[MUSIC PLAYING]
SPEAKER 1: I'm pleased
introduce Dr. Geoffrey West.
He's very likely had the
origin of our 20% time.
It's a funny story.
We had it over lunch.
I invite you to ask
him about it sometime.
He's a theoretical physicist
whose primary interests
have been in fundamental
questions of physics,
especially those concerning
the elementary particles,
their interactions, and
cosmological implications.
His long-term fascination
in general scaling phenomena
evolved into a highly
productive collaboration
on the origin of
universal scaling laws
that pervade biology, from
the molecular genomic scale
up through mitochondria and
cells to whole organisms
and ecosystems.
And many of these
ideas, I think,
formed the basis of
his book that he's here
to talk about today, "Scale, the
Search for Simplicity and Unity
in the Complexity of Life,
from Cells to Cities, Companies
to Ecosystems,
Milliseconds to Millennia.
Please join me in giving
him a warm welcome today.
[APPLAUSE]
GEOFFREY WEST: Thank you.
Thanks.
[APPLAUSE]
Thanks.
Thanks for inviting me here.
Oops.
I'm going to get
out of that light.
So I'm going to give a
relatively short presentation,
short meaning I hope I can
keep it to about 30 minutes,
giving an overview of
what this book is about.
And it covers a huge territory.
So it's very impressionistic.
It will be non-technical.
The book is non-technical.
The presentation will
be non-technical.
But it deals with
a lot of deeply
technical and
challenging problems.
And it's encapsulated in
the title, which is here.
But there was this cute
thing that Penguin Press, who
produced the book, produced.
And I can't help
showing it to begin.
This was kind of fun.
There you go.
Isn't that cute.
[LAUGHTER]
So I'm a physicist.
And as was said in
an introduction,
I spent most of my career doing
high energy physics, quarks
and gluons and string
theory and dark matter
and so on and so forth.
And in a way, what
this book is about
and what this talk
is about is sort
of taking that way of
thinking and asking,
how far can you push
it in terms of thinking
about biology, organismic
life, and socioeconomic life?
And so a lot of what
I'm going to talk about
is going to be about
cities and companies.
But I'm also going to talk
a little bit about biology.
But the framework really is--
the whole is trying
to ask, to what extent
can you make a quantitative,
predictive, mathematizable,
computer, science of
biological phenomena,
biomedical phenomena,
and in some ways
more importantly for what
I want to talk about,
socioeconomic phenomena?
And really, the
passion behind all this
was the fundamental
question as to
whether this extraordinary
enterprise that we've
developed over the last--
well, almost 10,000
years-- maybe only really
the last couple hundred
years, is actually
sustainable in principle.
So that's what I'm going
to try to lead up to.
And as I said, it's
inspired by work
that I've done in
physics and in biology.
So the first thing
is that everybody
knows that we live in an
exponentially-- whoops.
Is this going to be a--
Someone help me.
If I press that-- oh,
a picture of a bridge.
I don't know if that's--
[LAUGHS] Actually, I talk
about bridges in the book,
ironically--
[LAUGHTER]
--the design of bridges.
Ah, very good.
So everybody knows we live
in an exponentially expanding
universe, but what is
often not appreciated
is that of course we live in
an exponentially expanding
socioeconomic universe.
And there's some
figures that you're
probably familiar with
in one form or another,
that 200 years ago, when
this country was founded,
it was almost entirely
agricultural rural.
And it's now become
overwhelmingly urbanized.
It's about 82% of the US
population lives in cities.
And just a couple of
years ago, the world
crossed this amazing point
of being over half urbanized.
And it's moving
towards that 70% to 80%
mark in the next 50 years.
So this is extraordinary.
But it's sort of interesting
to put it into sort
of more categorical terms.
Whoops.
And that is if you just
average over the next 30
to 40 years, that is equivalent
to adding about almost 1
and 1/2 million people
a week to cities,
building that infrastructure.
That means building a New
York metropolitan area
every couple of months or the
city of Seattle every few days.
That's what's going.
It's sort of extraordinary.
So everything that we do is
sort of with that backdrop,
this phenomenon that
is taking place.
And it's again
represented by this.
This is China.
China was very slow to urbanize.
The yellow there is the rural.
The red is the urban.
And it's crossed
the halfway mark.
And it is heading towards
this 80% mark very rapidly.
And that is represented
by its claim
to be building 200 to 300
new cities in the next 15
to 20 years, each in
excess of a million people.
So the stress and strain
on energy, resources,
and in particular on the
social fabric are phenomenal.
And this is just China.
We have India.
We have Latin America.
And of course we have Africa.
And of course, all of
this is a reflection
of this incredible
super exponential
growth of population.
This begins when we
started to urbanize,
when we stopped being
hunter-gatherers.
And it's sort of extraordinary.
I look at this graph.
And I'm looking up at it there.
I was born in 1940,
when there were just
over 2 billion
people on the planet.
There's now 7 and 1/2 billion.
And by the time many
of you are dead,
it will be 10 to 12 billion.
So that's unbelievable.
And so somehow we've
got to sort of think
of this in much bigger terms
than what its effect is
even just on global warming
or on the environment
or on health and energy.
And so we need to sort of
develop a much bigger picture.
And all of this is
manifested again
in this idea of
open-ended growth, which
has been so extraordinarily
successful in bringing us
to this point.
And just to give another
marker, another metric of it,
this is the growth in the US
GDP since roughly the Civil War.
If you'd invested $1 in the
stock market in about 1870,
it would now be
worth $1 million,
even allowing for inflation.
