cc

>> I'm Jessica Flack and I'm the

co-director of the Center for

Complexity and Collective

Computation in the WID, and

before I introduce tonight's

topic and speakers, specials

guests, I would like to first

thank some of the folks in WID

who made tonight happen.

Maria Peot, Marianne English,

Jenny Eagleton, Sarah Shapiro,

and Beth Misco, WID's

outstanding administrative

outreach team.

I'd also like to thank Wisconsin

Public Television, who's in the

back filming the lectures

tonight.

Everybody at University

Communications who's done a

great job with help with

advertising.

Folks at the Isthmus for

accommodating changes the last

minute.

And, of course, the IT team,

Alan Ruby in particular here at

WID for their support tonight.

And, finally, the Center for

Complexity and Collective

Computation is indebted to

John Wiley and to the Templeton

Foundation for very much

appreciated critical support,

financial support of tonight's

lecture series.

Without this support, we could

not have these kinds of events.

So, tonight's lecture on the

roles of energy and information

in the 21st century biology is

part of a new public lecture

series sponsored by C4, that's

the Center for Complexity and

Collective Computation, and the

series is called John

von Neumann Public Lectures

in Complexity and Computation.

And the lectures are going to be

each month at about 7:00 PM the

second or third Wednesday

over the course of the fall

and spring semesters in 2012

and 2013.

We have a great lineup this

fall.

The next speaker, the next

lecture is on October 17th, and

it's by Graham Spencer who you

might know as one of the

original founders of Excite, and

he's currently a partner at

Google Ventures.

And he's going to be talking

about programming the Internet.

He's a super smart, broadminded

computer scientist.

He gives fun, insightful

lectures, so I hope to see you

guys all there.

Energy and information are two

of the most fundamental concepts

in science, effecting phenomena

over a vase range of scales.

From quantum mechanics to the

origin of life to the evolution

of cooperation in human and

animals society to, also as

you'll hear about tonight, the

size and pace of cities.

In tonight's lecture,

Dr. Geoffrey West and Dr. David

Krakauer, who I will introduce

momentarily, will explore how

information and energy have

shaped nature, and they'll

discuss the implications and the

principles of the future of

science, the future of biology,

and for society.

Some of the questions that we

will tackle over the course of

the evening tonight include,

what really are energy and

information?

The laws of physics, and

specifically the first law of

thermodynamics, tells us that

energy cannot be created or

destroyed.

As such, it's said to be a

conserved quantity.

Meaning that the total energy in

the universe is a constant.

It is this property of energy

coupled to the fact that it can

be hard to transform into

something useful that makes it

seem like a finite expendable

resource.

Now, is the same true for

information?

Or can information be creative

and destroyed?

And if so, what does this mean?

So, as you'll hear tonight,

existing theories of information

stress that information

increases our understanding of

the world around us.

In formal terms, we say that

information reduces the

uncertainty about the

environment.

But there's nothing in this

definition that captures, I

think, the fact that most of us,

for most of us in this audience,

information is really about

meaning.

It's the knowledge itself, not

about reducing uncertainty about

the knowledge that we're

interested in.

So existing formal theories of

information have nothing to say

about this, what you might call,

semantics.

Do we need a new theory of

information that includes

meaning or semantics?

That's one of the questions

we'll ask tonight.

Can we use existing formal

theories of information?

Or the new ones that we might

build to overcome energetic

constraints.

Or does information only help us

find solutions to harness energy

more efficiently?

These are deep and unanswered

questions in physics and

biology.

So, finally, we are going to

ask, what would a science of

energy and information mean for

physics, for computer science,

for biology, and for society?

So our first speaker is

Dr. Geoffrey West.

Geoffrey is a theoretical

physicist.

His primary interests have been

in fundamental questions in

physics, especially those

concerning the elementary

particles, their interactions in

cosmological implications.

Geoffrey received his BA from

Cambridge in 1961, his doctorate

from Stanford in 1966 where he

returned in 1970 as a member of

the faculty.

He was the leader and founder of

the Higher Energy Physics Group

at Los Alamos National Lab.

And then he moved to the

Santa Fe Institute in 2003 where

he became a distinguished member

of the faculty and eventually

became president of SFI for 2005

to 2009.

His long-term fascination has

been in the origin of universal

scaling laws that pervade

biology from the molecular

genomic scale up through

mitochondrian cells to whole

organisms and ecosystems.

He's currently working to extend

the theory of scaling as it was

developed in biology to social

systems, with the goal of

understanding the structure and

dynamic of social organizations

such as cities and corporations.

Tonight, he will talk on energy

scaling the future of life on

Earth for about 45 minutes.

Our second lecturer is David

Krakauer.

David is the director of the

Wisconsin Institute for

Discovery, co-director of the

Center for Complexity and

Collective Computation,

professor of genetics at UW

Madison and external professor

at the Santa Fe Institute.

A graduate of University of

London and Oxford University,

he's authored more than a

hundred scientific papers,

served on several journal

editorial boards, and works to

bridge the gap between academia

and business and also between

the physical and biological

sciences on the one hand and the

social sciences and humanities

on the other.

His research focuses on the

evolutionary history of

information processing

mechanisms in adaptive systems.

