Faraway, so close! Nobel prize to complex systems

Yesterday, it was announced that Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi get to share the 2021 Nobel prize in physics. Woo hoo! I had a smile on my lips running through the night streets of Tokyo (my usual exercise). The best part is the motivation: “For groundbreaking contributions to our understanding of complex physical systems.” Complex systems are explicitly there! Keep ’em coming!

Obviously, complexity is the least common denominator between the climate modeling of Manabe/Hasselmann and Parisi’s contributions to spin glass theory. Complex systems is a very inclusive term; still, Manabe and Hasselmann are polar opposite to Parisi on Planet Complexity. If either one of these two sides got the prize independently, complex systems would hardly have been mentioned.

As I see it, from a complex-systems perspective, Parisi’s most important contribution has been to channel the ideas of spin-glass theory to an impressive range of interdisciplinary problems (distributed computing, neuroscience, collective animal behavior, etc.). This is excellently described in the final chapter of Stein & Newman’s highly topical book Spin Glasses and Complexity. But the Nobel prize focuses on one discovery, and of course, since it is a physics prize, the early PRLs about spin glasses feature heavily in the motivation. Those papers are, of course, statistical physics, but are they more complex systems than other statistical physics awarded the Nobel? An interesting question. I find it is easier to argue for a no than a yes.

The spin-glass community has been a bit of outsiders within statistical physics. Moreover, Parisi represents one faction within the spin-glass community. I guess, by now, those borders are dissolved, but the process of spin-glass theory to reach the highest strata of scientific recognition has been unnecessarily long. This socio-epistemological aspect mirrors complex systems’ struggle for recognition, even though spin-glass theory has always been a division of physics rather than interdisciplinary.

My illustration, from ages ago, of a vortex in a vortex glass, moving through a disordered (glassy) landscape—the topic of my first project as a grad student was a mix of the 2016 and 2021 Nobel physics prizes.

Manabe and Hasselmann belong to a very different tradition among the integrative, system-focused sciences. Usually, their line of research is called “systems science,” but nothing is very clear-cut, as I tried to describe in this and this blog post. The computational global circulation models of Manabe and Hasselmann have become the most intricate and complicated theories that humans ever developed. Today, they are so complex that not a single research group, let alone an individual researcher, knows them in their entirety. Rather, the models themselves need to be studied as complex networks (as we argue here) and the figure below.

Statistical physicists getting into complex systems like simple models of emergent phenomena. After all, that’s what statistical physics uses. They would typically frown upon multi-parameter models like global circulation models, arguing that too many parameters obscure understanding. To study reality via a model, one needs to understand the model in its entire parameter space, which is impossible with anything as complicated as global circulation models. Instead, statistical physicists seek minimal models—models explaining a phenomenon, where removing any part would destroy their predictive power. In the development of global circulation models, components are very rarely removed but often added. On the other hand, global circulation models are shockingly carefully validated. Spin-glass theory, of course, also has empirical support, but anyone doubting climate models should read this report carefully.

The causal structure of a climate model. The network in panel A, adapted from Knutti and Rugenstein, illustrates some components and their interdependencies in a typical large-scale climate simulation model. Panel B shows the same network as a directed graph where we keep the color coding of the initial illustration.

There have been Nobel prizes in the past going to heroes of complexity and systems science—I think Ilya Prigogine, Herbert Simon, and Thomas Schelling are the most influential for complex systems science of today. Next on the list are probably Kenneth Wilson, Elinor Ostrom, Philip Anderson and John Nash. I don’t think Manabe, Hasselmann, or Parisi are more representative of complex systems than these names; the difference is that now the Nobel committee actually used the phrase.

As a field, complexity science exists in an orthogonal division of academia to the Nobelian one (Physics, Chemistry, Medicine). What delights me most with this choice of winners is that this division gets recognition from the most conservative of ivory towers. For the same reason, it is also great to see these very different aspects of complex systems and systems science together. I’m so curious about what they will chat about in (a virtual?) Stockholm.

4 thoughts on “Faraway, so close! Nobel prize to complex systems

  1. Philip Anderson also comes to mind among the past winners, especially for complex physical systems (including in modern complex-systems investigations in materials science).

    Like

  2. Thank you for the educational blog post.

    This is the first time I know the Nobel laureates close to us: Herbert Simon and Thomas Schelling, besides Ilya Prigogine.

    I hope that 20-30 years later, somebodies in our field win the prize!

    Like

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