Friday, December 24, 2010

Policy Analytics

First of all let me apologize, there is not such a thing as Policy Analytics, at least not yet. However, as I will argue, it will be worth to invent it.
Policies are derived from frameworks that represent our understanding of a field. They cannot exist isolated without this reference.
For example if we understand Innovation in terms of economic growth, our policies will probably focus in the creation of capacities and on providing incentives to the different actors to use these capacities.
If, on the contrary, we understand innovation as the result of complex interactions among many agents, we will be busy trying to ease bottlenecks, optimize flows and maximize capacities.
This small example illustrates an important phenomenon, as our understanding gets more complex, the associated policies get also more complex.
However, in this path that otherwise looks natural, there is an important gap. The indicators that we use are still static and most of the times cannot match the objectives of the new policies derived from theoretical insights.
For the sake of the example, let’s continue with the case of Innovation Policy. In order to ease bottlenecks you have first to detect them and in order to optimize flows you have to be able to measure them … capacities is probably the easy part of the equation.
Why is that happening? There is really no data available? Well, yes and no. Certainly the instruments traditionally used in policy modeling are cannot provide this type of data. Nevertheless, there is a growing trend in the private and public sector to tap into the wealth of data lying in web 2.0 portals.
One good example is computer-savvy traders, also known as quants, according to Aite group, a financial services company, about 35% of quantitative trading companies are exploring whether to use trend and sentiment mining compared to about 2% two years ago (http://www.nytimes.com/2010/12/22/technology/22trading.html?_r=2) .
We can find similar examples in public policy, such as the use of Crowd-Sourcing  by the Obama administration (challenges.gov) or the FCC (broadband.gov) among others.
Closely looking at the different strands we can witness to kind of objectives that nevertheless intermingle one with another. On one side we have the ones that go for Policy co-creation with potentially large constituencies. Here good example are all sorts of Crowdsourcing or Innovation exercises.
On the other we find the development of novel indicators aimed at tapping into the wealth of data available on-line. Here we can find the very active field of trend and sentiment mining.
Therefore, it is clear that there are new opportunities lying ahead, however, what are the problems that could justify this new field or this new understanding?
Basically, as policies get more complex, policy makers have to dealt with two new situations.
We can characterize the first one as the asymmetry of knowledge. In fact, it is very difficult to foresee the impact, the level of adoption or the problems of a certain policy if you are not an insider. Exactly the same in the case of detecting bottlenecks, missuses, insufficient flows or institutional governance problems. Policy makers are at a clear disadvantage when they have to deal with the micro-level, with the detail and implement in concrete terms general frameworks.
Policy co-creation involving large groups of constituencies in exercises like crowd-sourcing could certainly alleviate this problem.
There is however, another kind of information asymmetry that we have to solve. The one of the citizens. They lack the macro data that informs the framework. Here Open Data could have, rendering available to everybody this specialized data.
However, policy design is only half of the problem. Complex policies demand detail monitoring and for that real-time indicators that could tap into the micro-level are needed. Technologies such as trend and sentiment mining also in groups or constituencies can probably help in determining what is not working and needs to be cancelled, what has to be modify and what is working well.
We are in the early beginnings of this understanding. Today there is certainly a gap between the ambitions of our policies and our frameworks and the capacities of our instruments to measure and control them. The good news is that  there are insights on how to close this gap.
Why don’t we name it? Policy Analytics is my proposal for a name. 

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