The acclamation consensus state and an associated ranking rule

The study of conditions, under which the existence of an “absolute” best winner can be assured, is a hot topic in the field of social choice. Unanimity is an evident example of a condition under which the winner is obvious. However, many more properties weaker than unanimity have been analysed in literature: the presence of a Condorcet winner, strong stochastic transitivity, the presence of a candidate that Borda dominates all other candidates, etc. Unfortunately, one could easily find a prominent ranking rule, for which the outcome does not agree with these relaxed conditions. In this study, we aim to identify a condition weaker than unanimity, but under which the social outcome is still obvious. This condition, defined as the conjunction of three properties already studied by the present authors and hereinafter referred to as acclamation, will be proven to be a meeting point for the most prominent ranking rules in social choice theory, and will be used for introducing an intuitively appealing ranking rule

Extensión del concepto de utopía para el problema de la agregación de rankings sin empates

The use of rankings and how to aggregate or summarize them has received increasing attention in various fields: bibliometrics, web search, data mining, statistics, educational quality, and computational biology. For the Optimal Bucket Order Problem, the concept of Utopian Matrix was recently introduced: an ideal and not necessarily feasible solution with an unsurpassed quality for the feasible solutions of the problem. This work proposes an extension of the notion of Utopian Matrix to the Rank Aggregation Problem in which ties are not allowed between elements in the output ranking. Beyond the extension that is direct, the work focuses on studying its usefulness as an idealization or super optimal solution. As the Rank Aggregation Problem can be solved exactly based on its definition as an Integer Linear Programming Problem, an experimental study is presented where it is analyzed the relationship that exists between utopian (and anti utopian) values and the optimal solution in several instances solved by using the open source software SCIP. Among the 47 instances analyzed, in 19 the Utopian Value turned out to be equal to the optimal value (40.43 % feasibility) and in 18 the Anti Utopian Value also turned out to be feasible (38.00 %). This experimental study demonstrates the usefulness of utopian and anti utopian values to be considered as extreme values in the Rank Aggregation Problem, thus being able to find higher and lower bounds for optimization very quickly.El uso de los rankings y la forma de agregarlos o resumirlos ha recibido una atención creciente en diversos campos: bibliometría, búsquedas web, minería de datos, estadística, calidad educativa y biología computacional. Para el Problema de Ordenamiento Óptimo con empates fue introducido recientemente el concepto de Matriz Utópica: una solución ideal y no necesariamente factible con una calidad insuperable para las soluciones factibles del problema. Este trabajo propone una extensión de la noción de Matriz Utópica para el Problema de Agregación de Rankings en que no se permiten empates entre elementos en el ranking de salida. Más allá de la extensión que es directa, el trabajo se centra en estudiar su valor como idealización o solución súper óptima. Como el Problema de Agregación de Rankings puede resolverse de forma exacta a partir de su definición como Problema de Programación Lineal Entera, se presenta un estudio experimental donde se analiza la relación que existe entre los valores utópicos (y anti utópicos) y la solución óptima en instancias resueltas con la ayuda del software de código abierto SCIP. Entre las 47 instancias analizadas, en 19 el Valor Utópico resultó ser igual al valor óptimo (40,43 % de factibilidad) y en 18 el Valor Anti Utópico también resultó ser factible (38,00 %). Este estudio experimental demuestra la utilidad de los valores utópicos y anti utópicos para ser considerados como valores extremos en el Problema de Agregación de Rankings, pudiendo así encontrase muy rápidamente cotas superiores e inferiores para la optimización

Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem

The optimal bucket order problem consists in obtaining a complete consensus ranking (ties are allowed) from a matrix of preferences (possibly obtained from a database of rankings). In this paper, we tackle this problem by using evolution strategies. We designed specific mutation operators which are able to modify the inner structure of the buckets, which introduces more diversity into the search process. We also study different initialization methods and strategies for the generation of the population of descendants. The proposed evolution strategies are tested using a benchmark of 52 databases and compared with the current state-of-the-art algorithm LIA. We carry out a standard machine learning statistical analysis procedure to identify a subset of outstanding configurations of the proposed evolution strategies. The study shows that the best evolution strategy improves upon the accuracy obtained by the standard greedy method (BPA) by 35%, and that of LIA by 12.5%