Evaluating the Effects of Randomness on Missing Data in Archaeological Networks

Abstract

Network science shows promise for archaeologists who want to explore past social dynamics using material culture. Yet, archaeological data is subject to important caveats that exist for all datasets. Almost all archaeological datasets are biased, and these biases are often unknown or only partially understood. Prior research has examined the effects of missing nodes on archaeological networks. Instead, in this paper, we focus on the impact of missing links on such networks. We used an agent-based model (ABM) – namely ArchMatNet – to generate a simulated, unbiased assemblage of artefacts deposited at sites. We link those sites through the similarity of their artefacts to form the complete network. We also include an obsidian dataset from the US Southwest to compare differences between real and simulated data. We explore how random and non-random sampling of the two datasets affect the accuracy of the reconstructed network. Our analysis confirms prior research, demonstrating that random samples are representative of the original network, even when they are small, but biased samples of any size are significantly problematic. This research highlights the need to consider bias in archaeological data and demonstrates the utility of ABMs in testing archaeological methods. Furthermore, this simulated dataset can better inform how archaeologists judge bias and will help us develop new methods to mitigate the effects of biased data.