Sorry, this post in Italian only. I am holding a seminar (open access, in Italian) on the frontiers of collaborative public policies; and participate in the kickoff meeting of a research project on complexity science (invitation only – but I might be able to get you in, in English). Machine-translate for details.

La prossima settimana sarò a Venezia. Lunedì 23, insieme a Luigi Di Prinzio, Silvia Rebeschini e gli amici della Scuola di dottorato Nuove tecnologie dell’informazione territorio-ambiente, faremo il punto sulle frontiere delle politiche pubbliche collaborative al tempo della crisi. A quasi un anno mezzo dalla pubblicazione di Wikicrazia, queste frontiere sono in rapido movimento, e ha molto senso fermarsi un momento per aggiornarne le mappe. Info pratiche qui.

Il seminario è ovviamente collaborativo. Se avete delle esperienze di politiche pubbliche collaborative e volete condividerle (in un formato sintetico, per stimolare la discussione) scrivete a Silvia: srebeschini[chiocciola]gmail[punto]com.

Martedì e mercoledì mi fermo in laguna. Sarò ospite dell’European Center for Living Technology per l’incontro di inizio del progetto MD – Emergence by Design, nell’ambito del quale dirigerò lo sviluppo di un software per assistere i managers di comunità online (nome in codice: Dragon Trainer). L’incontro dell’ECLT non è aperto al pubblico, ma se ti interessa questa roba prova a scrivermi e vedo se riesco a farti entrare.

Good news: a research project I helped to write has been approved for funding by the European Commission’s Future and Emerging Technologies program. The project is led by one of the scientists I admire the most, David Lane, and rests firmly in the complexity science tradition associated to the Santa Fe Institute. We intend to attack a big, fundamental problem: innovation is out of control. Humans invent to solve problems, but they end up creating new and scary ones. Which they tackle by innovating more, and the cycle repeats itself. Cars improve mobility, but they come with global warming and the urban sprawl. Hi tech agriculture mitigates food scarcity, but it also gives rise to the obesity epidemics. To quote one of our working documents:

While newly invented artifacts are designed, innovation as a process is emergent. It happens in the context of ongoing interaction between agents that attribute new meanings to existing things and highlight new needs to be satisfied by new things. This process displays a positive feedback [...] and is clearly not controlled by any one agent or restricted set of agents. As a consequence, the history of innovation is ripe with stories of completely unexpected turns. Some of these turns are toxic for humanity: phenomena like global warming or the obesity epidemics can be directly traced back to innovative activities. We try to address these phenomena by innovation, but we can’t control for more unintended consequences, perhaps even more lethal, stemming from this new innovation.

We want (1) build a solid theory that concatenates design end emergence in innovation and (2) use it to forge tools that the civil society can use to prevent the nefarious consequences of technical change. It does not get any bigger! And in fact we got a stellar evaluation: 4.5 out of 5 for technical and scientific excellence and 5 out of 5 for social impact.

The project commits to building Dragon Trainer, an online community management augmentation software. The idea is to make a science of the art of “training” online communities to do useful things (like policy evaluation), just as you would train an animal too large and strong to push around. I am responsible for producing Dragon Trainer, and it is quite a responsibility.

I am superhappy, but worried too. Taxpayers foot most of the bill, and this makes it even more imperative to produce the absolutely best result we can. I will need to work very, very hard. I am seriously thinking of devoting myself to full time research for a couple of years starting in 2012. Does this make sense? What do yo think?

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).

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.

According to David Lane, sometimes we need to make decisions in a condition that he calls of ontological uncertainty. That means we have no means of painting an exhaustive picture of the situation and of the full range of moves we can possibly make; and certainly we are unable to foresee the consequences of the few moves we can imagine. In a famous article, David asks us to consider the situaton of a Bosnian diplomat trying to bring an end to the bloodshed in his country in early September 1995:

It is very difficult to decide who are his friends and who his foes. First he fights against the Croats, then with them. His army struggles against an army composed of Bosnian Serbs, but his cousin and other Muslim dissidents fight alongside them. What can he expect from the UN securiy forces, from the NATO bombers, from Western politicians, from Belgrade and Zagreb, from Moscow? Who matters and what do they want? On whom can he rely, for what? He doesn’t know – and when he thinks he does, the next day it changes.

How to make decisions in such a situation? Answer: by telling yourself stories. Humans are good at storytelling: if you recognize yourself as the hero of a story, he will inspire your course of action, just like Don Quixote changed his life to model it in on medieval chivalry epics.

