Spatial community structure impedes language amalgamation in a population-based iterated learning model

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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.
Original languageEnglish
Title of host publicationProceedings of the Artificial Life Conference 2023 (ALIFE 2023)
EditorsHero Iizuka, Keisuke Suzuki, Ryoko Uno, Luisa Damiano, N Spychalav, Miguel Aguilera, Eduardo Izquierdo, Reiji Suzuki, Manuel Baltieri
PublisherMassachusetts Institute of Technology (MIT) Press
Pages377-385
Number of pages9
DOIs
Publication statusE-pub ahead of print - 24 Jul 2023
EventThe 2023 Conference on Artificial Life - Sapporo, Japan
Duration: 24 Jul 202328 Jul 2023

Publication series

NameALIFE : proceedings of the artificial life conference.
PublisherMIT Press
ISSN (Electronic)2693-1508

Conference

ConferenceThe 2023 Conference on Artificial Life
Country/TerritoryJapan
CitySapporo
Period24/07/2328/07/23

Keywords

  • language
  • evolution
  • iterated learning model
  • Agent-Based Model
  • network
  • space
  • community structure

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