Preface

The aim of the series of workshops on Coping with Uncertainty (CwU) organized since over a decade at the International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria, has been to provide researchers and practitioners from different areas with an interdisciplinary forum for discussing various ways of effective dealing with uncertainties and risks in diverse areas, including environmental and social sciences, economics, policy making, management, and engineering. The workshops proved to be successful, especially in cross-disciplinary sharing methods, ideas, and open problems. Science-based support for effective coping with uncertainties and risks in complex policy-making and engineering problems needs practical solutions for fundamentally new scientific problems that in turn require new concepts and tools. A key issue concerns a vast variety of practically irreducible uncertainties, including potential extreme events of high multidimensional consequences, which challenge traditional models, and thus require new concepts and analytical tools. Robust decisions for problems exposed to extreme events are essentially different from over-simplified decisions that ignore such events. Specifically, a proper treatment of extreme/rare events requires new paradigms of rational decisions, new performance indicators, and new spatio-temporal dimensions of heterogeneous interdependencies, including network externalities and risks. Traditional scientific approaches usually rely on real observations and experiments. Yet no sufficient observations exist for new problems; “pure” experiments and “learning by doing” are dangerous, very expensive, and thus practically impossible. Moreover, the available historical observations are often contaminated by “experimentator,” i.e., past actions or policies. The complexity of new problems does not allow to achieve enough certainty, e.g., by increasing the resolution of models or by bringing in more links. Such problems require explicit treatment of uncertainties using “synthetic” information derived by integration of “hard” elements, including available data, results of possible experiments, and formal representations of scientific facts, as well as “soft” elements based on diverse representations of scenarios, and opinions of public, stakeholders, and experts.