather than having trees and different life forms, competing for resources.

Shannon Mattern (34m 29s):
They’re actually working to kind of support one another. They’re kind of communication networks that are, if one thrives, they all thrive. And then everybody’s kind of thriving depends upon collaboration with others. So I feel like, again, these metaphors we’re using really shaped the way we understand whether we’re living in a competitive or a collaborative culture. So that’s one kind of a larger motivation for wanting to have a dashboard. But I also just didn’t in the context of the larger book, I started the book with the dashboard chapter because I think it gives people a concrete case study to think about all of these epistemological, methodological and political, and kind of cultural things that I talk about in the rest of the book. So the dashboard really kind of crystallizes this way of thinking about smartness that we were talking about before.

Shannon Mattern (35m 13s):
So what is it that matters in a city? Is it really only the stuff that can fit on a dashboard that you can then show them in a control room? What can you actually measure? What can you create a widget for what actually lends itself to presentation and the heat map, or a dial or a ticker, they’re all kinds of things that probably mattered to us in our everyday life that don’t readily lend themselves to operationalization in those ways. So I’m looking at kind of the history of the dashboard, what made it possible, what technological developments made it possible, what methodological developments made it possible, why it persists, why it’s attractive and then what its limitations are. And at the end, I also look at how some artists and designers are trying to undo or kind of poke at or parody the dashboard to remind us that when we look at the COVID dashboard or a city control room, or if you see those kinds of classical examples of like a NASA mission control with all of the different things, they can be gauging and watching simultaneously, maybe we can do some speculative type of dashboards to remind us of, you know, what’s actually not capturable in those forms.

Jeff Wood (36m 13s):
I like how the word dashboard has evolved though. I mean, originally the origin was that it was to keep us from horse and wheel Splashback out of a vehicle. And now it’s something that kind of cleans your data. So it’s, we came from cleaning mud, which is such a basic kind of function to like cleaning data, which is, seems more complicated. So maybe as humans, we’re trying to make something more complicated than it, than it actually is.

Shannon Mattern (36m 36s):
Yes. The same. He goes back to connect to our discussion of maintenance earlier, too. So maybe there’s an inherent kind of maintenance operation of a dashboard. As you pointed out. Historically, we had it on a horse drawn carriage. The dashboard kept the mud from splashing up into the carriage where the people were sitting. And then we have the evolution of the car dashboard, which had over the course of car history. I am not a car buff, so this is not really my realm, but I had to study it a bit to write this piece. You know, we have originally just like an on and off and like the gas gauge, you know, so you have a limited number of gauges in part because systems engineering only allowed us to measure a certain number of things, other things you had to rely on analog cues. So if your radiator explodes, like there you go, that’s your, that is your dashboard to tell you like something is not working.

Shannon Mattern (37m 19s):
We didn’t have a way to measure. And then to visualize what was happening in these kind of more analog type of contexts, then we see the proliferation of gauges, look at the evolution of cockpits evolution of control rooms, mission control, and then the more contemporary ones and kind of smart contexts. So there is a way that it’s presenting kind of what we might regard as it’s cleaning the data that an individual agent or operator can seemingly reasonably deal with. So when you’re driving a Ford model T I mean, there, it’s kind of cleaning the data that was able to be gleaned at that time. The only things that kind of, we had the gauges and metrics to be able to both measure and then represent.

Shannon Mattern (37m 60s):
And now if we’re looking at a more digital dashboards, it’s again, kind of cleaning certain data sets and presenting for us in many cases, ostensibly it’s about so that we can act on these data. But if you look at a lot of dashboards, it’s more data than any one human brain can intake our synergize and be able to act on. So in some cases it’s more like a performance it’s a highly as set aside, you might say a clean aesthetic to present this, going back to your comment about like reading all the books and having all melody and presents the clean visualization of ubiquitous knowledge of kind of Survalent clairvoyance, knowing all the things that are happening simultaneously. But it’s a very clean version of that. And by that, I mean kind of delimited, because again, it’s only showing what can be measured and rendered visible in that full.

