Emergent Linguistic Grammar from Network Dynamics

Abstract

This paper investigates the possibility of emergent linguistic grammar arising from the dynamic interactions within a large, interconnected network of agents. The central premise is that complex linguistic structures, traditionally considered the product of human cognitive development, can spontaneously emerge through self-organization and evolutionary processes. We propose a computational model simulating a network where agents communicate using a simplified set of rules, allowing for the observation of pattern formation that resembles grammatical structures. The model leverages network dynamics, specifically focusing on synchronization and feedback loops, to drive the emergence of linguistic features. The results demonstrate that, under specific conditions, complex patterns consistent with syntactic rules – such as hierarchical structures and dependency relationships – can arise without explicit programming of these rules. This research offers a new perspective on the origins of language, shifting the investigation from solely human cognition to the dynamics of distributed systems, and suggesting that emergent properties can arise in complex networks with minimal centralized control. The key metric for assessing emergent grammar is the degree of regularity and predictability in the patterns observed within the network. Further research will explore the influence of network topology, agent interaction rules, and the diversity of communication strategies on the resulting linguistic structures.