An interesting story on The Economist about the myth that countries with wider spread of new technologies (in this case, broadband) should be more productive than the rest has a few interesting facts:

Paul David, an economist at Oxford University, has shown that electric power, introduced in the 1880s, did not immediately raise productivity. Not until the late 1920s—when around half of America's industrial machinery were finally powered by electricity—did efficiency finally climb.

In 1987 Robert Solow, a Nobel Prize-winning economist, famously said: 'You can see the computer age everywhere but in the productivity statistics.' It was only in 2003 that The Economist felt comfortable boldly proclaiming: 'The 'productivity paradox' has been solved.'

Can the Japanese and Koreans (who finish at the top of OECD's charts) do something at 100Mb/s that the Americans, British and Germans (in the middle tier) can't at 20Mb/s? The idea that “bigger is better broadband” is orthodoxy, not economics. So is its corollary, the neo-Cartesian logic that goes: “Broadband ergo innovation.” But we have yet to see innovation happen at a high speed that couldn't happen at a slower one.

In short, though technology allows innovation, it does not imply it. Kind of obvious, after you realize it, but I think it allows for the interesting insight of understanding there are two ways for innovating, and one of them does not necessarily pass through evolving the infrastructure.

I've been recently investing time in using activity similarity graphs as tools to understand the structure of sharing in BitTorrent and tagging communities in two works in collaboration with Elizeu, Matei and Adriana (all three of them have done interesting work on characterizing system usage patterns using similarity graphs in the past).

I'm still toddling on this, but some of the graphs we're looking at are pretty big, what calls for graph visualization tools which are both versatile and efficient. I've been playing with two which are quite interesting:

This was made with GUESS, which is quite easy to use and has the great functionality of understanding python scripts to interact with the graph:



The problem I ran into was that for very large graphs, the fact that GUESS does its rendering in a background thread renders it very uninteractive (you don't really know whether what you asked the GUI to do is going to take long, and you don't have a button to cancel it).

I then found Cytoscape, which deals very nicely with very large graphs and even has some nice plugins for doing topology analysis. I only managed to plot this in Cytoscape:

Not long ago, I came across the fascinating TED website. I didn't have time to explore it as much as I would like yet, but two talks I had time to watch caught my attention as greatly interesting:

The first one is Yochai Benkler on sharing systems. Benkler has some of the most fascinating ideas and analysis of sharing systems, which I have been greatly interested in from the research perspective. I believe the intersection between the perspective of Benkler's work, P2P and grid computing has still a lot to be digged into.

The other nice talk I had the chance to see was Cameron Sinclair's talk on Architecture for humanity. In a nutshell, he has founded an organization which has been applying the ideas of open source development to design in general and creating lots of innovation for acting in disaster relief and humanitary efforts. A great lesson on the potential of sharing for intellectual work and some very interesting points on designing with the needs of communities in mind.

Both worth watching.

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