Category Archives: complexity economics

Nation states vs. feral finance: the final battle?

The financial crisis has broadened the scope of the economic policy debate. Pressed by their respective public opinions, world leaders need new ideas, fast. Despite this, one might be surprised by the way German Chancellor Angela Merkel and French president Nicholas Sarkozy dusted off the idea of a levy on financial transactions. Nowadays journalists are calling it Robin Hood tax, but in the 90s it was called Tobin tax and it was part of the intellectual arsenal of the anti-globalization movement. Even then, it was old news: Nobel laureate James Tobin first proposed it in 1972 (the original formulation was aimed at transactions of foreign currency).

The idea is to tax sales of financial assets (equity and/or bonds and derivatives) with a low rate (0.01-0.05%). That’s meant to be too low to discourage the migration of capital from low-yield to structurally higher yield assets (such as from equity in a stagnating company to equity in a highly profitable one), because such a shift happens only once, and the cost of the levy is quickly offset by the increased yields: this way, the market maintains its efficiency property. But such rate is high enough to discourage speculation, that is based on buying and quickly reselling the same assets.

The main practical problem with a Tobin tax is that, unless it is introduced everywhere at the same time, speculators can elude it simply moving to a financial market that does not levy it. In fact, some variants have been tried out in Sweden in the 80s. Results: low revenue, a sharp drop in transaction volume and eventual migration of many of the most active titles from the Stockolm to the London stock exchange. The tax was eventually abolished. So it does not work, right? Why bring it back into the debate?

It’s not quite that simple. It (probably) does not work to stop speculation and generate fiscal revenue. But it could work well for a different goal: driving the more instability-generating fringes of the financial sector (the same ones that make money off that instability, as Nicholas Taleb reminds us) off the country. Their flight can be painful: these are wealthy taxpayers, who supposedly bring prosperity. But it also could be liberating, because the financial sector has become politically very powerful: this (1) makes it very difficult to make economic policy, because the super-rich veto any move that does not imply benefits for them (as Nobel laureate Joseph Stiglitz reminds us); (2) enhances economic inequality, exasperating the non-super-wealthy 99% of the population; and finally (3) it is not even clear that having these rich guys around does bring much prosperity. They sure don’t pay much taxes: Warren Buffett recently declared that his tax rate is lower than his cleaning lady’s.

Are we looking at a cultural shift? Granted, the man in the street never trusted finance, and never understood it. But this is news to me: two world leaders of the standing of Merkel and Sarkozy aligning with people like tax expert and progressive blogger Richard Murphy – who applauded their joint proposal as “a welcome and overdue move […] if the feral banking economy is to be brought under control” (with the Guardian’s blessing). Why, in the 90s Attac’s militants campaigning for the Tobin tax during G8 meetings were treated as a disturbance by the police forces of those same states.

I am tempted to read this story as the final battle between two different organizing principles, nation states and global finance. Am I seeing ghosts?

Dragon training: gestione di comunità online assistita dal computer

Nel mio libro Wikicrazia sostengo che il settore pubblico, il pezzo della società deputato al perseguimento dell’interesse comune, possa essere reso più intelligente mobilitando l’intelligenza collettiva dei cittadini. Ricorrere all’intelligenza collettiva vuole dire abilitare un gran numero di individui a lavorare in modo coordinato su obiettivi comuni. Questo in genere si traduce in comunità online, che usano Internet come infrastruttura tecnologica e in cui si interagisce sulla base di un patto sociale e di qualcuno che media i conflitti e fa in modo che non si perda di vista l’obiettivo.

Qui però si pone un problema. Da una parte, le comunità online non si possono gestire con il comando top-down: è proprio l’azione libera delle tante persone che le compongono a produrre la loro straordinaria efficienza nell’elaborare grandi quantità di informazione. Dall’altra, le politiche pubbliche hanno per definizione una missione da compiere che viene dall’esterno della comunità che le attua: mentre gli utenti di Facebook sono su Facebook per stare insieme, e non importa quello che poi faranno usando quella piattaforma, quelli di Peer to Patent sono lì per valutare domande di brevetto; quelli di Kublai per elaborare progetti di impresa creativa; quelli di Wikipedia (non è una politica pubblica, ma ne ha alcune caratteristiche) per scrivere un enciclopedia. I community managers, me compreso, si dibattono in questo dilemma come possono: quasi l’unico modo che hanno per interpretare le dinamiche sociali delle loro comunità è passare una quantità spropositata di ore online, e cercano di influenzarle con la persuasione, l’esempio, la retorica. Ma si lavora molto a istinto, questo è chiaro. E quando le comunità diventano relativamente grandi — anche solo qualche migliaio di persone – è davvero difficile capire cosa sta succedendo.

Ho pensato che il nostro lavoro migliorerebbe molto se avessimo un software che accresce le nostre capacità di lettura delle dinamiche sociali online. In essenza, una comunità di policy è una rete sociale, e quindi può essere rappresentata con un grafo di nodi e link e studiata matematicamente. Le dinamiche sociali della comunità si dovrebbero riflettere sulle caratteristiche matematiche del grafo che la rappresenta: per esempio, la creazione di un gruppo coeso di utenti senior in Kublai nel 2009 veniva segnalata dalla formazione di una struttura che si chiama k-core. Se riusciamo a costruire una specie di vocabolario che traduca le dinamiche sociali in cambiamenti nelle caratteristiche matematiche del grafo, possiamo usare l’analisi di rete per individuare le dinamiche di comunità che a occhio nudo non si vedono, perché sono “troppo macro”: e questo funziona anche per comunità molto grandi, almeno in linea di principio.

Sviluppare questo software sarà il lavoro della mia tesi di Ph.D. Mi aiuteranno i colleghi dell’Università di Alicante e dell’European Center for Living Technology. Per ora si chiama Dragon Trainer, perché gestire una comunità online che deve svolgere un compito esogeno è come ammaestrare un drago: che è troppo grosso e pericoloso per essere costretto a fare quello che vuoi, e quindi va sedotto o convinto. Se ti interessa capire come sarà fatto, guarda il video qui sopra (12 minuti).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).

Dragon training: computer-aided online community management

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).