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Analysis of the "naming game" with learning errors in communications.
1Department of Electronic Engineering, City University of Hong Kong, Hong Kong SAR, China.
Scientific Reports
|July 17, 2015
Summary
Communication errors in the naming game slightly slow convergence but increase memory needs. Higher error rates linearly increase word diversity, with a critical threshold impairing consensus in agent populations.
Area of Science:
- Network Science
- Computational Linguistics
- Agent-Based Modeling
Background:
- The naming game models how populations reach consensus on names through interactions.
- Communication errors can significantly impact consensus-building processes.
Purpose of the Study:
- To investigate the effects of learning errors on the naming game dynamics.
- To propose and evaluate strategies for mitigating these errors.
- To analyze error impacts across different network topologies.
Main Methods:
- Development of a model for the naming game with learning errors (NGLE).
- Simulations on random-graph, small-world, and scale-free network topologies.
- Analysis of convergence speed, memory requirements, and lexicon diversity.
Main Results:
- Learning errors minimally affect convergence speed but significantly increase agent memory demands.
- A linear relationship exists between error rate and the number of distinct words.
- A critical error threshold was identified that hinders consensus without error-mitigation strategies.
Conclusions:
- Learning errors play a crucial role in shaping lexicon diversity and memory requirements in agent communication.
- Understanding these error dynamics offers insights into human language development and network communication.
- Error-mitigation strategies are essential for efficient consensus in noisy communication environments.
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