Abstract
Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.
Highlights
Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network
As implemented in[8], when the memory of the picked speaker is empty, it will randomly pick a word from the vocabulary instead of randomly creating a new word; when learning error occurs, the hearer will randomly pick a word from the vocabulary and this word should be different from the word received
We proposed a novel model of naming game with learning errors in communications (NGLE) and study it by means of extensive and comprehensive computer simulations
Summary
Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. Three typical topologies of communication networks, namely randomgraph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. NG on small-world networks was studied in[4,5], and NG on random-graph networks and scale-free networks was studied[6]
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