Coping with Uncertainty [electronic resource] : Modeling and Policy Issues / by Kurt Marti, Yuri Ermoliev, Marek Makowski, Georg Pflug.

Uncertainty and Decisions -- Facets of Robust Decisions -- Stress Testing via Contamination -- Structured Modeling for Coping with Uncertainty in Complex Problems -- Modeling Stochastic Uncertainty -- Using Monte Carlo Simulation to Treat Physical Uncertainties in Structural Reliability -- Explicit Methods for the Computation of Structural Reliabilities in Stochastic Plastic Analysis -- Statistical Analysis of Catastrophic Events -- Scene Interpretation Using Bayesian Network Fragments -- Non-Probabilistic Uncertainty -- General Equilibrium Models with Discrete Choices in a Spatial Continuum -- Sequential Downscaling Methods for Estimation from Aggregate Data -- Optimal Control for a Class of Uncertain Systems -- Uncertainties in Medical Processes Control -- Applications of Stochastic Optimization -- Impacts of Uncertainty and Increasing Returns on Sustainable Energy Development and Climate Change: A Stochastic Optimization Approach -- Stochasticity in Electric Energy Systems Planning -- Stochastic Programming Based PERT Modeling -- Towards Implementable Nonlinear Stochastic Programming -- Policy Issues Under Uncertainty -- Endogenous Risks and Learning in Climate Change Decision Analysis -- Pricing Related Projects -- Precaution: The Willingness to Accept Costs to Avert Uncertain Danger.

Ongoing global changes bring fundamentally new scientific problems requiring new concepts and tools. A key issue concerns a vast variety of practically irreducible uncertainties, which challenge our traditional models and require new concepts and analytical tools. The uncertainty critically dominantes, e.g., the climate change debates. In short, the dilemma is concerned with enormous costs vs. massive uncertainties of potential extreme impacts. Traditional scientific approaches usually rely on real observations and experiments. Yet no sufficient observations exist for new problems, and "pure" experiments and learning by doing may be very expensive, dangerous, or simply impossible. In addition, available historical observations are contaminated by actions, policies. The complexity of new problems does not allow to achieve enough certainty by increasing the resolution of models or by bringing in more links. Hence, new tools for modeling and management of uncertainty are needed, as given in this book.