In my book Wikicrazia I claim that the public sector, society’s system to pursue the common good, can be made smarter by mobilizing the citizenry’s collective intelligence. Accessing collective intelligence entails enabling a large number of individuals to coordinate on some common goal. Normally, this is done by means of online commmunities, that use the Internet as their technological infrastructure and where interaction is mediated by some kind of social bargain, with somebody to resolve conflicts and keep the group focused on the goal.
There’s a problem here. On the one hand, online communities cannot be run by top-down command and control: it is exactly the free action of their different participants that make online communities so incredibly effective in processing large amounts of information. On the other hand, public policies have by definition a goal which is set exogenously with respect to the community itself: whereas Facebook users are on Facebook to hang out, and it does not really matter what they do with it, the users of Peer to Patent are there to process patent application; those of Kublai to write up creative business plans; those of Wikipedia (not a public policy, but similar in this respect) to write an encyclopedia. Community managers, myself included, are trapped in this dilemma: practically the only way we have to figure out the social dynamics in our communities is to spend an unreasonable amount of time participating in them, and we try to steer them by rhetoric and persuasion. We end up navigating pretty much by gut feelings. And as communities scale – even to just a few thousand participants – it gets really hard to understand what is really going on.
I thought our work would improve a lot if we could augment our ability to read social dynamics of online communities by using software. In essence, a policy community is a social network, and as such it can be represented by a graph with nodes and links, and studied mathematically. The community’s social dynamics should be encoded into the mathematical characteristics of the graph that represents it: for example, the creation of a cohesive group of senior users in Kublai in 2009 was picked up by the crystallization of a structure called k-core. If we managed to build some sort of dictionary that maps social dynamics onto mathematical characteristics of the graph, we could use network analysis to detect community dynamics that are invisible to the eye, because they happen at a scale too large for human participants: and this would work even for very large communities, at least in principle.
I intend to develop this software as my Ph.D. thesis. Colleagues at University of Alicante and the European Center of Living Technology will help. I call it Dragon Trainer, because doing policy through an online community is like training a dragon, an animal too large and dangerous to order around. If you are interested in learning how we plan to do this, you can watch the video above (12 mins).