Sensitivity analysis, which ascertains how a given model depends upon its input parameters and determines the degree of stability of the model, is increasingly being considered as essential for good modelling practice. Written by leading researchers active in developing sensitivity analysis strategies for a variety of applications, this volume will prove helpful in the solution of many modelling problems. The book guides the reader through the principles behind sensitivity analysis and then suggests suitable methods for particular types of problem. This approach allows the analyst to come to terms with the entire causal assessment chain, from data to predictions, in a way that makes the impact of source uncertainties and framing assumptions more transparent.