This repository contains the implementation of the algorithms and data of the experimental section of the paper "Aggregating Value Systems for Decision Support" by Roger X. Lera-Leri, Enrico Liscio, Filippo Bistaffa, Catholijn M. Jonker, Maite Lopez-Sanchez, Pradeep K. Murukannaiah, Juan A. Rodríguez-Aguilar, and Francisco Salas-Molina in Knowledge-Based Systems, 2024.
All experiments consider the European Values Study 2017: Integrated Dataset (EVS 2017) (dataset).
Our approach must be executed by means of the solve.py Python script, i.e.,
usage: solve.py [-h] [-p P] [-e E] [-f F] [-w W] [-i I] [-o O] [-v] [-l] [-t]
[-g G]
optional arguments:
-h, --help show this help message and exit
-p P p-norm (default: 2)
-e E epsilon used to compute limit p (default: 1e-4)
-f F CSV file with data (default: 'data.csv')
-w W weighting countries: 0 for unweighted problem, 1 for considering people that participated in the study and 2 for country population (default: 0)
-i I computes equivalent p given an input consensus
-o O write consensus to file
-v computes the preference aggregation
-l compute the limit p
-t compute the threshold p
-g G store results in csv
This repository contains the implementation of the pIRLS algorithm (article). This article should be cited when citing our work.