Abstract
Abstract We develop methods for analysing decision problems based on multi-attribute utility hierarchies, structured by mutual utility independence, which are not precisely specified due to unwillingness or inability of an individual or group to agree on precise values for the trade-offs between the various attributes. Instead, our analysis is based on whatever limited collection of preferences we may assert between attribute collections. These preferences identify a class of Pareto optimal decisions. We show how to reduce the class further by combining rules which are almost equivalent and introduce general principles appropriate to selecting decisions in an imprecise hierarchy. The approach is illustrated by the design of a university course module.
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