From Models To Decisions (M2D)

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Today many decisions are made using evidence from numerical models. Such models are often large and complex and often appear to be without uncertainty. However, even if the model is itself deterministic in that it gives the same result for the same set of inputs, the inputs are almost always uncertain and the model structure itself is not fully known. Thus any model result is uncertain. Determining the uncertainties in models is often referred to as uncertainty quantification. This is a hard problem; even for the forward problem where we know the uncertainty in the model inputs and we want to propagate it to the model outputs. Even harder is inverse modelling when (uncertain) observations are used to reduce the uncertainty in the inputs. Once we have done both inverse and forward modelling we can estimate the uncertainty in the model output that is being used to help make the decision. However, in any decision the evidence from the model is rarely the only consideration. The decision maker has his or her own uncertainties and biases and these must be taken into account and understood. Two decision makers given the same evidence will often make different decisions. Presenting the evidence to decision makers (often untrained in mathematics and statistics) is difficult and complex. The network will look at new ways of presenting narratives and visualisations of uncertainty to decision makers.