The research projects in this area are aimed at combining big data, machine learning, econometric tools, and decision support systems for the construction of Composite Indicators with applications to social sciences. 

Composite Indicators are increasingly recognized as a useful tool in policy analysis and are found in many application domains, ranging from the measurement of financial stress and economic uncertainty, to competitiveness, innovation, ranking of institutions in terms of quality of work, policies and inclusion, measurement of well-being, environmental quality and others.

Multi-indicator systems come into play when we want to measure a phenomenon on the basis of relevant features or rank a set of alternatives on the basis of multiple criteria.

A wide range of tools and techniques are of interest:

• The use of high-dimensional datasets: BIG DATA

• Machine learning techniques  for the identification of a subsets of most relevant features and for forecasting such as PCA,  SVM, RIDGE regression, LASSO, Elastic net, Bagging, Boosting and Random Forest, among the others

• Multi-criteria decision making 

• Composite indicators based on the OECD Handbook on Constructing Composite Indicators (OECD, 2008))

• Fuzzy rule-based systems• TOPSIS (Technique for Order Preference by Similarities to the Ideal Solution) 

• The partially ordered set (poset) 

• Methods based on eXplainable Artificial Intelligence

Referee

Prof. Silvia Muzzioli

Research Team

Prof. Silvia Muzzioli (Metodi matematici dell’Economia), Prof. Bernard De Baets (Esperto in Machine Learning e indici compositi), Prof. Alberto Fernandez (Esperto di Big data), Prof. Mario Forni (Esperto in Econometria), Prof. Tindara Addabbo (Economia del lavoro e valutazione delle politiche pubbliche), Prof. Massimo Baldini (Valutazione delle politiche pubbliche e misurazione ineguaglianze di reddito e povertà), Dott. Giovanni Campisi (RTDA presso Università Politecnica delle Marche), Dott. Luca Gambarelli (Post-doc Metodi matematici dell’Economia), Dott. Filippo Damiani (Post-doc Economia del lavoro), Dott. Filippo Ferrarini (Post-doc Organizzazione e gestione delle risorse umane).