Coping with Uncertainty

Robust Solutions

  • Kurt Marti
  • Yuri Ermoliev
  • Marek Makowski

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 633)

Table of contents

  1. Front Matter
    Pages 1-14
  2. Y. Ermoliev, M. Makowski, K. Marti
    Pages 1-7
  3. Modeling of Uncertainty and Probabilistic Issues

  4. Robust Solutions Under Uncertainty

    1. Front Matter
      Pages 58-58
    2. T. Ermolieva, Y. Ermoliev, G. Fischer, M. Makowski
      Pages 59-77
    3. T. Ermolieva, Y. Ermoliev, G. Fischer, M. Jonas, M. Makowski
      Pages 79-99
    4. Y. Ermoliev, A. Gaivoronski, M. Makowski
      Pages 101-137
  5. Analysis and Optimization of Technical Systems and Structures Under Uncertainty

  6. Analysis and Optimization of Economic and Engineering Systems Under Uncertainty

    1. Front Matter
      Pages 194-194
    2. G. Fischer, T. Ermolieva, L. Sun
      Pages 209-227
    3. Matthias Jonas, Thomas White, Gregg Marland, Daniel Lieberman, Zbigniew Nahorski, Sten Nilsson
      Pages 229-245
    4. K. J. Keesman, T. Doeswijk
      Pages 247-258
    5. Mitsuhiro Tomosada, Koji Kanefuji, Yukio Matsumoto, Hiroe Tsubaki, Tatsuya Yokota
      Pages 259-277

About this book

Introduction

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.

 

Keywords

Frames Modelling Uncertainty Optimal Decision under Uncertainty Robust Optimal Solution Technical Systems control learning linear optimization modeling optimal design optimization robust design uncertainty

Editors and affiliations

  • Kurt Marti
    • 1
  • Yuri Ermoliev
    • 2
  • Marek Makowski
    • 3
  1. 1.Aerospace Engineering and, TechnologyFederal Armed Forces University MunichNeubiberg/MünchenGermany
  2. 2.Applied Systems Analysis (IIASA)International Institute forLaxenburgAustria
  3. 3.Applied Systems Analysis (IIASA)International Institute forLaxenburgAustria

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-03735-1
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Business and Economics
  • Print ISBN 978-3-642-03734-4
  • Online ISBN 978-3-642-03735-1
  • Series Print ISSN 0075-8442
  • About this book