(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.
Isn’ti it clear to everyone, to you in the first place, that, through human effort, individuals can influence the place that they occupy within such networks?
Recognizing this does not mean denying that network forces exist, which are independently from human personality/identity.
The notion of “elegance” of a model in which members are all identical and non-differentiated is strange and unfamiliar to me. My feeling with regard to the modeling of these forces is very different from yours. I find more appealing the models that recognize differences, and appreciate human agency, for dirtier that they may be.
Oh, it is! And yes, explicit recognition of individual differences is indeed more appealing. But here is the thing: from an explanatory perspective, we sometimes find that – even when forcing individual differences across agents to disappear in the model – we don’t really lose that much explanatory power. This means that – while differences, say, in talent, between different individuals, are undeniable – they don’t go a very long way in explaining the difference in performance or behavior, once you account for connectivity (Ronald Burt makes this point convincingly in his famous 2004 article on creativity as a network-driven phenomenon).
And from a cognitive bias perspective, the fact that, intuitively, individual differences should explain most of the differences in performance, should make you even more wary. It could be a trap, evolved by our primate brains under very different conditions: just like our ancestors seeing lightning and being led by their intuition to the conclusion that some angry god was throwing a tantrum. When you prove something that clashes with your intuition you run a smaller risk of fooling yourself.
I allow myself a little intrusion in the conversation: Ron Burt’s theory is actually behind all I’m suggesting about multiple networks. Here is another point, in between both your point of views, the way you constrain your network (i.e. the function of entities from which you decide to draw your links) allows you to express individual differences. Once built, you treat the network as one proper object as you mention it.
Wow, thank you Alberto!
I’m very happy to hear and read from people like you, amazed, excited, and fascinated by networks, with a thirst for understanding what lays beneath.
It’s hard to comment on both my personal and scientific point of view, not to get overwhelmed. I really tend to be mesmerized by networks, and after the time I’ve spent studying them I tend to see networks literally everywhere.
Networks tend to be perfect objects for storytelling: they have actors and lines that bring interaction between them. They’re highly drawable, and you can read them from a global perspective (the role an actor plays in the overall network), but as well from a local perspective (the role of an actor in its community).
So yes, there is a lot to understand considering every node as equal, and we can also go beyond: there is not one network, but networks, we can overlay them and form very complex but nonetheless fascinating objects, multiple networks (and they bear a lot of different names within the scientific community). Now let’s understand what is the place of an actor within each of its possible community… We do tend to overestimate as human beings our power over things, others, and the world, but each of us is one node, part of a neighborhood formed throughout multiple networks. Humbling is to see how properties emerge from these very specific neighborhood (or communities), not from individual nodes (except for hubs as you mentioned). Sorry, over-excited by my work, going to far. But yes I follow your thought, the maths is very elegant, treating every entity as equal (even though you can make a difference within the linkage).
Well, overall, I mostly want to thank you, I hope a lot of people will read and listen to you, bringing more awareness of the networks around us: we are all connected (it sounds cheesy, but it’s now clearer than ever, and I’m not talking about the Internet), and our connectivity really defines us. So much to say…
What I take away from this discussion (thanks alberto, you always teach me something):
– in the observation of networks one can reach something close to “illumination”, an experience reported by scientists and some religions like Buddhism; Unfortunately i have never experienced it;
– some people I highly respect like you, don’t find the scientific determinism which is dominant I believe in much of today’s physics (theory of the matter, of the universe) as gloomy as I do, for the implication it has on human freedom. Again, I want to look into this further
My friend, I am just as scared as you. More scared I suspect, because I spent years teaching myself not to avert my gaze from very unsettling, very impersonal social dynamics. But I still want to know. Even if knowledge results in acknowledging humanity’s lack of control, even if it amounts to knowing we are all going down and there’s nothing we can do about it, I still want to know. It’s stupid, I know, but I find it more dignified, more human somehow, to go down with your eyes open. Humbled as I am, such is my goal.
Alberto, I think identity matters a lot if defined as “what you think of yourself” (self-definition, self-consciousness, self-representation, aspirations) rather than “how you could be described and classified”. It’s the relationship between the two concepts that is more interesting rather than one or the other.
Well, no brainer: within the space of your brain, identity is all-important (“The ego matters!” says the ego). Furthermore, individual differences are objectively there. What I am saying is that in many cases, for modelling purposes, you can assume them out and still get realistic behavior of the model. This does not negate individual differences (how could it?), but it does tell you they don’t have a lot of impact on the system’s dynamics.