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This study models how agents develop a shared language on complex networks. Vertical transmission (parental learning) speeds up language convergence and improves communication more than neighbor learning.

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Area of Science:

  • Computational Social Science
  • Evolutionary Linguistics
  • Network Science

Background:

  • Understanding the emergence of shared linguistic conventions is crucial for human communication.
  • Complex social structures, modeled by networks, likely influence language evolution dynamics.

Purpose of the Study:

  • To develop an evolutionary model for shared linguistic convention emergence.
  • To investigate the impact of network topology and agent learning strategies on language convergence and communicative efficiency.

Main Methods:

  • Agent-based simulations on complex networks.
  • Modeling of vertical (parental) and oblique (neighbor) transmission learning mechanisms.
  • Analysis of network topology effects (e.g., small-world networks) on language dynamics.

Main Results:

  • Small-world network effects accelerate language convergence but do not impact communicative efficiency.
  • Vertical transmission leads to faster convergence and higher communicability compared to oblique transmission.
  • Optimal languages emerge with dominant parental learning and a small degree of neighbor learning.
  • An exclusion effect drives core-periphery network formation when agents adapt towards better communicators.

Conclusions:

  • Social network structure and learning strategies significantly shape linguistic convention evolution.
  • Vertical transmission is a more effective strategy for rapid and efficient language development.
  • Adaptive network rewiring based on communication success can lead to emergent social structures.