How to pronounce "overall"

Translator: Joseph Geni Reviewer: Morton Bast

"When the crisis came,

the serious limitations of existing economic and financial models

immediately became apparent."

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

that bad or oversimplistic and overconfident economics

helped create the crisis."

Now, you've probably all heard of similar criticism

coming from people who are skeptical of capitalism.

But this is different.

This is coming from the heart of finance.

The first quote is from Jean-Claude Trichet

when he was governor of the European Central Bank.

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

Are these people implying

that we don't understand the economic systems

that drive our modern societies?

It gets worse.

"We spend billions of dollars

trying to understand the origins of the universe,

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

a functioning economy, or peace."

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

Do we really understand more about the fabric of reality

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

Unfortunately, the answer is yes.

But there's an intriguing solution

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

To explain what this means and what this thing is,

please let me quickly take a couple of steps back.

I ended up in physics by accident.

It was a random encounter when I was young,

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

in describing the reality we wake up in every day.

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

So you take a chunk of reality you want to understand

and you translate it into mathematics.

You encode it into equations.

Then, predictions can be made and tested.

We're actually really lucky that this works,

because no one really knows why the thoughts in our heads

should actually relate to the fundamental workings of the universe.

Despite the success, physics has its limits.

As Dirk Helbing pointed out in the last quote,

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

This paradox is what got me interested in complex systems.

So these are systems which are made up

of many interconnected or interacting parts:

swarms of birds or fish,

ant colonies, ecosystems, brains, financial markets.

These are just a few examples.

Interestingly, complex systems are very hard to map

into mathematical equations,

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

So what do we know about complex systems?

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

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

This means you can forget about the equations

and just start to understand the system

by looking at the interactions,

so you can actually forget about the equations

and you just start to look at the interactions.

And it gets even better, because most complex systems

have this amazing property called emergence.

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

which cannot be understood or predicted

by looking at the components of the system.

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

And all of this also means

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

how complex they are.

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

you just focus on the rules of interaction.

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

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

and the links are given by the interactions.

So what equations are for physics,

complex networks are for the study of complex systems.

This approach has been very successfully applied

to many complex systems in physics, biology,

computer science, the social sciences,

but what about economics?

Where are economic networks?

This is a surprising and prominent gap in the literature.

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

was the first extensive analysis of economic networks.

The study went viral on the Internet

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

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

Similar data has been around for quite some time.

What we looked at in detail was ownership networks.

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

And the links represent the shareholding relations,

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

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

So ownership networks reveal the patterns of shareholding relations.

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

with some of the many links highlighted.

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

because ownership networks are really, really boring to study.

Well, as ownership is related to control,

as I shall explain later,

looking at ownership networks

actually can give you answers to questions like,

who are the key players?

How are they organized? Are they isolated?

Are they interconnected?

And what is the overall distribution of control?

In other words, who controls the world?

I think this is an interesting question.

And it has implications for systemic risk.

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

A high degree of interconnectivity can be bad for stability,

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

Scientists have sometimes criticized economists

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

because a foundational guideline in science is:

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

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

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

"who rules the world,"

we decided to focus on transnational corporations,

or "TNCs," for short.

These are companies that operate in more than one country,

and we found 43,000.

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

so we took all the TNCs' shareholders,

and the shareholders' shareholders, etc.,

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

and ended up with a network containing 600,000 nodes

and one million links.

This is the TNC network which we analyzed.

And it turns out to be structured as follows.

So you have a periphery and a center

which contains about 75 percent of all the players,

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

which is made up of highly interconnected companies.

To give you a better picture,

think about a metropolitan area.

So you have the suburbs and the periphery,

you have a center, like a financial district,

then the core will be something like

the tallest high-rise building in the center.

And we already see signs of organization going on here.

36 percent of the TNCs are in the core only,

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

OK, so now we analyzed the structure,

so how does this relate to the control?

Well, ownership gives voting rights to shareholders.

This is the normal notion of control.

And there are different models

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

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

you get control,

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

And the network really matters.

About 10 years ago, Mr. Tronchetti Provera

had ownership and control in a small company,

which had ownership and control in a bigger company.

You get the idea.

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

So this means that, with each euro he invested,

he was able to move 26 euros of market value

through the chain of ownership relations.

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

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

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

which is the probability of imposing one's own will

despite the opposition of others.

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

this is what you have to do.

It's actually not that hard to understand.

Let me explain by giving you this analogy.

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

So similarly, the control is flowing in the ownership networks

and is accumulating at the nodes.

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

Well, it turns out that the 737 top shareholders

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

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

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

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

And it gets even more extreme.

There are 146 top players in the core,

and they together have the potential to collectively control

40 percent of the TNCs' value.

What should you take home from all of this?

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

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

could pose a significant systemic risk to the global economy.

And we could easily reproduce the TNC network

with a few simple rules.

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

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

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

like a global conspiracy.

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

It's not a street map."

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

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

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

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

And this is slowly starting.

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

which aim at understanding our networked world

from a complexity point of view.

But this journey has only just begun,

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

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

Ideas relating to finance, economics, politics, society,

are very often tainted by people's personal ideologies.

I really hope that this complexity perspective

allows for some common ground to be found.

It would be really great if it has the power

to help end the gridlock created by conflicting ideas,

which appears to be paralyzing our globalized world.

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

But this is just my own personal ideology.

Thank you.

(Applause)