Models in Decision Analysis

Abstract

Any statistical analysis or decision analysis contains numerical inputs of which we are unsure. Some of our uncertainty arises from physical randomness which we can model in various ways, ideally through probability. Some relates to judgmental estimates of qualities about which we may be unsure in many different respects. There are other uncertainties involved, however: some relates to ambiguity and imprecision of meaning; some relate to lack of clarity in the objectives which the analysis seeks to meet; some relate to the numerical accuracy of calculations. Suppose, for instance, that we need to consider the probability of a conservative government being returned at the next election. The probability, a subjective probability, itself is a number that the statistician or decision maker has to judge. In some ways it may be related to 'hard' data, but there will always be an element of judgement in setting its value. But one might also ask: what is meant by a 'conservative' government; ...

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