CESAR HIDALGO: So my
name is Cesar Hidalgo
and I'm a Professor
at the MIT Media
Lab, where I run a group
called Macro Connections.
And today I'm here
promoting this little baby.
It took me 2 and 1/2
years to give birth to it,
and it's called "Why
Information Grows."
The story of the book
actually is quite simple.
The idea is that the universe is
made of three things-- energy,
matter, and information.
But information is the one that
makes the universe interesting.
Information is the one
that gives the universe
its structure, its beauty,
and its complexity.
But for the most part,
we have struggled
to understand where
information comes from.
So what I'm going to do
in the next 40 minutes
is explain to you
what information is,
where it comes from, and
how it manages to grow.
And to understand the
origins of information,
we have to understand
how information
is related to matter
because, as Landauer
repeated an infinite number of
times, information is physical.
It's always embodied on physical
states, physical particles.
In some way, despite the
physicality of information,
it is one of the
latest things that we
have been able to understand
from a physical perspective.
Some of the first people to
start thinking about this
were people in the 19th
century like Ludwig Boltzmann.
Now, Boltzmann was a famous
statistical physicist, but also
a relatively sad man.
He ended his own life in 1906.
And he ended own
life after having
gone through a
scientific career that
was full of very
fruitful results,
but also results
that were sometimes
rejected by his colleagues.
Boltzmann had, for
instance, advocated
for the idea of atoms,
and many of his colleagues
believed that atoms were nothing
more than a convenient analogy
and they should
not be incorporated
into scientific theories
because they were not
directly observable.
Boltzmann also was
inspired by some people
that had produced great
scientific results a little bit
before him.
Two of where Charles
Darwin and Alfred Wallace.
And Boltzmann had seen in
Darwin's and Wallace's theory
of evolution through
natural selection,
an explanation of the
biological origins of order.
And what he wanted
to do in his life
was actually develop
an explanation
of the physical
origins of order.
So he tried to understand
systems that were
composed of many particles.
And what he realized is that
those systems eventually do not
go towards order, but
go towards disorder.
That was something
that also troubled him.
Because the thing
is that the universe
has a tendency to
average itself out.
This is known as the second
law of thermodynamics.
So think about a
very simple example.
I am now speaking in this room.
And as I speak in this
room, I'm technically
writing on pressure
waves on air.
But those pressure ways on
the air are very short lived.
These pressure waves
move around the room.
Basically, the particles that
have differences in pressure
average out and that
sound disappears,
and that information is not
embodied in the air anymore.
And the same is true for
a lot of physical systems.
So you take a cup of coffee and
you put milk in the beginning,
you have all of these
beautiful swirls that
are full of correlations and
very interesting informations
and fractural structures
that after a while
is just a hazy cloud in which
everything is kind of mixed.
And the universe has this
tendency of mixing things
in ways that are not mixable.
The thing that you cannot
unmix is temperature,
and that's what
Boltzmann discovered.
So the question of
where order comes from
was a question that actually
eluded not only Boltzmann,
but many great scientists
that came after him
and tried to continue to
develop the statistical physics
that Boltzmann had started.
This included Maxwell,
Einstein, and Gibbs.
And the true
breakthrough happened
in the first half
of the 20th century
when the Russian
born and Belgian
raised statistical physicist
Ilya Prigogine started
to look at systems that
were a bit different
than the ones that had been
looked at by Boltzmann.
Prigogine realized that
Boltzmann was right,
that physical systems that
are composed of many particles
when left to their own devices
would go to an equilibrium
state in which basically,
all particles more or less
are moving willy nilly.
And even though they
encode technically
a lot of information
in their quantum states
or in the positions and
velocities that they have,
they do not encode much order.
And Prigogine also
wanted to explain
the origins of
physical order, but he
realized that in
order to do so, you
had to look at a little
bit of a different system.
The systems that Boltzmann,
Einstein, Maxwell, and Gibbs
had looked at were systems
that were in equilibrium--
basically, when looking
at the state of the coffee
and the milk after the
milk had been in the coffee
for a long time and
everything had mixed up.
Prigogine realized that
what he had to look at
were systems that were
out of equilibrium.
And what he realized is
that the systems that
were out of equilibrium
satisfied a different objective
function.
The systems that are in
equilibrium maximize entropy.
The systems that are out
of equilibrium, Prigogine
showed in a very
particular example,
they minimize the rate
of entropy production.
What does that mean?
Well, the systems are
producing entropy,
but are producing as
little entropy as possible.
And when the systems
self organize
to produce as little
entropy as possible
is where order emerges.
So the classic example of
this is the bathtub whirlpool.
How many of you have used
a bathtub in your lives?
I assume most of you
have used a bathtub.
And you have made
this observation
that when you take
the plunger out
of the bathtub and the
water starts racing down
the drain, at some
point, there is
a little whirlpool that forms.
That whirlpool is actually
a very simple form
of physical order
that is emerging
in the steady state of
a dynamical system that
is out of equilibrium.
Why?
Because in this whirlpool,
if you think about it,
if you know the
velocity of a particle,
you very much know the
velocity of the particles
that are neighboring that.
They tend to be very similar.
But since these
particles are correlated
and they're moving
with velocities
that are similar to
that of their neighbors,
they're not dissipating
too much energy
because they don't
have too much friction,
or interactions that
are not happening
at speeds that are highly
dissimilar between these two
particles.
So they're minimizing the rate
at which they dissipate energy.
And in that state in
which they minimize
the rate at which
they dissipate energy,
order starts to emerge for free.
But the big lesson
here is actually
that in order to generate
physical order and information,
there is an energy cost.
You cannot produce order
without spending energy.
And that's a fundamental
thermodynamic relationship.
You cannot record a video on
your phone when your battery is
gone.
The thing is the origins
of physical order
were explained by
Prigogine by looking out
of equilibrium systems
does not help us explain
more complex forms
of physical order,
like the ones, for instance,
that we observe in our cities
or in our homes, or in
the economy at large.
For that maybe
we're going to need
to have a few other
additional principles that
help us understand
how order continues
to grow to the extent in
which it's able to produce
the type of complexity
that we observe
in nature and in society.
So the second ingredient for
the physical origins of order
was one what was hinted at by
Erwin Schrodinger in his 1944
book, "What is Life."
So Schrodinger in his book "What
is Life" already acknowledges
and recognizes that
life is a machine
that is going against the
second law of thermodynamics.
It's minimizing
entropy internally
and obviously, we
are producing heat
at our seams to make sure that
the entropy in the universe
at large continues to increase.
But locally, we
eat food and we use
that energy to produce order.
But the thing is
if we always have
to be producing
order by consuming
energy, the amount of
order that we would be
able to produce is very little.
So we have to have a way of
saving that order, marking sure
that order endures, perseveres.
So what Schrodinger
said in some sense,
one of the tricks that life had
figured out to help order grow
and to help information grow
was to store that information
in aperiodic crystals.
Think of proteins.
Think of DNA.
Think of RNA.
All of these molecules
are actually quite stable.
DNA is very stable.
You can store it
at room temperature
for a very long time.
And it's able to
preserve information,
and it's able to
in some way obviate
the second law of
thermodynamics, at least
temporarily.
And because solids
make order endure,
once you're able to store
information in solids,
you don't need to be
creating it all the time.
You can start recombining it
and you can help order grow.
So that's the second trick.
The second trick is that you
need to preserve information.
And for that, you need solids.
But there's one more trick
that nature has in its
[INAUDIBLE] that
helps order grow
in pockets of the universe.
This one is the capacity
of matter to compute.
People tend to think of
computation as something
that we invented recently.
But actually, computation
started with the big bang.
Every time two physical
particles interact,
they're actually
exchanging information,
and that's an act
of computation.
They're processing information.
And computation is actually
a prerequisite of life.