But his mind, and I know him

well, is perhaps best described

as sitting in an intellectual

spaces deeply influenced by, I

guess, I think, a somewhat

idiosyncratic group of some of

history's most interesting

contributors including John

Louis Borges, John von Neumann,

Turing, Bill Hamilton,

and Sir Thomas Browne.

Tonight, he will talk for

30 minutes on the power of

information to transfer in the

21st century.

Following the lectures of David

and Geoffrey, Steve Paulson from

Wisconsin Public Radio will lead

a panel discussion on the role

of energy and information on

21st century biology.

Steve is the executive producer

and interviewer with To the Best

of Our Knowledge, a well known

science radio program.

Paulson has written for Salon,

Slate, the Huffington Post, the

Chronicle of Higher Education,

The Independent, and many other

publications.

His radio reports have also been

broadcast on NPR's

Morning Edition

and All Things Considered.

His recent book, Atoms and Eden:

Conversations on Religion and

Science, was published from the

University Press.

So please join me for a bit of a

marathon session and welcome

Steven, David, and Geoffrey.

Thank you.

[APPLAUSE]

>> Okay, thank you.

Thank you, David, thank you,

Jessica, and thank you for

inviting me to give this talk

and be part of this interesting

discussion about energy and

information So, this is the

title that was given to me,

mostly because I was delinquent

in giving one myself, and it

does sort of cover what I'm

going to talk about, but this is

kind of a landscape that I'm

going to be talking about

tonight in terms, primarily, of

energy and the ideas surrounding

metabolism in biology.

And it's a very idiosyncratic

view, and as is clear from the

introduction, I come to it as a

theoretical physicists wondering

into areas of biology and the

social sciences.

And I'm going to begin it, I'm

going to take as a point of

departure, something that's

relevant to this, namely this is

supported by the Center for

Computation and Complexity, or

whatever, complexity was in it.

[LAUGHTER]

And I wanted to use as a point

of departure a statement

by Stephen Hawking,

who is not someone that works on

complexity and not someone that

works on biology, but is a

highly reductionistic

physicist.

And here's something he said

about 10 years ago: "Some say

that while the 20th century was

the century of physics, we are

now entering the century of

biology, what do you think of

this?"

And many people believe the 21st

century is the century of

biology and it certainly will

be, but more importantly, and

this I do believe, is what

Hawking says, "I think the next

century will be the century of

complexity" because many of the

problems we're going to face and

are facing to do with complex

adaptive systems, and I'll be

talking a little bit about them,

and I want to use this, as I

say, as a point of departure

because implicit in this is an

idea of what complexity is, and

that's something I'm not going

to discuss except to use it to

talk about what is not complex

first because it brings in the

whole role of energy.

And this is a system we're

familiar with, the solar system,

and this is a system that is

often thought of as not complex.

It is simple, and by simple, it

means that the whole

dynamic of that system, the

motion of the planets around the

sun and, therefore, of the

satellites that power our

communication systems, are

encoded in two very simple

looking equations.

This is a highly non-technical

talk.

You don't have to understand the

equations.

Everybody is familiar with the

idea F equal MA.

But there are two laws proposed

by Newton that kind of covers

everything to do with the motion

of the solar system, the planets

around the sun, but much, much

else.

Much of modern physics is based

on these equations or versions

of these equations, but critical

in this is the flow of energy.

The idea that energy is the kind

of fundamental thing that's

underlying this motion.

And one of the crucial aspects

of that has already been

mentioned by Jessica: the

conservation of energy.

But if you add to it another set

of equations which you don't

have to understand but important

to understand conceptually, the

equations of Maxwell for

unifying electricity and

magnetism and giving rise to

understanding radiation,

electromagnetic radiation.

That pretty much encompasses

much of the kind of world that

we live in, and just to take

these two equations with these

equations is how your cell phone

works and allows everybody to

communicate and everything else

that's pretty much around us.

And the word simple applies too

because they invoke the idea

that you can encode everything

in the symbolic fashion, and

from that, everything else

follows, even down to the

quantum level.

And one of the things I want to

emphasize is that, again, these

are to do with the flow of

energy and because they are

simple and they are written in a

mathematically precise form, you

can calculate things somehow in

principled to any degree of

accuracy.

And one of the great triumphs of

20th century science is this

calculation which is calculating

the strength of the magnet

associated with the electron,

and here's the theory to 12

decimal places and here's the

experiment agreeing with it to

12 decimal places.

It's kind of extraordinary, and

this is quite unusual, actually,

in the world that we live in

because the world that we live

in, oh, I forgot about that.

I put this slide in just to show

you some of the main characters

that you're familiar with that I

think played an important role

in developing ideas of energy.

One is Newton.

That's a kind of rock star image

of Newton.

[LAUGHTER]

This is Einstein

looking like Einstein.

And this is Max Planck,

the origin of photons.

And this is someone--

Oops.

This is someone that most people

are not familiar with.

It's another, there was a famous

Bill Hamilton here, biologist,

very famous ecologist/biologist

that Jessica actually mentioned.

This is a really deep

Bill Hamilton.

This is Bill Hamilton.

He was an Irishman who sadly

drank himself to death, but he

encoded something extremely

important about energy, and the

physicists in the audience will

forgive me, but basically he

said that all physical systems,

in some sense, minimize the

amount of energy that needs to

be expended.

