Mathematical Modeling of Adaptive Fishery Management Using Reinforcement Learning

The application of mathematical modeling plays a crucial role in understanding and managing marine species and their exploitation. This study explores the integration of bioeconomic modeling and artificial intelligence to optimize fishery management while promoting ecosystem sustainability. Following our previous research, “Bioeconomic Modelling for the Sustainable Exploitation of Three Key Marine Species in Morocco,” the present work builds upon that foundation by extending the existing model and integrating additional biological and economic parameters to enhance its realism and predictive capacity. We first provide a general overview of bioeconomic models, highlighting previous research and the dual objectives of profit maximization and ecological preservation. We then introduce a mathematical model representing the exploitation of a single species within a three-species marine ecosystem. A detailed mathematical analysis is conducted to identify equilibrium points that ensure the persistence of all species, along with their positivity, boundedness, and stability. Building on this foundation, we apply Reinforcement Learning (RL) to the model, demonstrating how AI techniques can guide adaptive harvesting strategies. Finally, we perform a sensitivity analysis on key parameters influencing the RL implementation,
providing insights into the robustness and effectiveness of the approach. Our findings highlight the promising potential of combining bioeconomic theory, mathematical modeling, and AI-driven optimization methods to achieve sustainable marine resource management, and encourage future interdisciplinary research at the intersection of mathematics, ecology, and artificial intelligence.