The physics of Trump?? Election renormalization.

Two things were high in my mind this last quarter: My course on advanced statistical mechanics and phase transitions, and the bizarre general elections that raged all around. It is no wonder then, that I would start to conflate the Ising model, Landau mean field, and renormalization group, with the election process, and just think of each and every one of us as a tiny magnet, that needs to say up or down – Trump or Cruz, Clinton or Sanders (a more appetizing choice, somehow), and .. you get the drift.

Elections and magnetic phase transitions are very much alike. The latter, I will argue, teaches us something very important about the former.

The physics of magnetic phase transitions is amazing. If I hadn’t thought this way, I wouldn’t be a condensed matter physicist. Models of magnets consider a bunch of spins – each one a small magnet – that talk only to their nearest neighbor, as happens in typical magnets. At the onset of magnetic order (the Curie temperature), when the symmetry of the spins becomes broken, it turns out that the spin correlation length diverges. Even though Interaction length = lattice constant, we get correlation length = infinity.

To understand how ridiculous this is, you should understand what a correlation length is. The correlation tells you a simple thing. If you are a spin, trying to make it out in life, and trying to figure out where to point, your pals around you are certainly going to influence you. Their pals will influence them, and therefore you. The correlation length tells you how distant can a spin be, and still manage to nudge you to point up or down. In physics-speak, it is the reduced correlation length. It makes sense that somebody in you neighborhood, or your office, or even your town, will do something that will affect you – after all – you always interact with people that distant. But the analogy to the spins is that there is always a given circumstance where some random person in Incheon, South Korea, could influence your vote. A diverging correlation length is the Butterfly effect for real.

And yet, spins do this. At the critical temperature, just as the spins decide whether they want to point along the north pole or towards Venus, every nonsense of a fluctuation that one of them makes leagues away may galvanize things one way or another. Without ever even remotely directly talking to even their father’s brother’s nephew’s cousin’s former roommate! Every fluctuation, no matter where, factors into the symmetry breaking process.

A bit of physics, before I’m blamed for being crude in my interpretation. The correlation length at the Curie point, and almost all symmetry-breaking continuous transitions, diverges as some inverse power of the temperature difference to the critical point: \frac{1}{|T-T_c|}^{\nu}. The faster it diverges (the higher the power \nu) , actually the more feeble the symmetry breaking is. Why is that? After I argued that this is an amazing phenomenon? Well, if 10^2 voices can shift you one way or another, each voice is worth something. If 10^{20} voices are able to push you around, I’m not really buying influence on you by bribing ten of these. Each voice is worth less. Why? The correlation length is also a measure of the uncertainty before the moment of truth – when the battle starts and we don’t know who wins. Big correlation length – any little element of the battlefield can change something, and many souls are involved and active. Small correlation length – the battle was already decided since one of the sides has a single bomb that will evaporate the world. Who knew that Dr. Strangelove could be a condensed matter physicist?

This lore of correlations led to one of the most breathtaking developments of 20th century physics. I’m a condensed matter guy, so it is natural that Ken Wilson, as well as Ben Widom, Michael Fisher, and Leo Kadanoff are my superheros. They came up with an idea so simple yet profound – scaling. If you have a system (say, of spins) that you can’t figure out – maybe because it is fluctuating, and because it is interacting – regardless, all you need to do is to move away from it. Let averaging (aka, central limit theorem) do the job and suppress fluctuations. Let us just zoom out. If we change the scale by a factor of 2, so that all spins look more crowded, then the correlation length also look half as big. The system looks less critical. It is as if we managed to move away from the critical temperature – either cooling towards T=0 , or heating up towards T=\infty. Both limits are easy to solve. How do we make this into a framework? If the pre-zoom-out volume had 8 spins, we can average them into a representative single spin. This way you’ll end up with a system that looks pretty much like the one you had before – same spin density, same interaction, same physics – but at a different temperature, and further from the phase transition. It turns out you can do this, and you can figure out how much changed in the process. Together, this tells you how the correlation length depends on T-T_c. This is the renormalization group, aka, RG.

Interestingly, this RG procedure informs us that criticality and symmetry breaking are more feeble the lower the dimension. There are no 1d permanent magnets, and magnetism in 2d is very frail. Why? Well, the more dimensions there are, the more nearest neighbors each spin has, and more neighbors your neighbors have. Think about the 6-degrees of separation game. 3d is okay for magnets, as we know. It turns out, however, that in physical systems above 4 dimensions, critical phenomena is the same as that of a fully connected (infinite dimensional) network. The uncertainty stage is very small, correlations length diverge slowly. Even at distance 1 there are enough people or spins to bend your will one way or another. Magnetization is just a question of time elapsed from the beginning of the experiment.

Spins, votes, what’s the difference? You won’t be surprised to find that the term renormalization has permeated every aspect of economics and social science as well. What is voting Republican vs Democrat if not a symmetry breaking? Well, it is not that bad yet – the parties are different. No real symmetry there, you would think. Unless you ask the ‘undecided voter’.

