Nicholas Christakis: The hidden influence of social networks

443,802 views ・ 2010-05-10

TED


Please double-click on the English subtitles below to play the video.

00:16
For me, this story begins about 15 years ago,
0
16260
3000
00:19
when I was a hospice doctor at the University of Chicago.
1
19260
3000
00:22
And I was taking care of people who were dying and their families
2
22260
3000
00:25
in the South Side of Chicago.
3
25260
2000
00:27
And I was observing what happened to people and their families
4
27260
3000
00:30
over the course of their terminal illness.
5
30260
3000
00:33
And in my lab, I was studying the widower effect,
6
33260
2000
00:35
which is a very old idea in the social sciences,
7
35260
2000
00:37
going back 150 years,
8
37260
2000
00:39
known as "dying of a broken heart."
9
39260
2000
00:41
So, when I die, my wife's risk of death can double,
10
41260
3000
00:44
for instance, in the first year.
11
44260
2000
00:46
And I had gone to take care of one particular patient,
12
46260
3000
00:49
a woman who was dying of dementia.
13
49260
2000
00:51
And in this case, unlike this couple,
14
51260
2000
00:53
she was being cared for
15
53260
2000
00:55
by her daughter.
16
55260
2000
00:57
And the daughter was exhausted from caring for her mother.
17
57260
3000
01:00
And the daughter's husband,
18
60260
2000
01:02
he also was sick
19
62260
3000
01:05
from his wife's exhaustion.
20
65260
2000
01:07
And I was driving home one day,
21
67260
2000
01:09
and I get a phone call from the husband's friend,
22
69260
3000
01:12
calling me because he was depressed
23
72260
2000
01:14
about what was happening to his friend.
24
74260
2000
01:16
So here I get this call from this random guy
25
76260
2000
01:18
that's having an experience
26
78260
2000
01:20
that's being influenced by people
27
80260
2000
01:22
at some social distance.
28
82260
2000
01:24
And so I suddenly realized two very simple things:
29
84260
3000
01:27
First, the widowhood effect
30
87260
2000
01:29
was not restricted to husbands and wives.
31
89260
3000
01:32
And second, it was not restricted to pairs of people.
32
92260
3000
01:35
And I started to see the world
33
95260
2000
01:37
in a whole new way,
34
97260
2000
01:39
like pairs of people connected to each other.
35
99260
3000
01:42
And then I realized that these individuals
36
102260
2000
01:44
would be connected into foursomes with other pairs of people nearby.
37
104260
3000
01:47
And then, in fact, these people
38
107260
2000
01:49
were embedded in other sorts of relationships:
39
109260
2000
01:51
marriage and spousal
40
111260
2000
01:53
and friendship and other sorts of ties.
41
113260
2000
01:55
And that, in fact, these connections were vast
42
115260
3000
01:58
and that we were all embedded in this
43
118260
2000
02:00
broad set of connections with each other.
44
120260
3000
02:03
So I started to see the world in a completely new way
45
123260
3000
02:06
and I became obsessed with this.
46
126260
2000
02:08
I became obsessed with how it might be
47
128260
2000
02:10
that we're embedded in these social networks,
48
130260
2000
02:12
and how they affect our lives.
49
132260
2000
02:14
So, social networks are these intricate things of beauty,
50
134260
3000
02:17
and they're so elaborate and so complex
51
137260
2000
02:19
and so ubiquitous, in fact,
52
139260
2000
02:21
that one has to ask what purpose they serve.
53
141260
3000
02:24
Why are we embedded in social networks?
54
144260
2000
02:26
I mean, how do they form? How do they operate?
55
146260
2000
02:28
And how do they effect us?
56
148260
2000
02:30
So my first topic with respect to this,
57
150260
3000
02:33
was not death, but obesity.
58
153260
3000
02:36
It had become trendy
59
156260
2000
02:38
to speak about the "obesity epidemic."
60
158260
2000
02:40
And, along with my collaborator, James Fowler,
61
160260
3000
02:43
we began to wonder whether obesity really was epidemic
62
163260
3000
02:46
and could it spread from person to person
63
166260
2000
02:48
like the four people I discussed earlier.