Innovation often happens in ontological uncertainty conditions. It is certainly possible to have a well defined goal in terms of producing an artefact, but the market system that depends on what people will use that artifact for – is always emergent. Movable type printing was a well-defined R&D project, but Gutenberg could not have forseen Aldus Manutius’s portable book and and the Umanesimo movement in Italy in the Renaissance; Henry Ford rationalized car production, but he could not have foreseen bedroom communities and mass commuting. To build and bring to market an innovation means acting in a changing context, like that of our Bosnian diplomat. And that requires storytelling.

Nadia El-Imam has come up with the idea to help people to tell stories about themselves and what they are doing with technology. She uses a special deck of tarot cards she designed herself (in lieu of the Hermit and the Magician she has arcana like the Server, the Developer and the Interface). Dressed up as a gypsy fortune teller, she offered to divine the future of the various geeks gathered at Mozilla Drumbeat in Barcelona. It was a roaring success, with a permanent queue of people waiting to interrogate her tarot. Among them, entrepreneur and venture capitalist Joi Ito (in the video). Engaging with Nadia and the cards, innovators make sense of what they are doing, and look for a way to complete their quests.

In their own unusual way, Nadia’s techno tarot are a platform, that lends itself to be used for collecting ethnographic data on innovation, for technology counseling and who knows for what else. I am quite curious to see how it all evolves.

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.

A few days ago I was giving a lecture to an audience of young creative people. Part of it was devoted to showing them how to use Kublai as a platform for developing their ideas into full-fledged projects. Since I thought the experience of joining Kublai would be more fun if students got early response from the community, I started to Skype people I saw online asking them to drop by and say hello to the newcomers. A few did; and, as the (about 15 students) started to interact with Kublai, the activity got noticed by a few more kublaians, who decided to drop by as well. < (lang_en>

As the number of people using Kublai simultaneously got in the 15-20 range, I got the almost physical feeling of the experience changing dramatically. Any action taken locally (in the classroom) would show up in the recent activity feed, and users across the country would pick up on it. Response was almost instantaneous: as soon as you finished writing something, somebody else had commented on it. It was quite exhilarating, for me and for the students.

I could not help being reminded of phase transitions (in physics that’s the transformation undergone by matter when it changes its state, say from solid to liquid to gas or viceversa). Kublai felt like a glacier: it had been moving in its solid state, pulled down by gravity and shaping the landscape with moraines, but now it was melting, and moving much faster as a result. This raises a fascinating question: is this the same process happening at a higher speed or does the higher speed imply a different process? In the example of the melting glacier the transition from ice to water gives rise a stream, which is most emphatically not the same thing as a faster moraine. My intuition would be that’s the case for Kublai too: specifically, I’m conjecturing that “liquid” Kublai tends to concentrate a higher share of the posts on the most active projects than the “solid” Kublai… but that’s very far from a founded conclusion.

In complexity science, matter at the phase transition threshold exhibits interesting properties and is said to be at the edge of chaos. So Ruggero and I got quite excited, and discussed ways to study this phenomenon through graph maths. Meanwhile, I enlisted some of the most active members of the community in running an experiment of using Kublai as a semi-synchronous environment: it comes down to doing a “project coaching jam” on a set date and time, trying to get 20-30 people to start posting at the same time, and we’ll see how that goes. Will the phase transition take place? Will other people get the same feeling that I did before? I’ll post any progress – if any – on the blog as we go.

I still want to travel less, but the occasion is worth an exception. I am in Turin to follow David Lane‘s course on what he calls “innovation in agent-artifact space”. David, I freely admit it, is one of my heroes. To begin with, he was in the economics program of the Santa Fe Institute – the cradle of complexity science and its multidisciplinary approach – from the very start: he was one of its directors after Brian Arthur got it its headstart. Sitting in one of his lectures is like riding in a rollercoaster designed by a sadistic architect: he darts from modelling ant behaviour in an anthill to flint axe bulding techniques in the Neolithic age. I hold on for dear life and hope my brain is still in one piece at the end of the lecture.

I’m convinced that the complexity approach to economics will bear fruit. It’s super-agile, because it borrows modeling strategies and hacks from biology, physics, computer science, network math, ethnography, you name it; and it’s very rigorous, because its champions tend to be better than traditional economists at math (though the latter are also very good in a different, more static kind of way). So I forge on, hoping to understand better the emergence phenomena unfolding right in my backyard – most recently the self-organization of the program for the Kublai Camp 2010. I’m stubborn enough that at some point I’ll see the light, I hope.

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?