Who controls the world? | James B. Glattfelder | TEDxZurich
-
0:18 - 0:21"When the crisis came,
-
0:21 - 0:25the serious limitations
of existing economic and financial models -
0:25 - 0:27immediately became apparent."
-
0:30 - 0:34"There is also a strong belief,
which I share, -
0:34 - 0:39that bad or oversimplistic
and overconfident economics -
0:39 - 0:41helped create the crisis."
-
0:41 - 0:43Now, you've probably all heard
of similar criticism -
0:43 - 0:46coming from people
who are skeptical of capitalism. -
0:47 - 0:48But this is different.
-
0:48 - 0:52This is coming from the heart of finance.
-
0:52 - 0:55The first quote
is from Jean-Claude Trichet -
0:55 - 0:58when he was governor
of the European Central Bank. -
0:59 - 1:04The second quote is from the head
of the UK Financial Services Authority. -
1:05 - 1:07Are these people implying
-
1:07 - 1:10that we don't understand
the economic systems -
1:10 - 1:12that drive our modern societies?
-
1:13 - 1:14It gets worse.
-
1:15 - 1:17"We spend billions of dollars
-
1:17 - 1:21trying to understand
the origins of the universe, -
1:21 - 1:27while we still don't understand
the conditions for a stable society, -
1:27 - 1:30a functioning economy, or peace."
-
1:34 - 1:36What's happening here?
How can this be possible? -
1:36 - 1:40Do we really understand more
about the fabric of reality -
1:40 - 1:44than we do about the fabric
which emerges from our human interactions? -
1:44 - 1:46Unfortunately, the answer is yes.
-
1:47 - 1:49But there's an intriguing solution
-
1:49 - 1:54which is coming from what is known
as the science of complexity. -
1:55 - 1:58To explain what this means
and what this thing is, -
1:58 - 2:01please let me quickly take
a couple of steps back. -
2:02 - 2:04I ended up in physics by accident.
-
2:04 - 2:07It was a random encounter
when I was young, -
2:07 - 2:12and since then, I've often wondered
about the amazing success of physics -
2:12 - 2:15in describing the reality
we wake up in every day. -
2:16 - 2:19In a nutshell, you can think
of physics as follows. -
2:19 - 2:22So you take a chunk of reality
you want to understand -
2:23 - 2:26and you translate it into mathematics.
-
2:26 - 2:28You encode it into equations.
-
2:29 - 2:32Then, predictions can be made and tested.
-
2:35 - 2:37We're actually
really lucky that this works, -
2:37 - 2:40because no one really knows
why the thoughts in our heads -
2:40 - 2:44should actually relate to the fundamental
workings of the universe. -
2:46 - 2:49Despite the success,
physics has its limits. -
2:50 - 2:53As Dirk Helbing pointed out
in the last quote, -
2:53 - 2:58we don't really understand the complexity
that relates to us, that surrounds us. -
3:00 - 3:04This paradox is what got me interested
in complex systems. -
3:04 - 3:06So these are systems which are made up
-
3:06 - 3:09of many interconnected
or interacting parts: -
3:09 - 3:12swarms of birds or fish,
-
3:12 - 3:17ant colonies, ecosystems,
brains, financial markets. -
3:17 - 3:18These are just a few examples.
-
3:21 - 3:26Interestingly, complex systems
are very hard to map -
3:26 - 3:28into mathematical equations,
-
3:28 - 3:32so the usual physics approach
doesn't really work here. -
3:33 - 3:35So what do we know about complex systems?
-
3:35 - 3:41Well, it turns out that what looks
like complex behavior from the outside -
3:41 - 3:45is actually the result
of a few simple rules of interaction. -
3:47 - 3:51This means you can forget
about the equations -
3:51 - 3:53and just start to understand the system
-
3:53 - 3:55by looking at the interactions.
-
3:55 - 3:58And it gets even better,
because most complex systems -
3:58 - 4:01have this amazing property
called emergence. -
4:01 - 4:06So this means that the system as a whole
suddenly starts to show a behavior -
4:06 - 4:09which cannot be understood or predicted
-
4:09 - 4:11by looking at the components
of the system. -
4:11 - 4:14So the whole is literally
more than the sum of its parts. -
4:15 - 4:17And all of this also means
-
4:17 - 4:22that you can forget about
the individual parts of the system, -
4:22 - 4:23how complex they are.
-
4:23 - 4:28So if it's a cell or a termite or a bird,
-
4:28 - 4:30you just focus on the rules
of interaction. -
4:33 - 4:38As a result, networks are ideal
representations of complex systems. -
4:40 - 4:44The nodes in the network
are the system's components, -
4:46 - 4:48and the links are given
by the interactions. -
4:49 - 4:52So what equations are for physics,
-
4:52 - 4:55complex networks are for the study
of complex systems. -
4:56 - 5:00This approach has been
very successfully applied -
5:00 - 5:03to many complex systems
in physics, biology, -
5:03 - 5:06computer science, the social sciences,
-
5:06 - 5:07but what about economics?
