In our paper Modeling Lexicon Emergence as Concept Emergence in Networks a model for lexicon emergence in social networks is presented. The
model is based on a modified version of classic Naming Games, where agents’
knowledge is represented by means of formal contexts.
That way it is possible
to represent the effect interactions have on individual knowledge as well as the
dynamics of global knowledge in the network.
Experiments lead us, roughly speaking that intend-extend based communication produces collective knowledge
fast, but will remain inconsistent (with respect to the global knowledge) until the
global knowledge has been achieved. While those based on implication bases do not
converge to the global knowledge, but produce partial knowledge consistent with the
global one. It is worthy to note that despite the convergence rate being slower using
network topology, both scenarios present the same behavior (exponential).