Emergent Linguistic Phenomena in Multi-Agent Communication Games
This work addresses the problem of modeling language evolution for researchers in computational linguistics and AI, though it is incremental as it builds on existing multi-agent communication frameworks.
The study tackled the problem of understanding linguistic evolution by simulating multi-agent communication games, demonstrating that complex linguistic phenomena like creole formation and mutual intelligibility gradients can emerge from simple social interactions, with results showing that balanced contact leads to novel creole languages of lower complexity.
In this work, we propose a computational framework in which agents equipped with communication capabilities simultaneously play a series of referential games, where agents are trained using deep reinforcement learning. We demonstrate that the framework mirrors linguistic phenomena observed in natural language: i) the outcome of contact between communities is a function of inter- and intra-group connectivity; ii) linguistic contact either converges to the majority protocol, or in balanced cases leads to novel creole languages of lower complexity; and iii) a linguistic continuum emerges where neighboring languages are more mutually intelligible than farther removed languages. We conclude that intricate properties of language evolution need not depend on complex evolved linguistic capabilities, but can emerge from simple social exchanges between perceptually-enabled agents playing communication games.