I joined a Council of Europe workgroup on Quality job creation through social links and social innovation (the social innovation part is a recent add to the group’s name, and I think I am partly responsible for the add). One of the issues we are discussing is this: given that there is an interesting group of people who started calling themselves social innovators; given that these people seem to have potential for improving the society they (and we) live in; given that they look like a new kind of social and economic agent, as such requiring a new kind of public policy – the ones in place for firms and nonprofit orgs might not work in their case; given all this, it follows that public authorities might soon be required to do new things, perhaps radically new ones. That’s great; but how do public authorities actually learn?

This looks like a relevant question to me. I have worked on pilot government initiatives hailed by some as innovative, like Kublai or Visioni Urbane; the challenge they now face is integration into mainstream policy, becoming a part of the default arsenal for their parent authorities to do their job. Thanks to the Council of Europe’s support I have been able to look deeper into the issue. My provisional conclusion is that the prevailing learning model for public authorities is rational-Weberian and way off the mark. Here’s how it works:

• a new issue, after its importance has been validated by the scientific community, gains importance in the eye of the public opinion.
• politicians, competing for votes, include it in the list of issues they promise to tackle once elected.
• after taking office, representatives embed action to be taken thereabout into law.
• new law is enacted into policy

This model is elegant but useless. It only works if (1) alternative courses of actions can be identified, discussed and selected already in the democratic debate phase; (2) the electorate has effective means to enforce their pact with its representatives, constraining them to keep their promise by making law; (3) law enactment is “linear”, i.e. a law translates unambiguously in a course of action at the level of the executive branch (the main tool for law enactment is generally assumed to be the impersonal, rational Weberian bureaucracy); (4) and policy is a one way street: government acts upon society, trying to mould it according to its goals, whereas society does not exert any influence on government, save through the democratic process. None of this is even remotely true.

So what? So it makes more sense to abandon Weber and the mechanism metaphor for framing governance, and embrace an ecosystem metaphor instead. I propose to look at public authorities as complex adapive systems, coevolving with society and the economy. Teaching them to deal with social innovation – or anything they never experienced before – means helping them to think of economic and social agents as driven by evolutionary forces that reward the fittest. Policy, then, works best by shaping the fitness landscape, and letting agents work their way through it towards the desired outcome. It is a policy that enables and incentivizes agents to give input, rather than forcing outcomes top-down. This has clear implication for designing policies in practice. One of them is that a constitutional architecture that enables bottom-up learning (like Common law) is inherently superior to one that does not.

If you care about this topic, you can read the paper: the Council of Europe authorized me to share it online. Thanks to Gilda Farrell and Fabio Ragonese for the kind concession.

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?

Er… I’m an economist actually, madam. True though, my working tools have been kind of unusual over the latest years, since I started working on the creativity/innovation/development nexus: not only blogs and social networks, but also parties, barcamps and Second Life-Real Life mashups. Much if the stuff I do feels right, and a lot of it goes surprisingly well, but it is by no means easy being confident that I really am getting it right, and that I am not missing out important opportunities simply because I do not see them. The economics I studied at college does not help me in this; nor do the intellectual contributions picked up along the way, from game theory to new economic geography, from the regional analysis of innovation à la Saxenian to public choice theory, important as they are. And when you find itself spending taxpayer money to improve your Second Life avatar to generate credibility (ok, that was about five dollars, but it’s the concept that matters), well, then it’s time to update your theoretical framework.

I looked around for a few years and I think I have spotted a promising thread in complexity economics. It is the attempt to apply to the economic domain a conceptual framework that developed in completely different disciplines, from biology to meteorology, in which the classic approach based on reductionism and determinism was not yielding results. Though this approach can be traced back to – surprise surprise – Viennese school economists, Von Hayek in particular, the birthplace of the concept of complex system is generally thought to be the Santa Fe Institute. So I started to hang out with David A. Lane, who worked in Santa Fe and now teaches in Italy, and to exchange some thoughts with him. Lately we have been speaking a lot about Kublai, and two of his students are trying to conceptualize the Kublai social network as a complex system: the approach seems interesting, we’ll see what comes out of it.

Meanwhile I have started to read Complexity Perspectives on Innovation and Social Change, by David and others (forthcoming). Compared with his articles of the late 90s and early 2000s there is a noticeable development: the “complexity approach” of that time is turning into a full-fledged economic theory. And it accounts for something that IMHO badly needs accounting for: namely, that technical innovation does not exist. What does exist in innovation, that happens in the agents-artifacts-culture space, and that involves people, modes of interaction, new artifacts and the set of attributions concerning all of the above. Technology is a part of this, and it does not make sense analytical sense to separate it out from the whole.

For a plain, rank-and-file economist like myself, spoonfed on Keynes and Edgeworth, reading this stuff is like going on some kind of psychedelic trip. I read of “Darwinian accounts”, “funcional orthogonality”, “agent-artifact space”, “attributional shifts” and even “exaptive bootstrapping dynamics” – a locution which I have not even been able to translate into Italian – until my brain starts to smoke like on old, overheated engine. But it’s good, very good, and well worh the effort. And I can’t help thinking – with some kind of warped professional pride – that David teaches in a department of economics: there must be something right in a discipline that can question itself so deeply, and to keep on giving me, after twenty odd years, new stimuli.