Modelling Under Risk and Uncertainty

Modelling has permeated virtually all areas of industrial,
environmental, economic, bio-medical or civil engineering: yet the
use of models for decision-making raises a number of issues to
which this book is dedicated:

How uncertain is my model ? Is it truly valuable to support
decision-making ? What kind of decision can be truly supported and
how can I handle residual uncertainty ? How much refined should the
mathematical description be, given the true data limitations ?
Could the uncertainty be reduced through more data, increased
modeling investment or computational budget ? Should it be reduced
now or later ? How robust is the analysis or the computational
methods involved ? Should / could those methods be more robust ?
Does it make sense to handle uncertainty, risk, lack of knowledge,
variability or errors altogether ? How reasonable is the choice of
probabilistic modeling for rare events ? How rare are the events to
be considered ? How far does it make sense to handle extreme
events and elaborate confidence figures ? Can I take advantage of
expert / phenomenological knowledge to tighten the probabilistic
figures ? Are there connex domains that could provide models or
inspiration for my problem ?

Written by a leader at the crossroads of industry, academia and
engineering, and based on decades of multi-disciplinary field
experience, Modelling Under Risk and Uncertainty gives a
self-consistent introduction to the methods involved by any type of
modeling development acknowledging the inevitable uncertainty and
associated risks. It goes beyond the "black-box" view
that some analysts, modelers, risk experts or statisticians develop
on the underlying phenomenology of the environmental or industrial
processes, without valuing enough their physical properties and
inner modelling potential nor challenging the practical
plausibility of mathematical hypotheses; conversely it is also to
attract environmental or engineering modellers to better handle
model confidence issues through finer statistical and risk analysis
material taking advantage of advanced scientific computing, to face
new regulations departing from deterministic design or support
robust decision-making.

Modelling Under Risk and Uncertainty:

• Addresses a concern of growing interest for large industries,
  environmentalists or analysts: robust modeling for decision-making
  in complex systems.

• Gives new insights into the peculiar mathematical and
  computational challenges generated by recent industrial safety or
  environmental control analysis for rare events.

• Implements decision theory choices differentiating or
  aggregating the dimensions of risk/aleatory and epistemic
  uncertainty through a consistent multi-disciplinary set of
  statistical estimation, physical modelling, robust computation and
  risk analysis.

• Provides an original review of the advanced inverse
  probabilistic approaches for model identification, calibration or
  data assimilation, key to digest fast-growing multi-physical data
  acquisition.

• Illustrated with one favourite pedagogical example crossing
  natural risk, engineering and economics, developed throughout the
  book to facilitate the reading and understanding.

• Supports Master/PhD-level course as well as advanced tutorials
  for professional training

Analysts and researchers in numerical modeling, applied
statistics, scientific computing, reliability, advanced
engineering, natural risk or environmental science will benefit
from this book.

Autorentext
Etienne De Rocquigny is Senior Research fellow in Statistics in Risk and Environment at Electricite' de France R&D He has 12 years of R&D and consulting experience in risk and environmental management. He has had consulting appointments and R&D contracts worldwide with the World Bank, the IMF, and UN as part of Sogreah consulting engineers. He is Chairman of the European Safety and Reliability & Data Society and Chairman of a consortium of French Industries on Uncertainty and Industry.

Zusammenfassung

Modelling has permeated virtually all areas of industrial, environmental, economic, bio-medical or civil engineering: yet the use of models for decision-making raises a number of issues to which this book is dedicated:

How uncertain is my model ? Is it truly valuable to support decision-making ? What kind of decision can be truly supported and how can I handle residual uncertainty ? How much refined should the mathematical description be, given the true data limitations ? Could the uncertainty be reduced through more data, increased modeling investment or computational budget ? Should it be reduced now or later ? How robust is the analysis or the computational methods involved ? Should / could those methods be more robust ? Does it make sense to handle uncertainty, risk, lack of knowledge, variability or errors altogether ? How reasonable is the choice of probabilistic modeling for rare events ? How rare are the events to be considered ? How far does it make sense to handle extreme events and elaborate confidence figures ? Can I take advantage of expert / phenomenological knowledge to tighten the probabilistic figures ? Are there connex domains that could provide models or inspiration for my problem ?

Written by a leader at the crossroads of industry, academia and engineering, and based on decades of multi-disciplinary field experience, Modelling Under Risk and Uncertainty gives a self-consistent introduction to the methods involved by any type of modeling development acknowledging the inevitable uncertainty and associated risks. It goes beyond the black-box view that some analysts, modelers, risk experts or statisticians develop on the underlying phenomenology of the environmental or industrial processes, without valuing enough their physical properties and inner modelling potential nor challenging the practical plausibility of mathematical hypotheses; conversely it is also to attract environmental or engineering modellers to better handle model confidence issues through finer statistical and risk analysis material taking advantage of advanced scientific computing, to face new regulations departing from deterministic design or support robust decision-making.

Modelling Under Risk and Uncertainty:

• Addresses a concern of growing interest for large industries, environmentalists or analysts: robust modeling for decision-making in complex systems.
• Gives new insights into the peculiar mathematical and computational challenges generated by recent industrial safety or environmental control analysis for rare events.
• Implements decision theory choices differentiating or aggregating the dimensions of risk/aleatory and epistemic uncertainty through a consistent multi-disciplinary set of statistical estimation, physical modelling, robust computation and risk analysis.
• Provides an original review of the advanced inverse probabilistic approaches for model identification, calibration or data assimilation, key to digest fast-growing multi-physical data acquisition.
• Illustrated with one favourite pedagogical example crossing natural risk, engineering and economics, developed throughout the book to facilitate the reading and understanding.
• Supports Master/PhD-level course as well as advanced tutorials for professional training
  Analysts and researchers in numerical modeling, applied statistics, scientific computing, reliability, advanced engineering, natural risk or environmental science will benefit from this book.

Inhalt
Preface xv Acknowledgements xvii

Introduction and reading guide xix

Notation xxxiii

Acronyms and abbreviations xxxvii

1 Applications and practices of modelling, risk and uncertainty 1

1.1 Protection against natural risk 1 …