Preference-neutral attribute weights in the journal-ranking problem

A linear programming model is proposed for assigning linear attribute weights in the journal-ranking problem. The constraints in the model are derived solely from any quasi-dominance relations that can be established between the journals. The objective function of the model minimizes the maximum difference between the implied valuations for the pair of journals that define a constraint. In the sense that personal inputs are not introduced, the derived weights are preference neutral. The feasibility of the procedure is demonstrated for two sets of data. By considering various random samples of journals from the larger data set, it is shown that large differences can emerge in the attribute weights and in the journal rankings from different samples of journals, even when the sample sizes are large relative to the population size.