So this kind of a least minimum,

least energy principle

Hamilton's principle is actually

called, technically, the

principle of least action, but

roughly speaking, this is the

framework in which all of

physics that encompasses

everything from the macroscopic

down to string theory operates

from is using those principles.

And it's has to do based on

energy.

So, here's a picture of energy.

This is energy.

We're familiar with it.

This is highly complex use of

energy because these are highly

non-linear effects and you're

familiar with them.

You're also familiar with this.

This is the energy that we

create on the planet through

objects like this, and I'm going

to talk a little bit about that

shortly.

And these are driven, of course,

by us, which involve energy.

So they're these multiple levels

of energy, and they go all the

way down to the thing that

drives these, us, that drives us

to create the light and the

energy by which we live, our

brains and ourselves.

And these, of course, also

operate by energy themselves,

but they invoke something new

that has typically not been

considered in physics.

And they invoke information, the

idea that information

necessarily is needed to be put

into the system in order to

understand the way these systems

have evolved and the way they

organize and the way they

behave.

And these systems are typically

outside physics, and they form

much of the work of biology and

the social sciences, and they

are complex but, more

importantly, they evolve and are

therefore adaptive.

So these are very different in

their nature from the kind of

simplistic system that we see in

the motions of the planets or

even, might I say, in thinking

about string theory as the

origins of the universe because

we can write definitive, precise

equations in principle for

those.

Whereas for these, we have this

extraordinarily challenging

problem of both integrating

information in it and

understanding the role of

energy.

And what I'm going to explore

tonight is how far one can take

the traditional physics thinking

without invoking, explicitly,

information to understand some

of the organization and dynamics

of these systems.

And then David will talk about

information, and maybe in the

discussion we'll talk a little

bit about the integration.

So here's another kind of system

which you're familiar with, a

forest.

And all of these systems obey

the second law of

thermodynamics, the conservation

of energy, and, therefore, the

production of entropy which is

the kind of random stuff, the

dissipative kind of energy that

comes from doing work and

creating order.

It is the resulting disorder

that comes from ordering the

system.

And one of the challenges, and I

would even go as far as to say I

think the major challenge

conceptually in 21st century

science is understanding this

integration between energy and

resources on the one hand,

metabolism and biology with

information genomics, maybe, and

integrate and get a complete

understanding of these.

These are typically considered

separately, roughly speaking, in

the kind of academic way in

which we think about these

things.

So, what I'm going to talk about

in these systems is, as a

physicist, I want a

quantitative, predictive,

mathematizable in principle,

understanding of the dynamics of

organization of these systems.

And it is clear for the kinds of

systems I just referred to, we

cannot do what we did for

understanding the magnetic

moment of the electron.

We're not going to get equations

that produce precise

calculations, but maybe what we

can get is what we call

coarse-grained descriptions.

So, for example, the kinds of

questions that we ought to be

able to answer quantitatively

are questions like this: why is

it that I can look across this

room and say everybody will be

dead in a hundred years?

In fact, if I look more closely,

I would say that...

[LAUGHTER]

Including myself, might I say.

But where does a hundred years

come from for a typical

life span of a human being?

Why isn't it 10 years or a

thousand years?

And how is that connected to

underlying molecular structures

of the genome and the complex?

Where in the hell does that come

from?

And why is it that the same

stuff, if it were a mouse, only

lives two or three years?

So those are the kind of

coarse-grained questions that

one ought to be able to answer

in a predictive framework, and

I'm not going to talk much about

these, but in what role does

energy and metabolism play in

these?

So here's another set of similar

kinds of coarse-grained

questions.

Why do we need eight hours of

sleep?

Why do we need to sleep?

Why is it not two hours or 18

hours like a mouse or four hours

like an elephant?

Where in the hell do these

numbers come from, and why is it

that mice get many more tumors

during their life span per unit

volume than human beings?

And, therefore, why in the hell

are we doing all these

experiments on mice and trying

to draw conclusions about human

beings?

We need to understand those

kinds of questions, and the one

I really love is, why is it very

good to grow babies in your body

but not grow cancer which is

also part of you?

So these are kind of

coarse-grained questions, and

one that has occupied me in the

last few years is, are cities

and companies just large

organisms that are biological or

are cities organisms just, is

New York a great big whale and

Google a great big elephant, and

if that's true, how come

organisms die, companies die,

but cities don't?

And in fact, I would say,

roughly speaking, almost no

cities die, although you can

think of counter-examples.

We have done awful experiments

to test that.

We've dropped two atom bombs on

two cities, and 30 year later,

they're functioning.

Companies, all companies, die.

And so one of the questions is,

in a coarse-grained way, can we

predict when Google or Microsoft

are going to go bust?

Because they surely will

eventually.

Okay, so those are the kind of

questions, and I want to lead

into all of this, this is

biology but I want to lead into

it via cities because we are

facing an extraordinary

challenge, and this is

addressing the question of how

biological our cities and can we

understand them that way.

We are facing this extraordinary

challenge that we live in this

exponentially expanding universe

of socioeconomic quantities And

just to give you an example,

we've gone from a few percent

being urban to over 80% being

urban in just 200 years.

The world's crossed the halfway

mark.

We're going to go to somewhere

close to 80% by 2050.

