Ricerc@Sapienza

In this paper, we propose a solution to the problem of scoring and ranking partially ordered data, by exploiting the spectral properties of so-called matrices of mutual ranking probabilities, a class of matrices which comprise and convey information on the dominance among statistical units. The procedure is optimal in many respects and overcomes the limitations of other ranking tools, which may fail to deliver acceptable solutions, even in trivial cases. We show the algorithm in action, on real data pertaining to the smartness of a subset of European cities.

In this paper, we develop a new statistical procedure for the comparison of frequency distributions on systems of ordinal indicators, based on a multidimensional fuzzy extension of the first order dominance (FOD) criterion. The procedure, named fuzzy-first order dominance (F-FOD), employs concepts and tools from partially ordered set theory and from fuzzy relational calculus and is designed to overcome the main limitations of previously developed algorithms for FOD analysis.

In this paper, we apply the novel Fuzzy First-Order Dominance (F-FOD) methodology to rank migrant subpopulations in Lombardy (Italy), in terms of multidimensional poverty and social fragility, for the year 2014, with the purpose to possibly provide useful support to policy-makers, in targeting relief interventions from poverty and discomfort.