Abstract
A new decisional tool, the consensus consistency matrix (CCM) has recently been proposed for dealing with AHP-group decision making (AHP-GDM) in a local context (a single criterion). Each entry of this matrix, based on the property of consistency, corresponds to the range of values or interval in which all the decision makers are simultaneously consistent in their initial matrices. The main limitation of the CCM is that, on many occasions, it is not possible to obtain a matrix with the minimum \(n-1\) judgments that are required to derive the priorities. In this local AHP context, using the row geometric mean as the prioritisation procedure, this paper presents an extension of the CCM, the precise consensus consistency matrix (PCCM), which significantly mitigates this problem. By identifying precise values in the common consistency intervals, the PCCM automatically allows the number of entries in the CCM to be increased. The PCCM provides more informed and participative GDM and offers more accurate estimations for the group’s priorities. It can also be used as a starting point for posterior negotiation processes between the actors and it can be employed in global AHP-GDM contexts (hierarchies). The new decisional tool has been applied to a real-life experience concerned with the analysis of the integral viability of public investment projects, more specifically, the economic valuation of social aspects.
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Notes
Redundancy in judgments provides more accurate estimations (Saaty 1980).
Defined as the difference between the inconsistency threshold allowed in the problem and the current inconsistency of the pairwise comparison matrix.
If there is no likelihood of confusion, in order to simplify the notation, this interval will be denoted as:\([\underline{a}_{rs} ,\overline{a} _{rs} ]\).
At least the (\(n-1\)) judgments connecting all the nodes are necessary.
PSOE: Partido Socialista Obrero Español; PP: Partido Popular; PAR: Partido Aragonés.
In this first iteration of the algorithm, the maximum variation allowed for the CGI of each of the decision-makers (\(\Delta ^\mathrm{(k)} = \mathrm{{GCI*}}- \mathrm{{GCI}}^\mathrm{(k)})\) is: \(\Delta ^{(1)}\) = 0.227, \(\Delta ^{(2)}\) = 0.067 and \(\Delta ^{(3)}\) = 0.072, respectively .
References
Aguarón, J., Escobar, M. T., & Moreno-Jiménez, J. M. (2003). Consistency stability intervals for a judgement in AHP decision support systems. European Journal of Operational Research, 145(2), 382–393.
Aguarón, J., & Moreno-Jiménez, J. M. (2003). The geometric consistency index: Approximate thresholds. European Journal of Operational Research, 147(1), 137–145.
Altuzarra, A., Moreno-Jiménez, J. M., & Salvador, M. (2007). A Bayesian priorization procedure for AHP-group decision making. European Journal of Operational Research, 182, 367–382.
Altuzarra, A., Moreno-Jiménez, J. M., & Salvador, M. (2010). Consensus building in AHP-group decision making: A Bayesian approach. Operations Research, 58, 1755–1773.
Aznar, J., Guijarro, F., & Moreno-Jiménez, J. M. (2011). Mixed valuation methods: A combined AHP-GP procedure for individual and group multicriteria agriculture valuation. Annals of Operations Research, 190(1), 221–238.
Barrett, C. R., Patanaik, P. K., & Salles, M. (1992). Rationality and aggregation of preferences in an ordinal fuzzy framework. Fuzzy Sets and System, 49, 9–13.
Barbier, E. B., Acreman, M., & Knowler, D. (1997). Economic valuation of wetlands: A guide for policy makers and planers. Gland, Switzerland: Ramsar Convention Bureau.
Brunelli, M., Canal, L., & Fedrizzi, M. (2013). Inconsistency indices for pairwise comparison matrices: A numerical study. Annals of Operations Research, 211(1), 493–509.
Bryson, N. (1996). Group decision making and the analytic hierarchy process: Exploring the consensus-relevant information content. Computers and Operations Research, 23, 27–35.
Condon, E., Golden, B., & Wasil, E. (2003). Visualizing group decisions in the analytic hierarchy process. Computers and Operations Research, 30(10), 1435–1445.
Crawford, G., & Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29, 387–405.
Dyer, R. F., & Forman, E. H. (1992). Group decision support with the analytic hierarchy process. Decision Support Systems, 8, 99–124.
Dong, Y., Zhang, G., Hong, W. C., & Xu, Y. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49, 281–289.
Escobar, M. T., & Moreno-Jiménez, J. M. (2007). Aggregation of individual preference structures in AHP-group decision making. Group Decision and Negotiation, 16(4), 287–301.
Escobar, M. T., Aguarón, J., & Moreno-Jiménez, J. M. (2004). A note on AHP group consistency for the row geometric mean priorization procedure. European Journal of Operational Research, 153, 318–322.
Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research, 108, 165–169.
Gargallo, M. P., Moreno-Jiménez, J. M., & Salvador, M. (2007). AHP-group decision making. A Bayesian approach based on mixtures for group pattern identification. Group Decision and Negotiation, 16(6), 485–506.
