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Aging in Language Dynamics

Author

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  • Animesh Mukherjee
  • Francesca Tria
  • Andrea Baronchelli
  • Andrea Puglisi
  • Vittorio Loreto

Abstract

Human languages evolve continuously, and a puzzling problem is how to reconcile the apparent robustness of most of the deep linguistic structures we use with the evidence that they undergo possibly slow, yet ceaseless, changes. Is the state in which we observe languages today closer to what would be a dynamical attractor with statistically stationary properties or rather closer to a non-steady state slowly evolving in time? Here we address this question in the framework of the emergence of shared linguistic categories in a population of individuals interacting through language games. The observed emerging asymptotic categorization, which has been previously tested - with success - against experimental data from human languages, corresponds to a metastable state where global shifts are always possible but progressively more unlikely and the response properties depend on the age of the system. This aging mechanism exhibits striking quantitative analogies to what is observed in the statistical mechanics of glassy systems. We argue that this can be a general scenario in language dynamics where shared linguistic conventions would not emerge as attractors, but rather as metastable states.

Suggested Citation

  • Animesh Mukherjee & Francesca Tria & Andrea Baronchelli & Andrea Puglisi & Vittorio Loreto, 2011. "Aging in Language Dynamics," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-7, February.
  • Handle: RePEc:plo:pone00:0016677
    DOI: 10.1371/journal.pone.0016677
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    References listed on IDEAS

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    1. Mark Pagel & Quentin D. Atkinson & Andrew Meade, 2007. "Frequency of word-use predicts rates of lexical evolution throughout Indo-European history," Nature, Nature, vol. 449(7163), pages 717-720, October.
    2. Erez Lieberman & Jean-Baptiste Michel & Joe Jackson & Tina Tang & Martin A. Nowak, 2007. "Quantifying the evolutionary dynamics of language," Nature, Nature, vol. 449(7163), pages 713-716, October.
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    Cited by:

    1. Fan, Zhong-Yan & Lai, Ying-Cheng & Tang, Wallace Kit-Sang, 2020. "Likelihood category game model for knowledge consensus," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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