The Effects of Time-Averaging on Archaeological Networks

  • Dries Daems
  • , Emily Coco
  • , Andrew Gillreath-Brown
  • , Danai Kafetzaki

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

It is well recognized that time-averaging of archaeological deposits results in significant biases in interpretations of the archaeological record. In this study, we investigate the biases introduced by time-averaging in the study of social and economic networks from the archaeological record. Using three different archaeological network datasets, we combine network slices from multiple periods to mimic the effects of time-averaging to understand how the palimpsest nature of the archaeological record affects our interpretations of the network. The results of our analysis indicate that time-averaging reduces the fidelity of network interpretations compared to the non-time-averaged networks when analyzing network or node properties. Our results also showed that the effects of time-averaging are highly dependent on initial network structures. This makes it difficult to establish general rules for how to interpret time-averaged networks in archaeology. However, our study shows that it is of paramount importance that archaeologists are aware of these biases and evaluate the reliability of their data accordingly.
Original languageEnglish
Pages (from-to)473-506
Number of pages34
JournalJournal of Archaeological Method and Theory
Volume31
Issue number2
Early online date6 May 2024
DOIs
Publication statusPublished - Jun 2024
Externally publishedYes

Funding

We are grateful to the organizers of Santa Fe Institute's Complex Systems Summer School (class of 2019), where we first discussed the ideas for this paper. We would also like to thank the many participants that we had discussions with and all the lecturers for CSSS 2019. More specifically, we thank Jack Shawn, Kate Wootton, and Anshuman Swain for their collaboration and discussions on network metrics and time-averaging. Thanks to Archaeology Southwest and Matt Peeples for providing the Southwest Social Network (SWSN) Database 1.0 data used in this article. We thank Matt Peeples and one anonymous reviewer for their thoughtful comments and helpful suggestions. This work was also supported in part through the NYU IT High Performance Computing resources and services. DD was supported by the Academic Foundation Leuven to attend the Complex Systems Summer School in 2019 and by C1 funding (C14/17/025) from KU Leuven. EC and AG-B received no funding for conducting this study. DK received funding from the Research Foundation Flanders (G088319N).

FundersFunder number
Santa Fe Institute’s Complex Systems Summer School
KU Leuven
Academic Foundation LeuvenC14/17/025
Fonds Wetenschappelijk OnderzoekG088319N

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