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A Framework for Reconstructing Archaeological Networks Using Exponential Random Graph Models.

Viviana Amati1, Angus Mol2, Termeh Shafie3

  • 1Department of Humanities, Social and Political Sciences, Social Networks Lab, ETH Zurich, Weinbergstrasse 109, 8092 Zurich, Switzerland.

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|June 9, 2020
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Summary
This summary is machine-generated.

This study introduces a new framework combining exponential random graph models with archaeological data to reconstruct ancient social networks. This method enhances understanding of past societies across diverse archaeological settings.

Keywords:
Caribbean networksEarly Ceramic AgeExponential random graph modelsNetwork reconstruction

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Area of Science:

  • Archaeology
  • Social Network Analysis
  • Computational Social Science

Background:

  • Reconstructing past social networks is crucial for understanding historical social phenomena.
  • Existing models for inferring archaeological networks have limitations due to restricted assumptions and dyadic formulations.
  • Diverse archaeological contexts require flexible network reconstruction approaches.

Purpose of the Study:

  • To present a general framework for reconstructing archaeological networks by integrating exponential random graph models with archaeological evidence.
  • To overcome limitations of existing models by incorporating specific archaeological mechanisms of network formation.
  • To provide a versatile method applicable to a wide range of archaeological settings.

Main Methods:

  • Combining exponential random graph models (ERGMs) with archaeological substantiations.
  • Developing a framework that incorporates mechanisms driving network formation.
  • Applying the framework to a dataset from Caribbean sites (AD 100-400).

Main Results:

  • Demonstrated a novel framework for inferring ancient network structures.
  • Illustrated the practical application of the framework using Caribbean archaeological data.
  • Showcased the potential for broader applicability in diverse archaeological research.

Conclusions:

  • The proposed framework offers a more robust and adaptable approach to reconstructing archaeological social networks.
  • Integrating statistical models with archaeological context provides deeper insights into past social dynamics.
  • This methodology facilitates a better understanding of societal structures in various historical periods and regions.