Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard.

Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications.

The book:

Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials.

Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls.

Features numerous exercises and solved problems to help illustrate the applications.

Is authored by leading sensitivityanalysis practitioners, combining a range of disciplinary backgrounds.

Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.

Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice, and is an implicit part of any modelling field.

• Offers an accessible introduction to sensitivity
• Covers all the latest research
• Illustrates concepts with numerous examples, applications and case studies
• Includes contributions from the leading researchers active in developing strategies for sensitivity analysis

Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB - a widely distributed freely-available sensitivity analysis software package developed by the authors - for solving problems in sensitivity analysis of statistical models.

Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis.Opens with a detailed worked example to explain the motivation behind the book.Includes a range of examples to help illustrate the concepts discussed.Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors.Contains a large number of references to sources for further reading.Authored by the leading authorities on sensitivity analysis.

----- One of the arguments most frequently raised by objectors to nuclear power production is the spectrum of the hazard from the nuc.lear waste. In the public's perception one of the most dangerous features of this waste is the threat to future generations due to the long life of the radio-emitting nuclides. People's concern for this hazard seems to be greater than that for other types of toxic wastes, widely produced and released to the environment (heavy metals, PCBs, arsenic etc.) though these might last even longer. In fact public attitude toward risk depends very strongly on the nature of the risk, as clearly illustrated ill the initial papers of these proceedings. On this basis it appears that tile safety of the nuclear practice, including waste disposal, cannot be assessed only by comparison with other practices or risks. For this reason the analysts have accepted the challenge of investigating the long term safety of this type of waste, taking it much further than for many other known pollution problems affecting mankind. Moreover, for one of the first times lhis is being dulle IJi2fure disp, )sal. The c, )ntributiolls tu these pruceedillljs attempt to describe the specific nature of this type of assessment, pi esent ing Ilew developments which have been made in the field. These include the recourse to system variability analysis, the use of natural and archaeulugical analogues and predictive geology.

The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists.

Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice and is an implicit part of any modelling field.Offers an accessible introduction to sensitivity analysis.Covers all the latest research.Illustrates concepts with numerous examples, applications and case studies.Includes contributions from the leading researchers active in developing strategies for sensitivity analysis.

The principles of sensitivity analysis are carefully described and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire casual assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A 'hitch-hikers guide' is included to allow the more experienced reader to readily access specific applications.

Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly from the numerous examples and applications.

"Presents many different sensitivity analysis methodologies and demonstrates their usefulness in scientific research." (Zentralblatt MATH)

Chapter 2, 'Pay no attention to the model behind the curtain', Chapter 4, 'Mind the hubris: Complexity can misfire', and Chapter 8, ' Sensitivity auditing: A practical checklist for auditing decision-relevant models' are published open access and free to read or download from Oxford Academic The widespread use of mathematical models for policy-making and its social and political impact are at the core of this book. While the discussion of mathematical modelling generally centres around technical features, use, and type of model, the literature is increasingly acknowledging that the social nature of modelling, its biases and responsibilities, are equally worth investigating. This book tackles these emerging questions by adopting a multidisciplinary approach to investigate how current modelling practices address contemporary challenges, and fills a gap in the field, which has historically focused on statistical and algorithmic modes of producing numbers. Thanks to its multidisciplinary appeal, this book will be essential reading for modellers, public officials, policymakers, and scholars alike.