Dynamic Decision

Dynamic decision-making research focuses on developing computational models that can make optimal choices in complex, evolving environments, mirroring human cognitive processes and improving efficiency in various applications. Current research emphasizes the use of neural networks, particularly deep reinforcement learning and large language models, often incorporating instance-based learning and attention mechanisms to capture individual differences and adapt to changing circumstances. These advancements are impacting fields like urban logistics, healthcare, and engineering design by enabling more efficient resource allocation, personalized interventions, and robust system control. The development of interpretable models and efficient algorithms remains a key focus.