So this is just a metric of
the extraordinary expansion
of the economy.
Now all of this is bound
up with the development
of cities, the urbanization
that I talked about briefly.
So one can immediately identify
that the fate of the planet--
if you tell me what
the press, I'll--
it's marvelous that this
is happening at Google.
I love it. this is a marvel.
[LAUGHTER]
Can you imagine the stories
I could tell about this?
[LAUGHTER]
By the way, I gave
a talk at the Google
up in Mountain View
about five years--
I forget, maybe about
seven years-- ago.
And I'm very old fashioned.
I'm got three times
as old as most of you,
as far as I can tell.
But I said, yeah, I'm coming.
And then two days before, they
said, send us the PowerPoint.
I said, PowerPoint?
You must be kidding.
I said, I have a bunch
of transparencies.
[LAUGHTER]
And you know what?
They got back and they
said, what's that?
So I explained.
And they had to
go out and Google,
had to go out and buy an
overhead projector so I could
give my talk, which
was-- are we--
I can just talk.
SPEAKER 1: Yeah, talk.
[INAUDIBLE]
GEOFFREY WEST: Maybe
I'll just have to talk.
SPEAKER 1: Yeah.
GEOFFREY WEST: If
you don't mind,
I'll use by PowerPoint
as notes to just talk.
You will miss the graphs.
And you're just going to have
to use your vivid imaginations.
So let's see.
So I have lovely
pictures of cities.
So--
[LAUGHTER]
So the point is that
the future of the planet
is now really the
future of the cities,
or the future of the cities
is the future of the planet.
The fate of the
planet is completely
intertwined with what's
happening in cities.
And so there is an
urgent need to develop
a deep understanding
of cities, not
just a qualitative narrative
version of cities, which
has been the traditional way
of thinking about cities,
via urban geography, urban
planning, urban economics,
but sort of a more scientific
viewpoint, and ask,
is it even possible?
Is it possible to have that?
Is it possible to
develop a science
of cities that could
lead to understanding
their structure, their dynamics,
their growth and evolution
so that we can
understand likewise
the growth of the socioeconomic
life that we have developed
and lead into what I try to
term a sort of, so to speak,
grand unified theory
of sustainability?
So that's been my passion
for the last several years.
And we all understand why
cities are very attractive.
It's a place where there's
greater opportunities.
There's greater buzz.
There's more cultural events.
There's greater access to
good restaurants and so forth.
And this has been
the great attractor.
Is it going to come?
No, no, no?
SPEAKER 1: No.
It just doesn't like me today.
GEOFFREY WEST: No?
And I have marvelous
pictures here.
[LAUGHTER]
But in all of this, all
of the marvelous things--
and look, my god,
you live in one
of the most energetic
cities on the planet.
I mean, something just--
my god.
I was looking at all the
construction downtown.
It's phenomenal.
But all of that requires energy.
So fundamentally, at
some fundamental level,
obviously energy has to come in.
And I'm sure most of you are
familiar with the second law
of thermodynamics, which
says, roughly speaking,
that if you try to create
lots of order, then you pay--
ah.
SPEAKER 1: Sorry.
GEOFFREY WEST: We have go back.
SPEAKER 1: [INAUDIBLE], yes.
GEOFFREY WEST: OK.
So one second-- let
me see where I can--
OK.
So that's what I was saying.
I didn't [INAUDIBLE].
And this is just the
images of cities, just
images of why cities are
exciting and important.
But all of that requires energy.
And as I said, from the second
law of thermodynamics, which,
after all, is the most
fundamental law of physics--
it's the one thing
that is immutable--
creating order necessarily
produces disorder,
produces entropy.
And so in creating cities, in
creating socioeconomic order,
we of course create
socioeconomic entropy,
which means that we
have-- we create things
like this and this and
this and this and this
and of course this,
social unrest and so on.
All of these are created by
this extraordinary phenomenon.
And much of what's going
on in the world in terms
of this kind of
unrest, even if it's
put in cultural and
political and ethnic terms,
is almost certainly--
a large part
of that is just due to this
extraordinary phenomenon that's
going on.
And one of the
questions to ask is,
is this what cities are
going to end up looking like?
Is it all going to
end up being sort
of slums and so forth,
which is happening
in many parts of the
world, or even this?
And so that's what I want
to sort of lead up to.
So when we think of cities--
and I just went and talked
about Seattle a moment ago.
You saw the image is one of the
physicality of the city, that
is the infrastructure,
the roads, the buildings
and so forth.
When you think of
Paris, you think
of the Eiffel Tower and the
boulevards and so forth.
Think of New York,
the skyscrapers.
But in fact, most
important part of the city
is of course the people, because
the whole point of the city
is people.
And the whole point of a city
is to facilitate interactions
like this.
So this is really what you
should think of as a city.
There's the backdrop.
The physicality, the roads
and the transport systems
and the buildings
are really the stage
upon which interaction
takes place.
And there's a marvelous
picture of Rome,
where you see that the backdrop
has been there for 2000 years.
People have changed,
but there's still
a place to provide interaction.
And this is a marvelous
picture of New York 120 years
ago or so.
And you can viscerally feel
the entrepreneurship, the idea
creation, and so forth
that's taking place there.
And this is what cities for.
And again, those
buildings are still there.
The people are no longer there.
There's different people.
They're not on the streets.
They're in buildings.
But those buildings
and that stage
is to facilitate interaction, to
create wealth, to create ideas,
to innovate, and so on.