So just to give you an idea of
how fundamental computation is,
I'm going to use as an example
a tree because people don't tend
to think of trees as computers.
But trees are computers.
They don't have
consciousness like you and I,
and they cannot run MATLAB or
R or Python like the digital
computers that we have built,
but they can run actually
programs that maybe are even
more sophisticated than those.
A tree knows how
to grow its leaves
in a certain direction and
its roots in a direction
of the world that it needs.
A tree knows how to turn genes
on and off to fight off pests.
A tree also knows how to
perform photosynthesis.
A tree knows when
to shed its leaves
and when to grow them again.
These are all acts
of computation
that a tree is performing
because in some sense,
the tree is a computer
that is building itself
from the intimacy of the cells
to the totality of its larger
structure.
So for information to
grow in the universe
and to exist despite the
second law of thermodynamics,
the universe has three
fundamental tricks.
The first one is that you
need to spend some energy.
You cannot get information
in an equilibrium system.
It has to get out of equilibrium
system because information,
order are not free.
The second thing
is that you need
to have ways of saving
that information.
You need to have
some sort of solid.
And here, be very in some sense
general in the use of the word
"solids."
When you have spins
in a hard drive,
you can also think
that in some sense,
as long as those spins are
stable in that hard drive,
that's a solid, too.
It doesn't have to be something
like a totem in which we're
passing on cultural information
by carving it on wood.
It can be actually a very
general thing about something
that is not a fluid, that
has a state of stasis that is
stable to thermal fluctuations.
That's what I mean
by a solid here.
And finally, by
using solids, energy,
matter develops the
capacity to compute,
which is the capacity to
recompile information.
So what we will see now is
that this capacity to compute
is actually the key to
make possible the growth
of information in the universe.
But it also has made the
growth of information
in the universe very difficult.
So what I'm going
to do next is I'm
going to take these three
fundamental mechanism
and I'm going to look now
at society and the economy.
And I'm going to try to explain
how information continues
to grow at the high level of
complexity that you and I live.
So the first thing
that I need to answer
to be able to explain
that is, what are
the types of information
that we produce in society
and that we use in society?
And to do that, I'm going
to tell you a story.
So I'm originally from Chile,
and I've been living in the US
for 11 years now.
And if anybody here
probably has moved out
of their home country, where
you find yourself is that when
you're procrastinating
online, often, you're
going to go and visit a
newspaper from the country
that you were born.
There's not many
interesting news there.
It's kind of like local news.
And once, there was this news,
"Chilean Bought the World's
Most Expensive Car."
And obviously, my first reaction
was like, what a douche.
Why do you need to buy a
car that's so expensive?
But then eventually, I
started thinking about,
what car was it, and
how much was it worth?
And the car was
a Bugatti Veyron.
So I searched online.
And a Bugatti Veyron
costs $2 and 1/2 million,
and that's $1,300 a kilo.
So obviously, you
think that, well,
why is this guy thinking
about how much does
a kilo of a car worth?
This is not rice or beans or
things that you buy per kilo.
But thinking of a
kilo of a Bugatti
is interesting
because it's going
to teach us an important lesson
about the nature of information
in the economy.
So imagine that
you have just won
this vehicle on the lottery,
and you're driving it
through the city, very excited
because this is a brand new car
and you're all pumped up.
And then boom!
You crash it against the wall.
Now, how much is that
kilo of Bugatti worth?
Obviously, you don't
need a PhD in economics
to know that a kilo
of a Bugatti now
is worth way less than before.
The value evaporated
when you crashed that car
against that wall.
And what this is telling you as
well that the value of that car
was not related to the
atoms, but was related
to the way in which those
atoms were ordered or arranged,
and that is information.
So the Bugatti was
made of information
even though it
was not a message.
It was an object.
So let's try to understand
a little bit more
about the types of
information that we have
in society and the economy.
And the second example
that I'm going to use
is I'm going to ask you guys
to compare apples and apples.
And on the left, you have the
Apples that here in Google
detest.
And on the right,
you have the ones
that you buy in the
supermarket that you eat.
And the question
I'm going to ask
you is, what is the fundamental
difference between these two
types of products?
Obviously, they both contain
information and physical order.
And some people might
say price, some people
might say complexity.
But I can basically
argue that the apple
on the right, the
one that you eat,
has molecular mechanisms
inside its cells
that are unparalleled by
anything that has been built
by an engineer in the world.
So the true difference
is not in their price,
it's not in their complexity.
It's not in the fact that
one of them has information
and the other one doesn't
because both have information.
The true difference is that
the first apple existed first
in the world and
then in your heads,
and the second
Apple existed first
in someone's head and
then in the world.
The first apple existed before
there was a name for apples,
a price for apples, and
a market for apples.
So what this telling us is that
the second Apple is special
because it is a
crystal of imagination.
And that's an observation
that is very important
because we live in a
world that is tangible,
but that tangibility is
actually made of fiction.
All of the objects that we
have-- the clothes that we
wear, the shoes, the
computers that we use,
the cars that we drive, the
buildings that we live in--
were all imagined
before they were built.
And by noticing that
the products that we
make in our economy are
crystals of imagination,
we can interpret important
economic processes.
So we can reinterpret the
economy based on the idea
that products are made of
crystallized imagination.
So as an example,
let's take some data
from the Observatory of
Economic Complexity, which
is a data visualization engine
that I created with my team
at MIT.
And here are the products that
Chile exports to South Korea.
And you see there
are two things.
On aggregate, you see that
Chile exports $4.6 billion.
But in this more
desirable view, you
see what Chile exports
to South Korea.
And you see that Chile exports
to South Korea mostly atoms--
refined copper, copper ores, and
pig meat, grapes, and so forth.
Now, what does Chile
buy from South Korea?
What Chile buys from South
Korea are cars, machines,
and delivery trucks.
In total, they're buying
only $2.5 million.
So what that means is that
Chile has a positive trade
surplus with Korea because
they're exporting 4.6 billion
and buying 2.5 But they have a
negative imagination surplus.
They have a negative
imagination balance
because Chile is
exporting atoms and Korea,
what it's sending back
is the way in which
those atoms are arranged.
And what this is
starting to tell
us is that eventually,
economies are computers, and are
computers with
different capacities.
And as we will see by
the end of this talk,
the different computational
capacity of different economies
can help us even explain
the level of income
that each economy
is able to achieve.
In this case, it
should be obvious
that the economy
of South Korea is
a more sophisticated
computer than that of Chile,
is able to generate
more sophisticated forms
of physical order.
Another thing that,
for example, thinking
about the world in terms
of crystallized imagination
helps us reinterpret
is the dynamics
of natural resource
exploitation that
are prevalent in many
developing countries.
I come from Chile.
And as you've seen, Chile
basically sells rocks.
Why the heck we
sell so many rocks?
Every year, Chile exports
roughly $50 billion
worth of rocks.
Why is the world willing to pay
so much money for those rocks?
Those green rocks
are rocks of copper,
and people are willing
to pay for copper.
Well, because copper, once
you melt it and make it
into a spaghetti,
you can actually
conduct electricity quite as
well-- not as good as gold,
but much cheaper than gold.
So some people say, well,
Chile is being exploited
because people are taking
these rocks out of Chile that
are very valuable.
And then basically, what
is Chile getting out
of this other than
some miners that
live in some towns that
are pretty desolate and $50
million?
And the thing is, well,
you have to think about,
where is that value
actually coming from?
Is the value on the atoms?
Is the value on the rocks?
Or was the value endowed
to the rocks by some sort
of computational process?
So we are buying
these rocks now.
And we were not buying these
rocks for a lot of money
300 years ago because
roughly 150 years ago, there
was a guy called Michael
Faraday that figured out
that if you jiggle a
magnet inside a coil,
you can produce electricity.
