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 address the extraction of rankings from multi-indicator systems, as a problem of approximation between the so-called “mutual ranking probability” matrices, associated to the partial order relations derived from the data. After providing a theoretical treatment of the topic, we propose a practical algorithm for ranking extraction and show it in action on a real example, pertaining to regional competitiveness

In this paper, we present a multidimensional fuzzy analysis of the levels and the patterns of poverty and social fragility of migrants’ families, in the Italian region of Lombardy, in year 2014. Migrants’ poverty emerges as a complex trait, better described as a stratification of nuanced patterns than in black and white terms; Lombard migrants are in fact affected, to different extents, by “a diffused sharing of deprivation facets” and cannot be trivially split into deprived and non-deprived.

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 address the measurement of vulnerability at municipality level, by using novel non-aggregative tools for the construction of synthetic indicators and for the extraction of rankings, based on the theory of partially ordered sets.