The more I use the Internet, the more I grow fascinated with networks, because they behave in unexpected, counterintuitive ways. They seem to summon order from chaos as if by magic. Consider the web: large masses of amateurs who don’t know each other and have no command structure should produce some kind of shapeless informational blob, right? Wrong. Day after day, people and content inexorably self-organize in such a way that they are one or few clicks away from each other. Building an exhaustive map of the Internet is impossible, but finding any one thing in it is quite easy. It is a bit like sticking your hand in the proverbial haystack and finding a needle, every time.

The more I study networks and the more they amaze me for their ability to organize information, in an apparently effortless way. Reading the history of scientific exploration of social networks is almost dizzying. Stanley Milgram gives random American letters for other random Americans asking the former to deliver through an unroken chain of aquaintances, and a surprising number of them reaches home in very few steps. Mark Granovetter discovers that aquaintances are more effective than close friends or family in finding us jobs. Fredrik Liljeros looks at a network of sexual contacts, and concludes that the existence of a small number of very promiscuous people renders AIDS impossible to eliminate. Nathan Eagle finds that the prosperity of a small area can be predicted from the pattern of allocation of calling time across their contacts of that area’s inhabitants (in poorer communities people spend a higher share of their calling time with one or two contacts). All these results seem independent of the actual people in the networks: in almost all models nodes are identical. All the action is in the link structure. Network papers are academic, but somewhat alien: Hogwarts comes to mind.

I am convinced that the properties of networks can help explain many phenomena that we experience every day but don’t really understand – and give us anxiety. Why do we feel surrounded by young, successful entrepreneurs (though there’s not that many of them)? Why were peer-to-peer file sharing services fatal to the recorded music industry? How does Wikipedia work so well?

My Holy Grail is to tame online social networks, forging them in a powerful, precise tool to design and deliver public policies. I have done it before in Visioni Urbane e Kublai, but a lot of time I had to steer by instinct. I was lucky, but for this to become a generalised method I need to understand it a lot better. So I study the language of networks: these days I am often at the European University Institute in Florence, to attend Fernando Vega-Redondo’s Complex Social Networks course. It’s a bit tough (I get up at 5 a.m., because Fernando usually lectures at 8.45 sharp), but so be it. I really need to understand this thing.

One of my New Year resolutions for 2010 was “study complexity economics”. In my job as consultant on public policy I find myself facing problems that standard economics cannot even describe, let alone solve them. The complexity approach – a weird interdisciplinary mix of biology, computer science, neuroscience and various add-ons, from statistics to archaeology, with math holding everything together – could hold some of the answers.

It’s looking like I’ll get plenty of chances to study this stuff: I have become a Ph.D. candidate in Quantitative Economics at University of Alicante, in Spain, effective academic year 2010-2011. David Lane, member of the Science Board of the legendary Santa Fe Institute, and – less problems – I shall defend my thesis in the fall of 2012. My line of research is going to be quite practical: I want to figure out how to train social networks to execute some tasks. It’s networks, as opposed to people participating in them, I want to train.

This is more entangled than it seems. We more or less agree that social dynamics are emergent. Most interesting societal strucures, from Common Law to cultures and even the Mob are complex adaptive systems, and their behavior is impossible to predict in the long run. Not because we have bad models: in a complexity framework it is unpredictable even in principle

On the other hand, I have theorized (in Wikicrazia) and tried to practice (in Kublai and elsewhere) that we can and should harness collective intelligence to improve public policies and, ultimately, the world we live in. How to reconcile the unpredictability of social networks with the agency that public policy requires? I would like to explore the possibility of training social networks, through appropriate design choices and stimuli, as you would train some huge animal: using their superhuman information processing capacity to the advantage of humans. This means first and foremost understanding their mathematical structure and trying to influence it: it’s what Ruggero Rossi (another newly enrolled Alicante Ph.D. candidate) and I have started to do. Anyway, I’m going back to school: at 44 it is really a luxury, and a wonderful adventure. My thanks to Giovanni Ponti, the director of Alicante’s doctoral programme, for awarding me the most important and prestigious academic title: that of student.