That knowledge
eventually is the one
that allows us to live
in this electrified world
that we live in nowadays
that allows us to produce
a large amount of information
because we have transformed
that energy that helps
us produce information
in the form of electricity.
But it's also that piece of
knowledge and that piece of
know how that endowed
those rocks with all
of that economic value.
So in some sense, we can
say that Chile is not
being exploited because people
are taking rocks out of Chile.
Chile is actually
exploiting knowledge
and know how that was
produced elsewhere.
So the question
then is, well, why
do we crystallize imagination?
One thing that you
might have not noticed
is that we make products
that are tangible
but that started as fiction,
and we're the only species
to do so.
There's no other
species that makes
products that are tangible and
that started as imagination.
And if you see pictures of
elephants making drawings
on the internet, they basically
get whipped the heck out
of in very
questionable practices
to make sure that they make
the same drawing over and over.
So it's not like the elephant
has this artistic expression.
And we do tons of
different things.
We crystallize
imagination [INAUDIBLE].
We're the only species to do so.
So there must have some
benefit, because certainly, it
has a cost.
Work is hard.
So what are the benefits of
crystallized imagination?
So to answer that
question, I'm going
to ask you another question,
which is, how many of you
used toothpaste today?
Please raise your hand if
you used toothpaste today.
Sir, did you use toothpaste?
Thank you.
And keep your hand
up because I'm
going to ask you a
follow-up question now.
Keep your hand up
only if you know
how to synthesize sodium
fluoride, if you know how
to synthesize sodium fluoride.
I don't know how to
synthesize sodium fluoride,
but what does that tell us?
It's that when we're
buying toothpaste,
we're not just buying
some goo on a tube.
What we're buying is access to
the practical uses of knowledge
and know how that resides
in the nervous systems
of other people.
So products augment us.
And this augmentation
is the one that
makes these products useful
because at the end of the day,
because of products, we're going
to live at a level of capacity
and a level of prosperity that
is much larger than the one
that we would be able to
achieve if we were to live left
to our own devices.
We don't have the
knowledge and know how
that we require to make all
of the things that we use.
But nevertheless, we
access the practical uses
of that knowledge and know
how-- not the knowledge
and know how-- through the
products that we consume.
So in that sense, as
a species, and we're
a social species-- we're somehow
similar to ants in the sense
that as a species, an
ability to coordinate things
comes from an ability to deposit
information in the environment.
And ants individually
are not very smart.
But they can do
great things, just
like solving problems of
routing and ventilation
and construction
and transportation
because they can put information
in their environments.
And that allows them
to coordinate and act
as a collective
computer that is much
smarter than an individual ant.
But the thing is
ants are somehow,
in a way, limited because ants
can deposit information that is
pre-specified in their genes.
So they have a
physical vocabulary.
We're a little bit
different than ants.
We don't just
produce information
that was encoded in
our genes, but we also
need to pull information
that comes from our brains.
And because of our ability
to pull information
that comes from
our brains, we're
able to actually coordinate the
production of activities that
are much more complex and create
a society that is infinitely
more complex than that of ants.
So the next question
that I want to ask is,
how do we actually manage
to create those things?
In some sense, we're even aware
that it's literally fantastic.
It's not metaphorically
fantastic.
It's literally fantastic
that the objects that we
use on a daily basis
started all as fiction
and we have managed to transform
that fiction into reality.
But sometimes, transforming
fiction into reality
is not that easy.
Different countries
or different teams
of people or different
companies struggle to do so.
So there must be
some things that
help us explain why some people
are better at crystallizing
imagination than others.
So what I want to
try to explain next
is what are the
mechanisms that enable
but also limit our ability
to crystallize information
and to make that
information grow.
So a few years
ago, Pep Guardiola
visited MIT Media Lab.
At that time, Guardiola
was between jobs.
He had just finished his
tenure as a coach at Barcelona,
and he was moving to be the
head coach of Bayern Munich.
And he visited the media lab.
It was a visit that was
very easy to organize.
I was put in charge of
organizing that visit,
and all the students
obviously wanted
to show something to
him, especially all
the students that like soccer.
So I organized the visit.
And as I was organizing
the visit during that week,
I started to receive emails
from other students, especially
students from Spain and
Catalan students, that
wanted to really meet him.
And I was supposed to keep
the visit kind of quiet.
So I said, you know what?
What I can do is I'm
going to do the demos.
I'm going to take
him around the lab.
And then when we
finish the demos,
I'm going to bring him to the
atrium, and then we've got Q&A.
So then people
started to show up.
And then during the
demos, there were a bunch
of students sitting there
with the Barcelona shirts
and scarves and
everything, waiting
to meet this soccer celebrity.
So obviously, the
Q&A gets started.
The students were
a little bit shy.
Eventually, a student
raises his hand and says,
well, at MIT, we have
to work with robots.
So my question to you, Pep, is
if we build a team of robots,
would you come and coach it?
And Pep's answer
was very cunning.
He said, when you're
trying to achieve something
with a group of people, like
in the case of a soccer team,
the most difficult thing is
not to figure out a game plan,
but actually to
transmit to everybody
that game plan in the
voice of the players.
In the case of robots, I don't
think that is a challenge.
I'll decline your offer.
So that answer was very
good because actually, it
points to the fact that
eventually, to achieve things,
we have to work in teams.
And having that
teamwork as a unit
is sometimes what
is very difficult.
And in some way, we
can ask ourselves,
why the heck do we
have to work in teams?
I assume all of you guys have
worked in teams in your life.
And how many of you had a
friction with a colleague?
All of you, exactly.
If you work in a team, you had
a friction with a colleague
sooner or later.
Even in playground,
kids know that lesson.
So [INAUDIBLE] a cost
of working in teams.
So there must be
benefits also to working
in teams that help teams
prevail and make them grow.
And to answer that
question of why
do we need to work in teams, I'm
going to ask you another very
simple dental question,
which is, how long does it
take for a person
to become a dentist?
How long does it take for a
person to become a dentist?
Any guesses?
AUDIENCE: Six years.
CESAR HIDALGO: Usually people
answer six or seven years.
But obviously, the answer
is 30 because the person
needs to learn how to talk,
how to walk, how to socialize,
how to read, how to
write, how to do math.
And then eventually, by the
time a person becomes a dentist,
it's in their late
20s, early 30s.
Their brain is half frozen and
there's not that much knowledge
or know how that you're
going to be able to get
in a person's brain.
So the point of this is that
people have a finite capacity
to compute.
And this is not just
a property of people,
but it's a property of
all computational systems.
All computational systems have
a finite capacity to compute.
Think of a cell.
A biological cell
has a finite capacity
to compute that
does not allow it
to do things that are as
sophisticated as the ones
that a human does.
But humans also have
a finite capacity
that they can only
transcend as long as they're
able to accumulate more capacity
in a social context as a team.
So in some way, we can
think that as a society,
we're a collective computer.
And this collective
computer is accumulating
computational capacity.
And in the beginning, when
things are relatively simple,
the computational capacity
we need to do things,
let's say, if what
we're doing is just
creating some
wooden spheres, can
very well fit without a
person's capabilities.
But at some point,
we start making
things that are more
complicated, more complex,
that require more knowledge,
more know how, more
computational capacity.
What we're going to
do is we're going
to cross a threshold which is
the personbyte threshold, which
means that the computational
capacity that we need
to do something is
larger than the one
that you can fit
inside a human being.
So when you're thinking
about information
in the context of math, bits
are a very natural unit.
But when you're thinking about
computational information
in the context of
society, the person,
the amount of
knowledge and know how
that you can embody in a
person, is a natural unit.
Why?
Because that unit
then forces you
to re-quantize computational
knowledge and know how.
That's the unit in which you
start parallelizing things
because your computer
cannot do much anymore.
