Our guest spot for this unit is Professor Luis Bettencourt.
Luis is a resident professor at the Santa Fe Institute. His background
is in theoretical physics, but like many of the Santa Fe Institute faculty, he’s worked
in a lot of different fields, ranging from computational neuroscience
to epidemiology. Much of his current work is focused
on studying cities as complex systems. So, welcome Luis.
Thank you. So Luis, in this course we’ve covered
metabolic scaling. We’ve talked to Geoffrey West.
And now we’re looking at how complex systems science is being applied to
studying cities. But let me ask -- why do you think it’s useful
to view cities as complex systems? Well I think
most people that start even just a little bit about cities know that
they are complex systems in this way we’ve just described, in that
they obviously look like they vary a lot. They’re made of many different
pieces. They look all very different. There are poor parts of the city, rich parts
of the city, parts of the city that are dedicated to some kinds of businesses
or other. So there’s a lot of diversity. However,
in whichever way you measure them, that makes the city look
very diverse. They also share with some other complex systems
the fact that cities by and large are open ended, so we’ve have cities
certainly for now over ten thousand years, and there’s something
recognizable about cities old and new, but they also have evolved obviously in terms
of technology, in terms of what it is that people do, and so
because of all this, I think cities are not just
something you can study from just their economic consequences
their social consequences, the way they are, they live in space and occupy
space, or structure space, so all of these things need to come together
social life, economic and material life, energy,
but also the way the city exists in space and in time, because cities
increasingly now with large suburbanized cities we cities that exist also
in time, where people come together during the day and then disappear again at night
people go back home. They really have the hallmark of something that
evolves, not in terms of biological evolution,
but in terms of things that we still struggle in the social sciences to define
what evolution is, and also, perhaps most importantly,
creates a new diversity, new ideas, new
spatial forms that reflect what we can do as a species when we
come together in large numbers in terms of our social life, and so
the reason I like cities is that all of these things come together into the same problem. Okay.
Well, it’s interesting that you use the term evolution. We’ve talked about evolution
in a lot of different contexts in this course, and
you say cities evolved maybe not in a biological sense, but in some sense
and people in the past have certainly
characterized cities as being sort of like organisms
so I guess one question is, are cities alive or
does it even make sense to talk about it in those terms?
They are not like an organism, but they evolve, where an organism per se does not evolve.
Other people, for example, compare cities to ecosystems famously
Jane Jacobs did this in The Economy of Cities. In the sense an ecosystem
has organisms and species that
reflect certain properties. Functional
properties, perhaps, functions that those creatures have in their niche
but it’s the interaction of all these different functions that creates the ecosystem
and in a city it’s a little like that, though with all the things they need
to change changed, in that the city only really exists as you have
division of labor between people, division of functions, and their integration back together
so that out of scale, this increasing diversity
and the productivity increases that division of labor and coordination
of labor and colocation permits. You can have a system that evolves
in that sense that at least superficially is like biological evolution that create new
things. So in that sense, the city is probably
in terms of human systems, the ultimate complex system, but it is a system
that is very interesting, when we compare different complex systems, in that
there’s a certain amount of
the creation of new things, and the selection of those
things, both of which occur also of course in biology, they become
endogenous, they become part of the city itself, that’s why the city evolves as a whole
just like, in sense, an ecosystem, although by very different means.
Whereas an organism only has a very limited way of creating new
information, which of course through reproduction, which is mostly random,
and then the selection is known at the level of the environment. Okay.