China's building several hundred

new cities in the next 20 to 30

years, and indeed, this

statistic ought to freak

everybody out that every week

from now to 2050, on the

average, one and a half million

people are being urbanized.

Therefore, every two months,

there's a New York metropolitan

area coming onto this planet.

So by the beginning of December,

there's another New York

metropolitan area.

By the beginning of February,

there's another one.

By the beginning of April,

there's another one and so on,

inextricably for the next 30-40

years.

Now, can I use expletives in

this talk?

>> Sure.

>> Okay, how the [###] are we

going to deal with that?

[LAUGHTER]

This is an extraordinary

challenge.

So I want to get into that, and

it is related to energy.

Okay, so, the other aspect to

recognize is that all of the

tsunami and problems we're

facing from global warming to

the environment to health

problems to crime to resource

problems, water, energy, and so

on, all are driven by

urbanization, by people in

cities.

So that's the problem, but

cities are also the solution

because they suck all of you

into Madison.

Cities suck in people, and they

are the sources of ideas,

innovation, and wealth creation.

So, this is what cities are

represented by.

The good of cities, this is

what, in fact, attracts people

to cities.

All these good things, culture,

music, goods and so on, driven

and participating in an ever

expanding, exponential expanding

economy, but if this is the

thermodynamic system, which it

is, it produces entropy.

So it produces socioeconomic

entropy.

Incidentally, side comment, if

you look in any of the standard

economics books, you never see

the word entropy, but what is

more amazing is we've looked in

five of the standard texts, only

in one did the word energy

appear.

That's a side comment.

Provocative comment.

So here's socioeconomic entropy.

Okay?

You're familiar with all this.

And the question is, is that

what New York and San Francisco

looked like 2050?

Or that?

Or like this?

Or this?

This is what we want.

Or even like that?

Beautiful.

Or like that in a hundred years.

Or that.

And most importantly, to

maintain that which is a social

buzz of a city.

The interaction of people.

So, here's the question, given

this extraordinary role of

cities, we need a science of

cities.

And I'm going to use energy as

the major piece of that.

So the question is, can we have

a science of that, and is that

another version of this?

Well, if it is, that would be

good, actually.

If this were another version of

that because you ask any

question that has a quantitative

metric associated with it, like

how many trees are there of a

given size, how many leaves are

there on a given branch of a

given size, how much energy is

flowing through each one, how

big is the canopy, what is the

mortality rate, what is the

growth rate, how far apart the

trees of given size, etc, all

those can be answered in a

quantitative conceptual

framework with a bunch of

mathematical formulas which,

in a coarse-grained way,

agrees with data from forests

all across the globe.

And the question is, can we do

the same for this?

Sorry, oops.

I went backwards.

So, well here's some of the

commonalities, metaphorically

at least, between social

organizations and biological

organisms which we're all

familiar with, and I'm going to

show you just very briefly some

data that substantiates the idea

that we understand a little bit

about generically the way

forests work.

So what this is, is just the

plot of the number of trees of a

given size.

The theory, which I'm going to

talk about momentarily, the

theory predicts that it goes as

the inverse square of the

diameter of the tree trunk.

So the number of trees of a

given size goes to the inverse

square of the diameter of the

tree trunks which means if you

look at trees that are twice the

size of any others, there's a

quarter of the number, two

squared, a quarter of the

number.

If it were three times, it'd be

eighth and so on.

So, you get the idea.

And there's data and what is

important about it, not only

does the data fit the theory,

but you can see it's just over a

period of 30-40 years, this

forest, which is a tropical

forest, has changed

dramatically.

Lots of trees have died, new

ones have grown.

Record turnover.

But this law has remained

robust, and there's data into

the '90s and 2000s that continue

with that.

I'm going to miss this.

So here's the kind of framework

that I want to invoke.

Here's a cartoon of said

ecosystem.

I could have done the city.

And this is the lens that I want

to look at it through, and all

of these equations actually are

really to do with energy flow.

Okay.

So, another concept I want to

talk about very briefly is the

concept of scalability that a

system needs to be scalable if

it is a system that is going to

be resilient and evolvable and

adaptive to change.

It needs to be scalable just in

the sense that we are scalable.

This is us.

We have scaled over a range of a

hundred million in size and

mass.

We go down to something that

sits on the palm of my hand all

the way up to something that's

much bigger than this room, and

we're all pretty much the same

thing.

We may look different, but in

terms of, in a coarse-grained

average way, in terms of our

life history and our physiology,

we're all pretty much the same

thing, and I'm going to show you

that in a moment.

But even going further in going

from the molecular levels, I

could have put the genome here,

this is what the cartoon of the

molecules that produce your

energy, all the way through

mitochondria to cells to

multicellular organisms like

this that produce things like

this, houses, and produce things

like this so that, in fact, what

goes on here scales all the way

through this to keep this going.

All of these have to be scalable

because they are highly complex

and continuously evolving and

continuously adapting.

And so there has to be a kind of

scalability, and out of that

comes the idea that you can't

have this being arbitrary.

There have to be emergent laws

that govern this, and I think

the next slide, ah, yes.

