In this method m alternatives composed of n attributes are represented as m points in the n-dimensional space. A decision maker is assumed to have his ideal point denoting his most preferred alternative location in this space and a set of weights which reveal the relative salient of the attributes. He prefers those variants, which are "closer" to his ideal point (in terms of a weighted Euclidean distance measure). A linear programming model is proposed for "external analysis" i. e., estimation of the co-ordinates of his ideal point and the weights (involved in the Euclidean distance measure) by analysing

paired comparison preference judgements on a set of stimuli, prespecified by their co-ordinate locations in the multidimensional space.