Managing Safety of Heterogeneous Systems

Managing safety of diverse systems requires decision-making under uncertainties and risks. Such systems are typically characterized by spatio-temporal heterogeneities, inter-dependencies, externalities, endogenous risks, discontinuities, irreversibility, practically irreducible uncertainties, and rare events with catastrophic consequences. Traditional scientific approaches rely on data from real observations and experiments; yet no sufficient observations exist for new problems, and experiments are usually impossible. Therefore, science-based support for addressing such new class of problems needs to replace the traditional deterministic predictions analysis by new methods and tools for designing decisions that are robust against the involved uncertainties and risks. The new methods treat uncertainties explicitly by using synthetic information derived by integration of hard elements, including available data, results of possible experiments, and formal representations of scientific facts, with soft elements based on diverse representations of scenarios and opinions of public, stakeholders, and experts. The volume presents such effective new methods, and illustrates their applications in different problem areas, including engineering, economy, finance, agriculture, environment, and policy making.

State-of-the-art analysis and optimization methods for effective coping with uncertainty and risks Advanced tools and techniques for dealing with uncertainty and risk in several applied areas, such as engineering, operations research, economics, environmental policy-making Proper treatment of stochastic and non-stochastic uncertainty for robust optimal decisions under uncertainty and risk Includes supplementary material: sn.pub/extras

Autorentext

Ermoliev, Y.: Professor, Institute Scholar, and Senior Researcher, Integrated Modeling Environment (IME) Project, International Institute for Applied System Analysis, Laxenburg, Austria. Research: modeling of decision-making processes under risks and uncertainties, stochastic and dynamic system optimization.

Makowski, M.: Dr., Leader, Integrated Modeling Environment (IME) Project, International Institute for Applied System Analysis, Laxenburg, Austria. Research: model-based decision-making support, structured modeling, integrated model analysis, multiple-criteria problem analysis.

Marti, K.: o.Univ.Professor for Engineering Mathematics, Federal Armed Forces University Munich, Aerospace Engineering and Technology, Neubiberg/Munich, Germany. Research: Stochastic Optimization Methods in Engineering

Klappentext

Tools and techniques are provided for dealing with uncertainties and risks in applied areas. A key issue concerns a vast variety of irreducible uncertainties, which require new concepts and analytical tools. The dilemma is concerned with enormous costs vs. massive uncertainties of potentially extreme impacts. Traditional approaches rely on real observations and experiments. Yet no sufficient observations exist for new problems, and learning by doing may be very expensive, dangerous, or impossible. In addition, historical observations are often contaminated by our actions and policies. The complexity of new problems require explicit treatment of uncertainties using "synthetic" information composed of available "hard" data from observations, the results of possible experiments, and scientific facts as well as "soft" data from experts' opinions, scenarios, and public opinion. Hence, the focus of the present volume is on novel approaches to supporting robust decision-making and design.