ABOUT THE SPEAKER
James B. Glattfelder - Complex systems theorist
James B. Glattfelder aims to give us a richer, data-driven understanding of the people and interactions that control our global economy. He does this not to push an ideology -- but with the hopes of making the world a better place.

Why you should listen

First a physicist and then a researcher at a Swiss hedge fund, James B. Glattfelder found himself amazed by the level of understanding we have in regards to the physical world and universe around us. He wondered: how can we move toward a similar understanding of human society?

This question led him to the study of complex systems, a subject he now holds a Ph.D in from the Swiss Federal Institute of Technology. Glattfelder is co-head of quantitative research at Olsen Ltd in Zurich, an FX investment manager focusing on market-stabilizing algorithms. In 2011, he co-authored the study “The Network of Global Corporate Control,” which went viral in the international media and sparked many controversial discussions. The study looked at the architecture of ownership across the globe, and computed a level of control exerted by each international player. The study revealed that less than 1% of all the players in the global economy are part of a highly interconnected and powerful core which, because of the high levels of overlap, leaves the economy vulnerable.

In his free time, Glattfelder enjoys snowboarding, rock climbing, surfing and listening to electronic music. 

More profile about the speaker
James B. Glattfelder | Speaker | TED.com
TEDxZurich 2012

James B. Glattfelder: Who controls the world?

Filmed:
2,753,507 views

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 world 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.
- Complex systems theorist
James B. Glattfelder aims to give us a richer, data-driven understanding of the people and interactions that control our global economy. He does this not to push an ideology -- but with the hopes of making the world a better place. Full bio

Double-click the English transcript below to play the video.