Places like this, that's
what cities are for.
So one of the dynamics of
any socioeconomic system--
matter of fact, of
any complex system--
is the tension between the
physicality, this physicality,
the energy, the resources,
the buildings in this case,
the flow of energy, the
metabolism of the system,
the tension between that and
the information exchange that
goes on between parts.
Yeah.
AUDIENCE: So in
your lecture, what's
a definition of a city
or urban [INAUDIBLE]??
GEOFFREY WEST: Can I
wait till near the end?
And I--
AUDIENCE: Yeah.
GEOFFREY WEST:
Bring that up again.
Because that's a very
challenging question
that's been a question for eons.
What is a city?
And we'll come back to that.
But I need to go through stuff.
And then I will give
you a definition
of a city, an
operational definition.
So there's this tension
between, so to speak,
the thermodynamics and
the information exchange
that those pictures we saw
show and also the recognition
that almost all the
problems that we face,
whether it is global warming
or it's the question of
do we have enough
energy and do we have
enough resources,
questions of crime,
pollution, health, and so
on, all these are generated
in an urban
environment primarily.
And so urbanization
can be thought
of as the source of
this tsunami of problems
that we feel we face
across the planet.
But they're also the solution
because of the very reason
I said earlier.
They are magnets
or vacuum cleaners
that suck up the smart people.
Almost all the great ideas,
almost all the wealth
that's created is created in
some kind of urban environment.
And associated with that is that
all of these various things--
this is just an incomplete
laundry list of many things
that are part of a
socioeconomic system.
Let's see.
Shall I [INAUDIBLE]?
Yeah, I'm going to press that.
Did that help?
Hey.
[APPLAUSE]
I'm not just going at flipping
transparencies apparently.
[LAUGHTER]
OK.
So an important aspect--
typically, we treat each
one of these, roughly
speaking, autonomously
or semi-autonomously.
That is, we think
only of global warming
or we only think of energy or
we only think of health issues
and so on.
But all of these are themselves
highly complex systems that
are each adapting and evolving.
And they are all, most
importantly, interacting
with each other.
And so if you only treat
one and only think of one
and stay quite stovepiped,
you have the classic problem
of all kinds of unintended
consequences because
of the neglected interactions
with everything else.
So it's very important to
develop an integrated bigger
picture in order to
see all these things
as part of an integrated whole.
So now I'm going to
switch gears and I'm
going to talk a little
bit about biology
as a way to leading back
into some of these questions
of cities and ultimately
that of companies
and then sustainability.
So first of all, if you're
going to have a science of one
of these systems, a kind
of physics of a city,
there's no way that there's a
Newton's laws of cities, That
meaning that there's a bunch
of principles from which you
can calculate anything,
so that you can--
physics is best
represented by the fact
that we know the
position of a satellite
to almost any degree of accuracy
so that we can use our cell
phones and bounce things off
and the whole IT enterprise
can work.
But that works because we can do
that to any degree of accuracy.
But the idea that you can
do that in complex adaptive
systems such as an
organism or a city
is clearly extremely unlikely.
Nevertheless, you
could have a theory
that is what I would
call coarse grained.
That is, you can get the
generic properties of the system
and understand those.
So there's kind of an
in between of not being
able to address any of the
questions to having something
predictable to any
degree of accuracy.
Maybe we can predict some
coarse grained low resolution
characteristics
of these systems.
So here's an example.
First question-- why do we
live 100 years and not 1,000?
So why is it that I can
say with absolute certainty
everybody in this room
will be dead in 100 years?
Where in the hell does
that 100 years come from?
Why isn't it 1,000
years, as I say here,
or a million years
or just 10 years?
Why isn't anybody dead
already, like a mouse?
If you were all mice--
and by the way,
mice tissue, mice
are almost identical to ours.
So why is it that they would all
be dead, if you were all mice?
So why is that?
Where does that
number come from?
And how is it generated
from molecular scales?
Or why is it that most of us had
to sleep of the order of eight
hours last night?
Why not 15 or 16, like a
mouse or three or four,
like an elephant?
An elephant only has to sleep
three or four years [INAUDIBLE]
only a couple of hours.
So where in the hell do
those numbers come from?
What is the dynamic that's
giving rise to those?
And so these are coarse
grained questions.
And their answers will
be coarse grained.
The idea that you can
predict the lifespan
of a particular individual is--
maybe you could do it
with some probability.
But the idea that you
can predict it accurately
is clearly not feasible.
So similarly, when we move into
these socioeconomic things,
are cities and companies
just large organisms?
They came out of biology.
To what extent is Google
or Seattle biological?
And why is it that,
given that, we all die,
which we've already
talked about,
but all companies die, but
cities almost never do?
It's impossible to kill a city.
You can think of
cities that have died.
But in modern times, you
could-- we've dropped atom bombs
on cities, and 25 years
later, they're fine,
whereas a small fluctuation,
relatively speaking,
in the stock market, and
you lose a TWA or a Lehman
Brothers, et cetera.
So companies are fragile.
Cities are very resilient.
And you could ask, could you
get a theory of companies?
And could you predict--
and here you're going
to throw me out-- when
Google will disappear?
Because I will show you all
companies eventually will,
like human beings.
So there's just a picture.
So what happens here
or what happened here,
for crying out loud?
[LAUGHTER]
Mick Jagger and I happen to come
from the same part of London
and are the same age.
So there you go.
So we're one of these.
We're a mammal.