So the question
obviously is then, well,
what determines the size of
the networks that we can form?
We have to do things
in a social context.
Computation is
distributed in society.
Society is
[INAUDIBLE] a computer
that's made of networks.
So one other network theory
I guess is important for us
to understand the
computational capacities
of society and the world.
And one of the first people
to start thinking about this
was Ronald Coase.
Ronald Coase was living
here in London in the 1930s.
He was a law student
and attended a seminar
in the Department
of Commerce of LSE.
And this seminar that I think
was taught by Professor Arnold
Plant, there, Coase
heard a phrase
that bothered him deeply.
And the phrase was "the
normal economic system
works itself out."
It's kind of these
classical phrases
that classical economists
love to throw around
that the economy
is self regulated,
and everything happens magically
through this business story
in which you have butchers and
bakers and horseshoe makers,
all trading things that find
prices optimally because
of the price mechanisms.
And this was 1931, 1932.
Ronald Coase was a
20-something-year-old.
This was the time of
the Great Depression.
And obviously, Coase
saw that the work
was very different than the one
that the Department of Commerce
professor was telling him.
Coase saw a world
in which a person
who from one desk
to another desk
to change the task that
the person is doing
is not because the price
mechanism in some way
realigned the incentives
of that person.
It was because the
boss told that person
to move from here to there.
So what Coase realized is
that the world was not just
like this fluid market that
economists talked about,
but it was made of conscious
islands of centralized planning
or power, which are firms and
organizations and institutions
and so forth.
So what he tried to
do, he said, well,
maybe I can try to
develop a theory of what
is the size of
this network, what
is the size of these firms.
And that is the classic
idea of the firm that
was invented by Ronald Coase.
And the theory is very simple.
It's an equilibrium theory
in which, on the one hand,
when you have a
company, you always
have to decide whether
you're going to buy
or you're going to make.
So on the one hand, let's
say at Google, you say,
hey, we need coffee.
Probably you're not going
to start your own coffee
plantation and procure
your own coffee.
In that case,
you're going to buy.
But hey, maybe we need to
improve our data visualization
skills.
Maybe we should hire
more people for the team.
And in that case, maybe it
will increase our net worth
because that's something
that we'd rather make.
And companies are always making
this decision of whether to buy
or whether to make.
And eventually, at the point
at which the cost of buying
and the cost of
making equalize, you
have this membrane
that determines
the size of the firm.
So that's Coase's
theory of the firm.
What is most important
for us, though,
in the case of Coase's
theory of the firm,
is the transaction
cost theory that
also comes from it, which
tells you the following.
It's that when you have a
system in which you are forming
networks, links have a cost.
The size of the networks
that you can form
are inversely proportional
to the cost of links.
So in a world in which
links are very cheap,
you've got very large networks
which then have a lot of links.
In a world in which
links are very expensive,
the networks that you're going
to be able form are very small.
So that is Coase's
theory of the firm.
And it gives you some idea
of the size of the networks
that people can form.
But always, as all
theories, this theory
also rubs some people in a
little bit of the wrong way.
Some of the people
started to think
of the world a
little bit different,
and they thought that
the theories that
were advanced by Coase and the
New Institutional Economists
were incomplete sociology,
such as Mark Granovetter.
So Mark Granovetter actually
argued that economists had it
completely backwards
because economists still
believe in a world in
which there are incentives,
and then people basically
connect with each other
based on those incentives.
So if someone has something
that I want to buy,
and that person is selling, and
we find ourselves miraculously,
through some sort of
magic of the market,
and we perform that transaction.
What that means is that the
link is an epiphenomenon
to economic activity.
But what Granovetter
said is, well,
obviously, when you're
buying a chocolate,
sure, you're going to go to
the closest Tesco or 7-11.
You don't have to become
friends with the clerk
to buy that little Snickers bar.
But in many cases,
actually, when
you're doing things that
are more complicated,
those type of
things are actually
highly constrained
by preexisting
social interactions.
So in some sense, what
Granovetter was arguing
is that the world
has a lot of networks
that are formed by
sociological process,
not by economic process.
And these networks
predate economic activity
and constrain economic activity.
So that idea of pipes through
which the economy can flow
are determined a priori by
other sociological processes.
And these are very simple
and obvious sociological
process to anyone.
The family.
You don't develop a relationship
with your dad or your mom
because you were doing
business together.
In some sense, you're
born into a family,
and those are things that
are important to everyone.
And also, when
you're a kid, you're
put, for example, in a school.
Even if the kids in that
school like you or not,
and if you don't
like them or not,
you basically spend so much
time with them that [INAUDIBLE]
that you get to know very well.
And you're not going to get to
choose that much who you end up
becoming friends with.
You have a lot of these
shared foci or institutions
that get passed on and
determine the people
that you interact with.
And the friends
that you're going
to be able to make
basically are determined
by that narrow set
of people that you
get to interact
with through reasons
that you don't get to choose.
You're not choosing among
seven billion people.
You're choosing your friends
out of a much smaller circle.
So obviously, this
idea that economies
are embedded in preexisting
social structures
makes a lot of sense.
But Granovetter also,
he's a very good academic.
So he decided to basically
put this theory to the test
by collecting data.
So what data could you get to
try to understand that better?
So markets.
What he said is, I'm going
to look at the labor market.
The labor market is
an important market
in which people basically
find other people
that they need to grow the
organizations that they form.
So what he did is he
made this big survey
in the Newton suburb of Boston.
In that survey, he asked
people how they got jobs.
And he realized that
basically, 50% of all people
got jobs through a
friend of a friend.
So the preexisting
social structure
allocated 50% of the
jobs in the economy.
What jobs were allocated through
this preexisting social links,
the best jobs or the worst jobs?
And it was the best jobs.
So if you're looking for a CTO,
a CFO, a talented engineer,
usually a recommendation
from someone that you trust
is a very important way
of getting that person.
You're getting
someone that is going
to do a job that is
much more standardized.
Usually, those jobs are
more likely to be assigned
through an agency or
through some sort of ad
in the newspaper or online.
And the thing is this
number is like 50%
that Granovetter for the
first time in the late '60s
has been measured over
and over and over again
in a number of
different situations.
And more or less, no
matter what country
you look at, what ethnic
group you look at,
or what technological period
you look at, between 40% and 60%
of all jobs are assigned
through friends of friends.
Even the internet has not
been able to change that.
So what does this tell you?
What is happening here is that
what these people were figuring
out is that they were developing
a more sophisticated theory
of what these networks are.
Because what social
networks embody is trust.
So trust is something
that is very real.
And trust is related to
the cost of interactions
that you're going to
have with another person,
but also about the
expectations that you're
going to have about
that person's behavior.
So for example, trust
my wife or a person
trusts their partner
when that person is not
going to cheat on
them, even though there
might be a very attractive
person that is hitting on them.
So Granovetter's
definition of trust
is that you trust a
person, would that person
do what is right
for you even when
incentives are to do
what is wrong for you.
So it's when people prioritize
the social relationship
over the short term gain.
And what this is
telling us also is
that trust, which
is this concept that
might appear to
get a little fuzzy,
is a real thing that
needs to exist somewhere
because everything in the world
has to be embodied somehow
in our physical reality.
In this case, trust is
embodied on social networks.
Just like water goes inside
a bottle inside a cup,
trust only exists in the
context of a social network.
Those social links
are meaningful
only as long as they embody
and accumulate trust.
If I just know you because we
exchanged a couple of emails
but I don't trust
you, that's not
a meaningful social
relationship.
But if I trust you,
that social relationship
is very meaningful.
It's going to allow you
to do a lot of things.
So people like Francis Fukuyama
then take this idea of trust
and they start to explain what
are the different things that
countries are able to make
based on the level of trust
that they have.
