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January, 2009

Thinking About Networks

One idea I've been turning over in my head lately is around the idea of desire lines. These are the unpaved paths people chose to take and eventually trample, turning what was one person's decision to stray from the pavement into an all-but-official route. I love desire lines as a metaphor because they expose the network of collective decision-making that tends to otherwise go unnoticed in the physical world.

On the internet, of course, things are very different. Every day we encounter the fruits of collective decision-making and most of the time are quite aware of the role we play in it. As I wrote in 2006, "I think the most important effect of the internet thus far is that it's exposed the network. For the first time everyone can understand what a network is and how it works. Now that we do, we're beginning to take that knowledge and exploit it." (By the way, I have trouble how much I love the fact that I can pull up forgotten thoughts from 2006 in an instant. It's an amazing power to possess.) This knowledge, of course, is what leads to people gaming the system, whether it be shady search engine optimization or manipulating Digg. Ethical issues notwithstanding, though, it's pretty amazing to think that so many people understand the core functionality of networks (even if they don't understand that they understand).

What's at the heart of this all is data: Before the web there was no real way to fully comprehend how networks functioned because the datasets were so small. In fact, I'd argue, that was true for most things. The web affords us the opportunity to play the role of amateur social scientist, looking at datasets that social scientists would have only dreamed of 30 years ago. As James Fowler explains in this Seed Salon with Albert-Laszlo Barabasi:

Well, the great thing about these massive, passive data sets is that we're going to have really deep information about a very, very large number of people. So we won't be forced anymore to make trade-offs between depth and breadth. But then the question becomes: What kind of preparation are we going to give our students? We've had a revolution in game theory in the past 30 years, so that a good number of political scientists all across the country work only on mathematical, closedform models. We've also had a revolution in the application of statistics.

But both of these revolutions have been built on this atomistic view of human beings. Statisticians make the assumption that all the observations are independent in order to be able to calculate statistical significance. Game theorists make it because, as you know, getting anything to work out in a closed-form model is nearly impossible if you assume that people are taking into account the preferences of other people.

We need not only to ramp up the amount of methodological training that people in social sciences have, but also to shift their perception into realizing that the relationships between people are important.

This is not constrained to social science, or even just academia, as people we all need to ramp up our understanding of the interconnection between individuals and their decisions. In fact, I think laymen may be ahead of the scientists in this respect. Social scientists (especially economists) have a lot invested in the individual view of human beings, the idea that we are rational actors generally unaffected by the world around us. This, of course, is wrong and behavioral economists are doing a great job of throwing a few wrenches into the field (of course the world economy collapsing isn't helping either).

Anyway, I feel like I'm rambling and have lost focus a bit. Partly that's because I don't really know where to go here. I know this is important, but there's a bit of a "what next" feeling left with me. What does a world look like where people understand the fundamentals of network science? How does the observation of group behavior in real time move us in directions we might not have expected? What does it mean for an individual to recognize their role within the mass?

Obviously, I'm not entirely sure how to answer those questions at the moment, but I'll keep thinking about it (and would love any thoughts you have).

January 25, 2009
Noah Brier | Thanks for reading. | Don't fake the funk on a nasty dunk.