00:16
"When the crisis came,
0
420
2316
00:18
the serious limitations of existing economic
1
2736
3120
00:21
and financial models immediately became apparent."
2
5856
5052
00:26
"There is also a strong belief, which I share,
3
10908
4982
00:31
that bad or oversimplistic and overconfident economics
4
15890
4999
00:36
helped create the crisis."
5
20889
2401
00:39
Now, you've probably all heard of similar criticism
6
23290
2267
00:41
coming from people who are skeptical of capitalism.
7
25557
3342
00:44
But this is different.
8
28899
1677
00:46
This is coming from the heart of finance.
9
30576
3868
00:50
The first quote is from Jean-Claude Trichet
10
34444
2861
00:53
when he was governor of the European Central Bank.
11
37305
3875
00:57
The second quote is from the head
12
41180
2239
00:59
of the U.K. Financial Services Authority.
13
43419
3365
01:02
Are these people implying
14
46784
1530
01:04
that we don't understand the economic systems
15
48314
2795
01:07
that drive our modern societies?
16
51109
3140
01:10
It gets worse.
17
54249
1922
01:12
"We spend billions of dollars
18
56171
2155
01:14
trying to understand the origins of the universe
19
58326
3224
01:17
while we still don't understand the conditions
20
61550
3862
01:21
for a stable society, a functioning economy, or peace."
21
65412
8726
01:30
What's happening here? How can this be possible?
22
74138
2835
01:32
Do we really understand more about the fabric of reality
23
76973
2956
01:35
than we do about the fabric
24
79929
1663
01:37
which emerges from our human interactions?
25
81592
3138
01:40
Unfortunately, the answer is yes.
26
84730
2527
01:43
But there's an intriguing solution which is coming
27
87257
3409
01:46
from what is known as the science of complexity.
28
90666
4488
01:51
To explain what this means and what this thing is,
29
95154
2843
01:53
please let me quickly take a couple of steps back.
30
97997
3579
01:57
I ended up in physics by accident.
31
101576
2390
01:59
It was a random encounter when I was young,
32
103966
3091
02:02
and since then, I've often wondered
33
107057
2105
02:05
about the amazing success of physics
34
109162
2079
02:07
in describing the reality we wake up in every day.
35
111241
4367
02:11
In a nutshell, you can think of physics as follows.
36
115608
3296
02:14
So you take a chunk of reality you want to understand
37
118904
3033
02:17
and you translate it into mathematics.
38
121937
3769
02:21
You encode it into equations.
39
125706
3436
02:25
Then predictions can be made and tested.
40
129142
3827
02:28
We're actually really lucky that this works,
41
132969
2564
02:31
because no one really knows why the thoughts in our heads
42
135533
3015
02:34
should actually relate to the fundamental workings of the universe.
43
138548
5577
02:40
Despite the success, physics has its limits.
44
144125
3562
02:43
As Dirk Helbing pointed out in the last quote,
45
147687
2722
02:46
we don't really understand the complexity
46
150409
2494
02:48
that relates to us, that surrounds us.
47
152903
3178
02:51
This paradox is what got me interested in complex systems.
48
156081
4648
02:56
So these are systems which are made up
49
160729
1904
02:58
of many interconnected or interacting parts:
50
162633
3480
03:02
swarms of birds or fish, ant colonies,
51
166113
3814
03:05
ecosystems, brains, financial markets.
52
169927
3434
03:09
These are just a few examples.
53
173361
4326
03:13
Interestingly, complex systems are very hard to map
54
177687
5243
03:18
into mathematical equations,
55
182930
1860
03:20
so the usual physics approach doesn't really work here.
56
184790
4493
03:25
So what do we know about complex systems?
57
189283
2193
03:27
Well, it turns out that what looks like complex behavior
58
191476
3942
03:31
from the outside is actually the result
59
195418
3019
03:34
of a few simple rules of interaction.
60
198437
4197
03:38
This means you can forget about the equations
61
202634
4225
03:42
and just start to understand the system
62
206859
1863
03:44
by looking at the interactions,
63
208722
2704
03:47
so you can actually forget about the equations
64
211426
2320
03:49
and you just start to look at the interactions.
65
213746
2473
03:52
And it gets even better, because most complex systems
66
216219
3237
03:55
have this amazing property called emergence.
67
219456
3068
03:58
So this means that the system as a whole
68
222524
2406
04:00
suddenly starts to show a behavior
69
224930
1735
04:02
which cannot be understood or predicted
70
226665
3144
04:05
by looking at the components of the system.
71
229809
2577
04:08
So the whole is literally more than the sum of its parts.
72
232386
3919
04:12
And all of this also means that you can forget about
73
236305
2346
04:14
the individual parts of the system, how complex they are.
74
238651
5349
04:19
So if it's a cell or a termite or a bird,
75
244000
4913
04:24
you just focus on the rules of interaction.
76
248913
4349
04:29
As a result, networks are ideal representations
77
253262
4446
04:33
of complex systems.
78
257708
2654
04:36
The nodes in the network
79
260362
2771
04:39
are the system's components
80
263133
2759
04:41
and the links are given by the interactions.
81
265892
4200
04:45
So what equations are for physics,
82
270092
2825
04:48
complex networks are for the study of complex systems.
83
272917
4615
04:53
This approach has been very successfully applied
84
277532
3224
04:56
to many complex systems in physics, biology,
85
280756
3263
04:59
computer science, the social sciences,
86
284019
3241
05:03
but what about economics?
87
287260
2297
05:05
Where are economic networks?
88
289557
2418
05:07
This is a surprising and prominent gap in the literature.
89
291975
4597
05:12
The study we published last year called
90
296572
2554
05:15
"The Network of Global Corporate Control"
91
299126
3326
05:18
was the first extensive analysis of economic networks.
