Category Archives: Dragon Trainer

Wikicrazia in Venice: the frontiers of collaborative public policies in a time of crisis

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.

The apprentice crowdsorcerer: learning to hatch online communities

I am working on the construction of a new online community, that will be called Edgeryders. This is still a relatively new activity, that deploys a knowledge not entirely coded down yet. There is no instruction manual that, when adhered to, guarantees good results: some things work but not every time, others work more or less every time but we don’t know why.

It is not the first time I do this, and I am discovering that, even in such a wonderfully complex and unpredictable field, one can learn from experience. A lot. Some Edgeryders stuff we imported from the Kublai experience, like logo crowdsourcing and recruiting staff from the fledgling community. Other design decisions are inspired from projects of people I admire, projects like Evoke or CriticalCity Upload; and many are inspired by mistakes, both my own and other people’s.

It is a strange experience, both exhalting and humiliating. You are the crowdsorcerer, the expert, the person that can evoke order and meaning from the Great Net’s social magma. You try: you say your incantations, wave your magic wand and… something happens. Or not. Sometimes everything works just fine, and it’s hard to resist the temptation of claiming credit for it; other times everything you do backfires or fizzles out, and you can’t figure out what you are doing wrong to save your life. Maybe there is no mistake – and no credit to claim when things go well. Social dynamics is not deterministic, and even our best efforts can not guarantee good results in every case.

As far as I can see, the skill I am trying to develop – let’s call it crowdsorcery – requires:

  1. thinking in probability (with high variance) rather than deterministically. An effective action is not the one that is sure to recruit ten good-level contributors, but the one that reaches out to one thousand random strangers. Nine hundred will ignore you, ninety will contribute really lame stuff, nine will give you good-level contibutions and one will have a stroke of genius that will turn the project on its head and influence the remaining ninety-nine (the nine hundred are probably a lost cause in every scenario). The trick is that no one, not even him- or herself, knows in advance who that random genius is: you just need to move in that general direction, and hope he or she will find you.
  2. monitoring and reacting rather than planning and controlling (adaptive stance). It is cheaper and more effective: if a community displays a natural tropism, it makes more sense to encourage it and trying to figure out how to use it for your purposes than trying to fight it. In the online world, monitoring is practically free (even “deep monitoring” à la Dragon Trainer), so don’t be stingy with web analytics.
  3. build a redundant theoretical arsenal instead of going pragmatic (“I do this because it works”). Theory asks interesting questions, and I find that trying to read your own work in the light of theory helps crowdsorcerers and -sorceresses to build themselves better tools and encourages their awareness of what they do. I am thinking a lot along a complexity science approach and using a little run-of-the-mill network math. For now.

These general principles translate into design choices. I have decided to devote a series of posts to the choices my team and I are making in the building of Edgeryders. You can find them here (for now, only the first one is online). If you find errors or have suggestions, we are listening.

Dragon Trainer begins

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?