So on the one hand, you
have low trust societies,
and on the other hand, you
have high trust societies.
And this is very much related
to the transaction cost theory
that was advanced
by Coase and the New
Institutional Economists
in earlier decades.
And Fukuyama says, well,
if you have a society where
you have low trust, creating
social relationships is
very expensive.
It takes a lot of time to become
a good friend with someone.
So links are expensive so
you can have few of them.
So the networks that you can
form are relatively small.
If you have a society that you
have high levels of trust--
so I meet someone, and
after a couple of meetings
we're already ready
to do business
or to start a partnership
or to do a collaboration--
then I can have a lot of links
because links are very cheap,
and I can create large networks.
So in the low trust society,
you have small computers.
And in the large trust
societies actually
have large [INAUDIBLE] computers
because-- for you guys,
probably it's easier to think
in terms of parallelization.
If I have a lot of
high speed links
between the different computers
that I'm parallelizing,
I'm going to build a
very efficient cluster.
So Fukuyama divides the
world in these two extremes
not because the world is
either black and white,
but sometimes to
understand gray,
the best thing is to
understand what black is
and what white is.
And on the one hand, you
have familial societies.
Familial societies are the
ones in which there's no trust.
So when there's no
trust, people have
to take advantage to the
last reservoir of trust
that they have left,
and that's the family.
Those are the strongest links.
So in a society that
nobody trusts each other,
they trust their family members.
And those societies, they are
dominated by small networks
that are composed of kin.
And these small networks
that are composed of kin
have a limited pool of talent.
So because they have a
limited pool of talent,
they're going to gravitate
towards simpler industries that
can be managed by a few people,
like agriculture, mining,
and retail.
These are societies in
which when people organize
themselves, they tend
to demand the government
to change things.
So people organize
themselves to protest,
not to create some sort of
association in which they're
going to self provide what they
think that they're missing.
On high trust societies,
on the other hand, built
from large networks
of non-kin-- I
don't know if the
Smurfs are kin or not.
I don't know if anybody does.
But it's built of these
large networks of non-kin.
And these large
networks of non-kin
gravitate towards
complex entities.
So I assume here,
there's no brothers
or cousins in this room, no?
No brothers or
cousins in this room?
No?
So they gravitate towards
complex industries
like aerospace, microprocessors.
These are networks
that accumulate
a lot of professional
talent and are also networks
that sometimes self generate
these instances to accumulate
the social capital
that they need
through events, through
associations, and so forth.
So what this tells
us, though, is
that no matter what
mechanism we use
to try to explain the
networks that we form,
we end up bumping into the fact
that the networks that we form
have a finite size.
That finite size
can be determined
by the level of
trust that you have,
can be determined by
preexisting social ties,
or by transactional
costs that also
are mandated by technology.
And if you have a
finite size of networks,
what you're going to have is
another quantization limit,
which is what I call
the firmbyte limit.
So just like before,
computation needs
to be distributed among
a large number of people.
Because people have a
finite capacity to compute,
firms also have a finite
capacity to compute.
And that means that in our
society and our economy,
we have to re-quantize
these computational networks
of firms.
So as we have been accumulating
knowledge and know how
and we have an increasing
computational capacity,
we have transcended
the personbyte limit,
and now for a long time have
transcended the firmbyte limit
in which the products
we create are actually
produced by networks of firms.
So as an example, think
of the personal computer.
So you know a personal
computer usually
tends to have some sort of brand
in its body, the parts that
make that computer
were obviously produced
by a large ecosystem of
other networks of people that
were at other firms.
And then that computer
is useful only as long
as you have software.
And that software
is yet produced
by another group of people.
And you use that
software to go online.
And all of those
websites are produced
by yet another group of people.
And then those websites
work-- well, why?
Because people are willing
to advertise on them.
And what are the advertisements?
They're advertising
products that are produced
by yet another group of people.
So in some sense,
we live in a world
in which we are creating
these very, very, very, long
[INAUDIBLE] that
are made possible
as we're creating value in
the context of these networks
of firms that are
also facilitated
by standards, by trust, by
associations, by a number
of different things that allow
these large computers to come
together.
So in some sense, we can say
that Pep Guardiola was right.
That what is difficult
to do when you're
trying to accomplish
something, you're not just
figuring out what
you are going to do,
but you try to create
a network of people
that has the capacity to do it.
And that embodiment of knowledge
and know how in the context
of a social network
is what makes
the growth of
computation, and therefore
the growth of information
inside an economy, difficult.
So what I want to
do next is now I'm
going to start bringing
in data to try to validate
these theories by using several
observations that connect
cultures to the
products that they make,
and also that look at how
communication technologies have
changed our ability to record
information and the types
of information that we record.
But also, I'm going to
give you guys a break,
because I think you have
heard a lot of things.
So what I'm going
to do next is I'm
going to show you a
very short video that
shows the other part
of the research agenda
that I do at MIT, which is not
just the scholarly work that I
translate into books and papers.
But it's the work that
involves invention.
And what we have been doing
is we have been creating
the opposite of a database.
For many years now,
there are many companies
like Oracle and
SAP, and you guys
as well, that have
developed great technologies
to accumulate and store
vast volumes of data.
But in some sense,
much of that data
is very obscure to the
people that own it,
or even to the people that
would want to access it.
Because databases are organized,
but they're not transparent.
So what we have been
doing is we have
been building what I call data
visualization engines, which
are these stacks of
software that you
can use to point into
any part of database
and obtain not a
table as a result,
but obtain a visualization.
And then by taking
these visualizations
and putting them
together, you're
going to start creating stories
that allow you to understand
the systems that
are being described
and the information that
is embodied in that data.
So I'm going to
show you that video.
And then we're going
to continue by looking
at how we can use now
[INAUDIBLE] observations
to validate our results.
[VIDEO PLAYBACK]
[MUSIC PLAYING]
-Welcome to the MIT Media
Lab Macro Connections Group.
Today, we're going to show
you seven different projects.
These are the Observatory of
Economic Complexity, DataViva,
Pantheon, Immersion, Place
Pulse, StreetScore, and Dive.
Most of these projects are
data visualization engine.
That is, they're
online tools that
empower people to visualize
any aspect of a data set.
The Observatory of
Economic Complexity
is a tool that makes
available international trade
data for the last 50
years through more than 20
million visualizations.
The Observatory of
Economic Complexity
focuses on the mix of
products that countries
export because this product mix
is predictive of a country's
future patterns of
diversification, GDP
growth, and income inequality.
DataViva makes available
regional development data
for all of Brazil
through more than one
billion visualizations.
These visualizations include
trade data, employment data,
and education data for each
of Brazil's more than 5,000
municipalities and its hundreds
of products, industries,
and occupations.
Pantheon expands on
the ideas developed
in the Observatory of Economic
Complexity and DataViva
by focusing on historical,
cultural production.
Pantheon is allowing
us to achieve
a quantitative understanding of
our species collective memory
and of the role of languages
and communication technologies
in the production and diffusion
of cultural information.
Immersion is a design experiment
that inverts a person's email
inbox by centering it on
people, rather than messages.
Immersion reveals the
web of interactions
that people create over
long periods of time,
helping uncover the information
embodied in metadata,
but also facilitating the
study of the networks that
form organizations.
Place Pulse and
StreetScore are efforts
to create high resolution
maps of people's perceptions
of urban environments.
These maps have been shown to
correlate with crime levels.
But also, we're now using
them to create computer vision
tools that help us
study urban change.
Finally, Dive is an
effort to automate
the creation of simple
data visualization engines.
Unlike traditional
visualization tools
that ask users to decide
which visualizations
they want to make a priori,
Dive has the ability
to recommend visualizations
to users, facilitating
the exploration of their data.
