Culture Process and the Interpretation of Radiocarbon Data. Issue 2 (12th December 2017)

• Record Type: Journal Article
• Authors: Freeman, Jacob; Byers, David A; Robinson, Erick; Kelly, Robert L
• Is Part Of: Radiocarbon. Volume 60:Issue 2(2018)
• Page Start: 453
• Page End: 467
• Publication Date: 2017-12-12
• Subjects: deep-time economics, -- macroecology, -- prehistoric demography, -- summed probability distribution; Radiocarbon dating -- Periodicals; 930.1028505
• DOI: 10.1017/RDC.2017.124
• Languages: English
• ISSNs: 0033-8222

Abstract

Abstract: Over the last decade, archaeologists have turned to large radiocarbon ( 14 C) data sets to infer prehistoric population size and change. An outstanding question concerns just how direct of an estimate 14 C dates are for human populations. In this paper we propose that 14 C dates are a better estimate of energy consumption, rather than an unmediated, proportional estimate of population size. We use a parametric model to describe the relationship between population size, economic complexity and energy consumption in human societies, and then parametrize the model using data from modern contexts. Our results suggest that energy consumption scales sub-linearly with population size, which means that the analysis of a large 14 C time-series has the potential to misestimate rates of population change and absolute population size. Energy consumption is also an exponential function of economic complexity. Thus, the 14 C record could change semi-independent of population as complexity grows or declines. Scaling models are an important tool for stimulating future research to tease apart the different effects of population and social complexity on energy consumption, and explain variation in the forms of 14 C date time-series in different regions.