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.
Danovski, Kaloyan
e1d648c0-67a1-435a-b336-1e81bed69887
Brede, Markus
bbd03865-8e0b-4372-b9d7-cd549631f3f7
30 August 2022
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), [e0273608].
(doi:10.1371/journal.pone.0273608).
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.
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journal.pone.0273608
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Accepted/In Press date: 7 August 2022
Published date: 30 August 2022
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© 2022 Danovski, Brede. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Local EPrints ID: 470412
URI: http://eprints.soton.ac.uk/id/eprint/470412
ISSN: 1932-6203
PURE UUID: c1f19fbc-bc6f-4875-8eaf-688b16875e7d
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Date deposited: 10 Oct 2022 16:53
Last modified: 16 Mar 2024 22:13
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Author:
Kaloyan Danovski
Author:
Markus Brede
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