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Stefan Schubert's avatar

This reminds me of Ernest Gellner's chapter "The Need for Philosophic History" in Plough, Sword and Book, which aims to give an explicit general theory of human history. Gellner was very much a generalist, but his approach wasn't as rigorous and formal as that suggested here.

"We inevitably assume a pattern of human history. There issimply no choice concerning whether we use such a pattern. Weare, all of us, philosophical historians malgre nous, whether wewish it or not. The only choice we do have is whether we makeour vision as explicit, coherent and compatible with availablefacts as we can, or whether we employ it more or lessunconsciously and incoherently. If we do the latter, we risk usingideas without examination and criticism, passed off tacitly assome kind of "common sense". ...

The joint result of our inescapable need for possessing somebackcloth vision of history, and of the low esteem in whichelaboration of global historical patterns is at present held, is amost paradoxical situation: the ideas of nineteenth-centuryphilosophers of history such as Hegel, Marx, Comte, or Spencerare treated with scant respect and yet are everywhere in use."

http://14.139.206.50:8080/jspui/bitstream/1/2215/1/Gellner,%20Ernest%20-%20Plough,%20Sword,%20and%20Book%20The%20Structure%20of%20Human%20History%201989.pdf

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Flavio Abdenur's avatar

"I’d suggest just picking some more limited category, such as perhaps government regulations, collecting some plausible data points, making some guesses about what useful features might be, and then just doing a quick survey of some social scientists where they each fill in the data table with their best guesses for data point features. If you ask enough people, you can average out a lot of individual noise, and at least have a data set about what social scientists think are features of items in this area. With this you could start to do some exploratory data analysis, and start to think about what theories might well account for the patterns you see."

This might be less tedious and labor-intensive than it seems. The machine-learning methods that are currently booming look like a perfect fit for making (and testing) predictions based on these features. You don't have to sift through these features; the algorithms will figure out which ones are relevant, and in what sense.

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