WEBVTT

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"When the crisis came,

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the serious limitations
of existing economic and financial models

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immediately became apparent."

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"There is also a strong belief,
which I share,

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that bad or oversimplistic
and overconfident economics

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helped create the crisis."

NOTE Paragraph

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Now, you've probably all heard
of similar criticism

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coming from people
who are skeptical of capitalism.

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But this is different.

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This is coming from the heart of finance.

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The first quote
is from Jean-Claude Trichet

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when he was governor
of the European Central Bank.

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The second quote is from the head
of the UK Financial Services Authority.

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Are these people implying

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that we don't understand
the economic systems

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that drive our modern societies?

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It gets worse.

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"We spend billions of dollars

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trying to understand
the origins of the universe,

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while we still don't understand
the conditions for a stable society,

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a functioning economy, or peace."

NOTE Paragraph

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What's happening here?
How can this be possible?

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Do we really understand more
about the fabric of reality

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than we do about the fabric
which emerges from our human interactions?

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Unfortunately, the answer is yes.

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But there's an intriguing solution

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which is coming from what is known
as the science of complexity.

NOTE Paragraph

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To explain what this means
and what this thing is,

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please let me quickly take
a couple of steps back.

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I ended up in physics by accident.

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It was a random encounter
when I was young,

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and since then, I've often wondered
about the amazing success of physics

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in describing the reality
we wake up in every day.

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In a nutshell, you can think
of physics as follows.

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So you take a chunk of reality
you want to understand

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and you translate it into mathematics.

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You encode it into equations.

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Then, predictions can be made and tested.

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We're actually
really lucky that this works,

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because no one really knows
why the thoughts in our heads

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should actually relate to the fundamental
workings of the universe.

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Despite the success,
physics has its limits.

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As Dirk Helbing pointed out
in the last quote,

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we don't really understand the complexity
that relates to us, that surrounds us.

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This paradox is what got me interested
in complex systems.

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So these are systems which are made up

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of many interconnected
or interacting parts:

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swarms of birds or fish,

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ant colonies, ecosystems,
brains, financial markets.

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These are just a few examples.

NOTE Paragraph

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Interestingly, complex systems
are very hard to map

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into mathematical equations,

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so the usual physics approach
doesn't really work here.

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So what do we know about complex systems?

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Well, it turns out that what looks
like complex behavior from the outside

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is actually the result
of a few simple rules of interaction.

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This means you can forget
about the equations

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and just start to understand the system

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by looking at the interactions.

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And it gets even better,
because most complex systems

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have this amazing property
called emergence.

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So this means that the system as a whole
suddenly starts to show a behavior

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which cannot be understood or predicted

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by looking at the components
of the system.

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So the whole is literally
more than the sum of its parts.

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And all of this also means

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that you can forget about
the individual parts of the system,

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how complex they are.

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So if it's a cell or a termite or a bird,

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you just focus on the rules
of interaction.

NOTE Paragraph

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As a result, networks are ideal
representations of complex systems.

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The nodes in the network
are the system's components,

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and the links are given
by the interactions.

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So what equations are for physics,

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complex networks are for the study
of complex systems.

NOTE Paragraph

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This approach has been
very successfully applied

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to many complex systems
in physics, biology,

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computer science, the social sciences,

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but what about economics?

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Where are economic networks?

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This is a surprising
and prominent gap in the literature.

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The study we published last year, called
"The Network of Global Corporate Control,"

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was the first extensive analysis
of economic networks.

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The study went viral on the Internet

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and it attracted a lot of attention
from the international media.

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This is quite remarkable, because, again,
why did no one look at this before?

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Similar data has been
around for quite some time.

NOTE Paragraph

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Well, any way, what we looked at in detail
was ownership networks.

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So here the nodes are companies,
people, governments, foundations, etc.

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And the links represent
the shareholding relations,

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so shareholder A has x percent
of the shares in company B.

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And we also assign a value to the company
given by the operating revenue.

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So ownership networks reveal the patterns
of shareholding relations.

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In this little example,
you can see a few financial institutions

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with some of the many links highlighted.

NOTE Paragraph

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Now, you may think that no one
looked at this before

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because ownership networks
are really, really boring to study.

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Well, as ownership is related to control,

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as I shall explain later,

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looking at ownership networks

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actually can give you
answers to questions like,

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who are the key players?

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How are they organized?
Are they isolated?

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Are they interconnected?

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And what is the overall
distribution of control?

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In other words, who controls the world?

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I think this is an interesting question.

NOTE Paragraph

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And it has implications for systemic risk.

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This is a measure of how vulnerable
a system is overall.

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A high degree of interconnectivity
can be bad for stability,

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because then the stress can spread
through the system like an epidemic.

