Ethnoarchaeological Inductive Predictive Model: A Field Test in the Italian Alps

Abstract Inductive predictive modelling is a controversial tool in archaeology. Visibility, taphonomy and research history can affect the statistical reliability of an archaeological dataset to be used as a training sample for a predictive model. To overcome these biases, an ethnoarchaeological approach has been proposed. This methodology has been developed and tested on a pastoral context in the Eastern Italian Alps. The present research proposes an application to a different, more composite landscape in the Central Italian Alps, testing the reliability of the model in relation to a heterogeneous and diachronic dataset, collected through extensive fieldwork. The results show that the model has an excellent degree of accuracy in predicting past structures with a similar purpose of use as the training sample. In addition, we show that its discriminative power can be greatly improved by the use of contemporary environmental predictors. However, the use of variables non-quantifiable for the past is an issue for the full applicability of this type of model to archaeological datasets. The results also show that this methodology, regardless of predictive results, can give us a good insight into the relationship between humans and their environment. The field application of the methodology has led us to understand that ethnoarchaeology can already be considered a reliable approach to address various methodological concerns of archaeological predictive modelling, but the primary purpose of such models should be seen more as explanatory rather than predictive.

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Publication Details

Journal
Journal of Archaeological Method and Theory
Published
2025-05-08
DOI
https://doi.org/10.1007/s10816-025-09712-w
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
0.00

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article

Ethnoarchaeological Inductive Predictive Model: A Field Test in the Italian Alps

Diego E. Angelucci, Francesco Carrer, E. Croce
Journal of Archaeological Method and Theory
Image Processing and 3D Reconstruction
article

Ethnoarchaeological Inductive Predictive Model: A Field Test in the Italian Alps

Diego E. Angelucci, Francesco Carrer, E. Croce
article en

Abstract

Abstract Inductive predictive modelling is a controversial tool in archaeology. Visibility, taphonomy and research history can affect the statistical reliability of an archaeological dataset to be used as a training sample for a predictive model. To overcome these biases, an ethnoarchaeological approach has been proposed. This methodology has been developed and tested on a pastoral context in the Eastern Italian Alps. The present research proposes an application to a different, more composite landscape in the Central Italian Alps, testing the reliability of the model in relation to a heterogeneous and diachronic dataset, collected through extensive fieldwork. The results show that the model has an excellent degree of accuracy in predicting past structures with a similar purpose of use as the training sample. In addition, we show that its discriminative power can be greatly improved by the use of contemporary environmental predictors. However, the use of variables non-quantifiable for the past is an issue for the full applicability of this type of model to archaeological datasets. The results also show that this methodology, regardless of predictive results, can give us a good insight into the relationship between humans and their environment. The field application of the methodology has led us to understand that ethnoarchaeology can already be considered a reliable approach to address various methodological concerns of archaeological predictive modelling, but the primary purpose of such models should be seen more as explanatory rather than predictive.

Journal of Archaeological Method and TheoryVol. 32(3)
University of Milan (IT), University of Trento (IT), Newcastle University (GB)
Università degli Studi di Milano
Openalex Percentile: Top 92%
Image Processing and 3D Reconstruction
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