Together, these
technologies are helping
us understand the evolution
of global economies,
regional economies,
cultural production, cities,
and organizations.
But unlike traditional
research methods,
these tools help
us include millions
of people in the exploration
of the knowledge that
is hidden in our data.
Join us in our quest.
[MUSIC PLAYING]
Atlas.media.mit.edu.
Dataviva.info,
pantheon.media.mit.edu,
immersion.media.mit.edu,
pulse.media.mit.edu,
streetscore.media.mit.edu,
dive.media.mit.edu,
macro.media.mit.edu.
[END PLAYBACK]
CESAR HIDALGO: That
gives you a bit
of a taste of some
of the other things
that we do in my
lab, which is is we
create these large data
visualization engines.
We've created some
that are very specific,
for example, the ones that are
looking at international trade
data, some that are growing to
become less specific and more
general.
For instance, DataViva
started looking only
at industrial data for Brazil.
Now it also includes
location data,
and we're even
looking to incorporate
even environmental
data in the future.
So what I want to
do next is I want
to validate some of the
theories that I told you before.
This is the idea that
economies are computers
and that the computational
capacity of these computers
is determined by their ability
to form networks that they
can use to model computation.
And that eventually,
we can use this theory
to explain differences in
income and economic growth.
And to be able to
validate those theories,
I first have to make sure
that we're on the same page
according to a very
important relationship, which
is the relationship within
genotypes and phenotypes.
So a genotype is the information
that is encoded in your genes.
A phenotype is a trait
that is observable
to an external observer.
So in principle, obviously,
you can genotype people
by sequencing, but
sequencing is kind of hard.
And also, figuring out
what genes are associated
to which traits is kind of
difficult. So in some sense,
one thing that we
can do is let's say
I'm curious about the genes
that encode for height.
And I have two people, Danny
DeVito and LeBron James.
Obviously, one
cumbersome way of trying
to figure out who has the
genes that encode for height
would be to sequence
them and try
to figure out what are the many
genes that encode for height.
There are known to be more or
less more than 10 genes maybe
that encode height.
Or the other thing is I can just
look at Danny DeVito and LeBron
James and say, hey,
probably LeBron James
has genes that encode
for being tall,
and Danny DeVito doesn't
have those genes.
So in some sense, even if I
don't have a technology that
allows me to look
at the genotypes,
I can know a lot about
a person's genotype
by looking at their
phenotype because
of the genotype-phenotype
relationship.
By the same token, economies are
computers that run on knowledge
and know how.
And measuring knowledge and
know how is very difficult.
So I can look at
knowledge and know how
by looking at the products
that economies make.
So if an economy produces
cars and produces good cars,
I know something about
the fact that they
know how to shape metal and
they have mechanical engineers.
In a company like Google,
I know that you guys
know how to deal with data
and to build websites.
In a company like Apple,
I know that they're
very good at consumer
products and the design
of those products.
So in some way, what
I want to do now
is I want to look at
the mix of products
that a country or a
region makes as a way
to try to understand
the type of knowledge
and know how that they have.
I'm going to in some
way measure the capacity
of a computer
based on the output
that it's able to produce.
So I'm going to look
at a number of facts
because obviously,
no rope of evidence
is built on a single strand.
And the first fact that
I'm going to look at
is at the structure of
the network that connects
industries to locations.
So here on the left,
I have a network
that connects municipalities
and industries.
These are all
municipalities in Chile.
On the right, I have
a network that's
connecting countries to the
products that they export.
A black dot represents
a link, meaning
that an industry is present in
that municipality or a product
is being exported
by that country.
And these matrices are
not sorted at random.
On the top, I put the most
diverse municipalities
or the most diverse countries.
And on the left, I put the
most ubiquitous industries,
the ones that are present
in most locations,
or the most ubiquitous
products, the ones
that are exported by the
largest number of countries.
And what you see is that
this matrix is nested.
Nestedness is a
pattern that was first
discovered by an ecologist
looking at insular habitats.
If you have islands
of increasing size,
the ecosystems of those
islands become more diverse,
but it also becomes more diverse
in discovering nested patterns.
Because I think that is--
islands of increasing size
are also more sophisticated
ecological computers.
And what this
nested pattern tells
you is that where the places
that produce few things,
they don't produce a
random set of few things.
They produce subsets
of the things that
are produced by more places.
And they produce
subsets of the ones that
are produced by more places.
This is not a
[INAUDIBLE] matrix,
but it's like a nested matrix.
They are subsets.
And this nestedness is at first
strand in the rope of evidence
because it tells you, well, the
places that are little diverse
are places, obviously,
that can only
form relatively
small networks that
are unsophisticated computers.
And the places that
are more diverse,
they can produce large
networks that are
more sophisticated computers.
And they can produce
the things that
are done by the
unsophisticated computers,
but also the ones
that are done only
in a sophisticated computer.
So if you think about it,
think of an Atari 2600.
An Atari 2600 by
today's standards
is a very
unsophisticated computer.
And you can run Pac-Man on it.
Now, on my Mac, I
can also run Pac-Man,
but I can run things that I
cannot run on an Atari 2600.
So in some sense, the
more sophisticated
the computer, the
more diverse it's
going to be able
to have as input.
But also, it's going be
able to produce inputs
that are very rare,
that it would only
be able to produce in very
sophisticated computers.
So that's the first strand
in the rope of evidence.
The second strand goes
into ideas of learning.
So according to some computers,
just like you, me, and cells,
and computers, in some
way, they need to learn.
Sometimes, the way
that computers is
that we don't let them learn.
We just tell them what to do.
But someone has to
do the learning.
And economies as computers, they
learn at this collective level.
And what happens is that
one property of learning
is that it's always
easier to learn
something that is
similar to something
that you already know.
So if I know Spanish, it's
relatively easy for me
to learn Portuguese and it's
hard for me to learn Chinese,
not because Portuguese might be
inherently easier than Chinese,
but simply because
I have knowledge
that is very similar
and very redundant.
A lot of words I
can simply reuse.
A lot of grammar I can
simply reuse from Spanish
to Portuguese, but I cannot
reuse it from Spanish
to Chinese.
And economies is some
way do the same thing.
So they diversity towards
related varieties.
What that means is that if
you know the mix of products
that a country makes, you
can very accurately predict
the mix of products that
it's going to make next.
So here, I have a picture of the
product space for Chile, 1979.
So what does this picture mean?
Each dot represents a product,
and the dots that are painted
are the products that Chile
exported in the year 1979.
The ones that are
gray, like fresh fish,
are products that Chile did
not export in that year.
Now we have links.
The links are proportional
to the property
that two products
are co-exported.
So if I know that two
products are co-exported,
I know that they must
be kind of similar.
Because I know
that, for example,
the countries that
export preserved fish
tend to export fresh fish.
I can predict that
since Chile already
knows how to export
preserved fish, crustaceans,
and mollusks, and
frozen fish, they
might become good at exporting
fresh fish in the future.
By the same token, if I go to
this other part of the product
space and I look at, say, parts
of metalworking machine tools,
that's a product that Chile
is not producing anything
that is related to that.
So learning how to
produce that product
would be very difficult
because we have nothing
that we could reuse.
So now I'm going to
fast forward in time.
So we're in 1979 and I'm
going to go to the year 1996.
If I go to the year 1996, I
see that Chile now also exports
fresh fish and also
exports frozen fish filets
that they didn't export before.
And they diversified in this
part of the product space that
was the part of
the product space
that was close to the part
that they were already in.
And this is something that
is actually true in general.
You guys probably here are
familiar with ROC curves
when you're doing a
prediction algorithm.
The product space has
a [INAUDIBLE] of 0.5
in an ROC curve.
You're trying to predict
the mix of products
that a country is
going to make next.
So this is actually quite
accurate at predicting
the mix of products
that a country
is going to make in the future.
