(dedicated to Benjamin Renoust)

For several years now I have been fascinated with networks. While I have grown to appreciate the internal coherence and beauty of the math, as soon as I lift my gaze from the models and try to use them to tell complicated, real-world stories I am a part of (like Edgeryders, or the unMonastery), I struggle with counterintuition. Duncan Watts’ beautiful book hits the nail on the head: since we are humans, we tend to overestimate the role of humans in how things unfold. By implication, we underestimate the role of other factors at play, like chance or, indeed, network effects. Highly connected individuals in a scale-free social network (say, people with million of Twitter followers) are, understandably, tempted to claim credit for their privileged position. And yet, we have rock-solid models that explain the emergence of hubs based purely on the (realistic) characteristic of the growth process of a network – even when nodes are identical.

Of course, you could build more sophisticated models, in which nodes are different from each other. That would make them even more realistic: indeed, people do have different abilities, and in many domains these abilities can be ranked. Clay Shirky’s blog posts are better than mine. He deserves to have more incoming links than I have. But here’s the thing: network math can explain rich, complex behavior by assuming identical nodes and focusing only on patterns of connectivity. In fact, that’s the whole point . As you make that move, your math gets much more elegant and tractable: you get a model building strategy that carries through to a very broad range of phenomena (networks of genes,of food ingredients in recipes, of intermediate goods in an economy, of relay stations in a power grid…). But most importantly, if you, like me, are ultimately interested in networks of humans, you find yourself staring at a counterintuitive, yet probably fundamental, conclusion:

Identity. Does. Not. Matter.

Or, more accurately, your pattern of connectivity – for modelling purposes – is your identity. In most models, you can start with identical nodes, add some randomness and watch the system create hubs of influence and power. Given how uncanny the predictive power of these models is, it is hard to escape the conclusion that they describe reality to some degree; in other words, that who we are is largely the product of chance and network math.

I find this thought beautiful and humbling, in a way that I can only describe as almost religious (even though I am not a believer in any faith). As I contemplate it, I feel somehow closer to my fellow humans, the powerful and connected as well as the weak and isolated. This may sound like not very scientific a conclusion, but I feel it is not a bad stance for social scientists and economists. Our disciplines can always use some extra empathy. Within the context of the Crossover project, I have been advocating for network analysis to be included in the toolkit of the modern policy maker; empathy is yet another argument for doing so.