Preference voting and ranking using DEA game cross efficiency model

Data envelopment analysis (DEA), a useful assessment tool, has been used to solve the problem of preference voting and aggregation which require 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 in order to maximize his/her own overall ratings subject to certain conditions. It is evident that competition exists among the candidates in a preferential election, while there is no literature considering the factor of competition. This paper proposes an approach to rank candidates based on DEA game cross efficiency model, in which each candidate is viewed as a player who seeks to maximize its own efficiency, under the condition that the cross efficiencies of each of other DMU's does not deteriorate. The game cross efficiency score is obtained when the DMU's own maximized efficiencies are averaged. The obtained game cross efficiency scores constitute a Nash Equilibrium point. Therefore, the results and orders based upon game cross efficiency analysis are more reliable and will benefit the decision-maker.