Nicholas Christakis: The hidden influence of social networks

442,676 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,
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when I was a hospice doctor at the University of Chicago.
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And I was taking care of people who were dying and their families
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in the South Side of Chicago.
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And I was observing what happened to people and their families
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over the course of their terminal illness.
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And in my lab, I was studying the widower effect,
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which is a very old idea in the social sciences,
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going back 150 years,
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known as "dying of a broken heart."
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So, when I die, my wife's risk of death can double,
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for instance, in the first year.
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And I had gone to take care of one particular patient,
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a woman who was dying of dementia.
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And in this case, unlike this couple,
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she was being cared for
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by her daughter.
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And the daughter was exhausted from caring for her mother.
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And the daughter's husband,
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he also was sick
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from his wife's exhaustion.
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And I was driving home one day,
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and I get a phone call from the husband's friend,
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calling me because he was depressed
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about what was happening to his friend.
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So here I get this call from this random guy
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that's having an experience
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that's being influenced by people
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at some social distance.
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And so I suddenly realized two very simple things:
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First, the widowhood effect
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was not restricted to husbands and wives.
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And second, it was not restricted to pairs of people.
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And I started to see the world
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in a whole new way,
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like pairs of people connected to each other.
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And then I realized that these individuals
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would be connected into foursomes with other pairs of people nearby.
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And then, in fact, these people
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were embedded in other sorts of relationships:
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marriage and spousal
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and friendship and other sorts of ties.
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And that, in fact, these connections were vast
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and that we were all embedded in this
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broad set of connections with each other.
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So I started to see the world in a completely new way
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and I became obsessed with this.
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I became obsessed with how it might be
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that we're embedded in these social networks,
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and how they affect our lives.
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So, social networks are these intricate things of beauty,
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and they're so elaborate and so complex
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and so ubiquitous, in fact,
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that one has to ask what purpose they serve.
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Why are we embedded in social networks?
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I mean, how do they form? How do they operate?
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And how do they effect us?
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So my first topic with respect to this,
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was not death, but obesity.
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It had become trendy
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to speak about the "obesity epidemic."
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And, along with my collaborator, James Fowler,
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we began to wonder whether obesity really was epidemic
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and could it spread from person to person
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like the four people I discussed earlier.
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So this is a slide of some of our initial results.
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It's 2,200 people in the year 2000.
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Every dot is a person. We make the dot size
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proportional to people's body size;
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so bigger dots are bigger people.
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In addition, if your body size,
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if your BMI, your body mass index, is above 30 --
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if you're clinically obese --
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we also colored the dots yellow.
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So, if you look at this image, right away you might be able to see
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that there are clusters of obese and
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non-obese people in the image.
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But the visual complexity is still very high.
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It's not obvious exactly what's going on.
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In addition, some questions are immediately raised:
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How much clustering is there?
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Is there more clustering than would be due to chance alone?
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How big are the clusters? How far do they reach?
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And, most importantly,
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what causes the clusters?
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So we did some mathematics to study the size of these clusters.
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This here shows, on the Y-axis,
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the increase in the probability that a person is obese
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given that a social contact of theirs is obese
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and, on the X-axis, the degrees of separation between the two people.
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On the far left, you see the purple line.
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It says that, if your friends are obese,
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your risk of obesity is 45 percent higher.
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And the next bar over, the [red] line,
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says if your friend's friends are obese,
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your risk of obesity is 25 percent higher.
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And then the next line over says
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if your friend's friend's friend, someone you probably don't even know, is obese,
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your risk of obesity is 10 percent higher.
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And it's only when you get to your friend's friend's friend's friends
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that there's no longer a relationship
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between that person's body size and your own body size.
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Well, what might be causing this clustering?
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There are at least three possibilities:
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One possibility is that, as I gain weight,
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it causes you to gain weight.
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A kind of induction, a kind of spread from person to person.
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Another possibility, very obvious, is homophily,
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or, birds of a feather flock together;
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here, I form my tie to you
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because you and I share a similar body size.