64
168260
3000
02:51
So this is a slide of some of our initial results.
65
171260
3000
02:54
It's 2,200 people in the year 2000.
66
174260
3000
02:57
Every dot is a person. We make the dot size
67
177260
2000
02:59
proportional to people's body size;
68
179260
2000
03:01
so bigger dots are bigger people.
69
181260
3000
03:04
In addition, if your body size,
70
184260
2000
03:06
if your BMI, your body mass index, is above 30 --
71
186260
2000
03:08
if you're clinically obese --
72
188260
2000
03:10
we also colored the dots yellow.
73
190260
2000
03:12
So, if you look at this image, right away you might be able to see
74
192260
2000
03:14
that there are clusters of obese and
75
194260
2000
03:16
non-obese people in the image.
76
196260
2000
03:18
But the visual complexity is still very high.
77
198260
3000
03:21
It's not obvious exactly what's going on.
78
201260
3000
03:24
In addition, some questions are immediately raised:
79
204260
2000
03:26
How much clustering is there?
80
206260
2000
03:28
Is there more clustering than would be due to chance alone?
81
208260
3000
03:31
How big are the clusters? How far do they reach?
82
211260
2000
03:33
And, most importantly,
83
213260
2000
03:35
what causes the clusters?
84
215260
2000
03:37
So we did some mathematics to study the size of these clusters.
85
217260
3000
03:40
This here shows, on the Y-axis,
86
220260
2000
03:42
the increase in the probability that a person is obese
87
222260
3000
03:45
given that a social contact of theirs is obese
88
225260
2000
03:47
and, on the X-axis, the degrees of separation between the two people.
89
227260
3000
03:50
On the far left, you see the purple line.
90
230260
2000
03:52
It says that, if your friends are obese,
91
232260
2000
03:54
your risk of obesity is 45 percent higher.
92
234260
3000
03:57
And the next bar over, the [red] line,
93
237260
2000
03:59
says if your friend's friends are obese,
94
239260
2000
04:01
your risk of obesity is 25 percent higher.
95
241260
2000
04:03
And then the next line over says
96
243260
2000
04:05
if your friend's friend's friend, someone you probably don't even know, is obese,
97
245260
3000
04:08
your risk of obesity is 10 percent higher.
98
248260
3000
04:11
And it's only when you get to your friend's friend's friend's friends
99
251260
3000
04:14
that there's no longer a relationship
100
254260
2000
04:16
between that person's body size and your own body size.
101
256260
3000
04:20
Well, what might be causing this clustering?
102
260260
3000
04:23
There are at least three possibilities:
103
263260
2000
04:25
One possibility is that, as I gain weight,
104
265260
2000
04:27
it causes you to gain weight.
105
267260
2000
04:29
A kind of induction, a kind of spread from person to person.
106
269260
3000
04:32
Another possibility, very obvious, is homophily,
107
272260
2000
04:34
or, birds of a feather flock together;
108
274260
2000
04:36
here, I form my tie to you
109
276260
2000
04:38
because you and I share a similar body size.
110
278260
3000
04:41
And the last possibility is what is known as confounding,
111
281260
2000
04:43
because it confounds our ability to figure out what's going on.
112
283260
3000
04:46
And here, the idea is not that my weight gain
113
286260
2000
04:48
is causing your weight gain,
114
288260
2000
04:50
nor that I preferentially form a tie with you
115
290260
2000
04:52
because you and I share the same body size,
116
292260
2000
04:54
but rather that we share a common exposure
117
294260
2000
04:56
to something, like a health club
118
296260
3000
04:59
that makes us both lose weight at the same time.
119
299260
3000
05:02
When we studied these data, we found evidence for all of these things,
120
302260
3000
05:05
including for induction.
121
305260
2000
05:07
And we found that if your friend becomes obese,
122
307260
2000
05:09
it increases your risk of obesity by about 57 percent
123
309260
3000
05:12
in the same given time period.