And despite the fact that
we look very different,
we look different from them
and they look different
from each other, and their
immediate environment
is quite different--
this swims in the ocean
and this scurries around
and we walk on two legs.
Nevertheless, to an
extraordinary degree
of accuracy, 80%,
90%, we aer actually
scaled versions of one another
when you look at anything
that you can measure
about them, anything
physiological or anything
about their life history.
So for example, here's the
most fundamental quantity.
It's called metabolic
rate, namely
how much food you need to
eat per day to stay alive.
And that's plotted
on the vertical axis.
And on the horizontal
axis is the size.
And it's plotted
logarithmically,
up by factors of 10.
And what you see is
something extraordinary.
Here's probably the
most complex phenomenon
in the universe, metabolism,
taking stuff and making it
into life.
Yet despite it being so
extraordinarily complex
and despite the fact that
each one of these organisms
has evolved by natural selection
with its own unique history--
each subcomponent, each organ
of the systems, each cell
type, each genome has evolved
with its own unique history,
in which case you see this
idea of sort of randomness
of natural selection,
in which case,
when you've plotted
something like this,
you would expect these points
to be all over this graph--
they've all lined up on a
very simple straight line
when plotted in this way.
And of course you
plotted logarithmically
at first because to get
a mouse and an elephant--
if you try to put the
mouse here and you
wanted to put the elephant
on the same graph,
and it were linear, of
course it would be somewhere,
I don't know, Tacoma, I guess.
Anyway, so this is a way of
getting it on the same graph.
And you can see there's an
extraordinary regularity.
The extraordinary regularity
is even more amazing
when you realize that the slope
of this line is less than 1.
Because if it were 1, you would
sort of vaguely understand it.
Because if it were
linear, you'd say,
well, you double the
size of an organism,
you double the number of cells.
Therefore you double the
amount of energy that's needed.
Not the case at all.
The slope of this, as
I've written down here,
is very close to 0.75, 3/4,
so that every time you double,
you actually save about 25%.
So there's an extraordinary
economy of scale
as you go up in size.
So an elephant is more economic.
Its cells work less
hard than yours.
But yours work less hard
than your cat's, for example.
So those two things are actually
scaled versions of one another.
So there it is.
I've said it again there.
But most importantly,
in addition to this,
is that this is true of any
physiological or life history
event.
Here's a mundane one.
This is heart rates,
your heart rate.
And you can see again it's
a pretty good straight line
on this log-log plot.
And the slope of this is
close to minus 1/4, minus
because it's negative.
And here's you.
This is your white matter to
gray matter in your brain.
This is the cables versus
the operating part.
And you can see there's a
fantastic straight line.
The slope of this is
very close to 5/4.
Here's genes.
There's more fluctuations here.
But the slope is
very close to 1/4.
And so what you see
emerge from this
is this incredible
simplicity underlying
this extraordinary complexity,
with something also amazing,
that all of these
seem to have slopes
that are simple multiples
of this number, 1/4,
so that the four plays some
fundamental role in all
of biology.
And I could spend the rest
of the afternoon showing you
75 or so of these damn graphs.
Almost everything that can
be measured looks like this.
Now this is not
exactly an equation,
but I decided to
leave this in anyway.
This actually is a
copy of a transparency
I showed at that Google event.
[LAUGHTER]
That's why I put it in.
So if you look, I said
heart rates decrease--
that's what that symbol
means, decrease--
in that we're plotting
it with a slope of 1/4.
But it turns out lifespan,
how long you live,
approximately increases
with that slope of 1/4.
So if you multiply these
two things together,
the increase of lifespan
going with plus 1/4, the heart
rate decreasing with 1/4--
you multiply them together--
the 1/4s cancels.
So heart rate times
lifespan is an invariant.
It doesn't change.
But what is heart
rate times lifespan?
It's the number of heart
beats in a lifetime.
And that's the
same for all of us,
which is spiritually beautiful
and scientifically challenging
to understand.
And that number is about
1 and 1/2 billion times.
And here's the data.
And you can see it.
So this is the number of beats
per lifespan versus their size
and [INAUDIBLE].
For those that quickly
do the calculation,
we are an exception,
for very good reasons
which, if I have time, I
will come back to at the end.
So what the hell?
Where in the hell do all these
extraordinary scaling laws
come from?
What is it that's driving them?
What is it that's constraining
the randomness and chaotic
nature of natural selection?
And the idea that I developed
with some marvelous biologists
was that underlying
them is something that's
common to all these systems
and is common, in fact,
in all socioeconomic systems.
And that is that
you've got to-- you
have huge numbers of components.
And you have to service those.
Whether they're cells
or customers or workers,
you have to service
those in a, roughly
speaking, democratic
and efficient manner.
And you do that of
course by networks.
And those networks are
typically branching
hierarchical networks.
So the idea is that
networks underlie this.
And it is the
mathematics and physics
of these networks,
these networks--
there's different kinds.
That's you.
This is also you.
That's your brain.
That's the white to gray matter.
This is also you.
That's inside your cells.
And that's a mitochondrion
get inside the cell.
And they're all networks.
And the idea is that even
though these networks have
different physical
structures, the mathematics
and the generic principles
underlying the structure
and dynamics of flow
on these networks
are universal and generic.
So now you have
this theory where
you understand all of these,
at least the scaling of all
of these.
But you also have a
theory for understanding
how these networks work, not
only their structure-- that
is, if you wanted to ask me,
what is the radius length blood
flow rate stress
on the 12th branch
of your arterial network,
there's a formula in my office.
