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© 2006

Coping with Uncertainty

Modeling and Policy Issues

Conference proceedings

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 581)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Uncertainty and Decisions

    1. Front Matter
      Pages 1-1
    2. Y. Ermoliev, L. Hordijk
      Pages 3-28
    3. J. Dupačová
      Pages 29-46
  3. Modeling Stochastic Uncertainty

  4. Non-Probabilistic Uncertainty

    1. Front Matter
      Pages 131-131
    2. M. Keyzer, Y. Ermoliev, V. Norkin
      Pages 133-154
    3. G. Fischer, T. Ermolieva, Y. Ermoliev, H. Van Velthuizen
      Pages 155-169
    4. F. L. Chernousko
      Pages 171-183
    5. A. G. Nakonechny, V. P. Marzeniuk
      Pages 185-192
  5. Applications of Stochastic Optimization

    1. Front Matter
      Pages 193-193
    2. A. Ramos, S. Cerisola, Á. Baíllo, J. M. Latorre
      Pages 217-239
    3. A. Gouda, D. Monhor, T. Szántai
      Pages 241-255

About these proceedings

Introduction

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.

Keywords

Analysis Monte Carlo Simulation Statistical Analysis Stochastic Optimization Stochastic Programming learning modeling optimization programming simulation

Authors and affiliations

  1. 1.Aero Space Engineering and TechnologyFederal Armed Forces University MunichNeubiberg/MunichGermany
  2. 2.International Institute for Applied System AnalysisLaxenburgAustria
  3. 3.Institute for Statistics and InformaticsUniversity of ViennaViennaAustria

Bibliographic information