Enhancing Decision -Making Processes: A Comparative Analysis of Artificial Intelligence Techniques in Decision Theory Models

In today’s world, where insights from data are incredibly important, this article looks into how artificial intelligence (AI) can change the way decisions are made in three key areas: healthcare, finance, and manufacturing. By using a comparative analysis approach, we explore different AI methods, such as decision tree classifiers, reinforcement learning, and neural networks, to create effective decision-making models. In healthcare, we use decision tree classifiers to accurately predict patient diagnoses, measuring the model’s performance with metrics like accuracy, precision, and recall to see how well it works. When it comes to finance, we apply Q-learning to fine-tune trading strategies based on simulated stock market data, showcasing how adaptable and profitable reinforcement learning can be. For manufacturing, we turn to neural networks to enhance demand forecasting and inventory management, measuring success with mean squared error (MSE). The results show that AI can significantly improve decision-making across various industries. This article emphasizes the importance of integrating advanced AI techniques into decision-making processes, providing helpful insights for organizations that want to use technology to boost efficiency and achieve better outcomes. We also suggest future research directions that focus on ethical issues and the broader use of AI techniques in decision theory.

کلیدواژه ها:

Decision Theory ، Model ، Comparative ، Artificial Intelligence ، Decision-Making