-
5:08 - 5:10Where are economic networks?
-
5:11 - 5:14This is a surprising
and prominent gap in the literature. -
5:16 - 5:22The study we published last year, called
"The Network of Global Corporate Control," -
5:22 - 5:27was the first extensive analysis
of economic networks. -
5:28 - 5:31The study went viral on the Internet
-
5:31 - 5:34and it attracted a lot of attention
from the international media. -
5:36 - 5:40This is quite remarkable, because, again,
why did no one look at this before? -
5:40 - 5:43Similar data has been
around for quite some time. -
5:43 - 5:48Well, any way, what we looked at in detail
was ownership networks. -
5:49 - 5:56So here the nodes are companies,
people, governments, foundations, etc. -
5:57 - 6:00And the links represent
the shareholding relations, -
6:00 - 6:05so shareholder A has x percent
of the shares in company B. -
6:06 - 6:10And we also assign a value to the company
given by the operating revenue. -
6:12 - 6:16So ownership networks reveal the patterns
of shareholding relations. -
6:18 - 6:22In this little example,
you can see a few financial institutions -
6:22 - 6:25with some of the many links highlighted.
-
6:27 - 6:29Now, you may think that no one
looked at this before -
6:30 - 6:34because ownership networks
are really, really boring to study. -
6:35 - 6:39Well, as ownership is related to control,
-
6:39 - 6:40as I shall explain later,
-
6:40 - 6:42looking at ownership networks
-
6:42 - 6:45actually can give you
answers to questions like, -
6:45 - 6:46who are the key players?
-
6:46 - 6:48How are they organized?
Are they isolated? -
6:48 - 6:50Are they interconnected?
-
6:50 - 6:53And what is the overall
distribution of control? -
6:54 - 6:57In other words, who controls the world?
-
6:57 - 7:00I think this is an interesting question.
-
7:00 - 7:03And it has implications for systemic risk.
-
7:04 - 7:08This is a measure of how vulnerable
a system is overall. -
7:10 - 7:13A high degree of interconnectivity
can be bad for stability, -
7:14 - 7:19because then the stress can spread
through the system like an epidemic. -
7:21 - 7:24Scientists have sometimes
criticized economists -
7:24 - 7:29who believe ideas and concepts
are more important than empirical data, -
7:29 - 7:32because a foundational
guideline in science is: -
7:33 - 7:35Let the data speak. OK. Let's do that.
-
7:35 - 7:42So we started with a database containing
13 million ownership relations from 2007. -
7:43 - 7:47This is a lot of data,
and because we wanted to find out -
7:47 - 7:49"who rules the world,"
-
7:49 - 7:52we decided to focus
on transnational corporations, -
7:52 - 7:53or "TNCs," for short.
-
7:54 - 7:57These are companies that operate
in more than one country, -
7:57 - 7:59and we found 43,000.
-
8:01 - 8:04In the next step, we built
the network around these companies, -
8:04 - 8:06so we took all the TNCs' shareholders,
-
8:06 - 8:08and the shareholders' shareholders, etc.,
-
8:08 - 8:11all the way upstream,
and we did the same downstream, -
8:11 - 8:15and ended up with a network
containing 600,000 nodes -
8:16 - 8:17and one million links.
-
8:17 - 8:20This is the TNC network which we analyzed.
-
8:21 - 8:23And it turns out to be
structured as follows. -
8:23 - 8:26So you have a periphery and a center
-
8:26 - 8:30which contains about 75 percent
of all the players, -
8:31 - 8:35and in the center,
there's this tiny but dominant core -
8:35 - 8:38which is made up of highly
interconnected companies. -
8:39 - 8:42To give you a better picture,
-
8:42 - 8:43think about a metropolitan area.
-
8:43 - 8:45So you have the suburbs and the periphery,
-
8:45 - 8:48you have a center,
like a financial district, -
8:48 - 8:50then the core will be something like
-
8:50 - 8:52the tallest high-rise building
in the center. -
8:54 - 8:57And we already see signs
of organization going on here. -
8:59 - 9:0436 percent of the TNCs
are in the core only, -
9:04 - 9:10but they make up 95 percent of the total
operating revenue of all TNCs. -
9:11 - 9:14OK, so now we analyzed the structure,
-
9:14 - 9:17so how does this relate to the control?
-
9:18 - 9:22Well, ownership gives
voting rights to shareholders. -
9:22 - 9:24This is the normal notion of control.
-
9:24 - 9:26And there are different models
-
9:26 - 9:29which allow you to compute
the control you get from ownership. -
9:30 - 9:33If you have more than 50 percent
of the shares in a company, -
9:33 - 9:35you get control,
-
9:35 - 9:38but usually, it depends
on the relative distribution of shares. -
9:40 - 9:41And the network really matters.
