A broad spectrum of applications, such as power systems operation, manufacturing, energy management, anomaly detection, and security, can be viewed as decision-making problems in uncertain and dynamic environments. This class of problems is naturally formulated in an optimization framework whose exact solution is often intractable. In this seminar, we consider tractable randomized (data-driven) counterparts of these programs and study a probabilistic bridge between these solutions and the original ones.