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## Comments

Welcome, Andre! Good to see you here.

One would think that spin glasses might be relevant to politics, since in both spin glasses and politics, frustration is fundamental.

This would be a serious remark if I knew what to do with it, but I don't, so let's say it's a joke for now...

Since you like statistical mechanics you might try to bring it a bit closer to environmental issues by thinking about how it's related to biology and evolution... I think in the long run this will become very important. You might try Matteo Smerlak's blog article, which will appear on the Azimuth Blog soon, or my more elementary talk on Diversity, entropy and thermodynamics.

`Welcome, Andre! Good to see you here. One would think that spin glasses might be relevant to politics, since in both spin glasses and politics, [frustration](https://en.wikipedia.org/wiki/Geometrical_frustration) is fundamental. <img src = "http://math.ucr.edu/home/baez/emoticons/rolleyes.gif" alt = ""/> This would be a serious remark if I knew what to do with it, but I don't, so let's say it's a joke for now... Since you like statistical mechanics you might try to bring it a bit closer to environmental issues by thinking about how it's related to biology and evolution... I think in the long run this will become very important. You might try Matteo Smerlak's blog article, which will appear on the Azimuth Blog soon, or my more elementary talk on [Diversity, entropy and thermodynamics](http://math.ucr.edu/home/baez/biodiversity/).`

Andre,

Hi. My random walk into environmental science: I originally did my PhD in computational statistical mechanics. I took my Monte Carlo simulation experience and applied it to learn Bayesian inference (nonlinear regression). With these tools, I now study climate change uncertainty.

It seems spin glasses are applicable to inference on networks. There are a few network applications in climate science (studying the patterns of correlation or "teleconnection" of climate variables across space and time). There are also applications to environmental impacts (such as the vulnerability of the power grid, transportation networks, etc. to weather disasters, environmentally-mediated epidemic dynamics across social networks, etc.). Not sure how spin glasses might fit into this, but just some ideas.

`Andre, Hi. My random walk into environmental science: I originally did my PhD in computational statistical mechanics. I took my Monte Carlo simulation experience and applied it to learn Bayesian inference (nonlinear regression). With these tools, I now study climate change uncertainty. It seems spin glasses are applicable to inference on networks. There are a few network applications in climate science (studying the patterns of correlation or "teleconnection" of climate variables across space and time). There are also applications to environmental impacts (such as the vulnerability of the power grid, transportation networks, etc. to weather disasters, environmentally-mediated epidemic dynamics across social networks, etc.). Not sure how spin glasses might fit into this, but just some ideas.`

John, thanks! I'll have a look into Smerlak's article. I have read some of your blog posts on entropy as a measure of biodiversity, it's a very interesting subject. I'll try to learn more about it.

Nathan, that's interesting! I have also worked, though not much, with Monte Carlo simulations and Bayesian inference.

Yes, I've recently became aware of things like this! I have looked through an article (arXiv:0904.0477) which treat the power grid issue using techniques from statistical mechanics. I'd like to work with something in these lines, but I don't have many ideas on what can be done. The use of belief networks to study correlations is also something that could be approached. Do you have any references on these applications you've mentioned?

Thank you!

`John, thanks! I'll have a look into Smerlak's article. I have read some of your blog posts on entropy as a measure of biodiversity, it's a very interesting subject. I'll try to learn more about it. Nathan, that's interesting! I have also worked, though not much, with Monte Carlo simulations and Bayesian inference. > There are also applications to environmental impacts (such as the vulnerability of the power grid, transportation networks, etc. to weather disasters, environmentally-mediated epidemic dynamics across social networks, etc.). Yes, I've recently became aware of things like this! I have looked through an article (<a href="http://arxiv.org/abs/0904.0477">arXiv:0904.0477</a>) which treat the power grid issue using techniques from statistical mechanics. I'd like to work with something in these lines, but I don't have many ideas on what can be done. The use of belief networks to study correlations is also something that could be approached. Do you have any references on these applications you've mentioned? Thank you!`

Interestingly, I work in the building next door to one of the authors of the article you cite (Chertkov). You might be interested in looking at the talks for the Optimization and Control for Smart Grids conference that he co-organized in the spring, which I attended. I think there is by now a large literature on stat mech applications to epidemiology on networks, but I'm not familiar with it. I'm not yet very knowledgeable in these applications myself (though I dabbled with the statistics of complex networks in grad school).

As for climate networks, you might try "The backbone of the climate network", "Complex networks in climate dynamics", and "The architecture of the climate network". These ideas haven't really been picked up by mainstream climate science, but may be worth investigating.

`Interestingly, I work in the building next door to one of the authors of the article you cite (Chertkov). You might be interested in looking at the talks for the [Optimization and Control for Smart Grids](http://www.cvent.com/events/optimization-and-control-for-smart-grids/event-summary-0068448ee06b45019681dc4d9f5a52e5.aspx) conference that he co-organized in the spring, which I attended. I think there is by now a large literature on stat mech applications to epidemiology on networks, but I'm not familiar with it. I'm not yet very knowledgeable in these applications myself (though I dabbled with the statistics of complex networks in grad school). As for climate networks, you might try ["The backbone of the climate network"](http://dx.doi.org/10.1209/0295-5075/87/48007), ["Complex networks in climate dynamics"](http://dx.doi.org/10.1140/epjst/e2009-01098-2), and ["The architecture of the climate network"](http://dx.doi.org/10.1016/j.physa.2003.10.045). These ideas haven't really been picked up by mainstream climate science, but may be worth investigating.`