Exploring the role of ecology and social organisation in agropastoral societies: A Bayesian network approach

• Published in: PloS one Vol. 17; no. 10; p. e0276088
• Main Authors: Palacios, Olga, Barceló, Juan Antonio, Delgado, Rosario
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 26.10.2022; Public Library of Science (PLoS)
• Subjects: Agricultural societies; Archaeology; Bayesian analysis; Behavior; Biology and Life Sciences; Computer and Information Sciences; Dependent variables; Domestication; Earth Sciences; Ecology; Ecology and Environmental Sciences; Farming; Food; Hypotheses; Independent variables; Learning algorithms; Machine learning; Mathematical models; Methods; Neolithic; Prediction models; Prehistoric era; Services; Shores; Small farms; Small-scale farming; Social factors; Social Sciences; Social-ecological systems; Specialization; Stone Age; System theory; Variables; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0276088

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.