Department of Prehistory, Laboratory of Quantitative Archaeology, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
Department of Mathematics, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Barcelona, Spain.
PLoS One. 2022 Oct 26;17(10):e0276088. doi: 10.1371/journal.pone.0276088. eCollection 2022.
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.
本研究旨在探讨小规模农业社会中人类与环境的相互作用。我们的研究集中在技术有限且社会分工尚未充分发展的情况下，定居点位置如何限制经济策略。我们的目的是研究当农业在旧世界伊比利亚半岛的黎凡特海岸开始发展时，新石器时代的史前社会经济组织。我们通过使用机器学习方法从大量民族志和民族考古案例中提取相关信息来研究这个主题。本文探讨了贝叶斯网络作为独立变量（环境）和因变量（社会决策）的解释模型的用途，以及作为预测模型的用途。该研究强调了生计策略如何受到定居点的生态和地形变量的影响，以及它们与社会组织的关系。它还确定了贝叶斯网络作为一种合适的监督机器学习方法在研究社会生态系统中的作用，引入了它们的使用，以构建有用的数据驱动模型来解决相关的考古学和人类学问题。
