BUSCADOR RECOLECTA

Quality of life indicators include measures of both objective and subjective well-being to broaden the traditional income-focused view towards a multidimensional view of welfare. In this paper, we develop a single reliable composite index that considers the effect of uncertainty and imprecision related to the specific nature of the phenomenon being measured. Additionally, we suggest a procedure for incorporating this new way of assigning priorities through multicriteria techniques. To this end, we propose a new multicriteria approach that introduces the recent notion of picture fuzzy sets to deal with uncertainty and imprecision in the assessment of objective and subjective dimensions of quality-of-life performance. To verify the effectiveness of the whole approach and the credibility of the results, we show its application for monitoring the quality of life in the European Union. Our results show that fuzzy numbers can be a useful tool when combined with multicriteria techniques in the prioritization phase of the criteria and that they increase the quality and reliability of the overall aggregate indicator. Moreover, we find that in a picture environment, the results are consistent regardless of the multicriteria approach used to provide a final ranking.

The numerical information in these datasets consists of two files. The first file (Dataset_SRI.xlsx) contains the asset data of monthly returns and ESG risk scores for three fund managers from January 2011 to December 2019. The second file (Pareto_Fronts.xlsx) contains the mean-variance-ESG Pareto fronts and the Pareto sets of non-dominated solutions for each manager obtained using the ev-MOGA algorithm. To get these Pareto fronts, we have considered an in-sample period of 72 monthly returns over six years from January 2011 to December 2016. To implement the ev-MOGA algorithm, we use an initial population of 50000 individuals and an auxiliary population of 500. The number of boxes defining the space of each function is 100.