Applications of stochastic processes and reinforcement learning in strategic decision support and personalized ad recommendation: An AIGC study

Abstract

This paper investigates the integration of stochastic processes and reinforcement learning (RL) in strategic decision support systems (SDSS) and personalized advertisement recommendations. Stochastic processes offer a robust framework for modeling uncertainties and predicting future states across various domains, while RL facilitates dynamic optimization through continuous interaction with the environment. The combination of these technologies significantly enhances decision-making accuracy and efficiency, yielding substantial benefits in industries such as financial services, healthcare, logistics, retail, and manufacturing. By leveraging these advanced AI techniques, businesses can develop adaptive strategies that respond to real-time changes and optimize outcomes. This paper delves into the theoretical foundations of stochastic processes and RL, explores their practical implementations, and presents case studies that demonstrate their effectiveness. Furthermore, it addresses the computational complexity and ethical considerations related to these technologies, providing comprehensive insights into their potential and challenges. The findings highlight the transformative impact of integrating stochastic processes and RL in contemporary decision-making frameworks.