So, in a similar way to the way

we scaled in a much shorter time

frame in terms of our growth,

and one of the things that I

will talk about briefly in a

moment is about growth and the

fact that the same theory, the

same theoretical framework,

which I will come to in a

moment, which purports to

explain the organization,

dynamics, growth of forests that

I talked about earlier applied

to us, to mammals, gives rise to

equations like this or pictures

like this for the,

what is called a growth curve.

This is the weight as a function

of age.

This is us.

It happens to be a rat version

of us.

And the line there, the solid

line is a prediction from the

theory and the points are the

data, and you see it's very

good.

And this can be done for any

organism, and if I had time, I

would show you lots of other

wonderful fits.

But the point is that we can

understand growth, and I will

come to that in a moment, but

the important thing I want to

stress now, moving to social

systems, is if this were taken

over to socioeconomic systems,

mainly that the organism grows

quickly and then stops like we

did, I mean one of the amazing

things about biology is that we

eat, we grow, and then we go on

eating but we don't grow, and

the theory explains that.

I don't have time to go into it.

Maybe we can discuss it later.

And it's all to do with the

input of energy and the

distribution of energy and the

scaling of the networks that are

inside us.

But the point is that this would

be very bad if it were a

socioeconomic system because

this is our image of

socioeconomic systems, and I

showed you a picture before of

the economy, always open and

expanding.

And I want to come back to this

in a moment.

So, here's a picture of the

extraordinary scaling in

biology.

So, let me spend a minute on it.

What you see that's plotted here

is the most fundamental

quantity, certainly from a

physicist's view, in biology

That is your metabolic rate.

How much energy do you need per

day to stay alive.

The 2,000 food calories a day

that you eat to stay alive, and

here it is plotted on the

vertical axis against the

weight, the mass of the organism

on the horizontal axis, and it's

plotted in a Byzantine way.

So I can put mice and elephants

on the same graph, and the

Byzantine way is that instead of

being linear, it goes up by

factors of 10.

One, 10, 100, 1,000 watts.

One, 10, 100, 1,000 kilograms.

So we can get everyone on the

same graph.

And what you see is something

extraordinary.

That there's an extraordinary

regularity when we're dealing

with maybe one of the most

complex and diverse phenomena in

the universe.

Something unbelievably simple,

ridiculously simple has emerged,

and this, at some level,

astonishing because we believe

that each one of these

organisms, each subsystem of

that organism, each organ, each

cell type, each genome has

evolved with its own unique

history and its own unique

environmental niche.

So, you might have expected from

that natural selection viewpoint

that if I plotted anything

versus the size of an organism,

it'd be all over the map

representing, manifesting the

historical contingency involved

in all of this.

No, that's not what you see.

You see something ridiculously

regular, and that regularity has

some extraordinary features.

For one, the slope of it is

approximately three-quarters,

and that three-quarters is less

than one.

One would be a slope,

a line like this.

And one is what you might

naively expect if you thought

everything were pretty much the

same at the most naive level.

You'd double the size of an

organism, double the number of

cells, therefore doubling the

amount of energy needed to keep

it alive.

That's not what you see.

You see something less than one

which means, in that language,

double the size of an organism,

approximately, instead of

needing twice the amount of

energy, you only need, roughly,

75%.

So there's this extraordinary

economy of scale as you get

bigger, and this is called

sub-linear behavior.

So it turns out that if you look

at any physiological variable

that you can measure or any life

history event that you can think

of, it all has this kind of

character.

And I'm going to show you a

couple of examples.

There's heart rate, a very

mundane kind of quantity, versus

body size.

I'm not going to explain it in

detail, but what you see is,

again, a very simple scaling

law.

This is your thinking.

This is your white matter to

gray matter in your brain.

Again, a very simple law

evolving.

This is your genomes, and what

you see from this is that you

have this regular behavior and

the other remarkable thing is

all the slopes of those, like

that three-quarters, are simple

multiples of one-quarter.

Four plays this amazing role in

these scaling laws of

extraordinarily complex

phenomena.

What is underlying them, and

this is work I did with

colleagues at the Santa Fe

Institute, and I'll tell you who

they are in a moment, the idea

is how in the hell can this be

that all of these phenomena have

simple scaling laws, and why is

it that the scaling laws of the

mammals are the same as for

birds, fish, crustacea, insects,

and so on?

How can that be?

Well, what could be universal

among them?

What could be universal is

somehow the supply of energy and

resources through networks has

to be a commonality among them,

and the idea here was, is that

it is the mathematics, the

universal mathematics and

geometric topological properties

of those networks that transcend

design that are constraining

natural selection.

That's the idea.

And I don't have time to go into

that other than to say that if

you look at these networks,

that's your brain, that's your

white to gray matter of the

brain, that's your lungs, that's

a tree, that's a little thing,

that's inside an elephant, but

they're ubiquitous.

And they have these, the

postulate is they have these

properties, and if you use those

properties, put them into fancy

mathematics and use Bill

Hamilton's principle of energy,

least action kind of ideas, out

pop all of these marvelous

scaling laws, including the

number four, the one-quarter,

which happens to be, for your

information, the dimension that

we live in three plus one.

And just to add to that, the

plus one has to do with the fact

that these networks have a

fractal-like behavior.

That's a kind of tangential

remark.

So, here's others.

This is down at the

intracellular level.

This is the mitochondrial level

and so on.