Jeff Wood (38m 45s):
Unfortunately I think if you, if you want to get all the knowledge off of your dashboard, you’re ended up colliding with something and then maybe dying, and then you’ll never get all the knowledge you’re reaching too far. I do like all the metaphors and the mixing and everything, I think personally, just because I like to make those connections, I like to connect some a to B and maybe seed it. And maybe I’m a tree. If you have a tree in that sense, I like to make those connections. Maybe that’s why I like my star wars. And it’s my own telanovela to a certain extent, making all those connections. What is something that you’ve written in the book or an idea that you have in the book that you don’t feel like you get asked about a lot, like people would talk about certain things probably when they talk to you about the book, but what are things that maybe you feel like have more importance or maybe something else you would like to get across?

Shannon Mattern (39m 31s):
Well, I’ve had a lot of really great conversations with people about the book. And I have to say like they’ve emphasized different things, depending upon kind of the personal interest of the person who’s asking me about it. And I really have to say, there’s not, it hasn’t been any disappointment. You know, some folks I had assumed that a lot of them were kind of urban tech folks would wonder, like, what is a chapter about a library doing it here? But I’ve had plenty of people who are kind of more on the digital tech realm. Who’ve been actually fascinated by the potential partnerships or what we can learn from libraries or how they could contribute to libraries to make them more kind of vibrant and well-resourced institutions. And then just the tree staff people. I was wondering if people would find that to be kind of an ornamental opening and closer, but people really found that to be a useful metaphor, to think about both formal way of organizing the book, but also a really useful aesthetic model to think about how the book is organized.

Shannon Mattern (40m 20s):
So folks have asked them about the trees and then also the grafting kind of the whole process of grafting, which I use as both a metaphor and a method in the introduction and conclusion to think about how important it is to graft these multiple ways of knowing onto each other, rather than starting tabula rasa or in the words of kind of Dan Doctoroff from formerly of sidewalk labs, like building from the internet up as if there was nothing there before is if data are the primary way of knowing things, people have also asked about the maintenance. So I’ve just been actually very pleased that people have found interesting connections between the chapters, even though they seem to be very disparate topics, trees, maintenance, libraries, smart cities dashboards, but I can’t say that there have been any gaps and I’m, I’m very grateful for that.

Jeff Wood (41m 1s):
I think they all are very connected though. I mean, as somebody who likes different topics and areas, and kind of tries to connect the dots to a certain extent, I think that they do connect very well. So he did a good job. You mentioned that the plant grafters and Kyrgyzstan, that was another interesting part that I was like, huh, okay. I like that. I think there’s kind of a positive connotation and then there’s a negative connotation to it that you brought in the book. And I think that’s fascinating too. Everything seems to have a ying and a yang almost to it. There’s always a negative way you could go or a positive

Shannon Mattern (41m 32s):
And maybe that’s, maybe that’s kind of a model, probably everything I write seems to have that I imagine. But if you think it’s important to be wrecking, to recognize the potential benefits that every new technology development practice kind of cultural forum can offer, but then the risks that it presents to and parts of that, you can decide which parts you want to emphasize, what you need to prepare for. You need to anticipate and maybe kind of remediate before it becomes a problem. But yeah, I’m glad, I’m glad you saw that as a recurring theme.

Jeff Wood (42m 1s):
Well, the book is A City is Not a Computer, Other Urban intelligence by Shannon matern. Where can folks find the book? If they want to get a copy,

Shannon Mattern (42m 7s):
Courage you to go to independent booksellers or to the Princeton university, press website, bookshop.org. And if you must use Amazon, you could do that too.

Jeff Wood (42m 16s):
Nah, go to go to your local bookstore, go to a bookshop that Oregon type in your local bookstore. I got mine at folio, which is my local bookstore right here in San Francisco. So I try to plug that as much as possible to get your books well, Shannon, thanks so much for joining us. We really appreciate your time and thanks for putting together such a great book.

Shannon Mattern (42m 33s):
Thank you so much for having me. It was a pleasure

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