This book describes a novel machine-learning based approach to answer some traditional archaeological problems, relating to archaeological site detection and site locational preferences. Institutional data collected from six Swiss regions (Zurich, Aargau, Grisons, Vaud, Geneva and Fribourg) have been analyzed with an original conceptual framework based on the Random Forest algorithm. It is shown how the algorithm can assist in the modelling process in connection with heterogeneous, incomplete archaeological datasets and related cultural heritage information. Moreover, an in-depth review of past and more recent works of quantitative methods for archaeological predictive modelling is provided. The book guides the readers to set up their own protocol for: i) dealing with uncertain data, ii) predicting archaeological site location, iii) establishing environmental features importance, iv) and suggest a model validation procedure. It addresses both academics and professionals in archaeology and cultural heritage management, and offers a source of inspiration for future research directions in the field of digital humanities and computational archaeology.

This book covers the topic of discourse and argumentation in archaeology with an aim to serve the archaeology community. The book presents discourse and argument analysis approaches and techniques in an affordable manner and applied to archaeological situations. It focuses on techniques and approaches that can be applicable to multiple situations, periods and cultures. The book begins with an introduction to discourse and argumentation analysis as a general field and also as an auxiliary technique to archaeology. The work includes conceptual applications, ranging from causality, ontological connections, vagueness, social production of discourse and public debates. The work also devotes a section to computational approaches and describes the specifics of some well-known families of algorithms such as lexical processing, information extraction or sentiment analysis. The conclusion comments on the future and which reflects on the previous chapters and discusses how the presented techniques and approaches should be adapted or improved for easier and more powerful application to archaeology. Contributing authors bring perspectives from archaeology, linguistics, and computer science.

Provides analytical theories offered by innovative artificial intelligence computing methods in the archaeological domain.

This volume deals with the pressing issue of uncertainty in archaeological modeling. Detecting where and when uncertainty is introduced to the modeling process is critical, as are strategies for minimizing, reconciling, or accommodating such uncertainty. Included chapters provide unique perspectives on uncertainty in archaeological modeling, ranging in both theoretical and methodological orientation. The strengths and weaknesses of various identification and mitigation techniques are discussed, in particular sensitivity analysis. The chapters demonstrate that for archaeological modeling purposes, there is no quick fix for uncertainty; indeed, each archaeological model requires intensive consideration of uncertainty and specific applications for calibration and validation. As very few such techniques have been problematized in a systematic manner or published in the archaeological literature, this volume aims to provide guidance and direction to other modelers in the field by distilling some basic principles for model testing derived from insight gathered in the case studies presented. Additionally, model applications and their attendant uncertainties are presented from distinct spatio-temporal contexts and will appeal to a broad range of archaeological modelers. This volume will also be of interest to non-modeling archaeologists, as consideration of uncertainty when interpreting the archaeological record is also a vital concern for the development of non-formal (or implicit) models of human behavior in the past.

This book offers insight into the relationship between prehistoric and protohistoric human populations and the world around them. It reconstructs key aspects of the palaeoenvironment – from large-scale drivers of environmental conditions, such as climate, to more regional variables such as vegetation cover and faunal communities. The volume underscores how computational archaeology is leading the way in the study of past human-environment interactions across spatial and chronological scales. With the increased availability of high-resolution climate models, agent-based modelling, palaeoecological proxies and the mature use of Geographic Information System in ecological modelling, archaeologists working in interdisciplinary settings are well-positioned to explore the intersection of human systems and environmental affordances and constraints. These methodological advancements provide a better understanding of the role humans played in past ecosystems – both in terms of their impact upon the environment and, in return, the impact of environmental conditions on human systems. They may also allow us to infer past ecological knowledge and land-use patterns that are historically contingent, rather than environmentally determined. This volume gathers contributions that combine reconstructions of past environments and archeological data with a view to exploring their complex interactions at different scales and invites scholars from varying disciplines and backgrounds to present and compare different modelling approaches.

This volume of original chapters written by experts in the field offers a snapshot of how historical built spaces, past cultural landscapes, and archaeological distributions are currently being explored through computational social science. It focuses on the continuing importance of spatial and spatio-temporal pattern recognition in the archaeological record, considers more wholly model-based approaches that fix ideas and build theory, and addresses those applications where situated human experience and perception are a core interest. Reflecting the changes in computational technology over the past decade, the authors bring in examples from historic and prehistoric sites in Europe, Asia, and the Americas to demonstrate the variety of applications available to the contemporary researcher.

Although many archaeologists have a good understanding of the basics in computer science, statistics, geostatistics, modeling, and data mining, more literature is needed about the advanced analysis in these areas. This book aids archaeologists in learning more advanced tools and methods while also helping mathematicians, statisticians, and computer scientists with no previous knowledge of the field realize the potential of the methods in archaeological experiments.