In Defense of Data: Realigning Archaeological Modeling Theory with Modern Statistical Learning Approaches

Summary

The acceptance of statistical modeling as common practice in archaeological studies is highly varied across applications and methodological focus. As a field, we lack a unified body of model building theory, best practices, and examples that demonstrate the successes and failures of various techniques applied specifically to archaeological data. The literature on archaeological predictive modeling (APM) provides a notable example in the form of the "Inductive" vs. "Deductive" debate. This false dichotomy unduly influences the general perception and approachability of these methods. Though there is a recent uptick in model-based analysis in archaeology, progress has been hampered by the APM tumult and anemic publication rate for quantitative methods research following the post-processual critique. Quantitative approaches in archaeology have lagged behind the trends in neighboring fields such as social sciences, ecology, and economics. Recent advances in statistical methods, analytical software, and the open science initiative present an opportunity for the construction of a framework for model based archaeology from which the evaluation of techniques and findings can be more accessible to the entire field. This presentation will discuss the motivations for such an approach, explore the theory of APM through examples, and offer potential routes for constructing such a framework.

This Resource is Part of the Following Collections

• Society for American Archaeology 81st Annual Meeting, Orlando, FL (2016)

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