Zoom and Enhance: high-resolution reconstruction of macro-scale processes with Bayesian hierarchical models

The usefulness of macro-level studies has been increasingly appreciated in archaeology over the last 10 years, not only since the publication of the book that is the reference point of this session, but also thanks to the greater accessi- bility of the data needed for such research. From this point of view, the call for a macroarchaeology is a culmination of a broader endeavour.
As exciting as the macroarchaeological research programme is, its aim of linking macro-scale processes to external drivers is a difficult task. In general, the prediction for recent prehistory is that only processes that take place over centuries can be identified. However, many relevant triggers have a much shorter duration, e.g. short-term climate fluctuations or epidemics.
One proven approach to increasing temporal sampling data is to combine data over large geographic areas. An- other is data assimilation, which combines a modelled representation with data. Hierarchical Bayesian models are able to do both and can be used to link different qualities of data and supplement missing data, e.g. from one region with data from another.
In this talk, we embed a hierarchical Bayesian approach to reconstructing population density within the framework of macroarchaeology. With this methodology it is possible to reconstruct macro-scale processes with higher resolution than with a single indicator, potentially exceeding the empirical limits proposed by Perreault. In this way, relevant causal relationships between different population-level triggers and responses can be more accurately and credibly demonstrated, offering many more opportunities to address issues of macroarchaeological relevance.