Complexity and Physics in a sentimental mood

Keith Mallet, “In a Sentimental Mood”

In a Sentimental Mood I can see
the stars come thru my room

I fell in love with network theory after reading “Linked“, one of the most insightful and inspiring popular science books I have ever read. Two or three years ago, I was very fortunate to meet the author, Albert-László Barabási. He was interested in using mobile cellular data records to model commuting and migration patterns and we talked about the possibility of collaborating. After that meeting, he kindly sent me a few of his research papers. The iPad had not yet transformed my reading habits and I was still a sentimental… I loved paper(s)!

Yesterday I happened to open a box with some old stuff I brought home from my office, and I came across a cardboard folder with the papers, together with László’s business card and a handwritten note (see figure)

Barabasi CardToday, I have been skimming through the papers and then looked for more recent stuff in Barabási’s web page to see where his research is heading. Do you know what I have discovered? That he is also a sentimental. In a note published on Nature Physics last January, he briefly reviews the implications and possibilities that data-based mathematical models offer us. The paper has a strong title: “The network takeover“; and a provocative intro:

Reductionism, as a paradigm, is expired, and complexity, as a field, is tired. Data-based mathematical models of complex systems are offering a fresh perspective, rapidly developing into a new discipline: network science

However the paper is written in a sentimental mood. First Barabási talks about the still important but diminishing role of reductionism as a fundamental tenet of science:

An increasing number of the big questions of contemporary science are rooted in the same problem: we hit the limits of reductionism

Then he explains how the data explosion and network theory are fundamentally reshaping the scientific assault on complexity, a thorny undertaking:

The daunting reality of complexity research is that the problems it tackles are so diverse that no single theory can satisfy all needs.

Barabási goes on to reflect on the role of physics in today’s scientific and technological scenario. As it happens, Barabási, like me, is a physicist by training and –I would say– at heart; and this is by far the most moving part of the whole article:

Who owns the science of complexity? Physics? Engineering? Biology, mathematics, computer science? All of the above? Anyone? (…) Although physics has owned complexity research for many decades, it is not without competition any longer. Computer science, fuelled by its poster progenies, such as Google or Facebook, is mounting a successful attack on complexity, fuelled by the conviction that a sufficiently fast algorithm can tackle any problem, no matter how complex.

What is the role of physics in this journey? We physicists do not have an excellent track record in investing in our future. For decades, we forced astronomers into separate departments, under the slogan: it is not physics (…) For decades we resisted biological physics, exiling our brightest colleagues to medical schools. (…) We let materials science be taken over by engineering schools just when the science had matured enough to be truly lucrative

The conclusion is clear:

Our children no longer want to become physicists and astronauts. They want to invent the next Facebook instead. Short of that, they are happy to land a job at Google.

Maybe the universe is the network, after all(*)

This is not the physical universe
Network Universe

(*) More on this coming soon

Featured Image: Network universe, a visualization generated in 1999 which represents a small portion of the World Wide Web. This map led to the discovery of scale-free networks. In Albert-László Barabási, “The Network Takeover“, Nature, Jan 2012

One comment

  1. I have yet to read Linked, but I’ve read a lot of Barabasi’s papers. I’ve always been struck by his ability to get at the heart of the matter. Pretty cool.

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