124
312260
2000
05:14
There can be many mechanisms for this effect:
125
314260
3000
05:17
One possibility is that your friends say to you something like --
126
317260
2000
05:19
you know, they adopt a behavior that spreads to you --
127
319260
3000
05:22
like, they say, "Let's go have muffins and beer,"
128
322260
3000
05:25
which is a terrible combination. (Laughter)
129
325260
3000
05:28
But you adopt that combination,
130
328260
2000
05:30
and then you start gaining weight like them.
131
330260
3000
05:33
Another more subtle possibility
132
333260
2000
05:35
is that they start gaining weight, and it changes your ideas
133
335260
3000
05:38
of what an acceptable body size is.
134
338260
2000
05:40
Here, what's spreading from person to person
135
340260
2000
05:42
is not a behavior, but rather a norm:
136
342260
2000
05:44
An idea is spreading.
137
344260
2000
05:46
Now, headline writers
138
346260
2000
05:48
had a field day with our studies.
139
348260
2000
05:50
I think the headline in The New York Times was,
140
350260
2000
05:52
"Are you packing it on?
141
352260
2000
05:54
Blame your fat friends." (Laughter)
142
354260
3000
05:57
What was interesting to us is that the European headline writers
143
357260
2000
05:59
had a different take: They said,
144
359260
2000
06:01
"Are your friends gaining weight? Perhaps you are to blame."
145
361260
3000
06:04
(Laughter)
146
364260
5000
06:09
And we thought this was a very interesting comment on America,
147
369260
3000
06:12
and a kind of self-serving,
148
372260
2000
06:14
"not my responsibility" kind of phenomenon.
149
374260
2000
06:16
Now, I want to be very clear: We do not think our work
150
376260
2000
06:18
should or could justify prejudice
151
378260
2000
06:20
against people of one or another body size at all.
152
380260
3000
06:24
Our next questions was:
153
384260
2000
06:26
Could we actually visualize this spread?
154
386260
3000
06:29
Was weight gain in one person actually spreading
155
389260
2000
06:31
to weight gain in another person?
156
391260
2000
06:33
And this was complicated because
157
393260
2000
06:35
we needed to take into account the fact that the network structure,
158
395260
3000
06:38
the architecture of the ties, was changing across time.
159
398260
3000
06:41
In addition, because obesity is not a unicentric epidemic,
160
401260
3000
06:44
there's not a Patient Zero of the obesity epidemic --
161
404260
3000
06:47
if we find that guy, there was a spread of obesity out from him --
162
407260
3000
06:50
it's a multicentric epidemic.
163
410260
2000
06:52
Lots of people are doing things at the same time.
164
412260
2000
06:54
And I'm about to show you a 30 second video animation
165
414260
3000
06:57
that took me and James five years of our lives to do.
166
417260
3000
07:00
So, again, every dot is a person.
167
420260
2000
07:02
Every tie between them is a relationship.
168
422260
2000
07:04
We're going to put this into motion now,
169
424260
2000
07:06
taking daily cuts through the network for about 30 years.
170
426260
3000
07:09
The dot sizes are going to grow,
171
429260
2000
07:11
you're going to see a sea of yellow take over.
172
431260
3000
07:14
You're going to see people be born and die --
173
434260
2000
07:16
dots will appear and disappear --
174
436260
2000
07:18
ties will form and break, marriages and divorces,
175
438260
3000
07:21
friendings and defriendings.
176
441260
2000
07:23
A lot of complexity, a lot is happening
177
443260
2000
07:25
just in this 30-year period
178
445260
2000
07:27
that includes the obesity epidemic.
179
447260
2000
07:29
And, by the end, you're going to see clusters
180
449260
2000
07:31
of obese and non-obese individuals
181
451260
2000
07:33
within the network.
182
453260
2000
07:35
Now, when looked at this,
183
455260
3000
07:38
it changed the way I see things,
184
458260
3000
07:41
because this thing, this network
185
461260
2000
07:43
that's changing across time,
186
463260
2000
07:45
it has a memory, it moves,
187
465260
3000
07:48
things flow within it,
188
468260
2000
07:50
it has a kind of consistency --
189
470260
2000
07:52
people can die, but it doesn't die;
190
472260
2000
07:54
it still persists --
191
474260
2000
07:56
and it has a kind of resilience
192
476260
2000
07:58
that allows it to persist across time.
