Decision Making Under Uncertainty

This lecture, presented by Daniel Kuhn, honors the late Kilian Schindler and his posthumous PhD thesis on scalable stochastic optimization. The lecture delves into decision-making under uncertainty, using examples like hydropower production and financial investment to illustrate the challenges of balancing known present profits with unknown future profits. Kilian's work on scenario reduction, a technique to simplify complex decision trees, is highlighted, showing how it can be applied to real-world problems like energy production planning. The lecture also discusses the implications of Kilian's research on clustering problems and the potential for reducing computational complexity in unsupervised machine learning methods.