Multistage Stochastic Optimization Multistage stochastic optimization problems appear in many ways in finance insurance energy production and trading logistics and transportation among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production asset liability management and inventory control concludes the book Introduction The Nested Distance Risk and Utility Functionals From Data to Models Time Consistency Approximations and Bounds The Problem of Ambiguity in Stochastic Optimization Examples