193
478260
2000
08:00
And so, I came to see these kinds of social networks
194
480260
3000
08:03
as living things,
195
483260
2000
08:05
as living things that we could put under a kind of microscope
196
485260
3000
08:08
to study and analyze and understand.
197
488260
3000
08:11
And we used a variety of techniques to do this.
198
491260
2000
08:13
And we started exploring all kinds of other phenomena.
199
493260
3000
08:16
We looked at smoking and drinking behavior,
200
496260
2000
08:18
and voting behavior,
201
498260
2000
08:20
and divorce -- which can spread --
202
500260
2000
08:22
and altruism.
203
502260
2000
08:24
And, eventually, we became interested in emotions.
204
504260
3000
08:28
Now, when we have emotions,
205
508260
2000
08:30
we show them.
206
510260
2000
08:32
Why do we show our emotions?
207
512260
2000
08:34
I mean, there would be an advantage to experiencing
208
514260
2000
08:36
our emotions inside, you know, anger or happiness.
209
516260
3000
08:39
But we don't just experience them, we show them.
210
519260
2000
08:41
And not only do we show them, but others can read them.
211
521260
3000
08:44
And, not only can they read them, but they copy them.
212
524260
2000
08:46
There's emotional contagion
213
526260
2000
08:48
that takes place in human populations.
214
528260
3000
08:51
And so this function of emotions
215
531260
2000
08:53
suggests that, in addition to any other purpose they serve,
216
533260
2000
08:55
they're a kind of primitive form of communication.
217
535260
3000
08:58
And that, in fact, if we really want to understand human emotions,
218
538260
3000
09:01
we need to think about them in this way.
219
541260
2000
09:03
Now, we're accustomed to thinking about emotions in this way,
220
543260
3000
09:06
in simple, sort of, brief periods of time.
221
546260
3000
09:09
So, for example,
222
549260
2000
09:11
I was giving this talk recently in New York City,
223
551260
2000
09:13
and I said, "You know when you're on the subway
224
553260
2000
09:15
and the other person across the subway car
225
555260
2000
09:17
smiles at you,
226
557260
2000
09:19
and you just instinctively smile back?"
227
559260
2000
09:21
And they looked at me and said, "We don't do that in New York City." (Laughter)
228
561260
3000
09:24
And I said, "Everywhere else in the world,
229
564260
2000
09:26
that's normal human behavior."
230
566260
2000
09:28
And so there's a very instinctive way
231
568260
2000
09:30
in which we briefly transmit emotions to each other.
232
570260
3000
09:33
And, in fact, emotional contagion can be broader still.
233
573260
3000
09:36
Like we could have punctuated expressions of anger,
234
576260
3000
09:39
as in riots.
235
579260
2000
09:41
The question that we wanted to ask was:
236
581260
2000
09:43
Could emotion spread,
237
583260
2000
09:45
in a more sustained way than riots, across time
238
585260
3000
09:48
and involve large numbers of people,
239
588260
2000
09:50
not just this pair of individuals smiling at each other in the subway car?
240
590260
3000
09:53
Maybe there's a kind of below the surface, quiet riot
241
593260
3000
09:56
that animates us all the time.
242
596260
2000
09:58
Maybe there are emotional stampedes
243
598260
2000
10:00
that ripple through social networks.
244
600260
2000
10:02
Maybe, in fact, emotions have a collective existence,
245
602260
3000
10:05
not just an individual existence.
246
605260
2000
10:07
And this is one of the first images we made to study this phenomenon.
247
607260
3000
10:10
Again, a social network,
248
610260
2000
10:12
but now we color the people yellow if they're happy
249
612260
3000
10:15
and blue if they're sad and green in between.
250
615260
3000
10:18
And if you look at this image, you can right away see
251
618260
2000
10:20
clusters of happy and unhappy people,
252
620260
2000
10:22
again, spreading to three degrees of separation.
