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Ranking Candidates Through Convex Sequences of Variable Weights

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  • Bonifacio Llamazares

    (Universidad de Valladolid)

Abstract

Scoring rules are a well-known class of positional voting systems where fixed scores are assigned to the different ranks. Nevertheless, since the winners may change according to the scores used, the choice of the scoring vector is not obvious. For this reason several methods have been suggested so that each candidate may be evaluated with the most favorable scoring vector for him/her. In this paper we propose a new model that allows to use different scoring vector for each candidate and avoid some shortcomings of other methods suggested in the literature. Moreover we give a closed expression for the score obtained by each candidate and, in this way, it is possible to rank the candidates without solving the proposed model.

Suggested Citation

  • Bonifacio Llamazares, 2016. "Ranking Candidates Through Convex Sequences of Variable Weights," Group Decision and Negotiation, Springer, vol. 25(3), pages 567-584, May.
    • Handle: RePEc:spr:grdene:v:25:y:2016:i:3:d:10.1007_s10726-015-9452-8
    DOI: 10.1007/s10726-015-9452-8
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    References listed on IDEAS

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    1. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
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    3. Foroughi, A.A. & Tamiz, M., 2005. "An effective total ranking model for a ranked voting system," Omega, Elsevier, vol. 33(6), pages 491-496, December.
    4. Llamazares, Bonifacio & Peña, Teresa, 2013. "Aggregating preferences rankings with variable weights," European Journal of Operational Research, Elsevier, vol. 230(2), pages 348-355.
    5. Green, Rodney H. & Doyle, John R. & Cook, Wade D., 1996. "Preference voting and project ranking using DEA and cross-evaluation," European Journal of Operational Research, Elsevier, vol. 90(3), pages 461-472, May.
    6. Wade D. Cook & Moshe Kress, 1990. "A Data Envelopment Model for Aggregating Preference Rankings," Management Science, INFORMS, vol. 36(11), pages 1302-1310, November.
    7. Llamazares, Bonifacio & Pea, Teresa, 2009. "Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates," European Journal of Operational Research, Elsevier, vol. 197(2), pages 714-721, September.
    8. Fishburn, Peter C., 1974. "Paradoxes of Voting," American Political Science Review, Cambridge University Press, vol. 68(2), pages 537-546, June.
    9. Obata, Tsuneshi & Ishii, Hiroaki, 2003. "A method for discriminating efficient candidates with ranked voting data," European Journal of Operational Research, Elsevier, vol. 151(1), pages 233-237, November.
    10. Bonifacio Llamazares & Teresa Peña, 2015. "Positional Voting Systems Generated by Cumulative Standings Functions," Group Decision and Negotiation, Springer, vol. 24(5), pages 777-801, September.
    11. Hashimoto, Akihiro, 1997. "A ranked voting system using a DEA/AR exclusion model: A note," European Journal of Operational Research, Elsevier, vol. 97(3), pages 600-604, March.
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    Cited by:

    1. Paolo Viappiani, 2024. "Volumetric Aggregation Methods for Scoring Rules with Unknown Weights," Post-Print hal-04440153, HAL.
    2. Paolo Viappiani, 2020. "Robust winner determination in positional scoring rules with uncertain weights," Theory and Decision, Springer, vol. 88(3), pages 323-367, April.

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