The Role of Social Cooperation in the Evolution of Recursive Language Structures

The emergence of recursive structures in human language has been a subject of significant interest in linguistic and cognitive sciences. Recursion allows humans to produce an infinite variety of complex expressions from a limited set of linguistic elements. It is often considered one of the fundamental features of human language. This paper proposes that the evolution of recursive language structures is intricately linked to social cooperation. Unlike prior theoretical work, this study uses agent-based modelling to empirically test how the demands of social coordination, such as the need to manage complex relationships and resource-sharing tasks, may have driven the development of recursion. In order to examine how social collaboration contributes to the development of recursive language structures, a computational model simulating agents doing cooperative tasks will be designed incorporating artificial neural networks, iterated learning frameworks, and evolutionary algorithms. Through computational models, the paper demonstrates how recursive language can evolve in virtual agents placed under cooperative social pressures, ultimately improving the agents' abilities to communicate and cooperate effectively. The study makes a contribution to understanding the co-evolution of language and social behaviour by offering a computational approach to studying recursive syntax within the context of social cooperation.