Tracking Prehistoric Relations With AI From The Middle Stone Age To Antiquity

Conny Waters - AncientPages.com - Who knows whom? Who has which desires and needs?

The answers to these questions are worth a lot of money for the advertising industry today. With the help of immense amounts of data as well as artificial intelligence, internet companies can answer them ever more precisely.

Obsidian artifacts found in 2022 in Gird-i Dasht (Soran district, Kurdistan Autonomous Region, Iraq). The raw material was once extracted several hundred kilometers from the site in eastern Anatolia. This connection is like a trace of human relations. The more such relationships can be studied using raw materials, the more precisely prehistoric networks can be analyzed. Image credit: im Kerig

In the international journal Antiquity, a team of archaeologists from seven countries led by Kiel University is now presenting the “Big Exchange” project, which uses similar questions and AI methods to better understand the networks and interactions of prehistoric and early historic people. “Archaeology, of course, does not find imprints of relationships in the ground. But we do find raw materials, such as flint, obsidian, jade, ivory, and even various metals, that have often travelled long distances from their sources to where they were found.

They are like shadows of relationships between people. With their help, we can investigate networks in the past,” states Dr. Tim Kerig, project leader and archaeologist in the ROOTS Cluster of Excellence at Kiel University.

The analysis of early networks based on raw material finds and the associated raw material sources is nothing new. Archaeology has already been using this possibility for about 50 years.

“It has provided us with many valuable insights into the past. But because of the effort involved and the specialisation of individual experts, for a long time the studies only dealt with one raw material at a time,” explains Dr. Johanna Hilpert, an archaeologist at the Institute for Prehistoric and Protohistoric Archaeology of Kiel University and a postdoc at Kiel’s Data Campus, in a press release.

More than 6000 sites with millions of finds recorded

Only recently has digitisation enabled more complex analyses with multiple raw materials at the same time. “The approach of our project ‘Big Exchange’ is now to include all recordable raw materials, their find locations and places of origin in the analyses for the period from the Middle Stone Age to antiquity. This can only be done by means of network analysis and with AI,” emphasises Dr. Hilpert.

So far, the project has already recorded more than 6000 sites with millions of individual finds from Western Europe to Central Asia. The network analyses made possible by this data allow statements to be made about how the simultaneous distribution of various goods is related to the more or less restricted access of the respective people to raw materials. This also concerns fundamental questions about social inequality and various power relations.

At the same time, the project is a social experiment. “It is not just about feeding datasets into appropriate databases and having them analysed automatically. We want to have archaeologists on board for every dataset," Dr. Kerig emphasises. Archaeological datasets vary widely, he says, and some are only available in analogue form. “

That is why it is important to involve colleagues who know the underlying excavations or surveys in the analysis. We do not just want to analyse prehistoric networks, but we also want to build scientific networks and link archaeology with data science.”

Call for collaboration

The authors are already presenting a first result of the project in Antiquity. The Linear Pottery culture is the first farming culture in Central Europe. For a long time, its northwestern characteristics were considered typical for its epoch. However, when considering recent excavations, the network analysis of “Big Exchange” shows that the product mix of the northwestern Linear Pottery is rather a very special case.

“We will probably experience even more surprises like this when we systematically analyse the available data,” says Dr. Kerig.

The authors also see their article as a call to colleagues to participate in “Big Exchange” and contribute their own data sets.

“The more participation, the better we can understand past relationship and network dynamics,” concludes Tim Kerig.

Background information:
The “Big Exchange” project has been funded by the Cluster of Excellence ROOTS at CAU Kiel since 2020.

Paper

Written by Conny Waters - AncientPages.com Staff Writer