Seshat bridges the gap between history and prehistory


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Archaeologist Peter Peregrine recently sat down with me at the beautiful Santa Fe Institute. He explained how Seshat incorporates a wide variety of source material to accomplish a feat that has eluded scholars for decades–bridging the gap between history and prehistory. The resulting databank provides researchers and policy makers with powerful new tools to explore long-term historical trends with high-quality data. Watch Peter explain how this works.

For more videos, see our Vimeo page.

To explore a related publication, see Turchin et al 2015.

Transcript:

Daniel Mullins: What can an archaeologist learn from a database like Seshat?

Peter Peregrine: Well archaeology is interested in long-term historical trends, so being able to move from prehistory into history is very important. My own research started when I was looking at the Jesuits and Indians around Green Bay, Wisconsin and as you move back in time, you run into the prehistory period, which is all archaeology. Move forward into the historic period and the whole nature of the dataset changes and that was very hard to deal with and that’s exactly what Seshat is dealing with.

Daniel Mullins: Why does Seshat need prehistory?

Peter Peregrine: Well the historical record is only a few centuries to a few millennia. We can go back ten thousand, twenty thousand, thirty thousand years, so we get enormous time depth to look at change.

Daniel Mullins: What do you see as Seshat’s biggest contribution to science?

Peter Peregrine: There are two major contributions to science that Seshat can make. One of them is just collecting the data and actually developing ways to analyse data to look at long-term trends, which economists have done and obviously climate scientists have done, but historians work in the realm of hundreds of years. We’re going to working in the realm of thousands of years and that’s a different level, a different scale of analysis…The other part of that question is the computational part and creating a data structure and facilities for covering and analysing and reformatting variables that will be easy and available to future scholars. That whole process of having thousand of variables that are readily accessible will be of tremendous use.

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