General Remarks on Robust Solutions -- Modeling of Uncertainty and Probabilistic Issues -- On Joint Modelling of Random Uncertainty and Fuzzy Imprecision -- On the Approximation of a Discrete Multivariate Probability Distribution Using the New Concept of -Cherry Junction Tree -- Robust Solutions under Uncertainty -- Induced Discounting and Risk Management -- Cost Effective and Environmentally Safe Emission Trading Under Uncertainty -- Robust Design of Networks Under Risks -- Analysis and Optimization of Technical Systems and Structures under Uncertainty -- Optimal Ellipsoidal Estimates of Uncertain Systems: An Overview and New Results -- Expected Total Cost Minimum Design of Plane Frames by Means of Stochastic Linear Programming Methods -- Analysis and Optimization of Economic and Engineering Systems under Uncertainty -- Uncertainty in the Future Nitrogen Load to the Baltic Sea Due to Uncertain Meteorological Conditions -- Planning Sustainable Agricultural Development Under Risks -- Dealing with Uncertainty in GHG Inventories: How to Go About It? -- Uncertainty Analysis of Weather Controlled Systems -- Estimation of the Error in Carbon Dioxide Column Abundances Retrieved from GOSAT Data.

Support for addressing the on-going global changes needs solutions for new scientific problems which in turn require new concepts and tools. A key issue concerns a vast variety of irreducible uncertainties, including extreme events of high multidimensional consequences, e.g., the climate change. The dilemma is concerned with enormous costs versus massive uncertainties of extreme impacts. Traditional scientific approaches rely on real observations and experiments. Yet no sufficient observations exist for new problems, and "pure" experiments, and learning by doing may be expensive, dangerous, or impossible. In addition, the available historical observations are often contaminated by past actions, and policies. Thus, tools are presented for the explicit treatment of uncertainties using "synthetic" information composed of available "hard" data from historical observations, the results of possible experiments, and scientific facts, as well as "soft" data from experts' opinions, and scenarios.