Computational Model Library

ArchMatNet: Archaeological Material Networks (1.1.0)

The purpose of the model is to investigate how different factors affect the ability of researchers to reconstruct prehistoric social networks from artifact stylistic similarities, as well as the overall diversity of cultural traits observed in archaeological assemblages. Given that cultural transmission and evolution is affected by multiple interacting phenomena, our model allows to simultaneously explore six sets of factors that may condition how social networks relate to shared culture between individuals and groups:

  1. Factors relating to the structure of social groups
  2. Factors relating to the cultural traits in question
  3. Factors relating to individual learning strategies
  4. Factors relating to the environment
  5. Factors relating to the context in which different types of cultural traits are learned
  6. Factors relating to the method used to reconstruct ancient social networks

The model follows the movement and interactions of people living in camps that belong to bands. People hunt in groups, create allies and visit them, aggregate at regular intervals, and migrate. When they interact with one another, people can trade objects and known object traits (ideas). They create objects with characteristics based on the known traits and can drop those objects at random times during a simulation run. This forms an “archaeological record” we can use to see if similar styles found at different sites are good indicators of the social contacts that occurred between the people who made them.

interface.png

Release Notes

In this version, changes were made to the visibility procedure so that highly visible traits are transmitted 98% of the time. Two new network comparison metrics were also added: adjCompare and adjCorr.

Associated Publications

Bischoff, R. J., & Padilla-Iglesias, C. (2023). A description and sensitivity analysis of the ArchMatNet agent-based model. PeerJ Computer Science, 9, e1419. https://doi.org/10.7717/peerj-cs.1419

ArchMatNet: Archaeological Material Networks 1.1.0

The purpose of the model is to investigate how different factors affect the ability of researchers to reconstruct prehistoric social networks from artifact stylistic similarities, as well as the overall diversity of cultural traits observed in archaeological assemblages. Given that cultural transmission and evolution is affected by multiple interacting phenomena, our model allows to simultaneously explore six sets of factors that may condition how social networks relate to shared culture between individuals and groups:

  1. Factors relating to the structure of social groups
  2. Factors relating to the cultural traits in question
  3. Factors relating to individual learning strategies
  4. Factors relating to the environment
  5. Factors relating to the context in which different types of cultural traits are learned
  6. Factors relating to the method used to reconstruct ancient social networks

The model follows the movement and interactions of people living in camps that belong to bands. People hunt in groups, create allies and visit them, aggregate at regular intervals, and migrate. When they interact with one another, people can trade objects and known object traits (ideas). They create objects with characteristics based on the known traits and can drop those objects at random times during a simulation run. This forms an “archaeological record” we can use to see if similar styles found at different sites are good indicators of the social contacts that occurred between the people who made them.

Release Notes

In this version, changes were made to the visibility procedure so that highly visible traits are transmitted 98% of the time. Two new network comparison metrics were also added: adjCompare and adjCorr.

Version Submitter First published Last modified Status
1.1.0 Cecilia Padilla-Iglesias Mon Feb 20 16:16:31 2023 Sun Mar 24 07:04:57 2024 Published Peer Reviewed https://doi.org/10.25937/08kx-4f24

Discussion

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept