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Related Experiment Video

Updated: May 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Naming game on adaptive weighted networks.

Dorota Lipowska¹, Adam Lipowski

¹Adam Mickiewicz University, Poznan, Poland. lipowska@amu.edu.pl

Artificial Life

|June 6, 2012

Summary

This summary is machine-generated.

This study models language evolution on adaptive networks. High communication success leads to a single language, while lower success can trap the system in a multi-language state, with one dominant language emerging.

Related Experiment Videos

Last Updated: May 21, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Complex systems
Computational social science
Network science

Background:

The emergence and evolution of language are complex phenomena studied through various models.
Agent-based models on networks offer insights into collective behavior and linguistic dynamics.
Adaptive networks, where connections change based on interactions, add realism to these models.

Purpose of the Study:

To investigate the dynamics of language formation in an adaptive weighted network model.
To explore how communication success rates influence linguistic diversity and convergence.
To compare model outcomes with real-world language distribution patterns.

Main Methods:

Simulating a naming game on a network where agent connection weights adapt based on communication success.
Analyzing model behavior under different parameter settings, varying the influence of communication success.
Comparing the resulting language distributions with empirical data from human populations.

Main Results:

In some parameter regimes, the model rapidly converges to a single language, similar to complete graph models.
In other regimes, a strong preference for successful communication leads to a multi-language state.
Gradual language extinction occurs in multi-language states, resulting in a dominant language alongside others.

Conclusions:

Adaptive weighted networks can exhibit diverse linguistic outcomes, from rapid convergence to persistent multilingualism.
The balance between network adaptivity and communication success is critical in determining language evolution.
The model provides a framework for understanding factors contributing to linguistic diversity and dominance.