92
302452
5930
05:24
The study went viral on the Internet
93
308382
2694
05:26
and it attracted a lot of attention from the international media.
94
311076
5072
05:32
This is quite remarkable, because, again,
95
316148
2711
05:34
why did no one look at this before?
96
318859
1421
05:36
Similar data has been around for quite some time.
97
320280
3292
05:39
What we looked at in detail was ownership networks.
98
323572
3640
05:43
So here the nodes are companies, people, governments,
99
327212
5440
05:48
foundations, etc.
100
332652
3552
05:52
And the links represent the shareholding relations,
101
336204
2828
05:54
so Shareholder A has x percent of the shares in Company B.
102
339032
5188
06:00
And we also assign a value to the company
103
344220
2272
06:02
given by the operating revenue.
104
346492
3037
06:05
So ownership networks reveal the patterns
105
349529
3099
06:08
of shareholding relations.
106
352628
2521
06:11
In this little example, you can see
107
355149
2183
06:13
a few financial institutions
108
357332
2120
06:15
with some of the many links highlighted.
109
359452
4393
06:19
Now you may think that no one's looked at this before
110
363845
2680
06:22
because ownership networks are
111
366525
2336
06:24
really, really boring to study.
112
368861
3127
06:27
Well, as ownership is related to control,
113
371988
3864
06:31
as I shall explain later,
114
375852
1596
06:33
looking at ownership networks
115
377448
1358
06:34
actually can give you answers to questions like,
116
378806
2558
06:37
who are the key players?
117
381364
1840
06:39
How are they organized? Are they isolated?
118
383204
2192
06:41
Are they interconnected?
119
385396
1488
06:42
And what is the overall distribution of control?
120
386884
3875
06:46
In other words, who controls the world?
121
390759
3476
06:50
I think this is an interesting question.
122
394235
2369
06:52
And it has implications for systemic risk.
123
396604
4088
06:56
This is a measure of how vulnerable a system is overall.
124
400692
5010
07:01
A high degree of interconnectivity
125
405702
2863
07:04
can be bad for stability,
126
408565
2867
07:07
because then the stress can spread through the system
127
411432
3444
07:10
like an epidemic.
128
414876
2952
07:13
Scientists have sometimes criticized economists
129
417828
2816
07:16
who believe ideas and concepts
130
420644
2328
07:18
are more important than empirical data,
131
422972
3011
07:21
because a foundational guideline in science is:
132
425983
3149
07:25
Let the data speak. Okay. Let's do that.
133
429132
3336
07:28
So we started with a database containing
134
432468
2594
07:30
13 million ownership relations from 2007.
135
435062
4143
07:35
This is a lot of data, and because we wanted to find out
136
439205
2857
07:37
who rules the world,
137
442062
2558
07:40
we decided to focus on transnational corporations,
138
444620
3832
07:44
or TNCs for short.
139
448452
1348
07:45
These are companies that operate in more than one country,
140
449800
3596
07:49
and we found 43,000.
141
453396
2608
07:51
In the next step, we built the network around these companies,
142
456004
3952
07:55
so we took all the TNCs' shareholders,
143
459956
2448
07:58
and the shareholders' shareholders, etc.,
144
462404
2092
08:00
all the way upstream, and we did the same downstream,
145
464496
2876
08:03
and ended up with a network containing 600,000 nodes
146
467372
4041
08:07
and one million links.
147
471413
1429
08:08
This is the TNC network which we analyzed.
148
472842
3850
08:12
And it turns out to be structured as follows.
149
476692
2528
08:15
So you have a periphery and a center
150
479220
2715
08:17
which contains about 75 percent of all the players,
151
481935
4477
08:22
and in the center there's this tiny but dominant core
152
486412
3528
08:25
which is made up of highly interconnected companies.
153
489940
4824
08:30
To give you a better picture,
154
494764
2435
08:33
think about a metropolitan area.
155
497199
1611
08:34
So you have the suburbs and the periphery,
156
498810
2291
08:37
you have a center like a financial district,
157
501101
2697
08:39
then the core will be something like
158
503798
1743
08:41
the tallest high rise building in the center.
159
505541
3439
08:44
And we already see signs of organization going on here.
160
508980
4875
08:49
Thirty-six percent of the TNCs are in the core only,
161
513855
5733
08:55
but they make up 95 percent of the total operating revenue
162
519588
4371
08:59
of all TNCs.
163
523959
2581
09:02
Okay, so now we analyzed the structure,
164
526540
2840
09:05
so how does this relate to the control?
165
529380
3562
09:08
Well, ownership gives voting rights to shareholders.
166
532942
3927
09:12
This is the normal notion of control.
167
536869
2719
09:15
And there are different models which allow you to compute
168
539588
3207
09:18
the control you get from ownership.
169
542795
2781
09:21
If you have more than 50 percent of the shares in a company,
170
545576
2780
09:24
you get control,
171
548356
1624
09:25
but usually it depends on the relative distribution of shares.
172
549980
5176
09:31
And the network really matters.
173
555156
2889
09:33
About 10 years ago, Mr. Tronchetti Provera
174
558045
2631
09:36
had ownership and control in a small company,
175
560676
3404
09:39
which had ownership and control in a bigger company.
176
564080
3452
09:43
You get the idea.
177
567532
1479
09:44
This ended up giving him control in Telecom Italia
178
569011
3263
09:48
with a leverage of 26.
179
572274
3633
09:51
So this means that, with each euro he invested,
180
575907
3943
09:55
he was able to move 26 euros of market value
181
579850
3685
09:59
through the chain of ownership relations.
182
583535
3376
10:02
Now what we actually computed in our study
183
586911
3080
10:05
was the control over the TNCs' value.
184
589991
3699
10:09
This allowed us to assign a degree of influence