Obviously, there's a
shitload of false positives,
too, because the process
of diversification
has very future positives.
But ROC curves are
still very good.
So the second string
on our rope of evidence
is the fact that
economies learn by moving
into related varieties.
The third string relates
to economic growth.
And the idea of economic
growth is an old idea
that we can say started
with a Scotsman, Adam Smith.
And he conceptualized
the economy
in terms of two things,
capital and labor.
Labor were people.
Capital were things
that people did.
And then people saved the things
that they did to produce things
in the future.
And those ideas were
eventually mathematized
by Robert Solow in the 1950s.
And what happened is that when
Solow advanced these models,
there was a big
gap that was left.
That gap was between the
model and the empirical data.
And that gap
eventually, other people
tried to explain it by using
information on other factors
that they thought that
Solow had left out.
So for instance, in
the '80s and '90s,
people like [? Robert ?]
Mankiw and Paul [? Robert ?]
started to introduce the
idea of human capital.
It's not just people and things.
People have to know things also.
And then people in
the '90s and 2000s,
they started to
include social capital.
People might know things,
but if they're all assholes
and they don't
talk to each other
and they don't get
along well, eventually,
what's going to happen is
that that's not going to work.
So people need to come together.
And social capital
is another factor
that was not being accounted.
And when they
included human capital
and they included
social capital,
the gap that Solow
had left behind
started to shrink a little bit.
The models were doing
better and better.
But also, now that we understand
that economies are computers,
we can try to reinterpret
all of these ideas
and try to see if
there was something
that is still missing.
So now, within that we
have two firms here.
We can start to reinterpret
all of these factors
in terms of the [INAUDIBLE]
that I've introduced.
On the one hand,
firms produce things.
And I think that this is
physical capital or crystals
of imagination.
On the other hand, we
can think that people
need to know things,
and that's engineered
knowledge or human capital.
People need to
collect, and that's
social capital and trust.
But what is missing
from this picture
is something else,
which is the knowledge
and know how that the
systems accumulate
at the collective level.
And that is different
than the knowledge
and know how that people
have at the individual level.
And that's what is missing.
But we can figure
out what societies
or what economies
have accumulated
vast amounts of
knowledge and know how
at the collective level by
looking at the mix of products
that they make, and that's what
I call learning complexity.
So using the information
on the matrix
that I've got of
countries and products,
and taking some [INAUDIBLE]
measures out of it,
we can actually create measures
of the computational capacity
of economies.
And these measures are not only
highly correlated with income
per capita, but the deviations
between these correlations
are predictors of
future economic growth.
So for example, a
country like China,
that you would see
down here, is a country
that in 1985 had a
computational capacity that
was much larger than what would
be expected by its income.
So it was doomed to grow.
A country like Greece had
an income that was too high,
given its
computational capacity,
so it was going to have
problems sustaining
that level of income.
So actually, when we think
about economies as computers,
we realize that the level of
income that an economy achieves
tends to converge to the
computational capacity.
Moreover, actually,
economic complexity
is a very good
explanatory factor
when you're trying to
explain income inequality.
Just to go very
briefly here, if you
take two countries
like Chile and Malaysia
that have basically the
same level of income
and the same level of
education, if you want
to try to explain why Chile
is more unequal than Malaysia,
you can explain that
very well if you
look at the mix of
products that Chile makes
vis a vis the products
that Malaysia makes.
And this is because the mix of
products that a country makes
tend to co-evolve
with the institutions.
So imagine you were
trying to run Google
as people used to run tobacco
plantations in the 1700s.
Probably, it would not work
because those were companies
that had very exploitative
institutions that did not
consider ways in
which they could
incorporate the knowledge,
know how, creativity
of their workers.
But for the tobacco
industry, in some sense,
that kind of worked because
it was a very simple industry.
Now when you have
something that is much more
complex in which you
have to include people,
eventually, you have to discover
the institutions that can
make your company productive.
And those institutions
for complex industries
tend to be institutions
that are very inclusive.
So the way that we behave
also is shaped and co-evolves
with the type of
things that we do.
I'm going to skip this part,
even though it's so nice.
So another thing that
we can use to try
to understand the computational
capacities of economies
and systems is not by
looking only at products,
but by looking at
cultural production.
Because you can think of
the United States as a place
that exports soybeans
and cars and jet engines,
or you can think of
the United States
as the birthplace of Miles
Davis and Neil Armstrong.
And in some way, that
second description,
I would say, is very
important and is very valid.
So what we started
doing a few years ago,
we started to collect data
on cultural production
to try to understand the rate
at which our society remembers
things and the types of
things that we remember,
and how those are
affected by technology.
So here, what we have is two
charts with two different data
sets.
The one on the top is
the Pantheon data set,
and this is data that
includes every person that
has a presence in more
than 25 different languages
in Wikipedia.
So you have to be
famous in 25 languages.
You have to be globally famous
to be part of that data set.
And what we see on the
y-axis is the probability
that one of these
globally famous people
was born in a year as a
fraction of the population
of the world in that year.
And you see that basically,
that line jiggles
in what is statistically
proven Gaussian noise
until the printing
press comes along.
And then there's a
discontinuity and it jumps,
and it goes to another state.
And then the Industrial
Revolution comes, and then
it jumps again, and
it keeps on jumping.
Now, this the Human
Accomplishment Data Set,
which is a compendium of 4,000
historically famous biographies
published in the year 2003.
It's a much smaller data set,
but we find the same pattern
and the same transition points.
And what this is telling us is
that changes in communication
technology are changing the
capacity of our computer
to remember collectively.
Before writing, we all
remembered nothing.
Nobody knows the name of anybody
that was born 20,000 years ago.
Then with the
invention of writing,
we started to record
some information.
And then with the
invention of printing,
the amount of information
that we were able to record
started to explode.
And now with modern [INAUDIBLE]
technology, obviously,
we have moved from a
world in which history
used to be something that was
reserved only for the elite
to a world in which everyone
has a personal history hidden
in the metadata that you guys
and other companies also have.
Also, [INAUDIBLE] technology
changed the type of information
that we produce.
So if you look at the era
of writing, pre-printing,
you will see that most
of the famous people that
are recorded in our data set
are either political figures
or religious figures.
But when the printing
press comes along,
these measures of cultural
production change dramatically.
And now you have famous
scientists and famous artists
that become globally provocative
because the printing press did
not only change the number
of books that produce.
But it also changed what
those books were about.
And if you're
interested in that,
the best reference there is
Elizabeth Eisenstein's "The
Printing Press as
an Agent of Change."
But the printing press obviously
was not the last revolution.
When film and radio
came along, the arts
shifted dramatically
because what
happened is that the arts
transformed into something that
was about performance, not about
the creation of pieces anymore.
So before film and radio,
the ones that become famous
was the painter or the composer.
After the invention
of film and radio,
it's the actor, the
musician, the singer
because the new medium
that was invented there
recorded the performance,
and that changed [INAUDIBLE].
And then when
television comes along,
now you invent the
famous sports figure.
[INAUDIBLE] these things are
causal because obviously,
for instance, if you look
at the ability of actors,
there were actors at
the times of the Greeks,
and there were
actors, obviously,
at the time of Shakespeare
that interpret and perform
his plays.
But the actors were
not the ones that
became famous at that time.
They were the playwrights
until we developed
a medium in which
the performance was
the one that was recorded.
So I started by telling
you that the universe is
made of three things, energy,
matter, and information,
and that information
is the one that
makes the universe interesting.
Information endows the universe
with its structure, its beauty,
and its complexity.
I argue that there's
three mechanisms that
help us explain the existence
and growth of information
despite the second
law of thermodynamics.
The first one is that
information is not free.
You need to spend some energy
to produce information.
