Computational Model Library

Seasonal Social Networks and Learning Opportunities Under Unbiased Cultural Transmission (version 1.0.0)

Understanding the relationships between seasonal social networks and diversity in artifact styles, is crucial for examining the production and reproduction of knowledge among complex foraging societies such as those of the Pacific Northwest Coast. This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission). The results of these simulations suggest that the relationship between learning opportunities and innovation rate has more impact on artifact style richness and evenness than seasonal social networks. Seasonal aggregation does appear to result in a higher amount of one-off rare variants, but this effect is not statistically significant. Overall, the restriction of learning opportunities appears more crucial in patterning cultural diversity among complex foragers than the potential impacts from individuals drawing on different seasonal social networks.

Release Notes

Version Submitter First published Last modified Status
1.0.0 Adam Rorabaugh Mon May 18 00:46:47 2015 Mon May 18 00:46:47 2015 Published

Discussion

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