  Naming game simulates the process of naming an objective by a population of
agents organized in a certain communication network topology. By pair-wise
iterative interactions, the population reaches a consensus state
asymptotically. In this paper, we study naming game with communication errors
during pair-wise conversations, where errors are represented by 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 with different parameters, 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 whole population increases
linearly as the value of the error rate increases; 3) without applying any
strategy to eliminate learning errors, there is a threshold value of the
learning errors which impairs the convergence. The new findings help to better
understand the role of learning errors in naming game as well as human language
development from a network science perspective.
