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Policy and technology: a "longue durée" view

Random thoughts on policy for technology and on technology for policy

Month

May 2015

Podemos in power / 2

Just noticed today in the press that the former mayor envisages a possible alternative to Ada Colau, through a “saint alliance” of basically all the other parties.

What is interesting in this is that the single point of substance mentioned by the mayor (“what most worries me”) is precisely the Mobile World Congress in Barcelona. He says that many cities “would fight for such an event and that we need to handle it with care”.

UPDATE: on may 29th, Colau validated the offer from the previous alcalde for the MWC. Apparently her party “has always been in favour of MWC”. However, in her first press conference, she said she was in favour “but that its benefits should spread all over the city, the jobs should not be temporary, and all the social benefits of the the apps should be exploited”. A bit of bipartisanship and diplomacy emerging here.

Correlation is not causation. Is non-correlation non-causation? #offthetopofmyhead

Caveat: this is another “off the top of my mind” posts.

“Correlation is not causation” is a concept well familiar to all analysts – and to policy analysts in particular. It is so mainstream that it has its own wikipedia entry. The web is full of examples of weird correlations.

However, can we say that the absence of correlation indicates the absence of causation?

Certainly not: there might be other factors in place that affect a phenomenon and thereby “hide” the correlation.

At the same time, the absence of correlation is a much more reliable sign of the absence of causation, than its presence. It is much more likely that no correlation is confirmed (after more in depth analysis) as no causation, than correlation is confirmed as causation.

I would even say (as a rule of thumb) that the majority of non-correlations turns out to be non-causation, while the minority of correlations turns out to be causations.

Podemos takes the power: notes from Barcelona

In the elections this sunday, Ada Colau’s party  (affiliated to Podemos) was the most voted.

It is likely that she will be the new major of Barcelona, my current city.

This will be VERY interesting. Today, Barcelona is very much startup and business oriented, focussing on design and innovation, and the Mobile World Congress is probably its foremost symbol. Just as a sign of this culture, our previous building was recently renamed “Barcelona Growth Center”.

Obviously, we expect this to change quickly. It is a unique opportunity to see to what extent a city can change, in case of a power shift from right to radical left.

From today onwards, I will start taking notes to document this change.

The first sign appeared today in El Pais: it appears that the Mobile World Congress is less sure to remain in Barcelona, as the new “alcaldesa” warns that is should “provide benefits to the whole city”.

Quantifying the quantified self

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I finally bought a smart wristband, a device that measures how much you sleep and how much you walk.

After one month, I like it more then ever. It’s not very accurate, but it gives me a rough idea of how much I walk and rest. It nudges me in doing the right thing: if I see that I did not walk much, I’m more likely to choose not to take the bus back and walk instead.

What matters is not the actual number of steps, but the capacity to be aware of myself. I can easily see if I am more or less active compared to my normal rate. I have trend data, and that’s all I care. I don’t care much for comparing against other people. But its great to be able to detect whether I’m having a lazy or active day.

And that’s probably true for big data in general. Despite the hype, it’s very very difficult to make sense of many large datasets. Cross analyzing data, identifying patterns and correlation is harder than we expected [1,2]. Basically, the problem is to make sense of big data.

But big data means also that as many things get measured, you will soon start having trend data for everything. You will uncover anomalies much earlier because everything will be measured.

In other words, while it remains difficult to cross analyze a huge amount of large datasets to uncover correlation, it will become much easier to simply uncover anomalies by comparing new data with old data. This suggests that big data will become much more important in the data: we’re today deploying sensors and struggling to make sense of the data, but in the future trend data from these sensors will become available, and simply detecting an anomaly will raise attention on potential problems.

Speaking of which, I am currently looking for data on the market for “quantified self”. How many people have smart wristbands? how many have fitness or health apps? Where can I find key data points?

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