COMPUTATIONAL MODELS OF LANGUAGE EVOLUTION

The evolution of language represents a complex and multifaceted phenomenon central to human cognitive and social development. Understanding the mechanisms that underpin language emergence, change, and stabilization over time has been a longstanding challenge in linguistics, anthropology, and cognitive science. Recently, computational modeling has emerged as a powerful approach to investigate language evolution by simulating the interaction of agents engaged in learning, communication, and cultural transmission of language. This article explores the major computational paradigms employed to model language evolution, including agent-based models, iterated learning frameworks, and evolutionary game theory models. These computational approaches illuminate how linguistic structure can arise, be transmitted, and evolve within populations. Furthermore, advances in neural network-based learning models and large-scale empirical data integration are discussed as promising avenues for future research. Despite limitations, computational models continue to provide valuable insights into the dynamic processes shaping language and hold the potential to bridge gaps between theoretical predictions and observed linguistic phenomena.