So here's that growth again.

So, the idea is we have this

theory.

The claim is we have this

wonderful theory based on

networks, the mathematics of

networks, and that from that we

can understand these scaling

laws, but also, if you like, we

understand the network.

We can understand the structure

of the network within each of

the organisms.

And you can use that to go back

to this growth and use this kind

of idea that if you eat, you

metabolize.

That energy goes through the

network, controls the network

which maintains the cells that

are there, replaces ones that

are dead, and grows new ones.

And then, from that, you can

mathematize it, and that gives

rise to this equation which

actually invokes all of the

properties of the network and

gives rise to these kinds of,

what we call, sigmoidal, meaning

they stop growing, curves.

Okay.

So here's kind of a summary of

this part.

We have these amazing non-linear

quarter power scaling laws.

They represent an economy of

scale.

The one-quarter is intimately

related to flows of energy in

networks.

One thing I did not emphasize is

the pace of life systematically

slows with size.

So that, for example, heart

rates decrease systematically

when in to these quarter power

scaling laws.

The rate of diffusion of oxygen,

say, across membranes decreases

with size.

Organisms live longer

systematically.

And so on and so forth.

Everything to do with time slows

down the bigger you are.

Growth is sigmoidal.

It reaches a stable size of

maturity.

And to emphasize again, the idea

is this is all based on

mathematics and properties of

networks.

So, and this is a sustainable

system.

It's sustainable,

phenomenologically, because it's

been around a couple billion

years or more.

Part of that sustainability, I

believe, is actually invoked in

the fact that things stop

growing.

They stabilize and most

organisms, not all, we can

discuss this, most organisms

spend most of their mature life

in a stable size configuration.

Not all and there are

exceptions, and we can discuss

that.

But this leads to a sustainable,

resilient system.

And the question is, I'm sorry.

Yes, so this is the motley crew

that I work with.

Some biologists, some

physicists, some chemists.

But I then took the work over

into social organizations, and

there's another motley crew.

The top guys did all the work.

All the guys at the bottom are

famous and did none of the work.

[LAUGHTER]

And the guys in the middle

started off with me and then

moved on to other things.

Okay, so here's the question,

the first question about whether

cities and companies are

biological.

The first thing is, do they

scale?

So what we said before was that

whales live in the ocean and

elephants have trunks and

giraffes long necks and we stand

on two feet and mice scurry

around, but in fact, at the kind

of 85% level, they're scale

versions of one another in this

non-linear quarter power

fashion.

In this mathematical fashion,

they're scaled versions of one

another.

Is that true of cities?

And is it the kind of universal

scaling as it is in biology?

So, are cities scaled versions

of one another?

Is New York a scaled up

San Francisco, which is

a scaled up Madison,

which is a scaled up Santa Fe?

Even though they look completely

different.

Well, you can only answer that,

at first light it's hard to

believe, every city feels so

unique.

But of course, you can only

answer that by looking at data.

But one of the reasons you might

think there are scaling

phenomena, there are these kind

of generic universal

similarities, is because cities

are, indeed, networks.

There's the obvious networks of

roads and electrical lines and

all the rest of it, like this,

and transportation systems, but,

more importantly, there's this

network which never occurs,

essentially, in biology.

It is the network of us

talking to one another.

It's us, social networks,

that is unique.

It's only been around on this

planet for maybe 10,000 years.

Maybe in the universe for all we

know for 10,000 years.

Who in the hell knows.

But, certainly, in our solar

system.

This is it.

It's new.

And it's this.

This is just people talking to

one another, how they interact

with one another.

So, oops, I seem to have lost

the slide.

So, I use this slide.

What is the city but the people.

Indeed, the city is not the

buildings and the roads and all

this other stuff.

It's us.

We are the city, and that stuff

is a manifestation of the

interactions between us.

And that's something that is

outside of biology but is

integral to biology because it's

driven by biology, and it's

driven, in large part, by

energy, and I'm going to talk

about that.

So, the other part of the

network is not just we interact

with each other but we prosper.

So, I'm going to not say much

more about that but go straight

to the data and ask the question

about scaling.

So this is a graph plotted in

the same way as those biological

graphs were, but this is a

mundane one.

The number of petrol stations,

I was working with European

colleagues, the number of gas

stations in a city as a function

of its size in various

countries.

And you can see, there's pretty

good evidence of scaling again,

there's a very simple line on

this plot, plotted this way.

Very much like biology, and like

biology, this would be linear so

that you don't, when you double

the size of a city, you don't

need twice as many gas stations.

In fact, what you find from

this, you only need about 85%

more gas stations.

The slope of this line is not

three-quarters as it is in

biology.

It's about .85 it turns out.

Most importantly, though, is

that they're pretty much the

same.

The slopes of these are the

same.

The same economy of scale occurs

in all these European countries

but it occurs in Columbia, it

occurs in Chile, it occurs in

China.

Anywhere you look across the

globe, you get the same scaling,

and if you look at any

infrastructural quantity that

you can measure, you get the

same scaling as this.

There's a consistent, always

roughly 15% saving every time

you double.

Okay?

>> Geoffrey?

>> Yeah.

>> Ten.

>> Good, perfect.

So this is kind of a mundane

quantity, and that's like

biology.

Looks just like biology

except it's .85

instead of .75.

