Category Archives: Open government

“Not so bad, actually”: an interview of the state of open data in Europe

Not just shiny toys: future policy is about distributed power and decentralized permission

I am just back from Dublin. I was at Policy Making 2.0, a meetup of people who care about public policies, and try to apply to them advanced modelling techniques and lots of computation. Big data, network analysis, sentiment analysis: the whole package. What results, if any, are we getting? What problems are blocking our way? What technology do we need to make progress? Lots of notes to compare. Thanks should be given (again!) to David Osimo, the main hub of this small community, for organizing the conference and bringing us together.

At the end of it all, I have good news, bad news and excellent news.

Good news: we are starting to see modeling that actually works, in the sense of making a real contribution to understanding intricate problems. A nice example is a href=”http://www.gleamviz.org/”>GLEAM, that allows to simulate epidemics. What’s interesting is that it uses real-world data, both demographic (population and its spatial distribution) and on transportation networks (infection agents travel with the people infected, by plane or by train). To these, you add the data describing the epidemics you are trying to simulate: how infectious is it? How serious? Where does the first outbreak start? And so on. The modeler, then, patches it all together into a simulation scenario.

Bad news: making rigorous AND legible models is very hard – no wonder we normally can’t. The rigorous ones fully take on board the complexity of the phenomena they attempt to describe, with the result that often they cannot really give a simple answer beyond “it depends”; the legible ones (in the sense that their results are easy to understand, and often based on shiny visualizations) pay for such surface visibility by sweeping under the carpet the understanding of how they get to those results – at least as far as most citizens and decision makers are concerned. This problem is further complicated when Big Data come into play, because Big Data force us to rethink what we mean by “evidence” (this argument deserves its own post, so I will not make it here).

Excellent news: the community of researchers and policy makers seem to be converging on what follows. Public policies will make the real leap into the future when they are able to devolve power and leadership to an ever smarter and better informed citizenry. That is, if they will be transparent, participatory, enabling, humble. Technology is ok: we need it. But without a deep fix in the way we think and run policy, future public institutions risk looking much like the Habsburgers Empire’s Cadastral Service, circa 1840 (rigid hierarchies, tight formal rules, bad exceptions management, airtight separation between administrations and civil society, communication with citizens only through regulation…), only with computers and perhaps infographics. Over coffee breaks, we mused a lot about iatrogenics (public policies that, though well-meaning, end up doing harm for lack of the intellectual humility to leave alone a complex system that is not properly understood); transparency as a trust generator, as well as a goal in itself; and we phantasized about public-private partnerships to troubleshoot policy when the normal mode of operating mode fails, a sort of commandos of social innovators and civic hackers. This would be my dream job! The Dutch Kakfa Brigades gave it a try, but based on the website the project does not seem very active.

The community has spoken. We’ll see if the Commission and the national policy makers will pick up on this consensus, and how. Of course, reform that goes so deep is really hard, and does not depend on the goodwill of the individual decision makers. The wisest thing we can do, maybe, is push the edge a little further out, without too many expectations. But without giving up, either. Because – and today I am a little more optimistic – we have not quite lost this one yet.

Policy making for smart swarms

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My friend Vinay Gupta has come up with the idea to start the Big Picture Days series with an event on what he calls swarm cooperatives, meaning instant campaigns, unconferences, hackathons and other unorthodox constellations of people in action that are both collaborative and non-hierarchical. He got in touch and asked me to give a talk about it in the context of public policy. That sounds crazy, but it got me thinking. For years now I have been involved in policy initiatives that incorporate an element of that openness, of that fluidity. Can we really speak of policy making for swarms? If so, what does that mean?

At the heart of this concept lies a fundamental paradox. Swarms derive their uncanny efficiency from radical decentralization of decision making and action; yet, decentralization might (and does) cause the swarm to lose coherence, and its action to lose directionality. That does not work well with public policy, that requires agency: no agency, no policy. The main tool I use to debunk this paradox is network theory: I think about swarms as people in networks. In networks, nodes might be equal in the amount of top-down power over others, but they will typically be very unequal in terms of connectivity, hence the ability to spread information (including narratives and calls to action) across the network. Uneven connectivity adds some directionality to the swarm, in the sense that the most connected people get it to go their way most of the times.

Public policy is generally understood as a top-down process: some leader somewhere makes a decision and that decision is enacted. I call this the linear model. Since it misses all of the feedbacks and adaptation, the linear model does not work if the context of your policy is a complex adaptive system: the system simply changes its shape to route around the policy, or even push it back (more details). Not only do its recipes not deliver: they might cause serious harm. This provides a good case for trying to apply swarm thinking to government.

It can be frightfully difficult, because the linear model is encoded into law and hardwired into organizational charts, remits and procedures: but the potential rewards are immense. Why? Because if you want to build a swarm, you need people to want to join it. By definition, these people can’t be your employees, or anyone you have command and control over; they have to be free agents that want to cooperate. Now, there are already very many opportunities to collaborate out there, and few of them have attracted the lion’s share of available “swarming” (think Wikipedia, with tens of millions of participants). This means that people will cherrypick, and you will have to work extra hard to win them over. Swarm building is a buyers’ market. That’s a big reality check for your project right there.

The first consequence of all this: the swarm-builder’s payoff to bullshit immediately becomes negative. Well-packaged bullshit might fill a report or a PowerPoint presentation that gets past your boss, but has no chance of whipping up enthusiasm in a bunch of strangers that are not taking your money. I believe this has given some competitive edge to my own projects. Cutting corners would not do it: I had to work at full steam, or call it quits.

Vinay’s invitation gave me the opportunity to lay out tips and tricks to policy making for swarms. I ended up with a weird list, with items like Falkvinge’s Law, randomness (my favorite), timebombs, the fishing rod model, dogfood and parties. It is tentative and incomplete, but does represent the very frontier of my thinking (and my practice!) of public policy. If you are interested in this kind of stuff, you might like my slides. I added my notes, so you get a reasonable rendition of my 10-minute talk at Big Picture Days.