You can put in the numbers,
put it into the program,
and you'll get the right
answer for a cat, a dog,
a hippopotamus, or whatever.
So it's quite powerful.
And once you have
such a theory, you
can apply it to many things.
And some of them are
things that I talked about,
which I'm not going to
talk about now, like aging
and sleep.
But something that I'll now talk
about very briefly is growth.
Because that's crucial in
your development obviously.
And-- ah, it's come
up with something.
Maybe that will work.
I just saw an opening.
Yes.
[LAUGHTER]
You got that?
Ah, there you are.
That's you.
So how did you grow?
Let's do this quickly
before it goes.
[LAUGHTER]
Oh, goodness me.
So what is it?
So you eat.
You just had lunch.
You metabolize the food.
It turns it into
ATP and molecules
that carry your energy.
And that gets delivered through
the networks to your cells.
At the cells, it repairs
and maintains them.
It replaces those that have
died and then grows new ones.
So there's the
equation in English.
Some goes to maintenance
and some goes to growth.
And by the way, in
a few minutes I'll
do this for cities
and companies,
and it's the same
equation basically.
I mean, you have to adjust
what you mean by these things,
but it's fundamentally the same.
If you do that and you put that
into mathematics, that English
into mathematics, with the
theory that has been developed,
then here's an example.
This is our growth,
"our" meaning mammals.
Here's a rat.
And these are data points.
And that's the
prediction of the theory.
And amazingly, with
the same parameters,
the same few parameters
like the mass of a cell
and so on, you can predict the
growth curve for any animal.
And then the theory tells
you how to re-scale all those
results--
I could have shown you lots
of those growth curves--
so that everybody appears
to grow in the same way.
And here's just some
small sampling of those.
And you see they all
grow and they all
fit on the same
universal curve that
is predicted by the theory, even
by the way cancer tumors, which
I will come back to a minute.
But the important
thing is there's
a universal theory for growth.
And most importantly
for what I want to say
is that it explains why it
is that you grow quickly
at the beginning.
And something that should
have maybe bothered you--
why is it that you go on eating
but you don't go on growing?
I mean, you do a little bit--
[LAUGHTER]
--this way.
But you don't grow what's
called ontogenetically.
You stay pretty
much the same size.
And then you have this stable
configuration you get into.
And by the way, this
idea that most organisms
reach a stable size
and stop growing
is crucial in the
sustainability of life
and why life has been around
for 2 to 3 billion years.
And of course,
just jumping ahead,
this is terrible when
you think of an economy
or a company or a city.
You're supposed to be--
you're not supposed to go
and bend over like that.
It's not supposed to stop.
And what is also very
important is the reason,
if you trace back why
it is you stop growing,
it is intimately
related to this what
I call sublinear scaling, the
fact that metabolic rate scaled
with an exponent, meaning
the slope of that graph
was less than 1, the 3/4, was
crucial that this bends over.
And I could elaborate on that
if anyone's interested later.
So it's still showing, great.
So here's a summary of
some biological things.
We have these
extraordinary scaling laws.
They're non-linear.
There's these quarter powers.
It expresses an
economy of scale.
The bigger you are, the less
is needed per cell per capita
to stay alive.
I didn't emphasize
this, but going along
with that sublinear scaling
is that the pace of life
systematically slows.
Hearts beat slower.
Oxygen diffuses across
membranes slower.
You live longer.
And in fact, you could contract
a elephant's life completely
down so that it overlaps
exactly that of a mouse
if you follow
these scaling laws.
I say growth stops.
And I only intimated it--
you die.
It bends over, you stop
growing, and then you die.
OK, let's use this now
as a point of departure
to talk about cities.
So I said this already.
First question is, are cities
scaled versions of one another?
Is Seattle just a scaled
down New York City, which
is a scaled up Santa Fe, where
I live, or scaled down Chicago?
Are they scaled
versions of one another?
Well, they sure as
hell don't look alike.
They have different histories,
cultures, geographies,
and so on.
On the other hand, they're
all network systems.
There are roads and
electrical lines and so on.
They have transport systems.
And they're supplied
by energy networks.
And perhaps most importantly
that I emphasized earlier,
they all have people in them.
And people form social networks.
This is a classic
social network,
a picture of a social network
with the nodes of people.
And those are the interactions.
You're probably more
familiar with this than I am.
But these are social networks.
And two points I want to
emphasize about this--
one is that there's a
modularity to these networks
generally, because
people have families
and they have jobs with groups
and departments and so on.
So people have-- so it's
not just individuals.
But we form groups
of various sizes.
And most importantly,
illustrated on this--
we tend to draw
pictures like this
when we do our network theory.
But we tend to
forget, especially
when you use words like "the
cloud," that all of these
have to be somewhere.
People have to
actually be somewhere
when they're on their iPhone or
on their computer or whatever,
doing Facebook.
They have to be on a road, in
their office, in the bathroom.
Somewhere they have to be.
So they are glued.
People are glued, whether
they like it or not,
to being in place.
So there's an integration
between place and information.
So are cities scaled
versions of one another?
Well, here's some first
data that we look at.
And this was something mundane.
This is gas stations.
It's called petrol here because
these people I worked with
were at the ETH in Zurich.
And you can see there's
four European cities.
And you see they
scale pretty well.
And the dotted line is linear.
Just like biology,
it's sublinear.
That is, the bigger the city,
the less gas stations needed
per capita, not surprising.
But what is surprising is
they're roughly the same slope,
meaning that you save the same
amount every time you double,
no matter where you are.
