Research projects in this area aim to combine big data, machine learning, econometric tools and decision support systems to build composite indicators for the social sciences.
Composite indicators are recognised as useful policy tools and have applications in many areas, including measuring economic and financial uncertainty, competitiveness, innovation, ranking institutions in terms of quality of work, policies and inclusion, measuring well-being, environmental quality and others.
Composite indicators come into play when we want to measure a phenomenon on the basis of some relevant characteristics or when we want to classify and rank a set of alternatives on the basis of a variety of criteria.
From this perspective, the research area addresses issues such as
• The use of high-dimensional data sets; BIG DATA
• Machine learning techniques for identifying a subset of relevant features and for prediction (PCA, Support Vector Machines, RIDGE Regression, LASSO Regression, Elastic Net, and other supervised and unsupervised learning algorithms: Bagging, Boosting, Random Forest).
• Multi-criteria decision methods
• Composite indicators according to the methodology developed by the Organisation for Economic Co-operation and Development (OECD, 2008),
• Fuzzy rule-based classification systems
• TOPSIS (Technique for Order Preference by Similarities to the Ideal Solution)
• POSET (Partially Ordered Set)
• Methods based on eXplainable Artificial Intelligence
Potential applications in the field of development, innovation and sustainability include:
• Composite indicators for measuring and managing risk in financial markets. Volatility and risk asymmetry indices for markets, sentiment indices.
• Composite indicators for measuring territorial competitiveness: Regional competitiveness indices (and comparison with indicators such as the Regional Competitiveness Index)
• Composite indicators for measuring regional innovation (and comparison with indicators such as the Regional Innovation Scoreboard)
• Composite indicators of climate risk and related financial instruments such as green bonds
• Composite indicators for sustainable finance and ESG ratings
• Composite indicators to assess the impact of green buildings on the community (green buildings)
• Composite indicators for assessing innovation in tourism services and the impact of tourism policies
• Composite indicators of LGBTQI+ inclusion in education and policies and practices for LGBTQI+ inclusive universities.
Referee
Prof. Silvia Muzzioli, Prof. Bernard De Baets
Disciplinary areas
Mathematical Methods of Economics, Labor Economics and Evaluation of Public Policies