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When I describe an increasing correlation between density and Democratic voting that took off after the 1980s, this is the rise not only of globalization and the knowledge economy in that period, but also the rise of politics related to religion, gender, and the social transformations that came about in the ‘60s and ‘70s and then were politicized in the ‘80s. Before the 1980s, it was not clear if one was a social conservative and one was anti-abortion whether one should be a Democrat or a Republican. That became much more clear in the 1980s when the parties took very sharply different positions on those issues. One’s preferences on those issues are also highly correlated with population density.

Once we add this additional set of issues, it all starts to bunch together. The parties become increasingly separated in their geographies. The Democrats go from not only being a party of urban workers, but also being a party of urban social progressives, which leads to further sorting of individuals into the parties. Knowing someone’s preferences and whether they call themselves a liberal or a conservative becomes much more predictive of whether they vote for Democrats or Republicans.

There's a real geographic story to that as well. These people who are sorting into the parties in this period are geographically located in ways that are quite clear. It all leads to an increase in this correlation between population density and Democratic voting. All that comes together and we end up with these two parties that offer a set of policies that might not even make that much sense anymore to refer to them as left and right. It makes more sense to refer to them as urban and rural because of the way they’re packaged together.

JONATHAN RODDEN is a professor in the Political Science Department at Stanford and a Senior Fellow at the Hoover Institution. Jonathan Rodden's Edge Bio Page

The general project that I’m working on is about the self and technology—what we understand by the self and how it’s changed over time. My sense is that the self is not a universal and purely abstract thing that you’re going to get at through a philosophy of principles. Here’s an example: Sigmund Freud considered his notion of psychic censorship (of painful or forbidden thoughts) to be one of his greatest contributions to his account of who we are. His thoughts about these ideas came early, using as a model the specific techniques that Czarist border guards used to censor the importation of potentially dangerous texts into Russia. Later, Freud began to think of the censoring system in Vienna during World War I—techniques applied to every letter, postcard, telegram and newspaper—as a way of getting at what the mind does. Another example: Cyberneticians came to a different notion of self, accessible from the outside, identified with feedback systems—an account of the self that emerged from Norbert Wiener’s engineering work on weapons systems during World War II. Now I see a new notion of the self emerging; we start by modeling artificial intelligence on a conception of who we are, and then begin seeing ourselves ever more in our encounter with AI.

Economic failures cause us serious problems. We need to build simulations of the economy at a much more fine-grained level that take advantage of all the data that computer technologies and the Internet provide us with. We need new technologies of economic prediction that take advantage of the tools we have in the 21^st century.  

Places like the US Federal Reserve Bank make predictions using a system that has been developed over the last eighty years or so. This line of effort goes back to the middle of the 20th century, when people realized that we needed to keep track of the economy. They began to gather data and set up a procedure for having firms fill out surveys, for having the census take data, for collecting a lot of data on economic activity and processing that data. This system is called “national accounting,” and it produces numbers like GDP, unemployment, and so on. The numbers arrive at a very slow timescale. Some of the numbers come out once a quarter, some of the numbers come out once a year. The numbers are typically lagged because it takes a lot of time to process the data, and the numbers are often revised as much as a year or two later. That system has been built to work in tandem with the models that have been built, which also process very aggregated, high-level summaries of what the economy is doing. The data is old fashioned and the models are old fashioned.

It's a 20th-century technology that's been refined in the 21st century. It's very useful, and it represents a high level of achievement, but it is now outdated. The Internet and computers have changed things. With the Internet, we can gather rich, detailed data about what the economy is doing at the level of individuals. We don't have to rely on surveys; we can just grab the data. Furthermore, with modern computer technology we could simulate what 300 million agents are doing, simulate the economy at the level of the individuals. We can simulate what every company is doing and what every bank is doing in the United States. The model we could build could be much, much better than what we have now. This is an achievable goal.

But we're not doing that, nothing close to that. We could achieve what I just said with a technological system that’s simpler than Google search. But we’re not doing that. We need to do it. We need to start creating a new technology for economic prediction that runs side-by-side with the old one, that makes its predictions in a very different way. This could give us a lot more guidance about where we're going and help keep the economic shit from hitting the fan as often as it does.

J. DOYNE FARMER is director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, professor in the Mathematical Institute at the University of Oxford, and an external professor at the Santa Fe Institute. He was a co-founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. J. Doyne Farmer's Edge Bio Page

An even more recent and exciting revolution happening now is this connectomic revolution, where we’re able to map in exquisite detail the connections of a part of the brain, and soon even an entire insect brain. It’s giving us absolute answers to questions that we would have debated even just a few years ago; for example, does the insect brain work as an integrated system? And because we now have a draft of a connectome for the full insect brain, we can absolutely answer that question. That completely changes not just the questions that we’re asking, but our capacity to answer questions. There’s a whole new generation of questions that become accessible.

When I say a connectome, what I mean is an absolute map of the neural connections in a brain. That’s not a trivial problem. It's okay at one level to, for example with a light microscope, get a sense of the structure of neurons, to reconstruct some neurons and see where they go, but knowing which neurons connect with other neurons requires another level of detail. You need electron microscopy to look at the synapses.

ANDREW BARRON is the Australian Research Council Future Fellow and Deputy Head of the Department of Biological Sciences at Macquarie University. He is a neuroethologist with a particular focus on studying the neural mechanisms of honey bees. Andrew Barron's Edge Bio Page