And that is about 15%.
The slope of this is about 0.85,
rather than 0.75 in biology.
Furthermore, if you look at
any infrastructure, that is,
if you look at the
length of all the roads,
you look at the length of
all the electrical lines,
the power, the water lines,
the gas line, whatever
you can measure
about infrastructure,
they all have this character.
They all scale in this way.
They all scale with the
same slope, about 0.85.
And it's the same not just in
these four European countries,
but across Europe, across the
United States, across China,
across Japan, Colombia,
Chile, wherever.
Wherever you can get data,
they look exactly like this.
All infrastructure
scales in the same way.
And one interesting
consequence of that
is that since you get
this economy of scale,
that means the bigger the
city, the less entropy
that is produced.
And therefore, as
I've got up here,
the carbon footprint gets lower
per capita the bigger the city.
So the greenest city in the
United States is New York City.
And one of the
shittiest in that sense
is Santa Fe, where I live,
even though it's idyllic
compared to living in New York.
So that's fantastic.
But what is even more
amazing is if you
look at socioeconomic
quantities, things that never
existed until we started
learning language and building
communities and
ultimately cities, things
like wages and something
that someone named
Richard Florida called super
created people like everybody
here, professional people, if
you ask, how does that scale
with city size, plotted in
this same logarithmic way,
then you get some of those
more fluctuations, more noise,
but you get scaling,
but you get a slope
that is now bigger than 1.
This is called
superlinear scaling, which
means that instead of every
time as you increase, you get
an economy of scale, the bigger
you are, the less per capita,
for socioeconomic quantities,
wages, super creative people,
the bigger you are,
the more per capita.
And the slopes of these are
quite similar, around 1.15.
What is amazing is that that's
the same for almost everything
else.
I didn't write it on here.
This is the patents produced
as a function of city size.
This is just over 1.15.
This is a crime in Japan,
a little bit bigger.
This is police, tax receipts,
construction, restaurants
in Holland, and so on.
And here's just a small
panel of half a dozen
of these different variables.
Here's wages I just showed you.
Here's the GDP of France.
Here's crime in Japan.
And you can see they all have
pretty much the same slope,
so that any socioeconomic
variable, no matter what
it is, no matter where it
is across the globe, scales
in the same way, which
is sort of extraordinary,
because there wasn't
a Congress in 1782
where all of the countries
of the world decided,
this is how we're going
to build citizens and this
is how we're going to build
our socioeconomic system.
It all evolved organically.
And amazingly, out
of all that came
something universal and similar.
And here's just for
the United States.
This is just income,
GDP, crime, and patents,
re-adjusted but
to the same scale.
And you can see the same slope.
There's quite a
bit of noise in it,
but you can see overall
it's the same kind of thing.
And one of the big questions
is, where in the hell
does all this come from?
Why?
Why should that be?
So here it is in English.
If you double the
size of a city,
a given urban
system, on average,
systematically the
income, wealth, patents,
number of colleges, educational
places, creative people,
but also all these bad things,
police, AIDS, flu, crime,
[INAUDIBLE],, all these things
all increase by about 15%.
And at the same time, you save
15% on all infrastructure.
So big cities are good if
you sort of ignore this--
which people are very good at
suppressing-- crime, pollution,
and disease.
Big cities are good and big
bigger cities are even better.
So where in the hell
does all this come from?
So you ask yourself, what is
universal about all cities?
What is common
across all cities?
And what you quickly
realize is something
I've emphasized earlier.
The thing that's common is
that they are for people.
And it may sound
sort of obvious,
but amazingly, if you become
familiar with urban planning,
urban architecture,
urban economics,
what you discover is people
rarely play any role in those.
Those subjects are
almost dominated
by the physicality and pure
economics of the system.
And so the idea is it is the
universality of the mathematics
of physics of social networks,
the interaction of people,
that is underlying this.
And I don't have time again to
go into any of the mathematics
and the details of this to
show you where it comes from.
But one thing I will show
you is the following.
One crucial thing, of
course, about science
is not just to have a theory
that explains the observations,
but to predict things.
And so I'm going to give
you one prediction that you
can make without having
to see the theory, just
based on that idea.
That's what I just showed you.
And the idea is that
the universality of that
is because of the universality
of human interactions.
So if we could measure the
number of the interactions
between people as a
function of city size,
it should track this.
OK, so how do you do that?
Well, that's been very
hard until recently.
But now everybody caries,
of course, a cell phone,
which means everybody has a
little detector with them.
I'm sure you're very
familiar with this.
In fact, many of you probably
exploit it in many ways
I'm sure.
But everybody has one.
And so it's like the molecules
in this room of course.
It's almost as if
everyone is tagged.
So we know exactly where it is.
We know where it's moving, who
it's interacting with and so
on.
So with some
colleagues at MIT, we
were able to get hold of this
data of billions and billions
of cell phone calls between
people in various places
and then just do the
analysis and count
the number of interactions
between people
scaled with city size.
And by the way, an
interaction was I call you
and within six months,
you call me back.
So we have a relationship.
Now, six months is-- you
could make it three months.
You could make it two.
It's up to you.
But we-- to establish an
interaction, a relationship.
So we did that.
And here's the result. You
can see down at the bottom.
And you can see it's got
the same slope as this.
And there's two
countries on this plot.
One is the UK and the
other is Portugal.
And you can see it agrees
with the prediction.
So it's very strong
evidence that
underling this is this
theory of social networks.
Now I'm going to finish
off very quickly.
