Language dynamics model with finite-range interactions influencing the diffusion of linguistic traits and human dispersal

Clément Zankoc, Els Heinsalu () and Marco Patriarca
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Clément Zankoc: National Institute of Chemical Physics and Biophysics
Els Heinsalu: National Institute of Chemical Physics and Biophysics
Marco Patriarca: National Institute of Chemical Physics and Biophysics

The European Physical Journal B: Condensed Matter and Complex Systems, 2024, vol. 97, issue 6, 1-15

Abstract: Abstract We study a multi-agent model of language dynamics that incorporates diffusion of linguistic traits and human dispersal, both influenced by local linguistic environment. We assume that each individual is characterized by a string, representing a language in terms of a set of linguistic features. Each individual can interact only with other individuals located within a finite neighborhood. The interaction between two individuals results in copying or passing a linguistic trait; the direction of the learning process is determined by the level of linguistic similarity with the neighborhood, estimated through the average Levenshtein distance. The latter determines also the diffusion coefficient of the random walk performed by the individuals. The dynamics of the model is investigated through numerical simulations over a wide range of parameters. Our results show a rich variety of possible final scenarios, ranging from language segregation and dialects formation to linguistic continua and consensus. The obtained language size distribution, spatial distribution of languages, and the correlation between geographic and linguistic distance at equilibrium resemble well the results observed in real systems. Graphical abstract The model dynamics incorporates diffusion of linguistic traits and human dispersal, both influenced by the local linguistic environment, in the spirit of the Axelrod and Shelling model, respectively. The system can reach different final scenarios ranging from consensus to fragmentation, like the equilibrium configuration shown that shows self-organized clusters: different symbols correspond to different languages (strings in the dendrogram) and each color represents a different dialect defined by the group emerging from the clustering analysis

Date: 2024
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DOI: 10.1140/epjb/s10051-024-00706-3

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