Noisy Preferential Attachment and Language Evolution

摘要

We study the role of the agent interaction topology in distributed language learning. In particular, we utilize the replicator-mutator framework of language evolution for the creation of an emergent agent interaction topology that leads to quick convergence. In our system, it is the links between agents that are treated as the units of selection and replication, rather than the languages themselves. We use the Noisy Preferential Attachment algorithm, which is a special case of the replicator-mutator process, for generating the topology. The advantage of the NPA algorithm is that, in the short-term, it produces a scale-free interaction network, which is helpful for rapid exploration of the space of languages present in the population. A change of parameter settings then ensures convergence because it guarantees the emergence of a single dominant node which is chosen as teacher almost always.