Spatial community structure impedes language amalgamation in a population-based iterated learning model
Abstract
The iterated learning model is an agent-based model of language evolution notable for demonstrating the emergence of compositional language. In its original form, it modelled language evolution along a single chain of teacher-pupil interactions; here we modify the model to allow more complex patterns of communication within a population and use the extended model to quantify the effect of within-community and between-community communication frequency on language development. We find that a small amount of between-community communication can lead to population-wide language convergence but that this global language amalgamation is more difficult to achieve when communities are spatially embedded.
Cite
@article{arxiv.2305.11962,
title = {Spatial community structure impedes language amalgamation in a population-based iterated learning model},
author = {George Sains and Conor Houghton and Seth Bullock},
journal= {arXiv preprint arXiv:2305.11962},
year = {2023}
}
Comments
8 pages, 7 figures, to be published in Artificial Life 2023