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And the last possibility is what is known as confounding,
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because it confounds our ability to figure out what's going on.
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And here, the idea is not that my weight gain
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is causing your weight gain,
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nor that I preferentially form a tie with you
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because you and I share the same body size,
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but rather that we share a common exposure
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to something, like a health club
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that makes us both lose weight at the same time.
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When we studied these data, we found evidence for all of these things,
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including for induction.
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And we found that if your friend becomes obese,
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it increases your risk of obesity by about 57 percent
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in the same given time period.
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There can be many mechanisms for this effect:
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One possibility is that your friends say to you something like --
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you know, they adopt a behavior that spreads to you --
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like, they say, "Let's go have muffins and beer,"
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which is a terrible combination. (Laughter)
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But you adopt that combination,
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and then you start gaining weight like them.
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Another more subtle possibility
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is that they start gaining weight, and it changes your ideas
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of what an acceptable body size is.
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Here, what's spreading from person to person
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is not a behavior, but rather a norm:
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An idea is spreading.
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Now, headline writers
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had a field day with our studies.
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I think the headline in The New York Times was,
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"Are you packing it on?
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Blame your fat friends." (Laughter)
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What was interesting to us is that the European headline writers
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had a different take: They said,
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"Are your friends gaining weight? Perhaps you are to blame."
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(Laughter)
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And we thought this was a very interesting comment on America,
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and a kind of self-serving,
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"not my responsibility" kind of phenomenon.
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Now, I want to be very clear: We do not think our work
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should or could justify prejudice
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against people of one or another body size at all.
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Our next questions was:
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Could we actually visualize this spread?
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Was weight gain in one person actually spreading
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to weight gain in another person?
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And this was complicated because
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we needed to take into account the fact that the network structure,
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the architecture of the ties, was changing across time.
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In addition, because obesity is not a unicentric epidemic,
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there's not a Patient Zero of the obesity epidemic --
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if we find that guy, there was a spread of obesity out from him --
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it's a multicentric epidemic.
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Lots of people are doing things at the same time.
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And I'm about to show you a 30 second video animation
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that took me and James five years of our lives to do.
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So, again, every dot is a person.
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Every tie between them is a relationship.
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We're going to put this into motion now,
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taking daily cuts through the network for about 30 years.
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The dot sizes are going to grow,
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you're going to see a sea of yellow take over.
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You're going to see people be born and die --
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dots will appear and disappear --
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ties will form and break, marriages and divorces,
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friendings and defriendings.
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A lot of complexity, a lot is happening
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just in this 30-year period
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that includes the obesity epidemic.
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And, by the end, you're going to see clusters
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of obese and non-obese individuals
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within the network.
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Now, when looked at this,
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it changed the way I see things,
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because this thing, this network
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that's changing across time,
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it has a memory, it moves,
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things flow within it,
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it has a kind of consistency --
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people can die, but it doesn't die;
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it still persists --
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and it has a kind of resilience
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that allows it to persist across time.
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And so, I came to see these kinds of social networks
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as living things,
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as living things that we could put under a kind of microscope
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to study and analyze and understand.
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And we used a variety of techniques to do this.
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And we started exploring all kinds of other phenomena.
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We looked at smoking and drinking behavior,
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and voting behavior,
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and divorce -- which can spread --
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and altruism.
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And, eventually, we became interested in emotions.
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Now, when we have emotions,
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we show them.
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Why do we show our emotions?
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I mean, there would be an advantage to experiencing
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our emotions inside, you know, anger or happiness.
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But we don't just experience them, we show them.
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And not only do we show them, but others can read them.
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And, not only can they read them, but they copy them.
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There's emotional contagion
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that takes place in human populations.
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And so this function of emotions
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suggests that, in addition to any other purpose they serve,
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they're a kind of primitive form of communication.
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And that, in fact, if we really want to understand human emotions,
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we need to think about them in this way.