Golany, B., & Kress, M. (1993). A multicriteria evaluation of methods for obtaining weights from ratio-scale matrices. European Journal of Operational Research, 69, 210–220.
González-Pachón, J., & Romero, C. (2007). Inferring consensus weights from pairwise comparison matrices without suitable properties. Annals of Operations Research, 154(1), 123–132.
Iz, P. H., & Gardiner, L. R. (1993). Analysis of multiple criteria decision support systems for cooperative groups. Group Decision and Negotiation, 2, 61–79.
Kalai, E., & Schmeider, D. (1977). Aggregation procedure for ordinal preferences: A formulation and proof of Samuelson’s impossibility conjecture. Econometrica, 45(6), 1431–1438.
Keeney, R. (1976). A group preference axiomatization with cardinal utility. Management Science, 23(2), 140–145.
Lin, Ch., Kou, G., & Ergu, D. (2013). An improved statistical approach for consistency test in AHP. Annals of Operations Research, 211(1), 289–299.
Moreno-Jiménez, J. M. (2003). Los Métodos Estadísticos en el Nuevo Método Científico. In J. M. Casas & A. Pulido (Eds.), Información económica y técnicas de análisis en el siglo (Vol. XXI, pp. 331–348). Madrid: INE.
Moreno-Jiménez, J. M. (2011). An AHP/ANP multicriteria methodology to estimate the value and transfers fees of professional football players. In Proceedings ISAHP 2011, Sorrento.
Moreno-Jiménez, J. M., Aguarón, J., & Escobar, M. T. (2008a). The core of consistency in AHP-group decision making. Group Decision and Negotiation, 17(3), 249–265.
Moreno-Jiménez, J. M., Aguarón, J., Raluy, A., & Turón, A. (2005). A spreadsheet module for consistent consensus building in AHP-group decision making. Group Decision and Negotiation, 14(2), 89–108.
Moreno-Jiménez, J. M., Gómez-Bahillo, C., Sanaú, J. (2009). Viabilidad Integral de Proyectos de Inversión Pública. Valoración económica de los aspectos sociales. In J. R. Pires & J. Dioniso Monteiro (Eds.), Anales de Economía Aplicada (Vol. XXIII, pp. 2551–2562). Madrid: Delta Publicaciones.
Moreno-Jiménez, J. M., Gómez-Bahillo, C., & Sanaú, J. (2013). Integral viability of public investment projects: A multicriteria approach, based on input–output models, for the economic valuation of social aspects. Working Paper.
Moreno-Jiménez, J. M., & Salvador, M. (2008). Collaborative decision making in e-cognocracy. Maximizing the outer compatibility. In Collaborative decision making (CDM08), IRIT/RR-2008-4-FR (CD), Toulouse.
Moreno-Jiménez, J. M., Salvador, M., & Gargallo, M. P. (2008b). Compatibilidad interna y externa en la e-cognocracia. XXII Anales de Economía Aplicada, 377–396. ISBN 978-84-92453-25-2.
Ramanathan, R., & Ganesh, L. S. (1994). Group preference aggregation methods employed in AHP: An evaluation and intrinsic process for deriving members’ weightages. European Journal of Operational Research, 79, 249–265.
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology., 15(3), 234–281.
Saaty, T. L. (1980). Multicriteria decision making: The analytic hierarchy process. New York: Mc Graw-Hill. (2nd impression 1990, RSW Pub. Pittsburgh).
Saaty, T. L. (1989). Group decision making and the AHP. In B. L. Golden, E. A. Wasil, P. T. Harker (Eds.), The analytic hierarchy process: Applications and studies (pp. 59–67).
Saaty, T. L., & Vargas, L. G. (2005). The possibility of group welfare functions. International Journal of Information Technology and Decision Making, 4(2), 167–176.
Saaty, T. L., & Vargas, L. G. (2012). The possibility of group choice: Pairwise comparisons and merging functions. Social Choice and Welfare, 38, 481–496.
Srdjevic, B., & Srdjevic, Z. (2013). Synthesis of individual best local priority vectors in AHP-group decision making. Applied Soft Computing, 13, 2045–2056.
Xu, Z. (2000). On consistency of the weighted geometric mean complex judgement matrix in AHP. European Journal of Operational Research, 126, 683–687.
Acknowledgments
This paper has been partially funded by the research project “Social Cognocracy Network” (Ref. ECO2011-24181), supported by the Spanish Ministry of Science and Innovation.
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Aguarón, J., Escobar, M.T. & Moreno-Jiménez, J.M. The precise consistency consensus matrix in a local AHP-group decision making context. Ann Oper Res 245, 245–259 (2016). https://doi.org/10.1007/s10479-014-1576-8
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DOI: https://doi.org/10.1007/s10479-014-1576-8