Let me ask a slightly different question. We looked a lot
at scaling, different scaling laws and
particularly in the context of metabolic scaling so far, and I know you’ve done
a lot of work on scaling laws in cities, so let me ask first, what do you
see as the main new thing that you
discovered that hasn’t been known before? What we
know now is essentially, in a nutshell, that cities
are basically social reactors. They’re sort of open-ended places where a lot of
people can come together and interact with each other and when things
go well, and necessary organizations
and technologies come together, there are places where we can do lots of good things
together, in terms of having new ideas, producing things economically
more productive, and so forth, but of course we can also do a lot of bad things
so what I mean by that is that there are places where a lot of people can
interact, react with each other, and create things that we can only
create socially, there’s sort of a famous quote by
E. O. Wilson, and he’s very interested in ants, and he calls a set of ants
an ant colony a superorganism in the sense that even
in genetic terms, they of course are very related, and that’s part of the explanation for their
sociality, but he says that an ant alone is a disappointment
you only find what ants can do when you understand how
the colony works. But to a much larger extent
you could say that a human alone is a little bit of a disappointment, that it’s when
you actually find ways of integrating a lot of what humans can do
together that you understand
what we can do in terms of our creativity and our originality and
how we deal also with resources, and that’s what cities are, cities are
technologies to do just that. When you look at organisms
versus cities, you find this interesting distinction that’s subtle but it’s
important, which is that by and large the way we understand organisms
to some extent also river networks, which may be something that you covered in your lectures, I’m not sure
is that these are systems that exist in some sense to preserve
information or certainly organisms, information
is already there in the genome, but then they want to do that using
the least amount of energy possible, and they form these networks that reach all
parts of the organism that allow you to do that. In a city
as I just described, it’s a little different, because in some sense you want to
enable this reactivity of people and organizations, so you want
to have people move around -- your cells in your body don’t move around and conspire
to do new things. In fact when they try to do that, you kill them.
You have many mechanisms to kill them. You don’t want anything inside the organism
Yes, exactly. So what happens in cities is the opposite in that you need
to promote all of these interactions, it’s out of the recombination that
these heterogeneous elements that have different information, that new ideas
new organizational forms come about. In order for a city to promote
that, it still needs to occupy, in this case, cities live in space, so
you need to have networks that allow you to cover all of space, and that’s the sense in which these networks
bear some resemblance to those in biology for an organism
that also need to irrigate all parts of space, but in these networks
what you have is by and large information, people
interacting with each other and creating new things, and that form of transport
actually takes more energy, so it’s less efficient than the organism, but
also creates an output that is
greater, so this is what we call superlinear, so you find essentially there’s
superlinear output in terms of products or social reactions
like ideas, innovation, wealth, and so forth, but you also
find a superlinear amount of energy, not so much in the network, which
actually tends to be spatially smaller, just like in an organism a little bit,
but because in that smaller network you need to move more things around
faster, and that ends up creating more dissipation. So when you say
superlinear, you mean when you look at
some functionality with respect to the size of the
city, right? Yes, so in some sense what you find is that
superlinear just means that if you measure some property of the city,
let’s say GDP or it’s number of crimes,
and if you look at cities that you think are comparable,
usually inside the same nation, but across different sizes, so you have small
cities, medium cities, large cities, and you ask how that property varies
with the number of people in the city, with the population, you find that
typically social and economic things increase faster than
population, so its per capita rates go up, and this is why
large cities are more expensive, but also people earn more money, often they are
more dangerous, and so forth. This is basically this characteristic
which is essentially an acceleration on the rate of these social and economic
interactions, this reactivity.
So the reactor is basically getting a little hotter, in some sense
much like a star. I like to use sometimes the analogy of a star. It’s
a lot like a star, in that we have more mass, the reactions in the center
are faster, and the star is hotter and everything is faster.
In that respect, it’s a lot like a star, although quantitatively it’s a little different in that the physics
and the social dynamics are different.
What do you get out of predicting all of this? What kinds of applications are there of all this measurement?
So the first thing is just to understand what kind of
complex system a city is, because
well, as you know, and probably as people watching this know,
at least since Plato and Aristotle, people have been debating what kind of system a city is
and how should we deal with it, in terms of policy or development infrastructure,
etc. Should we want more cities, less cities, what kind of cities, what can
actually someone like the mayor do to make the city better? If you think of a city
primarily as a social network that lives in space and time,
and if you think then of the infrastructure as a way to
enable its socioeconomic activity, then you
understand that there’s a value to infrastructure, for example, in terms of
allowing a more efficient use of people’s time or
costs, in terms of doing what people do well,
which is their socioeconomic activity, so there's
a sense in which one can start in some sense putting a value to what
infrastructure actually does.