I've gone on too long, being
delayed by some of this.
But I'll try to finish
off very quickly.
First of all, I
mentioned earlier
that the network
dynamics in biology
leads to the slowing
of the pace of life.
That's the sublinear, the 3/4.
The superlinear behavior
of socioeconomic metrics
and the network underlying
it-- and by the way,
I should say one thing
about the network.
The superlinearity
comes about because
of the positive
feedback loops that we
create when we interact.
I talk to you, we
have a discussion,
and then you talk to him, and
then we have a discussion.
Well, it's what goes
on here of course.
That's what makes the
whole system work.
And that has this positive
exponentiating behavior,
which leads to this
superlinear behavior.
That's sort of the words
for the mathematics that
goes into this.
But that leads-- it also
has this curious thing
that if you have this
superlinear behavior, instead
of the pace of
life slowing down,
it speeds up in a
predictable fashion.
And here's-- so we've looked
at many things at that.
And here's one whimsical one.
This is heart rates.
This is biology,
decreasing with size.
This is walking speed in
cities as a function of size.
And this is pushing the
theory a little bit,
but you're still part of a
network when you're walking.
And you can see
there's fluctuations.
This is ancient
data, by the way.
It wasn't plotted this way.
But you can see it's very good.
And that was basically the
prediction from the theory.
And interestingly,
here's something
that I found in a British
newspaper a few months ago.
This is the city of Liverpool.
And here's what it
says underneath.
This is amazing.
"Research revealed
almost half the nation
found the slow
pace of highstreets
to be their biggest
shopping bugbear."
Not the noise and the
pollution and all the rest,
it's the other fucking
people are going to slow.
[LAUGHTER]
Sorry.
[INAUDIBLE]
[LAUGHTER]
Take it is
frustration from this.
[LAUGHTER]
And here it is.
So here is a fast
lane they put in--
[LAUGHTER]
--fast lane for workers.
[INAUDIBLE],, that was the
first day they did it.
So I'm going to miss-- there's
a marvelous quote here.
Oh, here's something.
This is for you.
"I see you buying the iPhone
5 and then shortly after Apple
launching--" this is an
old one-- "the iPhone 6."
[LAUGHTER]
And now we got the bloody
iPhone 7 driving us nuts.
OK, onward.
So here's the growth equation.
I want to go quickly
through growth.
So it's the same kind of thing.
I said it earlier.
You have to, of course,
know all the vari--
you have to add up
everything that's coming in.
And some goes to maintain
everything that's here,
maintain all the city
streets and the roads
and maintain all the people.
And some grows.
It grows new
infrastructure, new roads,
grows new people, and so on.
So you have to put that on.
This is just a cartoon of it.
And you have to put
that into mathematics.
And when you did it in
biology, we got this, you saw.
But now the input, all
of this is superlinear.
And that, instead of
giving rise to this,
which would be a disaster
in a socioeconomic system,
from our modern
capitalist viewpoint,
you get-- and I just
do cartoon versions
of this to you and idea.
It gives you something
very satisfying.
It gives you this faster
than exponential growth,
which is what we've seen.
And I'm not going to have time
to show you all the data that
agrees with this.
And that's fantastic.
So it's very satisfying.
The theory is very
self-consistent
and is very predictive.
But it has a fatal flaw.
And you've heard this in
a different form I'm sure.
And that fatal flaw
is this dotted line.
Because what the
theory says is that you
go to an infinite size
of whatever the metric is
that you're looking
at in a finite time.
This is called mathematically
a finite time singularity.
That's obviously nonsense.
You can't have an infinite
number of AIDS cases
or an infinite amount
of wages or whatever.
You can't.
So somewhere along
here, something changes.
And the theory tells you that.
The theory tells
you what happens
is that as you approach
that singularity,
the system stagnates
and then it collapses.
And that's what I've done here.
So I've just symbolically
written it here.
And that's terrible of course.
And that's what we don't want.
And so the question
is, how do we avoid it?
Well we avoid it--
as we're going
along here, when you
realize that of
course this growth was
done within a
certain set paradigm,
a certain major innovation--
we discovered coal
or we discovered iron 20,000
years ago, whenever it was.
We've invented computers.
We discovered IT.
Whatever it is, something
sets the, so to speak,
backdrop for this.
So it's clear if
you want to have
open-ended growth,
continued open-ended growth,
somewhere along here before
stagnation and collapse sets
in, you better start over again.
And you do that by
major innovation.
So innovation--
it's nothing new--
is the thing that
resets the clock.
And you can start over
again and avoid the collapse
inherent in that singularity.
So you start over again
and, boom, off you go.
And that's great.
But there's another singularity
that's going to come,
because it's the same dynamic.
So you better do it again--
Sorry.
Somehow it didn't
come out proper.
That's supposed to be there--
and so on.
I'm sorry.
I don't know what happened here.
Yeah, but you get the idea.
So you have this idea.
So you have a theorem
you can produce.
If you want to have
open-ended growth,
you have to have
continuous cycles
of paradigm-shifting
innovations.
Great.
However, here's the hitch.
I said, as you go along one of
these curves, life gets faster,
life is speeding up.
But along with that, the time
between these innovations
has to systematically
and predictably
get shorter and shorter.
So the time between innovations
necessarily gets shorter.
And so, just to make
up some numbers,
if it took 100 years to make a
major change 1,000 years ago,
now it only takes
15 to 20 years.
And so indeed the time
between, so to speak,
computers, laptops, and
IT was maybe 20, 30 years,
I don't know, whatever.