-
9:43 - 9:46About 10 years ago, Mr. Tronchetti Provera
-
9:46 - 9:49had ownership and control
in a small company, -
9:49 - 9:52which had ownership and control
in a bigger company. -
9:52 - 9:54You get the idea.
-
9:54 - 9:59This ended up giving him control
in Telecom Italia with a leverage of 26. -
10:01 - 10:04So this means that,
with each euro he invested, -
10:04 - 10:08he was able to move 26 euros
of market value -
10:08 - 10:10through the chain of ownership relations.
-
10:12 - 10:18Now what we actually computed in our study
was the control over the TNCs' value. -
10:19 - 10:23This allowed us to assign
a degree of influence to each shareholder. -
10:24 - 10:28This is very much in the sense
of Max Weber's idea of potential power, -
10:29 - 10:33which is the probability
of imposing one's own will -
10:33 - 10:35despite the opposition of others.
-
10:37 - 10:42If you want to compute the flow
in an ownership network, -
10:42 - 10:43this is what you have to do.
-
10:43 - 10:45It's actually not that hard to understand.
-
10:45 - 10:48Let me explain by giving you this analogy.
-
10:48 - 10:53So think about water flowing in pipes,
where the pipes have different thickness. -
10:54 - 10:59So similarly, the control is flowing
in the ownership networks -
10:59 - 11:02and is accumulating at the nodes.
-
11:04 - 11:08So what did we find after computing
all this network control? -
11:08 - 11:13Well, it turns out
that the 737 top shareholders -
11:13 - 11:19have the potential to collectively control
80 percent of the TNCs' value. -
11:20 - 11:23Now remember, we started out
with 600,000 nodes, -
11:23 - 11:30so these 737 top players
make up a bit more than 0.1 percent. -
11:31 - 11:35They're mostly financial institutions
in the US and the UK. -
11:36 - 11:37And it gets even more extreme.
-
11:39 - 11:42There are 146 top players in the core,
-
11:43 - 11:47and they together have the potential
to collectively control -
11:47 - 11:5040 percent of the TNCs' value.
-
11:53 - 11:55What should you take home
from all of this? -
11:56 - 12:03Well, the high degree of control you saw
is very extreme by any standard. -
12:04 - 12:09The high degree of interconnectivity
of the top players in the core -
12:09 - 12:14could pose a significant systemic risk
to the global economy. -
12:15 - 12:18And we could easily
reproduce the TNC network -
12:18 - 12:19with a few simple rules.
-
12:20 - 12:24This means that its structure is probably
the result of self-organization. -
12:24 - 12:30It's an emergent property which depends
on the rules of interaction in the system, -
12:30 - 12:34so it's probably not the result
of a top-down approach -
12:34 - 12:35like a global conspiracy.
-
12:37 - 12:40Our study "is an impression
of the moon's surface. -
12:40 - 12:41It's not a street map."
-
12:41 - 12:45So you should take the exact numbers
in our study with a grain of salt, -
12:45 - 12:51yet it "gave us a tantalizing glimpse
of a brave new world of finance." -
12:53 - 12:58We hope to have opened the door
for more such research in this direction, -
12:58 - 13:02so the remaining unknown terrain
will be charted in the future. -
13:02 - 13:04And this is slowly starting.
-
13:04 - 13:09We're seeing the emergence of long-term
and highly-funded programs -
13:09 - 13:13which aim at understanding
our networked world -
13:13 - 13:14from a complexity point of view.
-
13:15 - 13:20Like the FuturICT Flagship intitaive,
which is actually spearheaded from Zurich. -
13:22 - 13:24But this journey has only just begun,
-
13:24 - 13:28so we will have to wait
before we see the first results. -
13:30 - 13:33Now there is still
a big problem, in my opinion. -
13:34 - 13:40Ideas relating to finance,
economics, politics, society, -
13:40 - 13:44are very often tainted
by people's personal ideologies. -
13:46 - 13:50I really hope that this
complexity perspective -
13:50 - 13:54allows for some common ground to be found.
-
13:56 - 13:58It would be really great
if it has the power -
13:58 - 14:03to help end the gridlock
created by conflicting ideas, -
14:03 - 14:06which appears to be paralyzing
our globalized world. -
14:08 - 14:12Reality is so complex,
we need to move away from dogma. -
14:13 - 14:16But this is just my own personal ideology.
-
14:16 - 14:17Thank you.
-
14:17 - 14:23(Applause)
- Title:
- Who controls the world? | James B. Glattfelder | TEDxZurich
- Description:
-
James Glattfelder studies complexity: how an interconnected system -- say, a swarm of birds -- is more than the sum of its parts. And complexity theory, it turns out, can reveal a lot about how the economy works. Glattfelder shares a groundbreaking study of how control flows through the global economy, and how concentration of power in the hands of a shockingly small number leaves us all vulnerable.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDxTalks
- Duration:
- 14:39
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The English transcript was updated on 2/3/2017