Assessing Global Social Progress Through Shannon Entropy and Fuzzy Logic

Abstract

This study proposes a methodological approach that combines fuzzy logic and Shannon entropy to analyze the structure of the Social Progress Index (SPI). The objective of this paper is to quantify the entropy among the different dimensions that constitute the Social Progress Index across nations. To achieve this, data from 146 countries for the period 2014–2018 were analyzed, applying the humanistic algorithm to construct fuzzy subsets and estimate their entropy levels. The results reveal differentiated regional patterns: European countries, particularly the Nordic ones, exhibit low entropy levels, indicating greater structural coherence in social progress; in contrast, several Sub-Saharan African countries show high entropy levels, reflecting greater dispersion and structural fragility. This approach provides a useful tool for comparative analysis of social development, as it allows for modeling uncertainty, ambiguity, and imprecise data.