Uncertainty And Sensitivity Analysis In Archaeological Computational Modeling
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Uncertainty and Sensitivity Analysis in Archaeological Computational Modeling
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.
Computational and Machine Learning Tools for Archaeological Site Modeling
Author: Maria Elena Castiello
language: en
Publisher: Springer Nature
Release Date: 2022-01-24
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.
Discourse and Argumentation in Archaeology: Conceptual and Computational Approaches
Author: Cesar Gonzalez-Perez
language: en
Publisher: Springer Nature
Release Date: 2023-11-03
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.