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Agreement dynamics on interaction networks with diverse topologies
Alain Barrat1, Andrea Baronchelli, Luca Dall'Asta
1LPT, CNRS, UMR 8627, Univ Paris-Sud, Orsay, F-91405, France.
Chaos (Woodbury, N.Y.)
|July 7, 2007
Summary
The Naming Game model shows how agents reach agreement. Small-world networks balance local consensus and global convergence for efficient communication systems.
Area of Science:
- Complex systems
- Computational social science
- Agent-based modeling
Background:
- The Naming Game models the self-organized emergence of linguistic conventions.
- It studies the establishment of communication systems in populations with local interactions.
Purpose of the Study:
- To review the behavior of the Naming Game model.
- To analyze the impact of interaction networks on agreement dynamics.
- To explore strategies for faster global convergence.
Main Methods:
- Agent-based simulation of the Naming Game.
- Analysis of convergence dynamics on different network topologies (mean-field, lattice, small-world).
- Investigation of alternative adaptive strategies.
Main Results:
- Mean-field networks are inefficient due to high memory demands.
- Lattice networks show slow global convergence (coarsening).
- Small-world networks offer a tradeoff, enabling local consensus and efficient global convergence via long-range links.
Conclusions:
- Network topology critically influences agreement dynamics in the Naming Game.
- Small-world networks provide an effective structure for establishing communication systems.
- Adaptive strategies can further enhance the speed of global convergence.
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