Predicting Language Shifts and Extinction Using Reinforcement Learning Models

Language evolution is influenced by various sociocultural and economic factors, leading to shifts in usage patterns and, in some cases, extinction. Predicting these shifts is essential for language preservation efforts and policymaking. Existing methods rely on statistical models and linguistic surveys, which struggle with dynamic adaptation to real-world language use and fail to provide long-term predictive accuracy. To address these limitations, we propose a reinforcement learning (RL)-based framework that models language adoption, shift, and extinction risks. Our approach leverages Markov decision processes (MDPs) to simulate language competition and reward mechanisms for sustained usage. By incorporating agent-based simulations and real-world linguistic data, the model dynamically adjusts to evolving sociolinguistic factors. The proposed method enables proactive policy formulation by simulating interventions, such as education reforms or media influence, to assess their impact on language survival.Additionally, it provides insights into critical transition points where a language may face an irreversible decline. Our findings demonstrate that RL models effectively predict language shifts more accurately than traditional statistical approaches. The results offer valuable guidance for linguists, policymakers, and cultural organizations in designing strategies to sustain endangered languages and understand global linguistic trends

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