TY - JOUR
T1 - Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach
AU - Palacios Martínez, Olga
AU - Barceló, J.A.
AU - Delgado de la Torre, Rosario
N1 - Publisher Copyright:
© 2022 Palacios et al.
PY - 2022/10/26
Y1 - 2022/10/26
N2 - 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.
AB - 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.
KW - Agriculture
KW - Archaeology
KW - Bayes Theorem
KW - Humans
KW - Population Groups
KW - Technology
UR - http://www.scopus.com/inward/record.url?scp=85140814735&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/8581c95a-aa35-340d-8c84-7a7691a67f52/
U2 - https://doi.org/10.1371/journal.pone.0276088
DO - https://doi.org/10.1371/journal.pone.0276088
M3 - Article
C2 - 36288335
VL - 17
IS - 10 October
M1 - e0276088
ER -