Dating the Neolithic of South India: new radiometric evidence for key economic, social and ritual transformations

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Abstract

The Neolithic period in South India is known for its ashmounds, superseded (in its Iron Age) by megalith builders with craft specialisation. Thanks to a major radiocarbon dating programme and Bayesian analysis of the dates, the authors have placed this sequence in a new chronological framework: the ashmounds, formed by burning cattle dung, are created by a few generations of people. In many cases the mounds are then succeeded by villages, for which they may have acted as founding rituals. The new tightly dated sequence also chronicles the cultivation of particular crops, some indigenous and some introduced from Africa.

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Radiocarbon Calibration and Analysis of Stratigraphy: The OxCal Program

Christopher Bronk Ramsey (1995)

People usually study the chronologies of archaeological sites and geological sequences using many different kinds of evidence, taking into account calibrated radiocarbon dates, other dating methods and stratigraphic information. Many individual case studies demonstrate the value of using statistical methods to combine these different types of information. I have developed a computer program, OxCal, running under Windows 3.1 (for IBM PCs), that will perform both 14C calibration and calculate what extra information can be gained from stratigraphic evidence. The program can perform automatic wiggle matches and calculate probability distributions for samples in sequences and phases. The program is written in C++ and uses Bayesian statistics and Gibbs sampling for the calculations. The program is very easy to use, both for simple calibration and complex site analysis, and will produce graphical output from virtually any printer.

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Development of the Radiocarbon Calibration Program

Christopher Bronk Ramsey (2001)

This paper highlights some of the main developments to the radiocarbon calibration program, OxCal. In addition to many cosmetic changes, the latest version of OxCal uses some different algorithms for the treatment of multiple phases. The theoretical framework behind these is discussed and some model calculations demonstrated. Significant changes have also been made to the sampling algorithms used which improve the convergence of the Bayesian analysis. The convergence itself is also reported in a more comprehensive way so that problems can be traced to specific parts of the model. The use of convergence data, and other techniques for testing the implications of particular models, are described.