From Reviewer, too by Elise Cutts
Republished with permission
Eddie Lee mapped the political landscape underpinning the increasingly polarized US Senate using the same physics that sparked the AI boom
The study of complex systems owes a lot to the humble magnet.
A magnet’s magic emerges from the collective coordination of countless “spins,” units of intrinsic rotation that make certain atoms, like iron, act like tiny bar magnets. Like their macroscopic counterparts, these spins like to align: put mismatched spins next to each other, and they’ll flip-flop until they point the same way unless random thermal jitters knock them askew.
In the 1920s, physicist Ernst Ising captured this push and pull in a simple schema that would go on to become physics’ go-to mathematical lasso for wrangling collective phenomena. And decades later, in 1982, condensed matter physicist John Hopfield realized that it might do much more. The way spins influence each other, he realized, looked a lot like how neurons exchange signals. By tweaking the Ising model, Hopfield built a simplified neural network that could “remember” and “recall” patterns. Geoffrey Hinton and others expanded on these ideas to spark the deep learning revolution that ushered in modern AI.
Magnets, physics, AI — this all might sound rather far away from the messy, human world of politics. But Eddie Lee, a physicist at the Complexity Science Hub in Vienna, recently discovered an unexpected connection between voting in the US Senate and the bare-bones neural networks that won Hopfield and Hinton the Nobel Prize in physics last year.
Eddie uses statistical physics to study how information flows through life and society. That could mean anything from understanding aggression in monkey troupes to decision-making on the US Supreme Court. And in a recent preprint with co-author Gavin Rees, he turned his physics toolkit on the US Senate to develop a mathematical representation of how individual inclinations manifest in votes. Much to Eddie’s surprise, the simple model he devised to capture this straightforward intuition about votes reflecting personal preferences ended up recreating a simple neural network that Hinton used decades ago to unleash the power of deep learning: the restricted Boltzmann machine.
Boltzmann machines were among the first neural networks designed to dream up new data rather than simply remember it — an early form of generative AI. They typically have two layers of nodes: a visible layer that takes inputs and gives outputs, and a hidden layer that represents patterns internally. By adjusting how strongly different nodes interact with each other, the network can absorb statistical patterns that appear over and over again in their inputs and output similar patterns. Restricted Boltzmann machines are leaner versions that cut down on internal chatter: visible nodes connect only to hidden ones, not to each other. These trim networks played a vital role in the 1990s AI renaissance after Hinton and colleagues showed that stacks of them could be used to “pretrain” deep neural networks and dramatically boost their performance.
Eddie inadvertently discovered that an old idea from political science, translated into mathematics, ends up reproducing the two-tiered network of a restricted Boltzmann machine.
Traditionally, political science thinks of voting the way economists think of well, everything: as a matter of individual utility. Votes represent the revealed preferences of individuals. Eddie has historically taken a different view: voting not as the sum of individual choices, but as an emergent, collective one. For instance, he recently analyzed Supreme Court decisions as the outcome of interactions between justices with help from none other than the trusty Ising model.
But for this latest study of Senate voting over the last 5 decades, Eddie wanted to see what would happen if they took the traditional view seriously — without flattening “individual preferences” down to a Republican/Democrat split, as often occurs in the political science literature.
The unexpected result was a restricted Boltzmann machine: Senators with their “yea” or “nay” votes sat in the visible layer and their individual preferences in the hidden layer. Perhaps predictably, this simple model revealed deepening political polarization between Senate Republicans and Democrats. But the landscape of voter preference was far more varied than one big, deep partisan canyon. Based only on senators’ voting behavior — not on their campaign promises or portrayal in the media — Eddie and Gavin were able to pick out politicians whose individual politics doesn’t fit neatly into a simple two-party view.
I spoke with Eddie about his new paper on polarization in the Supreme Court. Is the US Senate getting more polarized? If so, why? What do the simple neural networks that ushered in modern AI have to do with modeling politics? And at the end of the day, is voting truly the outcome of individual values — or might the political meanings of our beliefs emerge out of our interactions with others?
The interview has been edited for clarity.