And if elections are affected by such correlated dynamics, what about revolutions? Here the analogy with phase transitions is so much more prevalent even in our language – resistance to a regime solidifies, crystallizes, and aligns – just like solids and magnets. When people are fed up with a regime, the crucial question is – if I would go to the streets, will I be joined by enough people to affect a change?

Revolutions, therefore, seem to rise out of strong fluctuations in the populace. If you wish, think of revolutions as domains where the frustration is so high, which give a political movement the inertia it needs.

Domains-: that’s exactly what the correlation length is about. The correlation length is the size of correlated magnetic domains, i.e.,groups of spins that point in the same direction. And now we remember that close to a phase transition, the correlation length diverges as some power of the distance ot the transition: \frac{1}{|T-T_c|^{\nu}}. Take a magnet just above its Curie temperature. The closer we are to a phase transition, the larger the correlation length is, and the bigger are the fluctuating magnetized domains. The parameter \nu is the correlation-length critical exponent and something of a holy grail for practitioners of statistical mechanics. Everyone wants to calculate it for various phase transition. It is not that easy. That’s partially why I have a job.

The correlation length aside, how many spins are involved in a domain? \left[1/|T-T_c|^d\right]^{\nu} . Actually, we know roughly what \nu is. For systems with dimension $latex  d>4$, it is ½. For systems with a lower dimensionality it is roughly $latex  2/d$. (Comment for the experts: I’m really not kidding – this fits the Ising model for 2 and 3 dimensions, and it fits the xy model for 3d).

So the number of spins in a domain in systems below 4d is 1/|T-T_c|^2, independent of dimension. On the other hand, four d and up it is 1/|T-T_c|^{d/2}. Increasing rapidly with dimension, when we are close to the critical point.

Back to voters. In a climate of undecided elections, analogous to a magnet near its Curie point, the spins are the voters, and domain walls are the crowds supporting this candidate or that policy; domain walls are what becomes large demonstrations in the Washington Mall. And you would think that the world we live in is clearly 2d – a surface of a 3d sphere (and yes – that includes Manhattan!). So a political domain size just diverges as a simple moderate 1/|T-T_c|^2 during times of contested elections.

Something happened, however, in the past two decades: the internet. The connectivity of the world has changed dramatically.

No more 2d. Now, our effective dimension is determined by our web based social network. Facebook perhaps? Roughly speaking, the dimensionality of the Facebook network is that number of friends we have, divided by the number of mutual friends. I venture to say this averages at about 10. With about a 150 friends in tow, out of which 15 are mutual. So our world, for election purposes, is 10 dimensional big!

Let’s simulate what this means for our political system. Any event – a terrorist attack, or a recession, etc. will cause a fluctuation that will involve a large group of people – a domain. Take a time when T-T_c is a healthy 0.1 for instance. In the good old 2d world this would involve 100 friends times 1/0.1^2\sim 10000 people. Now it would be more like 100\cdot 1/0.1^{10/2}\sim 10-millions. So any small perturbation of conditions could make entire states turn one way or another.

When response to slight shifts in prevailing conditions encompasses entire states, rather than entire neighborhoods, polarization follows. Over all, a state where each neighborhood has a slightly different opinion will be rather moderate – extreme opinions will only resonate locally. Single voices could only sway so many people. But nowadays, well – we’ve all seen Trump and the like on the march. Millions. It’s not even their fault – its physics!

Can we do anything about it? It’s up for debate. Maybe cancel the electoral college, to make the selecting unit larger than the typical size of a fluctuating domain. Maybe carry out a time averaged election: make an election year where each month there is a contest for the grand prize. Or maybe just move to Canada.

10 thoughts on “The physics of Trump?? Election renormalization.

  1. As long as we don’t end up at the critical point, I don’t want to fall on my head every time I turn around…

    • That should be “tricritical point…”

      More seriously, I really like the idea! I wonder if different countries have different correlation lengths, or if multiparty elections have different correlations (think 2 vs 3 state Potts).

  2. Your estimate, “our world, for election purposes, is 10 dimensional big”, is of the same order of the statement that everyone in the world is connected (by friendship) by at most six people.

    • It is related, but let me explain a small difference. The 6 degrees of separation emerges as we ask – through how many rings of acquaintances we need to go to any one in the world. If I know a 100 people, then 100^5=10 billion=size of the world. Here, the 100 is the connectivity of a Bethe lattice, and 5 is the distance on the Bethe lattice. The Bethe lattice is actually infinite dimensional. I’m envisioning our world as a finite dimensional space, with ‘social media’ dimensionality of about 10. The ‘linear size’ of our world will be given by (size)^10=10^10 (10 billion people). This, incidentally, also gives size-10. So same order of magnitude as the Bethe lattice calculation. In a Bethe lattice, however, the correlation volume I talk about would be (connectivity)^(correlation length), and not (correlation length)^dimension.

    • I would say tipping point is the transition point between the two phases. In renormalization group language we call it the unstable critical fixed point.

  3. I think this is an interesting analogy …
    Dietrich Stauffer investigated this type of sociological model in some detail and econophysics etc. goes back to the 90s if I remember correctly.

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