Kailin Hatlestad: Exploring Uncertainty and Significance: Analysing Human Response to Environmental Risk with Computational Archaeology

Abstract

As humanity confronts the escalating challenges posed by rapid climate change, it becomes increasingly urgent to understand the complex dynamics of human-environment interactions to mitigate its multifaceted impacts. Archaeology, with its long-term perspective, offers the opportunity to examine past societal responses to environmental risks across diverse locations in Northwestern Europe and temporal scales. 

This dissertation aims to contribute to this critical endeavour by exploring the socio-environmental dynamics and adaptive strategies of past societies, to inform effective responses to climate change challenges in both the present and future. Utilizing computational archaeology, which integrates digital technologies and computational methods to analyse big data, the dissertation employs probabilistic approaches, including Bayesian modelling like summed probability distributions of radiocarbon (14C) data, to confront uncertainties inherent in reconstructing past human-environmental dynamics from interdisciplinary datasets. Additionally, quantitative methods, such as correlation tests and null hypothesis testing of 14C data, are employed to identify significant shifts in these dynamics, translating insights into quantitative terms for enhanced integration with policy-making processes. 

The primary objective of the dissertation is to illustrate how the integration of archaeological and environmental big data can enrich the understanding of human responses to environmental challenges. The papers in this thesis demonstrate how computational methods can be applied to big data to understand spatiotemporal changes in human-environmental variables, uncovering risk management strategies and societal vulnerabilities. The papers highlight cases where human communities experienced mitigated adverse effects from severe environmental shifts due to diverse socioeconomic strategies. Simultaneously, the results emphasize regional variations in the impacts of climate change, crucial for understanding the effectiveness of human responses. Moreover, the thesis exhibits how big data analytics both complement and challenge existing archaeological interpretations, contributing to the development of new theories. Importantly, it underscores the significance of diverse socioeconomic strategies in mitigating risks, especially in the face of abrupt environmental events.