Modelling diffusion of innovation curves using radiocarbon data

Authors: Crema, E.R., Bloxam, A., Stevens, C.J. and Vander Linden, M.

Journal: Journal of Archaeological Science

Volume: 165

eISSN: 1095-9238

ISSN: 0305-4403

DOI: 10.1016/j.jas.2024.105962

Abstract:

Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain.

https://eprints.bournemouth.ac.uk/39742/

Source: Scopus

Modelling diffusion of innovation curves using radiocarbon data

Authors: Crema, E.R., Bloxam, A., Stevens, C.J. and Vander Linden, M.

Journal: JOURNAL OF ARCHAEOLOGICAL SCIENCE

Volume: 165

eISSN: 1095-9238

ISSN: 0305-4403

DOI: 10.1016/j.jas.2024.105962

https://eprints.bournemouth.ac.uk/39742/

Source: Web of Science (Lite)

Modelling diffusion of innovation curves using radiocarbon data

Authors: Crema, E.R., Bloxam, A., Stevens, C.J. and Vander Linden, M.

Journal: Journal of Archaeological Science

Volume: 165

ISSN: 0305-4403

Abstract:

Archaeological data provide a potential to investigate the diffusion of technological and cultural traits. However, much of this research agenda currently needs more formal quantitative methods to address small sample sizes and chronological uncertainty. This paper introduces a novel Bayesian framework for inferring the shape of diffusion curves using radiocarbon data associated with the presence/absence of a particular innovation. We developed two distinct approaches: 1) a hierarchical model that enables the fitting of an s-shaped diffusion curve whilst accounting for inter-site variations in the probability of sampling the innovation itself, and 2) a non-parametric model that can estimate the changing proportion of the innovation across user-defined time-blocks. The robustness of the two approaches was first tested against simulated datasets and then applied to investigate three case studies, the first pair on the diffusion of farming in prehistoric Japan and Britain and the third on cycles of changes in the burial practices of later prehistoric Britain.

https://eprints.bournemouth.ac.uk/39742/

Source: BURO EPrints