Phase transitions in a decentralized graph-based approach to human language
This provides a decentralized explanation for Zipfian properties in language, which is incremental to existing optimization-based theories.
The paper tackled the emergence of Zipf's law in human language by proposing a decentralized language game model using bipartite graphs, with numerical simulations showing a phase transition from disorder to three language formation phases.
Zipf's law establishes a scaling behavior for word-frequencies in large text corpora. The appearance of Zipfian properties in human language has been previously explained as an optimization problem for the interests of speakers and hearers. On the other hand, human-like vocabularies can be viewed as bipartite graphs. The aim here is double: within a bipartite-graph approach to human vocabularies, to propose a decentralized language game model for the formation of Zipfian properties. To do this, we define a language game, in which a population of artificial agents is involved in idealized linguistic interactions. Numerical simulations show the appearance of a phase transition from an initially disordered state to three possible phases for language formation. Our results suggest that Zipfian properties in language seem to arise partly from decentralized linguistic interactions between agents endowed with bipartite word-meaning mappings.