Two effective total ranking models for preference voting and aggregation

A Hadi-Vencheh

doi:10.1007/s40096-014-0115-8

Abstract

Data envelopment analysis (DEA), a useful assessment tool, has been used to solve the problem of preference voting and aggregation which requires the determination of the weights associated with different ranking places. Instead of applying the same externally imposed weighting scheme to all candidates, DEA models allow each candidate to choose his/her own weights to maximize his/her own overall ratings subject to certain conditions. This paper proposes two new models to assess the weights. The proposed models are linear programming, which determine a common set of weights for all the candidates. The proposed models are examined with two numerical examples and it is shown that the proposed models can not only choose a winner, but also give a full ranking of all the candidates.

Highlights

In a preferential voting system, each voter selects a subset of the candidates and places them in a ranked order
The key issue of the preference aggregation in a preferential voting system is how to determine the weights associated with different ranking places
To avoid possible more misapplications or spread in the future, we present in this paper two improved data envelopment analysis (DEA) models to determine the weights

Summary

Introduction

In a preferential voting system, each voter selects a subset of the candidates and places them in a ranked order. Wang et al [10] propose three new models to assess the weights and rank the candidates. Wang et al have not taken care of about making the weight of a certain rank zero means that we throw away the corresponding part of the obtained data.

Results

Conclusion