You see this a lot
in the history of urban planning, the last sixty years, you have cities that tend to be
very dense, and that’s not good,
because people cannot move around and meet people that they might actually want to and get organized
and so those cities benefit essentially from having better infrastructure
that allows movement not only of people, but also of things
and information faster, but you also have then
in the United States more recently, cities that become too large in terms of infrastructure
where the cost of just moving across, at least in time, if not
in other forms, is quite high, so
those cities are just too diaphanous, and they
cease to be easy places of human interaction, and so the theory
that I’ve developed, basically tells you what are the
right compromises in terms of
social interactivity and the characteristics
of infrastructure, and the structuring of space. The other answer
is that this also allows us to put quantitative expectations as to what a city
with certain characteristics like size and land area might
do in terms of its wealth, or in terms of its innovation, in terms of its
crime
Some of this is contextual, because it depends a little bit on the history of that urban system
but some of it depends on these general characteristics of cities, so that allows us to say a little bit
how well a city’s doing relative to its peers, if you wish.
One last question. So what’s the most exciting thing you’re working on
now? So
that’s a hard question, because you prefaced this by saying I work in too many
things. I’m excited about them all. I didn’t say that.
So what’s missing when you look at cities and human society more generally
is that even though we now have a
reasonable understanding of the properties of cities versus size
we have a pretty poor understanding
of the properties of cities in time.
That goes to may others. You mean how they evolve?
How they evolve over time? How they evolve
over time, and this is of course all entangled with
deep, deep questions like how is the US
or any other country going to grow economically in this
coming year, or is our society going to be more peaceful
or less peaceful, because basically what you find is that the properties
of cities, which vary across sizes, are then integrated in terms of an urban system
of cities of many sizes, and there are these flows of people and information
that occur between all of these places, and so
it’s important that when you have an idea in New York,
this idea is applied, for example, is applied
everywhere that it can be useful. So when you develop computing in Silicon Valley
the wealth of creating that computing is not in that idea per se,
but it’s in the application of that idea everywhere, including
to farming and agriculture, and mining and primary activities.
So that cascading of ideas and organizations all the way
through a society is what most people think is
going on when you think about economic progress, economic growth, but also
growth of other things, such as improvements in public health,
and so forth. This is important in developed countries, but
it’s especially important when you go to developing countries, where
many of the properties of cities and human societies that we haven’t developed, countries are not
there yet. And so there’s a question, for example, in terms of infrastructure and services
So there’s a question about what is the path which you can create these
properties of human settlements
in order to enable a self-sustained way of creating
again, social interactions, ideas, organizations, that allow this system
to go on and evolve, and grow, at least economically.
So that’s a very important question which has to do with economic growth and
other things, and the other question is the question of creating actually a statistical theory
of what cities are, both for organisms and for cities
and for most other complex systems, for the brain and for many other
systems, we have a very poor idea of
their statistical character. For example, all of these scaling laws only give you an idea of what
how a system behaves on the average, given its size
or any other characteristics. But when you look at a particular
creature, a particular city, it’s never quite that number
it’s something around that. So the question is what creates these deviations
all these quantities behave a lot like
fluctuations in the stock market, at least in terms of their correlations
multiple different processes. So they’re hinting to
what happens in a lot of social organizations, not just
in cities, as being the result of processes there are long chains
of causation there, more like ants, so a city gets rich because
it attracted people, it developed some infrastructure, that
allowed them to do what they do, and discovered some technology
and had finance, and more people came
it’s a long history of things that need to happen, they’re all necessary
in order to get to a certain place. There are interesting
theories, they’re all qualitative, in
the social sciences, that try to express this, and they are
good candidates for explaining some patterns like crime, or wealth
things like cumulative advantage and disadvantage, and so
I think I’m close to having a way of describing that statistically and therefore
end up with something that can actually make predications where we might expect
we might know how wrong we may be and why. Well that sounds really
interesting. We will look forward to hearing more about that later.
Okay, well, thank you so much. You’re welcome, it’s been a pleasure.