Bayesian Approaches for Attribute Analysis of Lithic Assemblages

Summary

This is an abstract from the "The Expanding Bayesian Revolution in Archaeology" session, at the 88th annual meeting of the Society for American Archaeology.

By studying stone tool technology, archaeologists and anthropologists shed light on big questions in human prehistory, including how ancient peoples adapted to changing environments, moved throughout landscapes, and interacted with other groups of people. There are many methodological approaches for characterizing stone tool technology, and several that involve the collection of metric data. However, the quantitative methods that archaeologists rely on to interpret these data remain relatively limited, and most often rely on frequentist null hypothesis significance testing. This paper presents a hierarchical Bayesian approach for parameter estimation in the analysis of lithic assemblages using a case study of several late Pleistocene assemblages from the Tràng An Landscape Complex, northern Vietnam. The results highlight several major advantages of the Bayesian hierarchical approach for lithic analysis, including (1) improved interpretability, (2) balance between overfitting and underfitting, and (3) explicit modeling of variation. The results of the attribute analysis indicate little technological variability over time at each site, but a high degree of variability between different sites. Due to the often hierarchically structured nature of archaeological data (e.g., contexts in trenches in squares), researchers studying similarly organized datasets should also consider the benefits of Bayesian hierarchical modeling.

This Resource is Part of the Following Collections

• The Expanding Bayesian Revolution in Archaeology   •
• Society for American Archaeology 88th Annual Meeting, Portland, OR (2023)

Keywords

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Record Identifiers

Abstract Id(s): 37303.0