253
622260
2000
10:24
And you might form the intuition
254
624260
2000
10:26
that the unhappy people
255
626260
2000
10:28
occupy a different structural location within the network.
256
628260
3000
10:31
There's a middle and an edge to this network,
257
631260
2000
10:33
and the unhappy people seem to be
258
633260
2000
10:35
located at the edges.
259
635260
2000
10:37
So to invoke another metaphor,
260
637260
2000
10:39
if you imagine social networks as a kind of
261
639260
2000
10:41
vast fabric of humanity --
262
641260
2000
10:43
I'm connected to you and you to her, on out endlessly into the distance --
263
643260
3000
10:46
this fabric is actually like
264
646260
2000
10:48
an old-fashioned American quilt,
265
648260
2000
10:50
and it has patches on it: happy and unhappy patches.
266
650260
3000
10:53
And whether you become happy or not
267
653260
2000
10:55
depends in part on whether you occupy a happy patch.
268
655260
3000
10:58
(Laughter)
269
658260
2000
11:00
So, this work with emotions,
270
660260
3000
11:03
which are so fundamental,
271
663260
2000
11:05
then got us to thinking about: Maybe
272
665260
2000
11:07
the fundamental causes of human social networks
273
667260
2000
11:09
are somehow encoded in our genes.
274
669260
2000
11:11
Because human social networks, whenever they are mapped,
275
671260
3000
11:14
always kind of look like this:
276
674260
2000
11:16
the picture of the network.
277
676260
2000
11:18
But they never look like this.
278
678260
2000
11:20
Why do they not look like this?
279
680260
2000
11:22
Why don't we form human social networks
280
682260
2000
11:24
that look like a regular lattice?
281
684260
2000
11:26
Well, the striking patterns of human social networks,
282
686260
3000
11:29
their ubiquity and their apparent purpose
283
689260
3000
11:32
beg questions about whether we evolved to have
284
692260
2000
11:34
human social networks in the first place,
285
694260
2000
11:36
and whether we evolved to form networks
286
696260
2000
11:38
with a particular structure.
287
698260
2000
11:40
And notice first of all -- so, to understand this, though,
288
700260
2000
11:42
we need to dissect network structure a little bit first --
289
702260
3000
11:45
and notice that every person in this network
290
705260
2000
11:47
has exactly the same structural location as every other person.
291
707260
3000
11:50
But that's not the case with real networks.
292
710260
3000
11:53
So, for example, here is a real network of college students
293
713260
2000
11:55
at an elite northeastern university.
294
715260
3000
11:58
And now I'm highlighting a few dots.
295
718260
2000
12:00
If you look here at the dots,
296
720260
2000
12:02
compare node B in the upper left
297
722260
2000
12:04
to node D in the far right;
298
724260
2000
12:06
B has four friends coming out from him
299
726260
2000
12:08
and D has six friends coming out from him.
300
728260
3000
12:11
And so, those two individuals have different numbers of friends.
301
731260
3000
12:14
That's very obvious, we all know that.
302
734260
2000
12:16
But certain other aspects
303
736260
2000
12:18
of social network structure are not so obvious.
304
738260
2000
12:20
Compare node B in the upper left to node A in the lower left.
305
740260
3000
12:23
Now, those people both have four friends,
306
743260
3000
12:26
but A's friends all know each other,
307
746260
2000
12:28
and B's friends do not.
308
748260
2000
12:30
So the friend of a friend of A's
309
750260
2000
12:32
is, back again, a friend of A's,
310
752260
2000
12:34
whereas the friend of a friend of B's is not a friend of B's,
311
754260
2000
12:36
but is farther away in the network.
312
756260
2000
12:38
This is known as transitivity in networks.
313
758260
3000
12:41
And, finally, compare nodes C and D:
314
761260
2000
12:43
C and D both have six friends.
315
763260
3000
12:46
If you talk to them, and you said, "What is your social life like?"
316
766260
3000
12:49
they would say, "I've got six friends.
317
769260
2000
12:51
That's my social experience."
318
771260
2000
12:53
But now we, with a bird's eye view looking at this network,
319
773260
3000
12:56
can see that they occupy very different social worlds.
