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.
Difficulties surrounding the reconstruction of social systems in past communities have propitiated the development of multiple social theories and a variety of approaches to explain archaeological remains. The Bayesian Network approach has proved to be a crucial tool to model uncertainty and probability to estimate parameters and predict the effects of social decisions, even when some data entries are missing. This paper has the principal objective to present a research study centered on exploring how prehistoric early farmers survived in their environmental context by suggesting a causal complex model of a socio-ecological system. To achieve this, two different causal models are proposed, both based on probabilistic Bayesian Networks, one built from expert knowledge and the other learned from ethnoarchaeological data. These models are used to define what variables would have been relevant to the socioeconomic organization of early Neolithic communities and to predict their behavior and social decisions in hypothetical case scenarios. The ultimate outcome is exploring the use of the Bayesian Network for investigating socio-ecological systems and defining its potentialities as a research method.
Aunque las primeras aplicaciones de aprendizaje automático en arqueología datan de finales de los años 90, no ha sido hasta el año 2019 cuando su uso se ha empezado a extender. ¿Qué ventajas tiene esta metodología respecto a otros métodos con una trayectoria más larga en arqueología? ¿Se puede aplicar en todos los ámbitos de estudio? La presente contribución tiene el objetivo de dar respuesta a estas cuestiones a través de una exhaustiva revisión de los estudios arqueológicos realizados con esta metodología y desarrollando un modelo con un algoritmo concreto, las redes bayesianas, para explorar sus beneficios y limitaciones. Despite initial attempts to apply machine learning to archaeology dating back to the late 1990s, it was not until 2019 that its use began to become widespread. What advantages does this methodology have over previous methods? Can it be applied to all relevant fields of study? This article aims to answer these questions through an exhaustive review of archaeological studies that employ this methodology and by developing a model with a specific algorithm, based on Bayesian networks, to explore its benefits and limitations.
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