Entropy in multiattribute problems

Concepts involving entropy have intermittently been used in consideration of multiattribute problems. This paper discusses the main approaches that have been proposed. It is argued that the analogies drawn with information theory and between weights and probabilities are to some degree unsatisfactory and a reconsideration is given. The result is a new suggestion in the spirit of Jaynes' maximum entropy methodology, but now in two parts: ask those questions that are likely to be most discriminating (minimize entropy) but process the answers obtained conservatively (maximize entropy). In contrast to many previous methods, it is the distribution of the utilities of alternatives rather than of the weights given to attributes that is the focus of attention.