320
776260
3000
12:59
And I can cultivate that intuition in you by just asking you:
321
779260
2000
13:01
Who would you rather be
322
781260
2000
13:03
if a deadly germ was spreading through the network?
323
783260
2000
13:05
Would you rather be C or D?
324
785260
3000
13:08
You'd rather be D, on the edge of the network.
325
788260
2000
13:10
And now who would you rather be
326
790260
2000
13:12
if a juicy piece of gossip -- not about you --
327
792260
3000
13:15
was spreading through the network? (Laughter)
328
795260
2000
13:17
Now, you would rather be C.
329
797260
2000
13:19
So different structural locations
330
799260
2000
13:21
have different implications for your life.
331
801260
2000
13:23
And, in fact, when we did some experiments looking at this,
332
803260
3000
13:26
what we found is that 46 percent of the variation
333
806260
3000
13:29
in how many friends you have
334
809260
2000
13:31
is explained by your genes.
335
811260
2000
13:33
And this is not surprising. We know that some people are born shy
336
813260
3000
13:36
and some are born gregarious. That's obvious.
337
816260
3000
13:39
But we also found some non-obvious things.
338
819260
2000
13:41
For instance, 47 percent in the variation
339
821260
3000
13:44
in whether your friends know each other
340
824260
2000
13:46
is attributable to your genes.
341
826260
2000
13:48
Whether your friends know each other
342
828260
2000
13:50
has not just to do with their genes, but with yours.
343
830260
3000
13:53
And we think the reason for this is that some people
344
833260
2000
13:55
like to introduce their friends to each other -- you know who you are --
345
835260
3000
13:58
and others of you keep them apart and don't introduce your friends to each other.
346
838260
3000
14:01
And so some people knit together the networks around them,
347
841260
3000
14:04
creating a kind of dense web of ties
348
844260
2000
14:06
in which they're comfortably embedded.
349
846260
2000
14:08
And finally, we even found that
350
848260
2000
14:10
30 percent of the variation
351
850260
2000
14:12
in whether or not people are in the middle or on the edge of the network
352
852260
3000
14:15
can also be attributed to their genes.
353
855260
2000
14:17
So whether you find yourself in the middle or on the edge
354
857260
2000
14:19
is also partially heritable.
355
859260
3000
14:22
Now, what is the point of this?
356
862260
3000
14:25
How does this help us understand?
357
865260
2000
14:27
How does this help us
358
867260
2000
14:29
figure out some of the problems that are affecting us these days?
359
869260
3000
14:33
Well, the argument I'd like to make is that networks have value.
360
873260
3000
14:36
They are a kind of social capital.
361
876260
3000
14:39
New properties emerge
362
879260
2000
14:41
because of our embeddedness in social networks,
363
881260
2000
14:43
and these properties inhere
364
883260
3000
14:46
in the structure of the networks,
365
886260
2000
14:48
not just in the individuals within them.
366
888260
2000
14:50
So think about these two common objects.
367
890260
2000
14:52
They're both made of carbon,
368
892260
2000
14:54
and yet one of them has carbon atoms in it
369
894260
3000
14:57
that are arranged in one particular way -- on the left --
370
897260
3000
15:00
and you get graphite, which is soft and dark.
371
900260
3000
15:03
But if you take the same carbon atoms
372
903260
2000
15:05
and interconnect them a different way,
373
905260
2000
15:07
you get diamond, which is clear and hard.
374
907260
3000
15:10
And those properties of softness and hardness and darkness and clearness
375
910260
3000
15:13
do not reside in the carbon atoms;
376
913260
2000
15:15
they reside in the interconnections between the carbon atoms,
377
915260
3000
15:18
or at least arise because of the
378
918260
2000
15:20
interconnections between the carbon atoms.
379
920260
2000
15:22
So, similarly, the pattern of connections among people
380
922260
3000
15:25
confers upon the groups of people
381
925260
3000
15:28
different properties.
382
928260
2000
15:30
It is the ties between people
383
930260
2000
15:32
that makes the whole greater than the sum of its parts.