In my early foray into computer graphics in the late 80s I came across Symbolics, a spinoff of MIT AI Lab doing (among other things) research in advanced visualization. I was dumbfounded by this video, premiered by Symbolics at SIGGRAPH 1987. How could they achieve their flock of birds  to move in such a natural-looking way? At the time it looked like sorcery: I was a humble economics student in a small town in Italy, with not a chance in hell to grasp the extension of the knowledge wielded by MIT computer whizs. So I put it away in a corner on my mind. Until, in 2009, I chanced on a 1992 book, Mitchell Waldrop’s Complexity, that actually knows the answer to my 22-year old question. Each bird or fish in the flock follows three simple rules of behaviour:

1. It tries to maintain a minimum distance from other objects in the environment, including other birds/fish (Symbolics’ Craig Reynolds called them “boids”).
2. It tries to match velocities with nearby birds/fish.
3. It tries to move toward the perceived center of mass of nearby birds/fish.

The natural-looking flocking behaviour is emergent. As far as the program is concerned, there is no entity called flock: it is just moving about individual boids. Simple rules for local interaction among them produce an elegant and effective collective behaviour.

Wait a minute. This is not so different from what is happening in Kublai. Example: we wanted the community to go and say hello to new members. Of course you cannot issue a decree that this is to happen. So what we did was this: Walter and I, who are friends and also particularly active community members, agreed that we would do it, created a Welcome Group and started doing just that. This produced some sort of flocking behaviour: our “net neighbours” (at least some of them) started imitating us, and joined the group. Soon they developed a more effective way to keep track of who was doing what (after some trial-and-error Pico proposed a widget which everyone was happy with), and their net neighbours started following their example… including the initiators!

Communities are, by definition, impossible to control: but they are certainly possible to influence. This is no rocket science: most of us have some experience of it. This flocking behaviour intuition, if confirmed by analysis, could lead to developing techniques for influencing (not sure “managing” is the appropriate word) social networks based on establishing “islands” of local interactions where certain rules apply, and watching them spread out through the network’s links. Of course where you start matters: it just so happens that Walter and I are by far the most eigenvector central people in Kublai, according to Ruggero.

I am wondering whether this mechanism could somehow help us understand why people seem “too eager” to collaborate in social networks, and why, conversely, oppurtunistic behaviour is a lot less widespread than one would be inclined to think (this remark run in several talks at Public Services 2.0). Cooperation as an emergent property of networks, as opposed to an intrinsic property of individuals?

For some time now I have been designing and deploying interfaces between public authorities and citizens (in particular between authorities and creative people/creative firms). The strategy behind them all is very simple: connect people – those who work for public authorities and those who work in the creative industries – in a many-many-interaction environment with very transparent information. Web 2.0 tools and an appropriate value system – that David maintains coincide with hacker ethics – have so far brilliantly solved the filtering problem: civil servants in these networks are not clogged by people asking for favours. On the contrary, they give every sign of enjoying their proximity to citizens and what they do.

These interfaces allow a strong reduction of the distance between administrators and constituencies. The Ministry of economic development is a central authorities, but to the creatives populating Kublai it is just one click away. It is pretty obvious that Kublaians have a much, much closer relationship with it than with their local authorities, closed within their palaces (and almost always locked behind firewalls that inhibit their access to social networks).

I wonder if, in this situation, we should not rethink the subsidiarity principle. As far as I can see it says that public policy should be managed by the most local public authority which has the means to address the problem in discussion: so, in Europe, the European Union deals with global environmental planning, while urban planning in the smalltown of Pisticci is dealt with by the Pisticci municipality. This sounds simple; unfortunately, in a globalized world almost every local problem is inextricably linked to some broader context. Urban planning in metropolitan areas is a perfect example: it does not make sense to plan according to administrative borders if the economy and the society are integrated beyond it. So, it is not always easy to understand in the abstract which authority is closest to any particular problem. In practice, on the other hand, it is very simple: the closest authority is the one the least clicks away, the most accessible, the one with the best interface. For Kublai’s creatives it is a lot easier – not to mention more rewarding ad even fun – to talk to the Ministry of economic development than to their municipal governments, so they will tend to strentghen their ties with the former and ignore the latter. Old style subsidiarity is unsustainable.

It may be worth it to study the British setup (I have done it here). Policy competences are allocated not geographically, but by issues. Funding is centralized, so the central government has a lot of traction: when a strategy is adopted (like the first Blair government’s stance on the creative industries) things happen fast. But strategies are in general implemented locally, by small semipublic organizations who try out solutions competing with one another for funding from Whitehall. The system – at least for policies on creativity – works fairly well. It is no chance that the English – in a 2004 referendum – have decided that they do NOT want democratically elected regional authorities. A certain distance from the problem sometimes guarantees a broader perspective (and local politics can be toxic). All the more so since, with a little effort, central authorities can be just one click away.