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Now, we're accustomed to thinking about emotions in this way,
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in simple, sort of, brief periods of time.
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So, for example,
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I was giving this talk recently in New York City,
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and I said, "You know when you're on the subway
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and the other person across the subway car
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smiles at you,
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and you just instinctively smile back?"
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And they looked at me and said, "We don't do that in New York City." (Laughter)
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And I said, "Everywhere else in the world,
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that's normal human behavior."
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And so there's a very instinctive way
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in which we briefly transmit emotions to each other.
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And, in fact, emotional contagion can be broader still.
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Like we could have punctuated expressions of anger,
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as in riots.
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The question that we wanted to ask was:
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Could emotion spread,
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in a more sustained way than riots, across time
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and involve large numbers of people,
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not just this pair of individuals smiling at each other in the subway car?
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Maybe there's a kind of below the surface, quiet riot
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that animates us all the time.
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Maybe there are emotional stampedes
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that ripple through social networks.
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Maybe, in fact, emotions have a collective existence,
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not just an individual existence.
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And this is one of the first images we made to study this phenomenon.
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Again, a social network,
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but now we color the people yellow if they're happy
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and blue if they're sad and green in between.
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And if you look at this image, you can right away see
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clusters of happy and unhappy people,
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again, spreading to three degrees of separation.
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And you might form the intuition
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that the unhappy people
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occupy a different structural location within the network.
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There's a middle and an edge to this network,
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and the unhappy people seem to be
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located at the edges.
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So to invoke another metaphor,
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if you imagine social networks as a kind of
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vast fabric of humanity --
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I'm connected to you and you to her, on out endlessly into the distance --
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this fabric is actually like
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an old-fashioned American quilt,
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and it has patches on it: happy and unhappy patches.
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And whether you become happy or not
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depends in part on whether you occupy a happy patch.
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(Laughter)
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So, this work with emotions,
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which are so fundamental,
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then got us to thinking about: Maybe
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the fundamental causes of human social networks
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are somehow encoded in our genes.
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Because human social networks, whenever they are mapped,
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always kind of look like this:
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the picture of the network.
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But they never look like this.
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Why do they not look like this?
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Why don't we form human social networks
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that look like a regular lattice?
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Well, the striking patterns of human social networks,
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their ubiquity and their apparent purpose
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beg questions about whether we evolved to have
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human social networks in the first place,
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and whether we evolved to form networks
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with a particular structure.
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And notice first of all -- so, to understand this, though,
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we need to dissect network structure a little bit first --
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and notice that every person in this network
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has exactly the same structural location as every other person.
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But that's not the case with real networks.
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So, for example, here is a real network of college students
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at an elite northeastern university.
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And now I'm highlighting a few dots.
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If you look here at the dots,
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compare node B in the upper left
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to node D in the far right;
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B has four friends coming out from him
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and D has six friends coming out from him.
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And so, those two individuals have different numbers of friends.
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That's very obvious, we all know that.
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But certain other aspects
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of social network structure are not so obvious.
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Compare node B in the upper left to node A in the lower left.
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Now, those people both have four friends,
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but A's friends all know each other,
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and B's friends do not.
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So the friend of a friend of A's
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is, back again, a friend of A's,
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whereas the friend of a friend of B's is not a friend of B's,
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but is farther away in the network.
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This is known as transitivity in networks.
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And, finally, compare nodes C and D:
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C and D both have six friends.
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If you talk to them, and you said, "What is your social life like?"
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they would say, "I've got six friends.
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That's my social experience."
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But now we, with a bird's eye view looking at this network,
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can see that they occupy very different social worlds.
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And I can cultivate that intuition in you by just asking you:
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Who would you rather be
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if a deadly germ was spreading through the network?
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Would you rather be C or D?
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You'd rather be D, on the edge of the network.
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And now who would you rather be
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if a juicy piece of gossip -- not about you --
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was spreading through the network? (Laughter)
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Now, you would rather be C.