The second one is
that you need to have
ways to store that
information, and solids are
an excellent way of doing it.
And finally, matter needs
to develop this capacity
to compute.
And this capacity
to compute is what
is most important
because it's what
make the growth of
knowledge possible,
but also what makes it difficult
because computation always
needs to be physically
embodied, and the systems
that embody computation are
always of finite capacity.
And the only way that
those systems transcend
that finite capacity
is by actually creating
social structures
that allow them
to act as parallel
computers, as computers
that are able to process things
socially and collectively.
So the cell transcends its
limitation by multicellularity.
And multicellularity
eventually takes
us all the way to the human.
But then the human
is also limited.
In order for us to increase
our computational capacities
and make information grow,
we have to create teams,
we have to create
firms, and we have
to create networks
of [INAUDIBLE], which
are increasingly larger
social and economic computers
that help push the
growth of information
to what we have until today.
And obviously, it will
keep on pushing the growth
of information in the future.
And with that, I
would like to thank
you very much for
your time and I'm
happy to take any
questions you might have.
Thank you.
[APPLAUSE]
AUDIENCE: I have one.
I was thinking about how you
would apply it practically
if you were trying to get
companies to grow or to get
cities to grow.
Is there a way-- are
there some specific,
but are there
generalizations that you
can apply to
companies or to cities
to help them achieve more than
they have done [INAUDIBLE],
or does it still have to be
a very specific, case by case
basis?
CESAR HIDALGO: So the
more practical side
that I've been
doing, obviously, I
think that there
are some things that
are scholarly work that allow
you to understand the world.
And there are things that
have an immediate practical
application that you
can do in the short run
and allow people
to have benefits.
So for example, the Data
Visualization Engines
that we are building
are becoming
of increasing interest
of governments
beyond Brazil because these
data visualization engines allow
them to facilitate data tasks
that they had to perform
in the past very manually by
sending emails among people
and by producing charts in
Excel and then pasting them
in PowerPoint.
That is useful for
them to be able to do
automatically and faster and
with better designed tools.
So in the short run,
that's one of the things
that I can provide
that is a quick win
and I can have implemented in
the period of a year or so.
In the long run,
what this is starting
to tell you is that eventually,
if you think that economies are
computers, you have to
think about the ways
in which you allow people
to actually connect,
and how there might
be interaction
between social policies
and economic prosperity.
So for instance, in
the past, we tended
to have institutions that
were very discriminatory that
limit our ability to perform
connections between people
from different ethnicities,
from different nationalities,
from different genders.
And those policies, we would
say they were hurting us.
And it's good that we're moving
in this opposite direction
because that helps increase
computational capacity that
helps information grow, and
eventually economies grow.
Also, you have these very
simple and very obvious
policy applications, but
that can be derived easily
from the fact that economies
are computers that are embodied
on social networks.
AUDIENCE: On your
economic complexity chart,
you have countries.
Could you apply
that to companies
as well, where companies
might diversify?
CESAR HIDALGO: So that's a good
question because in some sense,
as you have this
re-quantization of computation,
you're going to
have matrices that
have different
structure along the way.
So for instance, if you
think about a country,
a country is a very
large computer.
It's a very large unit that
includes lots of people.
It includes networks of firms
and organizations and so forth.
In that case, the push is
going to be towards diversity.
But if you start looking
at simpler and simpler
and simpler, smaller
computers and smaller systems,
then the finite size
of those systems
pushes towards specialization.
So if I look at a company, like
Google doesn't do everything.
And they shouldn't do everything
because there's a lot of things
that you guys are good at,
and there's other things
that you guys are not good at.
So in some sense, you might
want to stay out of things
that you're not good at
and focus on the things
that you're good at,
and maybe discover
new things that you might
be good at that leverage
your capacities.
So in that case, the
measures, I would say,
do not apply immediately.
But I don't think
that it would be
impossible to create measures
that would take that data
and would be inspired by this
idea of economic complexity
and computation
to create measures
of the capacities of firms.
AUDIENCE: I guess
another similar question.
I was thinking if you tend
to maybe think strategies
that countries have adopted
to enable themselves to grow
as computers, as in the
amount of information
that they produce.
Because obviously, a
company might [INAUDIBLE]
diversification
because [INAUDIBLE].
But have there been any
other ways in which countries
have tried to diversify
their production of goods
or historically proven
ways that-- again,
things that they haven't been
doing well they start to do.
I guess that's pretty much it.
Or is one of the components that
were presented that contributes
towards the
potential information
growth in a country,
if one of them
is easier to modify than others.
Because obviously, for
example, social links,
predefined social links
like family connections
are not easy to
modify, like you said.
CESAR HIDALGO:
And in most cases,
obviously, one thing is
that you understand society.
Another thing is that
you can engineer it.
The same is true,
for instance, if you
think about Darwin's
theory of evolution.
You can develop a very
deep understanding of where
biological order comes from.
But going from Darwin's
theory of evolution
to genetic engineering
is a big step.
By the same token, I do think
that in our understanding
of social economic
systems, we're
just starting to realize that
these systems are computers.
And we have a pretty deep
understanding of them
that does not allow
us to engineer them.
One of the things
that I do think,
though, is that sometimes,
the question, for example,
that you're asking now might
be a little bit of a trick
question because you think
that economies are computers.
Maybe the existence of countries
is kind of like a bad idea.
In some sense,
what you want to do
is increase the computational
capacity of the planet
and the prosperity
of all people.
And countries are kind of like
a barrier for that to happen.
Nowadays, for
instance, one thing
that I found ridiculous-- I'm an
anti-nationalist about nations.
And as an anti-nationalist
about nations,
I find it ridiculous
that even though we
have learned that you should
not discriminate people because
of things that
they do not choose,
like your ethnicity
or your gender,
we still accept and
encourage discrimination
based on the color of
people's passports.
And that's bullshit,
and that's a barrier
to the organization of people
into more efficient computers.
So maybe the best way to
help countries prosper
is to get rid of these
divisions between countries
in a much more global context.
Obviously, sometimes those
really soothing effects
are going to mean that
there's some people that now,
because of some
protective measure,
are doing better
than they should.
And those people are
not going to want
that type of redistribution.
But I think that
over the long run,
it might be more fair
and better for everyone.
AUDIENCE: I was just curious.
According to this measure
of economic complexity
as a predictor of a country's
future economic growth, which
countries are likely to
have strong economic growth
in our future?
Which countries currently
have the biggest disparity
between economic complexity and
the current economic status?
CESAR HIDALGO: So there
is some obvious candidates
that everybody is aware
of, like China and India,
some a little bit less obvious,
like Vietnam, and some,
for example, that have a
lot of growth potential
but at the moment have
very low levels of income.
So in some sense,
the growth is not
going to translate into
those countries becoming
large economic powers.
But it's going to be substantial
nevertheless, like countries
in East Africa, for instance.
East Africa has
managed to become
a little bit more
industrialized than West Africa.
And that industrialization
of East Africa
that happened--
the garden center
with the export of flowers, with
other products that they have
been able to develop, as we see
that, it's implying that now,
the level of income
of East Africa
should start approaching
more like that
of Latin America than the
one that they have right now.
So those would be
a few examples.
Countries that are
on the opposite side,
obviously, we can
mention Greece,
but I think Greece already
gets too much of a bad rap here
in Europe.
You guys should
give them a break.
You have all of
the countries that
export a lot of raw materials.
So all of the countries
that export raw materials
are reliers on the relationship
because their income
is pegged to the national
resource endowments.
So countries like Saudi
Arabia or Qatar or Chile,
or even Australia, if
they were to run out
of natural resources,
or if the economy
were to change in such a way
that the resources that they
export is no longer needed,
it would be in deep trouble
because their computers
are not as sophisticated
as their income would suggest.
[APPLAUSE]