185
593690
2852
10:12
to each shareholder.
186
596542
2307
10:14
This is very much in the sense of
187
598849
2582
10:17
Max Weber's idea of potential power,
188
601431
3112
10:20
which is the probability of imposing one's own will
189
604543
3812
10:24
despite the opposition of others.
190
608355
3995
10:28
If you want to compute the flow in an ownership network,
191
612350
4643
10:32
this is what you have to do.
192
616993
1248
10:34
It's actually not that hard to understand.
193
618241
2545
10:36
Let me explain by giving you this analogy.
194
620786
2768
10:39
So think about water flowing in pipes
195
623554
2855
10:42
where the pipes have different thickness.
196
626409
3182
10:45
So similarly, the control is flowing in the ownership networks
197
629591
4744
10:50
and is accumulating at the nodes.
198
634335
4419
10:54
So what did we find after computing all this network control?
199
638754
3948
10:58
Well, it turns out that the 737 top shareholders
200
642702
5387
11:03
have the potential to collectively control
201
648089
2792
11:06
80 percent of the TNCs' value.
202
650881
4260
11:11
Now remember, we started out with 600,000 nodes,
203
655141
3316
11:14
so these 737 top players
204
658457
3777
11:18
make up a bit more than 0.1 percent.
205
662234
3823
11:21
They're mostly financial institutions in the U.S. and the U.K.
206
666057
4956
11:26
And it gets even more extreme.
207
671013
2548
11:29
There are 146 top players in the core,
208
673561
4297
11:33
and they together have the potential to collectively control
209
677858
4220
11:37
40 percent of the TNCs' value.
210
682078
5355
11:43
What should you take home from all of this?
211
687433
2796
11:46
Well, the high degree of control you saw
212
690229
3588
11:49
is very extreme by any standard.
213
693817
4890
11:54
The high degree of interconnectivity
214
698707
2550
11:57
of the top players in the core
215
701257
2312
11:59
could pose a significant systemic risk to the global economy
216
703569
5177
12:04
and we could easily reproduce the TNC network
217
708746
3720
12:08
with a few simple rules.
218
712466
1951
12:10
This means that its structure is probably the result
219
714417
2480
12:12
of self-organization.
220
716897
1640
12:14
It's an emergent property which depends
221
718537
3316
12:17
on the rules of interaction in the system,
222
721853
2844
12:20
so it's probably not the result of a top-down approach
223
724697
3446
12:24
like a global conspiracy.
224
728143
3426
12:27
Our study "is an impression of the moon's surface.
225
731569
2933
12:30
It's not a street map."
226
734502
1329
12:31
So you should take the exact numbers in our study
227
735831
2639
12:34
with a grain of salt,
228
738470
1440
12:35
yet it "gave us a tantalizing glimpse
229
739910
3392
12:39
of a brave new world of finance."
230
743302
4344
12:43
We hope to have opened the door for more such research in this direction,
231
747646
4440
12:47
so the remaining unknown terrain will be charted in the future.
232
752086
4737
12:52
And this is slowly starting.
233
756823
1445
12:54
We're seeing the emergence of long-term
234
758268
2992
12:57
and highly-funded programs which aim at understanding
235
761260
3570
13:00
our networked world from a complexity point of view.
236
764830
4690
13:05
But this journey has only just begun,
237
769520
2038
13:07
so we will have to wait before we see the first results.
238
771558
5438
13:12
Now there is still a big problem, in my opinion.
239
776996
3618
13:16
Ideas relating to finance, economics, politics,
240
780614
5152
13:21
society, are very often tainted
241
785766
3280
13:24
by people's personal ideologies.
242
789046
3816
13:28
I really hope that this complexity perspective
243
792862
4138
13:32
allows for some common ground to be found.
244
797000
5143
13:38
It would be really great if it has the power
245
802143
2919
13:40
to help end the gridlock created by conflicting ideas,
246
805062
5063
13:46
which appears to be paralyzing our globalized world.
247
810125
5130
13:51
Reality is so complex, we need to move away from dogma.
248
815255
4666
13:55
But this is just my own personal ideology.
249
819921
2886
13:58
Thank you.
250
822807
2035
14:00
(Applause)
251
824842
4677
Translated by Joseph Geni
Reviewed by Morton Bast

▲Back to top

ABOUT THE SPEAKER
James B. Glattfelder - Complex systems theorist
James B. Glattfelder aims to give us a richer, data-driven understanding of the people and interactions that control our global economy. He does this not to push an ideology -- but with the hopes of making the world a better place.

Why you should listen

First a physicist and then a researcher at a Swiss hedge fund, James B. Glattfelder found himself amazed by the level of understanding we have in regards to the physical world and universe around us. He wondered: how can we move toward a similar understanding of human society?

This question led him to the study of complex systems, a subject he now holds a Ph.D in from the Swiss Federal Institute of Technology. Glattfelder is co-head of quantitative research at Olsen Ltd in Zurich, an FX investment manager focusing on market-stabilizing algorithms. In 2011, he co-authored the study “The Network of Global Corporate Control,” which went viral in the international media and sparked many controversial discussions. The study looked at the architecture of ownership across the globe, and computed a level of control exerted by each international player. The study revealed that less than 1% of all the players in the global economy are part of a highly interconnected and powerful core which, because of the high levels of overlap, leaves the economy vulnerable.

In his free time, Glattfelder enjoys snowboarding, rock climbing, surfing and listening to electronic music. 

More profile about the speaker
James B. Glattfelder | Speaker | TED.com