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So different structural locations
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have different implications for your life.
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And, in fact, when we did some experiments looking at this,
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what we found is that 46 percent of the variation
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in how many friends you have
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is explained by your genes.
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And this is not surprising. We know that some people are born shy
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and some are born gregarious. That's obvious.
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But we also found some non-obvious things.
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For instance, 47 percent in the variation
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in whether your friends know each other
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is attributable to your genes.
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Whether your friends know each other
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has not just to do with their genes, but with yours.
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And we think the reason for this is that some people
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like to introduce their friends to each other -- you know who you are --
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and others of you keep them apart and don't introduce your friends to each other.
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And so some people knit together the networks around them,
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creating a kind of dense web of ties
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in which they're comfortably embedded.
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And finally, we even found that
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30 percent of the variation
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in whether or not people are in the middle or on the edge of the network
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can also be attributed to their genes.
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So whether you find yourself in the middle or on the edge
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is also partially heritable.
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Now, what is the point of this?
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How does this help us understand?
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How does this help us
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figure out some of the problems that are affecting us these days?
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Well, the argument I'd like to make is that networks have value.
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They are a kind of social capital.
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New properties emerge
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because of our embeddedness in social networks,
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and these properties inhere
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in the structure of the networks,
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not just in the individuals within them.
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So think about these two common objects.
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They're both made of carbon,
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and yet one of them has carbon atoms in it
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that are arranged in one particular way -- on the left --
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and you get graphite, which is soft and dark.
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But if you take the same carbon atoms
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and interconnect them a different way,
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you get diamond, which is clear and hard.
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And those properties of softness and hardness and darkness and clearness
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do not reside in the carbon atoms;
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they reside in the interconnections between the carbon atoms,
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or at least arise because of the
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interconnections between the carbon atoms.
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So, similarly, the pattern of connections among people
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confers upon the groups of people
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different properties.
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It is the ties between people
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that makes the whole greater than the sum of its parts.
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And so it is not just what's happening to these people --
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whether they're losing weight or gaining weight, or becoming rich or becoming poor,
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or becoming happy or not becoming happy -- that affects us;
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it's also the actual architecture
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of the ties around us.
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Our experience of the world
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depends on the actual structure
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of the networks in which we're residing
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and on all the kinds of things that ripple and flow
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through the network.
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Now, the reason, I think, that this is the case
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is that human beings assemble themselves
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and form a kind of superorganism.
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Now, a superorganism is a collection of individuals
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which show or evince behaviors or phenomena
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that are not reducible to the study of individuals
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and that must be understood by reference to,
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and by studying, the collective.
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Like, for example, a hive of bees
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that's finding a new nesting site,
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or a flock of birds that's evading a predator,
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or a flock of birds that's able to pool its wisdom
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and navigate and find a tiny speck
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of an island in the middle of the Pacific,
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or a pack of wolves that's able
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to bring down larger prey.
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Superorganisms have properties
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that cannot be understood just by studying the individuals.
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I think understanding social networks
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and how they form and operate
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can help us understand not just health and emotions
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but all kinds of other phenomena --
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like crime, and warfare,
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and economic phenomena like bank runs
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and market crashes
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and the adoption of innovation
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and the spread of product adoption.
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Now, look at this.
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I think we form social networks
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because the benefits of a connected life
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outweigh the costs.
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If I was always violent towards you
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or gave you misinformation
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or made you sad or infected you with deadly germs,
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you would cut the ties to me,
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and the network would disintegrate.
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So the spread of good and valuable things
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is required to sustain and nourish social networks.
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Similarly, social networks are required
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for the spread of good and valuable things,
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like love and kindness
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and happiness and altruism
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and ideas.
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I think, in fact, that if we realized
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how valuable social networks are,
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we'd spend a lot more time nourishing them and sustaining them,
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because I think social networks
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are fundamentally related to goodness.
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And what I think the world needs now
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is more connections.
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Thank you.
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(Applause)
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About this website

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