But this is something that

doesn't exist in biology.

Wages, super-creative people,

and you ask, how do those scale

with the size of the city?

And here they are.

This is wages at the top, and

this is super-creatives,

so-called super-creatives by a

man named Richard Florida.

And what you see is there's more

fluctuations in the data, but

there's pretty good evidence of

scaling.

But, most importantly, the

scaling is different than in

biology.

The slope of these are bigger

than one rather than less than

one, and this is critical.

Namely, the slope of these you

see is roughly about 1.15.

So what that says is that if you

double the size, instead of

needing less per capita, you

have more per capita, and I will

come back to this in a moment.

So here's wages, super-creative.

Here's patents.

This is some crude measure of

the innovation of a city.

The slope of this is also about

1.15.

This is the crime in Japan, a

little bit bigger 1.15.

This is police, tax receipts,

construction, all about 1.15.

And there they are, all plotted

on one another, just a few of

them to show a universality of

income, GDP, crime, and patents.

Very different things but they

all scale in the same way with

quite a lot of fluctuations, and

the slope of that is roughly

1.15.

And we believe that underlying

are the universality of social

networks.

The way we interact with each

other is a kind of universal

phenomenon whether you're in

China, Japan, Columbia,

United States, anywhere.

The fundamentals of the social

interaction is universal, and

that is what is being manifested

in these scaling laws.

These scaling laws are for

multiple socioeconomic

quantities anywhere in the

world.

We've looked at data in Europe,

United States, China, Japan,

Latin America, and so forth.

What is interesting to

substantiate that is this graph

which is very recent data which

is the number of cell phone, the

amount of people you call on

your phone, on the average, as a

function of city size.

So this is a kind of way of

tapping in to the network, the

social network, and if this

theoretical framework of the

mathematics of that social

network is the right one, the

slope of this should be about

the same as these other

networks.

And indeed, we could have

plotted this on top of this, and

it would fit right on top of it.

So this is the number of people

you talk to, on the average, as

a function of your city size.

So, I'm going to miss this.

That's what I said a moment ago.

And here's kind of a summary of

this.

The good, the bad, and the ugly.

If you double the size of a city

or if you look at a city that's

twice as big as another city,

then systematically across the

globe, and you double the size,

you can go from 50,000 to

100,000 or 5 million to 10

million, it doesn't matter where

you begin to double the size,

systematically, income, wealth,

patents, colleges, creative

people, police, crime, AIDS,

flu, all of these things, the

good, bad, and the ugly all go

up together to the same degree.

And, at the same time, you save

about 15% on the infrastructure.

Big cities are good, and the

bigger ones are better still

both at the collective level.

They save on infrastructure per

capita.

I had a graph which I flipped

through.

They save on carbon footprint

per capita.

And at the individual level,

everybody is attracted to cities

by the first lot of those,

income, wealth, number of

patents, colleges, and the

general buzz of city, and ignore

the other bad things.

The fact that they're coming

along with it, hand in glove,

there's more crime, disease, and

so forth.

Okay.

So, the network dynamics is the

one that dominates this again,

and the networks dynamics has

the following interesting

phenomenon that if it's

sub-linear scaling then we have

this pace of life slowing with

size, as I mentioned, but if

it's super linear, bigger than

one like we see in socioeconomic

systems in cities and driven by

the social networks, then the

theory tells you the pace of

life systematically increases

with size.

And so, the pace of life in

New York is systematically

faster than it is in Madison,

which is systematically faster

than it is in Espanola, which is

a tiny town near Santa Fe

in a systematic way.

And here's some whimsical data

to show you that.

We've looked at many things.

On the left is some data on

heart rate versus body size.

The one on the right is walking

speed.

I show you walking speed.

People have actually measured

walking speed perfect.

And you can see it

systematically rising, and it's

in reasonably good agreement

with the data.

But why?

Because walking, actually,

walking in a city is a social

phenomenon.

Okay, so I'm going to miss this

out, I think.

Yes.

So, this is kind of a summary,

and I'm going to finish off in

the last few minutes with the

last part of this.

So, unlike biology, super linear

scaling, socioeconomic, which

has to do with wealth creation

and innovation and ideas,

dominates, and so you get this

kind of 15% rule, which I'm not

going to talk about its origins

here, that also leads to a

systematic increase in the pace

of life.

And the last thing that I'm

going to talk about which is

very satisfactory is that if

this is true, which it is, and

it comes from these networks,

then I'm going to talk about

growth and the idea of

open-ended growth following from

this because we're going to use

the same kind of growth equation

that, again, based on energy

equivalent of the flow of energy

through these systems leading to

maintenance and growth, and

that's what we had before for

us.

The sub-linear scaling leading

to boundary growth, and if it's

super linear scaling, there's a

cartoon on the left, this leads

to actually faster than

exponential growth.

And that's very satisfying.

That's great because that's what

we see, and that's what we

apparently love or have loved

for the last couple hundred

years.

And that's great.

However, it has, if you believe

the theory, this has a fatal

flaw.

And the fatal flaw is denoted by

this line.

And it's called, in the

technical language, a finite

time singularity.

And to put it in very simple

language, what it says is that

in some finite time, this damn

growth curve will go to infinite

size, and that's obviously

impossible.

