Nonequilibrium dynamics of language games on complex networks.

Area of Science:

• Complex Systems
• Computational Linguistics
• Network Science

Background:

• The naming game models the emergence of communication systems through agent interactions.
• Understanding language evolution on complex networks is crucial for social dynamics.

Purpose of the Study:

• Investigate the dynamics of the naming game on complex networks.
• Analyze factors influencing consensus formation and convergence time.
• Examine memory capacity requirements and the role of network heterogeneity.

Main Methods:

• Agent-based modeling of the naming game.
• Simulations on various complex network topologies.
• Analysis of convergence times and memory scaling.

Main Results:

• Global consensus is typically reached rapidly on complex networks, faster than on lattices.
• Convergence time scales as N^1.4, with linear memory scaling.
• Network heterogeneity, particularly high-degree nodes, significantly impacts dynamics and convention spreading.

Conclusions:

• Complex networks facilitate efficient emergence of linguistic conventions.
• High-degree nodes act as crucial spreaders of communication norms.
• The naming game dynamics are robust across diverse network structures, with notable effects from heterogeneity.