And so what this says is if
you want to avoid collapse,
you got to do it again.
You got to do it soon.
And in fact, all
the analysis shows
you got to do another major
innovation analogous to IT
in the next 20 years.
And we can speculate as
to what that might be.
If not, we're going to have a
potential crisis on our hands.
Now this is not mine.
I don't know who this guy is.
But I picked it off the--
but I got permission to use it.
But it's someone that did
some analysis on just time
to reach 10 billion customers,
just to give you a sense.
And it's very similar.
And Ray Kurzweil has a different
view of the singularity.
He likes the singularity.
But singularity is sort of
like the coming of the messiah,
whereas I see it
quite the opposite.
But this is his graph actually.
And what you see
is that line is--
what's plotted along here is how
long ago an intervention took
place-- so cells took place
a billion years ago almost--
how long it took to make
the change, a billion years,
a billion years ago.
Now it's 15 years, 15 years ago.
And you can fit it all
on a logarithmic plot.
This line is exactly what's
predicted by this theory
that I just tried
to outline for you.
So I'm going to finish off very
quickly, extremely quickly.
And I leave hanging the
question as to what--
let's go back to this--
happens here, the question of
collapse, ultimate collapse.
Because if you take the argument
to its absurd conclusion,
you would have to be making a
major paradigm shift every two
years, then every one year,
then every six months,
obviously nuts.
So something has to change.
OK, so I want to finish off
extremely quickly with--
if I can get there--
companies.
So here's a summary of
just some companies.
I can show you all the data,
if anyone's interested,
of companies.
And what's plotted here
are assets and income,
just to give you a sense.
The important take-home
message is here.
I think this is Walmart,
this one over here.
And you can see there's
some fluctuations here.
But you see this is plotted
versus number of employees.
These are tiny companies
of 10 to 100 people.
But once it gets to
be a substantial size,
they follow the
scaling pretty well.
And the important thing about
this is that it's sublinear.
It's more like us.
It is not like cities.
And if you follow the
argument through--
and this is where
I'm going to zoom--
what did that say?
It said that you stop
growing and you die.
OK, so let's look at that.
Here's the data.
This is 30,000 US
publicly traded companies
from 1950 to 2010, just all on
one plot, a big spaghetti plot.
That's all of them,
every single one, OK?
And what you see is
what you sort of know.
Here are all these new ones.
They're zooming.
All these [INAUDIBLE]
still are zooming away.
But look at these old farts.
[LAUGHTER]
They're just sort
of hang in there.
They're just going
along the top like that.
There they are.
And as you see, inflation's
been taken out of this.
So the old farts are like me and
they're sort of limping along.
And all the young Turks like
you are zooming and so forth.
And here it is, re-adjusted
to the stock market, the GDP.
And there it is.
And you can see they're
dead flat, which
means that a big enough
fluctuation of the stock
market, and you're in trouble.
That's one reason
why companies die.
And I won't go--
I don't have time in
this, because I've
gone on too long, to tell you
about the dynamic of that.
That red line, by the way,
is Boeing going along.
And I can tell you about
Boeing because I've
worked with Boeing.
So here's the growth.
That's Walmart, looking
like a big hockey stick.
But you notice it
only goes till 1994.
And here it is taken to 2008.
And that red line is a
prediction of this theory.
And you can see it's
beginning to bend over
and will go the way
of all companies.
It will join-- it is
already joining this lot,
and will bend over.
And the question is--
it will eventually die.
And this is what
this last graph is.
This is plotted-- well, let's
just focus on one of these.
This is the probability of
dying versus the company age.
And it's broken down into
companies of different sizes.
And this is divided
into those that
disappear through
acquisition and merger, those
through bankruptcy
and liquidation.
But this is 1--
see, 1-- 100% probability.
So it's very unlikely.
It's very rare for a
company to live 100 years.
You know of many that do.
And it's very rare for a
company to go 200 to 300 years.
And I don't have
time to discuss that.
I discuss it in the book.
I discussed lots of
investigations that we've done
and analyses we've done.
So I'll finish there.
I didn't complete everything.
But I want to show
you one last graph.
I went on way too long.
That's this one.
One thing I didn't say when
we were discussing metabolism
is that everybody
who's sitting here,
desperately waiting
for me to finish,
is operating at 90 watts.
So that's all you're using.
You're that light bulb.
That's all you use,
about 90 Watts.
And what it says
on this graph is
that if you think of your--
that's how you evolved.
That you are as a
biological animal, a mammal
that fits perfectly on
that metabolic rate curve.
But if you ask the
question, how much energy
do need eat you
need to stay alive
with the kind of
socioeconomic activity you do,
the kind of quality and
standard of living you have,
that number is 11,000 watts.
So that's equivalent
to each one of us
in this room is acting as if
we are about a dozen elephants
or about 70% of a
blue whale, which
is almost as big as this
whole bloody complex.
And most people-- there's
7 and 1/2 billion people
on this planet.
And all of them
want to be like us.
They all want to
live at 11,000 Watts.
So this is a huge pressure.
So I haven't--
I sort of stopped
before telling you
some speculations about survival
and collapse, survivability,
sustainability.
But this just puts the
numbers into perspective.
So I'll finish there.
And I'm sorry I went
on for too long.
Some of it was due to this
and some was my own profligacy
in just getting carried away.
So thank you.
[APPLAUSE]
And to shamelessly promote--
I didn't realize I put this up
here-- it's all in the book.
[LAUGHTER]
It's all inside,
but no equations.
