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A description and sensitivity analysis of the ArchMatNet agent-based model.

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Archaeologists use material remains to infer past social networks. The ArchMatNet model simulates these networks, revealing how archaeological data relates to actual social interactions and guiding model use.

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Agent-based modelArchaeological recordCultural transmissionHunter-gatherer networksMaterial cultureNetwork analysisSensitivity analysisSocial network proxy

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Area of Science:

  • Archaeological science
  • Computational archaeology
  • Social network analysis

Background:

  • Archaeologists infer past social interactions from material culture, as direct observation is impossible.
  • Social network analysis methods are commonly applied to archaeological data to reconstruct past social dynamics.

Purpose of the Study:

  • To explore the relationship between archaeological material networks and the social networks that produced them.
  • To introduce and test the ArchMatNet agent-based model for simulating past social systems.
  • To analyze the sensitivity of the ArchMatNet model to various parameters.

Main Methods:

  • Development of the ArchMatNet agent-based model, adaptable to diverse social systems (hunter-gatherer to horticulturalist).
  • Incorporation of flexible agent activities (hunting, trading, migration, etc.) and adjustable parameters for social, demographic, and historical dynamics.
  • Application of the one-factor-at-a-time (OFAT) approach for sensitivity analysis of model parameters.

Main Results:

  • The study assesses the ArchMatNet model's sensitivity to parameter changes, providing insights into its behavior.
  • Sensitivity analysis identifies how different parameters influence the emergent network structures.
  • The research establishes a framework for understanding the connection between material culture and social networks.

Conclusions:

  • The ArchMatNet model offers a flexible tool for simulating and understanding past social interactions through material remains.
  • Sensitivity analysis results serve as a user guide, informing parameter choices and model interpretation.
  • This work enhances the ability to reconstruct past social networks from archaeological data.