Simulate radiocarbon dating
To do it, a portion of the calibration dataset or “curve” is selected and used as parameters in the standard equation that describes the probability density of the normal distribution (or alternatively, the distribution).
The resulting set of numbers is usually normalised so that the numerical values correspond to the correct boundary conditions of the probability density function of the “date”.
Defence of the technique has been maintained by many authors, however, as cumulative probability distributions can be seen as a useful way to explore trends in data aggregated from different sites, both in terms of environmental history ().
A number of recent papers attempt to improve summed probability as a modelling technique by confronting criticisms that they are unprobabilistic representations of research interest, not a true pattern borne from fluctuating activity levels in the past. () call the “University College London (UCL) method”, where simulation and back-calibration are used to generate a null hypothesis, which can also factor the expected rates of both population growth and taphonomic loss of archaeological materials through time.
These are reviewed in this paper, which also contains open-source scripts for calibrating radiocarbon dates and modelling them in space and time, using the R computer language and GRASS GIS.
The case studies that undertake new analysis of archaeological data are (1) the spread of the Neolithic in Europe, (2) economic consequences of the Great Famine and Black Death in the fourteenth century Britain and Ireland and (3) the role of climate change in influencing cultural change in the Late Bronze Age/Early Iron Age Ireland.
This allows for the direct comparison of archaeological data to other series and the coarse distribution of sites and their various phases.
It is a very useful tool for visualising the latter and probably deserves to be more widely used for exploratory work than it currently is.
Even in prehistory, the success of Bayesian chronologies in examining individual sites or certain cultural developments has transformed expectations of how much precision to expect from radiocarbon data.In this way, a time series is constructed that contains information about trends in the frequency of radiocarbon dates, which in turn becomes interpreted as a proxy measurement of past levels of activity.As many authors have pointed out, using the technique uncritically and as a direct proxy is applicable only to broad trends in very large datasets (Chiverrell et al. This is because the inherently statistical nature of radiocarbon measurements, together with the non-Gaussian uncertainty introduced by the calibration process, causes artefacts in the resulting curve that could be misinterpreted as “signal” but are, in fact, “noise”.Mid-points of confidence intervals or modal dates can be used, or the weighted mean (.However, it should be stressed that because the radiocarbon probability density of any calendar year is very low (typically never greater than 0.05), any point estimate of a radiocarbon date is much more likely to be “wrong” than “right”, underlining the need to work with the full probability distribution wherever possible.