384
932260
3000
15:35
And so it is not just what's happening to these people --
385
935260
3000
15:38
whether they're losing weight or gaining weight, or becoming rich or becoming poor,
386
938260
3000
15:41
or becoming happy or not becoming happy -- that affects us;
387
941260
3000
15:44
it's also the actual architecture
388
944260
2000
15:46
of the ties around us.
389
946260
2000
15:48
Our experience of the world
390
948260
2000
15:50
depends on the actual structure
391
950260
2000
15:52
of the networks in which we're residing
392
952260
2000
15:54
and on all the kinds of things that ripple and flow
393
954260
3000
15:57
through the network.
394
957260
2000
16:00
Now, the reason, I think, that this is the case
395
960260
3000
16:03
is that human beings assemble themselves
396
963260
2000
16:05
and form a kind of superorganism.
397
965260
3000
16:09
Now, a superorganism is a collection of individuals
398
969260
3000
16:12
which show or evince behaviors or phenomena
399
972260
3000
16:15
that are not reducible to the study of individuals
400
975260
3000
16:18
and that must be understood by reference to,
401
978260
2000
16:20
and by studying, the collective.
402
980260
2000
16:22
Like, for example, a hive of bees
403
982260
3000
16:25
that's finding a new nesting site,
404
985260
3000
16:28
or a flock of birds that's evading a predator,
405
988260
2000
16:30
or a flock of birds that's able to pool its wisdom
406
990260
3000
16:33
and navigate and find a tiny speck
407
993260
2000
16:35
of an island in the middle of the Pacific,
408
995260
2000
16:37
or a pack of wolves that's able
409
997260
2000
16:39
to bring down larger prey.
410
999260
3000
16:42
Superorganisms have properties
411
1002260
2000
16:44
that cannot be understood just by studying the individuals.
412
1004260
3000
16:47
I think understanding social networks
413
1007260
2000
16:49
and how they form and operate
414
1009260
2000
16:51
can help us understand not just health and emotions
415
1011260
3000
16:54
but all kinds of other phenomena --
416
1014260
2000
16:56
like crime, and warfare,
417
1016260
2000
16:58
and economic phenomena like bank runs
418
1018260
2000
17:00
and market crashes
419
1020260
2000
17:02
and the adoption of innovation
420
1022260
2000
17:04
and the spread of product adoption.
421
1024260
2000
17:06
Now, look at this.
422
1026260
2000
17:09
I think we form social networks
423
1029260
2000
17:11
because the benefits of a connected life
424
1031260
2000
17:13
outweigh the costs.
425
1033260
3000
17:16
If I was always violent towards you
426
1036260
2000
17:18
or gave you misinformation
427
1038260
2000
17:20
or made you sad or infected you with deadly germs,
428
1040260
3000
17:23
you would cut the ties to me,
429
1043260
2000
17:25
and the network would disintegrate.
430
1045260
2000
17:27
So the spread of good and valuable things
431
1047260
3000
17:30
is required to sustain and nourish social networks.
432
1050260
3000
17:34
Similarly, social networks are required
433
1054260
2000
17:36
for the spread of good and valuable things,
434
1056260
3000
17:39
like love and kindness
435
1059260
2000
17:41
and happiness and altruism
436
1061260
2000
17:43
and ideas.
437
1063260
2000
17:45
I think, in fact, that if we realized
438
1065260
2000
17:47
how valuable social networks are,
439
1067260
2000
17:49
we'd spend a lot more time nourishing them and sustaining them,
440
1069260
3000
17:52
because I think social networks
441
1072260
2000
17:54
are fundamentally related to goodness.
442
1074260
3000
17:57
And what I think the world needs now
443
1077260
2000
17:59
is more connections.
444
1079260
2000
18:01
Thank you.
445
1081260
2000
18:03
(Applause)
446
1083260
3000
About this website

This site will introduce you to YouTube videos that are useful for learning English. You will see English lessons taught by top-notch teachers from around the world. Double-click on the English subtitles displayed on each video page to play the video from there. The subtitles scroll in sync with the video playback. If you have any comments or requests, please contact us using this contact form.

https://forms.gle/WvT1wiN1qDtmnspy7