Investigating language change: A multi-agent neural-network based simulation

Multiple agents, equipped with a feature-based phonetic model and a connectionist cognitive model, interact via the naming game, with lexicon formation and change as emergent properties of this complex adaptive system. We present a new description of the naming game, situating it as a general, implementation-independent paradigm. Our addition of richer phonetic and cognitive models provides the agents with a greater degree of cognitive validity than does earlier work, while enhancing the flexibility of the system and reproducing empirical results. Feature-based phonetics, piecewise reinforcement learning, and a connectionist architecture with local representation allows language discrimination based on schemata instead of entire utterances.