2025/02/02 by Jonathan A. Hanna · 1 voice
Earth and Planetary Sciences · Social Sciences · #Archaeology and ancient environmental studies #Geology and Paleoclimatology Research #Pleistocene-Era Hominins and Archaeology
paper · pdf · doi:10.24072/pci.archaeo.100583
openalex publication_date 2025/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Archaeological data is inherently uncertain, which is probably why Bayesian approaches have become increasingly valued within the discipline.For radiocarbon analysis, instead of pinpointing one "true" date, Bayesian methods embrace probability, telling us how likely a sample falls within different date ranges based on what we already know (the prior) and what our data tells us (the posterior).In this new paper, Miguel de Navascus and colleagues offer a method for treating radiocarbon dates as count data (generated through a Poisson process), rather than viewing them as draws from a probability distribution.This shift allows them to model the expected number of samples per year and incorporate uncertainty in both the timing and total number of samples.The result is a more natural representation of how radiocarbon samples accumulate in the archaeological record over time.(They then demonstrate the method using data from Britain and Ireland, revealing patterns that both confirm and refine our understanding of population changes during key transitions, including a possibly earlier start to the Neolithic demographic expansion.)Overall, the paper represents a valuable contribution to quantitative archaeology that complements, rather than replaces, existing approaches like Sum Probability Distributions (SPDs) and end-to-end Bayesian methods (e.g., see Crema 2022 and Price et al. 2021).While mathematically heavy, the paper is accompanied by well-annotated R scripts that I encourage readers to experiment with.For researchers working with radiocarbon data, particularly those investigating demographic change or cultural transmission, the methods presented here offer important new analytical possibilities for understanding past human dynamics.Sometimes, to move forward, we just need to count differently. Reviewer 2 requests more detailed discussion of the method's practical implementation, particularly regarding parameter selection and model validation protocols.