So this is kind of a Malthusian

argument.

Namely, at some stage, you're

going to run out of whatever it

is that's keeping the system

alive, the resources, the

energy, whatever it is, and the

theory tells you what happens.

It says as you go through this

so-called singularity,

you stagnate and collapse.

That's terrible and we need to

avoid that, and we have avoided

it.

And this is how we avoid it.

We avoid it for the very reason

that Malthus and Paul Ehrlich

were rejected.

That is, they did not take into

account the fact that we

innovate ourselves out of these

problems.

So I want to finish on this

note.

So here's the situation.

This is, you start at some point

and you start growing and you

would hit this singularity and

collapse.

So somewhere along here you

better change something because

this growth was done in the

paradigm of whatever the major

innovation is.

It could be the discovery of

iron.

It could be the discovery of

coal.

It could be oil.

It could be the invention of

computers.

The invention of IT, but

something major that has a kind

of paradigm shift and changes

everything.

So, this suggests that the way

out of this dilemma of collapse

is that you innovate somewhere

here, and you start over again.

You reset the clock, and then

you can go on merrily.

And, of course, you're going to

hit another finite time

singularity and collapse unless

you innovate again.

So you have to keep innovating.

So there's a kind of theorem if

you like.

But if you want to continue to

have open-ended growth, you have

to have cycles of innovation.

Well, people have been saying

things like that for a long

time.

Here's the difference, going

back to what we said: as you

grow and you get bigger, the

pace of life increases.

So, first of all, the pace of

life is increasing.

The second thing is, it turns

out the theory says yes you can

do this, but the time to go from

here to here necessarily has to

be, and systematically, a lot

shorter than the time from here

to here.

So that you can innovate, you

have these cycles of innovation,

but they must come quicker and

quicker.

Okay?

So, the image is we're on this

treadmill that is going faster

and faster, which is difficult

to begin with, but at some

stage, you've got to jump from

that treadmill onto another

treadmill that's going even

faster.

And then very soon, you've got

to jump onto another one, and

you've got to kind of keep doing

this so you have this kind of

double acceleration.

And the question is, do you

suffer a kind of global heart

attack from that?

[LAUGHTER]

So, is this sustainable?

Obviously not, ultimately.

And this is not mine at all.

This is someone, I don't know

who he is, but he did it and he

showed it was great.

Oh, this is something I saw in

an English newspaper a week ago

which I thought illustrated the

point a little bit.

So here's something, some of you

may have heard the idea of

singularity from this, what I

consider, little bit looney work

of Ray Kurzweil.

I think it's looney, but he did

some wonderful things of

collecting data.

I don't agree with a lot of

this, but what he's plotted here

is that here's the paradigm

shift.

That is, how long it took to

create each one of these

paradigms versus how long ago it

happened.

Okay?

And what you see is it takes

shorter and shorter times to

make these shifts, and it's

happening faster and faster.

And he drew this.

All kinds of people have done

things like this.

I'm going to finish up.

And, whoops, and interestingly

enough, that straight line, if

you take this theoretical

framework that I just told you

about, this continuous

innovation and jumping from one

treadmill to another, it

predicts, almost exactly, that

orange line.

So that's data kind of

supporting it.

So I'm going to finish with

that, and I'm going to finish

with one last image which is

this just to give you a sense of

things in terms of energy.

Our metabolic rate, I didn't

point that out on the graph.

You remember that graph of

metabolic rate?

Our metabolic rate in watts is

about 90 watts.

A little bit less, women versus

men.

But roughly it's about 90 watts.

We're a light bulb.

One of these bloody light bulbs

is equivalent to one of us.

So, turning a light bulb off,

it's like your mother telling

you if you turn it off, you help

starving people in Sudan or

somewhere.

So it's ridiculous having all

these lights on, frankly.

They're us.

We are extraordinarily

efficient.

That 1,800-2,000 food calories a

day is unbelievably efficient.

It may seem like a lot of food,

but it's just a light bulb.

However, and incidentally, that

metabolic rate is the metabolic

rate we should have for a mammal

of our size.

That's what we should have.

Okay?

And if you add in activity,

hunting and gathering, which is

the way we evolved or versions

there of, that number changes

from 100 to about 250, the

amount of energy we do to hunt

and gather and so on.

And that also scales the active

metabolic rate for mammals.

So that's what we "should have."

Then we started talking to one

another.

We discovered economies of scale

to form communities, and then we

started to innovate.

And once we innovated,

we created all of this [###]

around us, all this wonderful

quality of life and standard of

living that we'd like to have

for everybody.

And we can ask, what is our real

metabolic rate?

What is our social metabolic

rate of having cars, and lights,

and buildings, and all the rest

of the stuff?

That number goes from about

90 watts to 11,000 watts.

And you can turn it around, this

has been done on this graph as I

did here, and ask, how big is

it?

What big an animal are we?

This is about a dozen elephants.

Each one of us in this room and

there are seven billion people

of us on this planet who all

want to be like us, and there's

three billion more coming in the

next 30-40 years, and they all

want to be like us

using 11,000 watts.

So this is an enormous problem,

and I cannot answer the

question, what is the future

and what does it bring?

But I can say

that I'm a pessimist.

So I'll finish on that.

[APPLAUSE]