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Seasonal Social Networks and Learning Opportunities Under Unbiased Cultural Transmission

Adam N Rorabaugh
Submitted By: adamant_2001
Submitted: May 17, 2015
Last Updated: Jul 13, 2015
118 Downloads (10 Downloads in the last 3 months)

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.

Cite This Model:
Rorabaugh, Adam N (2015, May 17). "Seasonal Social Networks and Learning Opportunities Under Unbiased Cultural Transmission" (Version 1). CoMSES Computational Model Library. Retrieved from:
Model Version: 1 [Latest]
Version Notes:

Platform: NetLogo 5.02
Programming Language: Logo (variant)
Operating System: Platform Independent
Licensed Under: Academic Free License 3.0
Instructions on Running This Model:
Open in Net Logo. Adjust sliders for desired variable settings. Press "setup." Then press "Go" to run simulation.

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