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Pàgina inicial > Articles > Articles publicats > Exploring the role of ecology and social organisation in agropastoral societies : |
Data: | 2022 |
Resum: | The present contribution focuses on investigating the interaction of people and environment in small-scale farming societies. Our study is centred on the particular way settlement location constraints economic strategy when technology is limited, and social division of work is not fully developed. Our intention is to investigate prehistoric socioeconomic organisation when farming began in the Old World along the Levant shores of Iberian Peninsula, the Neolithic phenomenon. We approach this subject extracting relevant information from a big set of ethnographic and ethnoarchaeological cases using Machine Learning methods. This paper explores the use of Bayesian networks as explanatory models of the independent variables-the environment- and dependent variables-social decisions-, and also as predictive models. The study highlights how subsistence strategies are modified by ecological and topographical variables of the settlement location and their relationship with social organisation. It also establishes the role of Bayesian networks as a suitable supervised Machine Learning methodology for investigating socio-ecological systems, introducing their use to build useful data-driven models to address relevant archaeological and anthropological questions. |
Ajuts: | Agència de Gestió d'Ajuts Universitaris i de Recerca 2019/FI-B-00966 Ministerio de Ciencia e Innovación PID 2019-109254GB-C21 Ministerio de Ciencia e Innovación PID2021-123733NB-I00 |
Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Llengua: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Publicat a: | PloS one, Vol. 17 (October 2022) , art. e0276088, ISSN 1932-6203 |
29 p, 2.3 MB |