Extension of the aggregation of preference rankings using an optimistic-pessimistic approach

Abstract

In a ranked voting system, candidates usually receive different votes in different ranking places. Many aggregation methods have been proposed to determine the ranking of the candidates competing for a limited number of positions. The most popular appears to be the weighted sum of votes that each candidate receives by different voters. Since the successful application of Data Envelopment Analysis (DEA) to preferential voting problems, many DEA-based models have been developed to aggregate the submitted ranked votes into a final ranking of candidates. In this study, we extend the preferential voting model by Khodabakhshi and Aryavash (2015) to an enhanced one that explicitly considers discriminating factors in the formulation, thereby generalizing previous results by other authors. The proposed model formulates a dual problem for resolving unknown discriminating factors in the primal problem, and then attempts to find its closed solution. © 2019 Elsevier Ltd