Mind the assumptions: quantify uncertainty and assess sensitivity

This chapter considers how to treat spoken and unspoken assumptions and how to use uncertainty quantification and global sensitivity analysis to make them transparent. As part of this, the broad role of assumptions (or hypotheses) in scientific modelling are analysed, and investigations are made into how they affect alternative elements of a model. We address models of data (machine learning) and models of phenomena (simulators). We then discuss the impact of varying assumptions on the output of a mathematical model, highlighting the role of uncertainty quantification and sensitivity analysis. We single out four main sensitivity analysis goals emerging from the literature.