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This paper proposes a critical analysis of the "Academic Ranking of World Universities", published every year by the Institute of Higher Education of the Jiao Tong University in Shanghai and more commonly known as the Shanghai ranking. After having recalled how the ranking is built, we first discuss the relevance of the criteria and then analyze the proposed aggregation method. Our analysis uses tools and concepts from Multiple Criteria Decision Making (MCDM). Our main conclusions are that the criteria that are used are not relevant, that the aggregation methodology is plagued by a number of major problems and that the whole exercise suffers from an insufficient attention paid to fundamental structuring issues. Hence, our view is that the Shanghai ranking, in spite of the media coverage it receives, does not qualify as a useful and pertinent tool to discuss the "quality" of academic institutions, let alone to guide the choice of students and family or to promote reforms of higher education systems. We outline the type of work that should be undertaken to offer sound alternatives to the Shanghai ranking.

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1 We wish to thank M. Abdellaoui, J.-P. Doignon, Ch. Gonzales, Th. Marchant, P.P. Wakker and an anonymous referee for their very helpful suggestions and comments on earlier drafts of this text. The usual caveat applies. Denis Bouyssou gratefully acknowledges the support of the Centre de Recherche de l'ESSEC and the Brussels-Capital Region through a "Research in Brussels" action grant. AbstractTraditional models of conjoint measurement look for an additive representation of transitive preferences. They have been generalized in two directions. Nontransitive additive conjoint measurement models allow for nontransitive preferences while retaining the additivity feature of traditional models. Decomposable conjoint measurement models are transitive but replace additivity by a mere decomposability requirement. This paper presents generalizations of conjoint measurement models combining these two aspects. This allows us to propose a simple axiomatic treatment that shows the pure consequences of several cancellation conditions used in traditional models. These nontransitve decomposable conjoint measurement models encompass a large number of aggregation rules that have been introduced in the literature.

1 We wish to thank Jose Figueira and Marc Pirlot for their helpful comments on an earlier draft of this text. Our greatest debt is to Salvatore Greco, Benedetto Matarazzo and Roman S lowiński who alerted us on the relation between our results on noncompensatory sorting models and the results in S lowiński et al. (2002) 3 Ghent University, Department of Data Analysis, H. Dunantlaan 1, B-9000 Gent, Belgium, tel: +32 9 264 63 73, fax: +32 9 264 64 87, e-mail: thierry.marchant@UGent.be. AbstractIn the literature on MCDM, many methods have been proposed in order to sort alternatives evaluated on several attributes into ordered categories. Most of them were proposed on an ad hoc basis. The purpose of this paper is to contribute to a recent trend of research aiming at giving these methods sound theoretical foundations. Using tools from conjoint measurement, we provide an axiomatic analysis of the partitions of alternatives into two categories that can be obtained using what we call "noncompensatory sorting models". These models have strong links with the pessimistic version of ELECTRE TRI. Our analysis allows to pinpoint what appears to be the main distinctive features of ELECTRE TRI when compared to other sorting methods. It also gives hints on the various methods that have been proposed to assess the parameters of ELECTRE TRI on the basis of assignment examples.

The standard data that we use when computing bibliometric rankings of scientists are just their publication/citation records, i.e., so many papers with 0 citation, so many with 1 citation, so many with 2 citations, etc. The standard data for bibliometric rankings of departments have the same structure. It is therefore tempting (and many authors gave in to temptation) to use the same method for computing rankings of scientists and rankings of departments. Depending on the method, this can yield quite surprising and unpleasant results. Indeed, with some methods, it may happen that the "best" department contains the "worst" scientists, and only them. This problem will not occur if the rankings satisfy a property called consistency, recently introduced in the literature. In this paper, we explore the consequences of consistency and we characterize two families of consistent rankings.