Tag Archives: complessità

Taming social networks: my Ph.D. at University of Alicante

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.

Il domatore di reti sociali: il mio Ph.D. all’università di Alicante

Ho iniziato il 2010 con il proposito di studiare l’economia della complessità. Nel mio lavoro di consulente sulle politiche pubbliche mi trovo a dovere risolvere problemi che l’economia che ho studiato all’università non riesce neppure a descrivere, non parliamo poi di risolverli. L’approccio delle scienze della complessità – un curioso miscuglio molto interdisciplinare di biologia, informatica, un po’ di neuroscienze e vari altri ingredienti minori, dalla statistica all’archeologia, con la matematica a tenere insieme il tutto – potrebbe avere qualche risposta.

Beh, pare proprio che avrò parecchie occasioni di studiare queste cose. A partire dall’anno accademico 2010-2011 sono infatti uno studente di dottorato in economia quantitativa all’università spagnola di Alicante. Il mio supervisore sarà David Lane, che fa parte dello Science Board del leggendario Istituto di Santa Fe, e se tutto va bene discuterò la tesi nell’autunno 2012. L’argomento della tesi è piuttosto pratico: voglio capire come usare le reti sociali per eseguire dei compiti. Le reti, non le persone che le compongono.

Il problema è molto più aggrovigliato di quanto sembra. Abbiamo sempre detto che le dinamiche sociali sono emergenti. La maggior parte degli oggetti interessanti nella società, dal sistema di Common Law alle culture e perfino alla criminalità organizzata, sono sistemi adattivi complessi, e il loro comportamento è imprevedibile a lungo termine. Non è questione di raffinare i modelli previsionali: secondo questo tipo di scienza, è imprevedibile in linea di principio.

D’altra parte io ho teorizzato (in Wikicrazia) e provato a mettere in pratica (in Kublai e altrove) l’idea di imbrigliare l’intelligenza collettiva per migliorare le politiche pubbliche e, in definitiva, il mondo in cui viviamo. Come conciliare l’imprevedibilità delle reti sociali con la direzionalità che le politiche pubbliche richiedono? Vorrei esplorare l’idea che sia possibile, attraverso scelte di progettazione e la somministrazione di stimoli adeguati, addestrare le reti sociali, come se fossero dei grandi animali; e sfruttare la loro capacità di elaborare l’informazione, che è molto più che umana, per fare vivere meglio gli umani. Questo vuol dire innanzitutto comprenderne la struttura matematica, e cercare di influenzarla; è quello che abbiamo cominciato a fare insieme a Ruggero Rossi, anche lui studente ad Alicante. Comunque sia, ritorno a scuola: a 44 anni, è davvero un lusso e un avventura meravigliosa. Grazie davvero a Giovanni Ponti, il direttore del programma di dottorato, per avermi conferito di nuovo il titolo accademico più importante e prestigioso: quello di studente.

Moving in flocks: local interaction rules as a social network management tool

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?