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In the field of modelling it is easier to find academic papers, guidelines tailored to specific disciplines and handbooks of numerical simulation rather than plain textbooks of broad appeal. The various academic communities go about modelling largely independently of each other. Is this an indication that modelling is not a science but a craft, as argued by epistemologists? In other words, is it because it is impossible to define a single set of rules to encode natural or man-made systems into sets of mathematical rules called models?\nIf modelling is in fact characterized by such heterogeneity and lack of systematization, it might seem overly ambitious to offer a set of good\npractices of universal application in sensitivity analysis. Furthermore, if one looks at the available literature, in most instances "snsitivities"are understood as derivatives of a particular output versus a particular input (such as elasticities in economics). This is not surprising, as contemporary researchers ¿ like the authors of the present volume ¿ are likely to have received more training in calculus than in Monte Carlo methods and to have seen more Jacobians and Hessians than Russian roulettes. A minority of senitivity analysis practitioners (mostly in statistics, risk analysis and reliability) actively use importance measures such as those described in this\nbook, whereby the influence of factors on outputs is assessed by looking at the entire input space rather than at a point in that space. Slowly these methods are finding their way into more recent modelling guidelines in other disciplines (see, for example, those of the Environmental Protection Agency in the USA, EPA, 2001). The purpose of this book is to offer to students an easy-to-read manual for sensitivity analysis covering importance measures and to show how these global methods may help to produce more robust or parsimonious models as well as to make models more defensible in the face of scientific or technical controversy.