NOTE Paragraph

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Scientists have sometimes
criticized economists

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who believe ideas and concepts
are more important than empirical data,

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because a foundational
guideline in science is:

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Let the data speak. OK. Let's do that.

NOTE Paragraph

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So we started with a database containing
13 million ownership relations from 2007.

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This is a lot of data,
and because we wanted to find out

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"who rules the world,"

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we decided to focus
on transnational corporations,

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or "TNCs," for short.

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These are companies that operate
in more than one country,

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and we found 43,000.

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In the next step, we built
the network around these companies,

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so we took all the TNCs' shareholders,

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and the shareholders' shareholders, etc.,

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all the way upstream,
and we did the same downstream,

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and ended up with a network
containing 600,000 nodes

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and one million links.

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This is the TNC network which we analyzed.

NOTE Paragraph

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And it turns out to be
structured as follows.

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So you have a periphery and a center

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which contains about 75 percent
of all the players,

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and in the center,
there's this tiny but dominant core

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which is made up of highly
interconnected companies.

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To give you a better picture,

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think about a metropolitan area.

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So you have the suburbs and the periphery,

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you have a center,
like a financial district,

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then the core will be something like

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the tallest high-rise building
in the center.

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And we already see signs
of organization going on here.

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36 percent of the TNCs
are in the core only,

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but they make up 95 percent of the total
operating revenue of all TNCs.

NOTE Paragraph

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OK, so now we analyzed the structure,

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so how does this relate to the control?

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Well, ownership gives
voting rights to shareholders.

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This is the normal notion of control.

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And there are different models

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which allow you to compute
the control you get from ownership.

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If you have more than 50 percent
of the shares in a company,

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you get control,

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but usually, it depends
on the relative distribution of shares.

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And the network really matters.

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About 10 years ago, Mr. Tronchetti Provera

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had ownership and control
in a small company,

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which had ownership and control
in a bigger company.

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You get the idea.

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This ended up giving him control
in Telecom Italia with a leverage of 26.

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So this means that,
with each euro he invested,

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he was able to move 26 euros
of market value

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through the chain of ownership relations.

NOTE Paragraph

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Now what we actually computed in our study
was the control over the TNCs' value.

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This allowed us to assign
a degree of influence to each shareholder.

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This is very much in the sense
of Max Weber's idea of potential power,

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which is the probability
of imposing one's own will

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despite the opposition of others.

NOTE Paragraph

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If you want to compute the flow
in an ownership network,

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this is what you have to do.

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It's actually not that hard to understand.

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Let me explain by giving you this analogy.

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So think about water flowing in pipes,
where the pipes have different thickness.

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So similarly, the control is flowing
in the ownership networks

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and is accumulating at the nodes.

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So what did we find after computing
all this network control?

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Well, it turns out
that the 737 top shareholders

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have the potential to collectively control
80 percent of the TNCs' value.

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Now remember, we started out
with 600,000 nodes,

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so these 737 top players
make up a bit more than 0.1 percent.

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They're mostly financial institutions
in the US and the UK.

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And it gets even more extreme.

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There are 146 top players in the core,

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and they together have the potential
to collectively control

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40 percent of the TNCs' value.

NOTE Paragraph

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What should you take home
from all of this?

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Well, the high degree of control you saw
is very extreme by any standard.

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The high degree of interconnectivity
of the top players in the core

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could pose a significant systemic risk
to the global economy.

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And we could easily
reproduce the TNC network

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with a few simple rules.

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This means that its structure is probably
the result of self-organization.

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It's an emergent property which depends
on the rules of interaction in the system,

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so it's probably not the result
of a top-down approach

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like a global conspiracy.

NOTE Paragraph

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Our study "is an impression
of the moon's surface.

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It's not a street map."

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So you should take the exact numbers
in our study with a grain of salt,

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yet it "gave us a tantalizing glimpse
of a brave new world of finance."

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We hope to have opened the door
for more such research in this direction,

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so the remaining unknown terrain
will be charted in the future.

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And this is slowly starting.

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We're seeing the emergence of long-term
and highly-funded programs

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which aim at understanding
our networked world

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from a complexity point of view.

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Like the FuturICT Flagship intitaive,
which is actually spearheaded from Zurich.

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But this journey has only just begun,

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so we will have to wait
before we see the first results.

NOTE Paragraph

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Now there is still
a big problem, in my opinion.

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Ideas relating to finance,
economics, politics, society,

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are very often tainted
by people's personal ideologies.

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I really hope that this
complexity perspective

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allows for some common ground to be found.

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It would be really great
if it has the power

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to help end the gridlock
created by conflicting ideas,

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which appears to be paralyzing
our globalized world.

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Reality is so complex,
we need to move away from dogma.

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But this is just my own personal ideology.

NOTE Paragraph

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Thank you.

NOTE Paragraph

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(Applause)