Real World Decision

Real-world decision-making research focuses on improving human and artificial agent choices in complex, uncertain environments. Current efforts concentrate on mitigating biases in AI models (like LLMs) through techniques such as benchmark datasets and algorithmic adjustments, and on developing methods to learn and teach optimal decision strategies, often leveraging reinforcement learning and deep learning architectures. This work is significant because it aims to enhance both human decision-making capabilities and the ethical development of AI systems, with potential applications ranging from project selection to financial markets and healthcare.