Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions

This collection contains the abstracts of the papers presented in the session entitled "Machine-Learning Approaches to Studying Ancient Human-Environmental Interactions" at the 90th annual meeting of the Society for American Archaeology.

Archaeologists have long used quantitative statistical analyses to understand past human-environmental interactions on a wide range of topics, including past foodways, landscape use, and social organization. Zooarchaeology and environmental archaeology, in particular, are well positioned to tackle these issues as analyses of faunal remains, climatic variability, and landscape dynamics, among other things, provides critical insights into past peoples and societies. Moreover, many of these analysts have begun using cutting-edge machine-learning statistical techniques to answer research questions on these same topics. The goal of this session is to highlight the applicability and analytical power of machine learning statistical approaches to answering questions about long term human-environmental interactions. These new tools have the power to significantly contribute to and help answer a diverse array of theoretically informed research questions.