Introduction to Sensitivity Analysis

Bertrand Iooss () and Andrea Saltelli
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Bertrand Iooss: EDF R&D, Industrial Risk Management Department

Chapter 31 in Handbook of Uncertainty Quantification, 2017, pp 1103-1122 from Springer

Abstract: Abstract Sensitivity analysis provides users of mathematical and simulation models with tools to appreciate the dependency of the model output from model input and to investigate how important is each model input in determining its output. All application areas are concerned, from theoretical physics to engineering and socio-economics. This introductory paper provides the sensitivity analysis aims and objectives in order to explain the composition of the overall “Sensitivity Analysis” chapter of the Springer Handbook. It also describes the basic principles of sensitivity analysis, some classification grids to understand the application ranges of each method, a useful software package, and the notations used in the chapter papers. This section also offers a succinct description of sensitivity auditing, a new discipline that tests the entire inferential chain including model development, implicit assumptions, and normative issues and which is recommended when the inference provided by the model needs to feed into a regulatory or policy process. For the “Sensitivity Analysis” chapter, in addition to this introduction, eight papers have been written by around twenty practitioners from different fields of application. They cover the most widely used methods for this subject: the deterministic methods as the local sensitivity analysis, the experimental design strategies, the sampling-based and variance-based methods developed from the 1980s, and the new importance measures and metamodel-based techniques established and studied since the 2000s. In each paper, toy examples or industrial applications illustrate their relevance and usefulness.

Keywords: Computer experiments; Uncertainty analysis; Sensitivity analysis; Sensitivity auditing; Risk assessment; Impact assessment (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-12385-1_31

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