The University of Southampton
University of Southampton Institutional Repository

On the evolutionary language game in structured and adaptive populations

On the evolutionary language game in structured and adaptive populations
On the evolutionary language game in structured and adaptive populations
We propose an evolutionary model for the emergence of shared linguistic convention in a population of agents whose social structure is modelled by complex networks. Through agent-based simulations, we show a process of convergence towards a common language, and explore how the topology of the underlying networks affects its dynamics. We find that small-world effects act to speed up convergence, but observe no effect of topology on the communicative efficiency of common languages. We further explore differences in agent learning, discriminating between scenarios in which new agents learn from their parents (vertical transmission) versus scenarios in which they learn from their neighbors (oblique transmission), finding that vertical transmission results in faster convergence and generally higher communicability. Optimal languages can be formed when parental learning is dominant, but a small amount of neighbor learning is included. As a last point, we illustrate an exclusion effect leading to core-periphery networks in an adaptive networks setting when agents attempt to reconnect towards better communicators in the population.
1932-6203
Danovski, Kaloyan
e1d648c0-67a1-435a-b336-1e81bed69887
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
Danovski, Kaloyan
e1d648c0-67a1-435a-b336-1e81bed69887
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7

Danovski, Kaloyan and Brede, Markus (2022) On the evolutionary language game in structured and adaptive populations. PLoS ONE, 17 (8 August), [e0273608]. (doi:10.1371/journal.pone.0273608).

Record type: Article

Abstract

We propose an evolutionary model for the emergence of shared linguistic convention in a population of agents whose social structure is modelled by complex networks. Through agent-based simulations, we show a process of convergence towards a common language, and explore how the topology of the underlying networks affects its dynamics. We find that small-world effects act to speed up convergence, but observe no effect of topology on the communicative efficiency of common languages. We further explore differences in agent learning, discriminating between scenarios in which new agents learn from their parents (vertical transmission) versus scenarios in which they learn from their neighbors (oblique transmission), finding that vertical transmission results in faster convergence and generally higher communicability. Optimal languages can be formed when parental learning is dominant, but a small amount of neighbor learning is included. As a last point, we illustrate an exclusion effect leading to core-periphery networks in an adaptive networks setting when agents attempt to reconnect towards better communicators in the population.

Text
journal.pone.0273608 - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)

More information

Accepted/In Press date: 7 August 2022
e-pub ahead of print date: 30 August 2022

Identifiers

Local EPrints ID: 470412
URI: http://eprints.soton.ac.uk/id/eprint/470412
ISSN: 1932-6203
PURE UUID: c1f19fbc-bc6f-4875-8eaf-688b16875e7d

Catalogue record

Date deposited: 10 Oct 2022 16:53
Last modified: 13 Oct 2022 16:31

Export record

Altmetrics

Contributors

Author: Kaloyan Danovski
Author: Markus Brede

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×