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Transcript

Question: How can startups or other new organizations design social networks for maximum effectiveness?

Nicholas Christakis: I want to emphasize that although I am obsessed with networks and a zealot for networks I do not think they’re a panacea. That is to say I don’t think that every social problem or every social challenge can readily or easily be mapped with some kind of network intervention. And you know we have all kinds of other longstanding policy levers at our disposal—taxation policy, legal policy, advertising—I mean there are lots of ways in which we try to change our society for the better let’s say. So I don’t think that for instance anything can be addressed with a network approach.

Now with respect to organizational networks, for example, startups that you asked about, I don’t have like some kind of general rule, but I’ll give you a little nugget of an idea, which is that in the network of organizations you don’t want to be overly networked or overly densely interconnected with entities that resemble you, nor do you wish to be too diffusely linked to entities that don’t resemble you. There is a kind of a sweet spot in the middle and my favorite example of this at the individual level is some work that was done by a colleague of mine, Brian Uzzi at Northwestern University. And what he did is he was very interested in the success of Broadway musicals and this is a very famous paper of his and he assembled a dataset of about 350 Broadway musical production companies—so the director, the producer, the actors and costume designer, so forth—and he mapped what the network of those people were, how interconnected they were and at one extreme you could have individuals who never had interacted before and that would be what we would call in network science very low transitivity or very low density in the ties between the people. So people didn’t really know each other, they hadn’t worked together before. At the other extreme you had a group of people all of whom had all worked together before, so very high density of ties. Everyone knew everyone else within this little network because they had all worked together before. And in between you had a mixed bag. Some people who had worked together before on the team and some were new to the team and had never worked with anyone on the team before. And when he plotted financial success and commercial success of these Broadway musicals on the Y axis and the density of ties or transitivity on the X axis, he found that if everyone had worked together before the show was a flop. And if nobody had worked with anyone else before the show was a flop. And the optimal success was in the middle, when you had the middle number of interactions or ties between people. Something between let’s say graphite... you know, coal, which is totally disconnected, and diamond, which is everyone rigidly connected, something in between was optimal... to pick up the example of carbon we discussed earlier. 

So I suppose then if one wanted to extrapolate and I’m not saying this is true, but I’m saying one could imagine this to be true. If you’re an organization and you’re embedded, a startup as you asked, embedded in a network of other organizations you want to have some relationships with firms that resemble you, but you also want relationships with firms that are very different to maximize your chances of success. And actually these ideas relate to some other ideas that James Fowler and I have explored on the genetics of human social networks and what we find is that people vary in their transitivity. People vary in the extent to which they’re friends know each other. So most people who are familiar with the idea that people vary in how many friends they have, some of us are born shy, some of us are born gregarious, some people have no friends or two friends, some have 80 friends or eight friends. People vary in how many social intimates they have and people are familiar of thinking about this as a kind of a genetic... maybe it’s partially genetic if you’re born shy or not. And in fact in our work we found that about 46% of the variation in how many friends you have can be ascribed in part to your genes.

Well we also find that whether or not your friends know each other also can depend in part on your genes, so we find that if you have Tom, Dick and Harry in a room, whether Dick knows Harry depends not just on Dick and Harry’s genes, but also on Tom’s genes. This is a very bizarre result. What I’m saying to you is whether your friends know each other has to do with something to do with your own genetic heritage. And we think the reason for this is that people vary in their tendency to introduce their friends to each other. So some of us you know knit our networks together and introduce all our friends to each other and other of us in a kind of worlds collide theory, you know, don’t introduce our friends to each other, keep them separate from each other. And it turns out that you can construct arguments for why people might vary in how many friends they have. Sometimes it’s to your advantage to have many friends. Sometimes it’s a disadvantage. Ad you can also construct arguments about why it’s to your…why you might introduce your friends to each other or not. Sometimes it’s to your advantage to have all your friends know each other and sometimes it’s not and so the example that we give in this particular paper and that we discuss in the book is that your friends knowing each other makes it easier for you to achieve an objective. 

For example, in evolutionary time, you know, maybe to bring down a big animal, so if you want to hunt a mastodon you want a group of people who all know each other very well, everybody knows everybody else. Let’s go kill the thing. On the other hand if you want to find a mastodon that’s not the group you want because if everyone is connected to everyone else your friend’s friend is right back again a friend of your own, so do you know where the mastodon is? No. Do you know where the mastodon is? Each guy comes right back to the first guy whereas in a network that has low transitivity your friend’s friend is not your friend. That person is now able to get information from a more distant location within the network, let’s say about where the mastodon is. So the point is, is that different microstructures of the network confer different advantages depending on what the challenge is. If the challenge is to acquire information you might want low transitivity. If the challenge is to work together you might want high transitivity. And therefore it’s not hard to imagine that these tendencies as well might be deeply rooted within our genetic heritage.  So we find this too and so what we find therefore is that human beings assemble themselves into particular kinds of networks. We do this naturally. We vary one person to the other in what kinds of networks we construct for ourselves and whatever network we construct for ourselves, turns out has rather profound implications for our experience in the world.

Recorded March 31, 2010
Interviewed by Austin Allen

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