Abstract
Systemic decision making is a new approach for dealing with complex multiactor decision making problems in which the actors’ individual preferences on a fixed set of alternatives are incorporated in a holistic view in accordance with the “principle of tolerance”. The new approach integrates all the preferences, even if they are encapsulated in different individual theoretical models or approaches; the only requirement is that they must be expressed as some kind of probability distribution. In this paper, assuming the analytic hierarchy process (AHP) is the multicriteria technique employed to rank alternatives, the authors present a new methodology based on a Bayesian analysis for dealing with AHP systemic decision making in a local context (a single criterion). The approach integrates the individual visions of reality into a collective one by means of a tolerance distribution, which is defined as the weighted geometric mean of the individual preferences expressed as probability distributions. A mathematical justification of this distribution, a study of its statistical properties and a Monte Carlo method for drawing samples are also provided. The paper further presents a number of decisional tools for the evaluation of the acceptance of the tolerance distribution, the construction of tolerance paths that increase representativeness and the extraction of the relevant knowledge of the subjacent multiactor decisional process from a cognitive perspective. Finally, the proposed methodology is applied to the AHP-multiplicative model with lognormal errors and a case study related to a real-life experience in local participatory budgets for the Zaragoza City Council (Spain).
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Notes
The stability of the priorities given by (16) against small judgement changes is guaranteed by having the T of Student with a reduced number of degrees of freedom (heavy-tailed distributions).
Extension to a global context (hierarchy) will be the subject of a future paper.
These values correspond to a diffuse prior centred on the level of inconsistency, as suggested by Genest and Rivest (1994).
References
Aguarón, J., Escobar, M. T., & Moreno-Jiménez, J. M. (2014). Precise consistent consensus matrix. Annals of Operations Research. doi:10.1007/s10479-014-1576-8.
Aguarón, J., & Moreno-Jiménez, J. M. (2003). The geometric consistency index. Approximated thresholds. European Journal of Operational Research, 147(1), 137–145.
Aitchison, J. (1986). The statistical analysis of compositional data. London: Chapman and Hall.
Alho, J. M., & Kangas, J. (1997). Analyzing uncertainties in experts’ opinions of forest plan performance. Forest Science, 43, 521–528.
Altuzarra, A., Gargallo, P., Moreno-Jiménez, J. M., & Salvador, M. (2013). Influence, relevance and discordance of criteria in AHP-global Bayesian prioritization. International Journal of Information Technology & Decision Making, 12(4), 837–861.
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(6), 1755–1773.
Bellucci, E., & Zeleznikow, J. (2005). Trade-Off Manipulations in the Development of Negotiation Decision Support Systems. In M. Conley Tyler, E. Katsh, D. Choi (Eds.) Proceedings of the Third Annual Forum on Online Dispute Resolution. International Conflict Resolution Centre. The University of Melbourne in collaboration with the United Nations Economic and Social Commission for Asia and the Pacific. (http://www.odr.info/unforum2004/bellucci_zeleznikow.htm).
Bryson, N. (1996). Group decision-making and the analytic hierarchy process: Exploring the consensus-relevant information content. Computers & Operations Research, 23, 27–35.
Bryson, N., & Joseph, A. (1999). Generating consensus priority point vectors: A logarithmic goal programming approach. Computers & Operations Research, 26(6), 637–643.
Chen, Y. M., & Huang, P.-N. (2007). Bi-negotiation integrated AHP in suppliers’ selection. International Journal of Operations & Production Management, 27(11), 1254–1274.
Cho, Y.-G., & Cho, K.-T. (2008). A loss function approach to group preference aggregation in the AHP. Computers & Operations Research, 35(3), 884–892.
Davidson, J. (1994). Stochastic limit theory. Oxford: Oxford University Press.
De Bono, E. (1970). Lateral thinking. Baltimore, MD: Penguin Books.
Dong, Y. C., Zhang, G. Q., Hong, W. Q., & Xu, Y. F. (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. Group Decision and Negotiation, 16(4), 287–301.
Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the Analytic Hierarchy Process. European Journal of Operational Research, 108, 165–169.
Gargallo, P., Moreno-Jiménez, J. M., & Salvador, M. (2007). AHP-group decision making: A Bayesian approach based on mixtures for group identification. Group Decision and Negotiation, 16(6), 485–506.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis. Texts in statistical science (2nd ed.). London: Chapman & Hall/CRC.
Genest, C., & Rivest, L. P. (1994). A statistical look at Saaty’s method of estimating pairwise preferences expressed on a ratio scale. Journal of Mathematical Psychology, 38, 477–496.
Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57, 1317–1340.
Hahn, D. (2003). Decision making with uncertain judgements: A stochastic formulation of the analytic hierarchy process. Decision Sciences, 34(3), 443–446.
Hahn, D. (2006). Link function selection in stochastic multicriteria decision making models. European Journal of Operational Research, 172, 86–100.
Hämäläinen, R. P., & Pöyhönen, M. (1996). On-line group decision support by preference programming in traffic planning. Group Decision and Negotiation, 5(4), 485–500.
Hämäläinen, R. P. (2003). Decisionarium-aiding decisions, negotiating and collecting opinions on the web. Journal Multi-Criteria Decision Analysis, 12(2–3), 101–110.
Hosseinian, S., Navidi, H., & Hajfathaliha, A. (2012). A new linear programming method for weights generation and group decision making in the analytic hierarchy process. Group Decision and Negotiation, 21(3), 233–254.
Huang, Y. S., Liao, J. T., & Lin, Z. L. (2009). A study on aggregation of group decisions. Systems Research and Behavioral Science, 26(4), 445–454.
Laininen, P., & Hämäläinen, R. P. (2003). Analyzing AHP-matrices by regression. European Journal of Operational Research, 148, 514–524.
Lipovetsky, S. (2009). Linear regression with special coefficient features attained via parameterization in exponential, logistic, and multinomial-logit forms. Mathematical and Computer Modelling, 49(7–8), 1427–1435.
Mikhailov, L. (2004). Group prioritization in the AHP by fuzzy preference programming method. Computers & Operations Research, 31(2), 293–301.
Moreno-Jiménez, J. M. (2003a). Los Métodos Estadísticos en el Nuevo Método Científico. In J. M. Casas & A. Pulido (Eds.), Información económica y técnicas de análisis en el siglo XXI (pp. 331–348). Madrid: Instituto Nacional de Estadística (INE).
Moreno-Jiménez J.M. (2003b). Las Nuevas Tecnologías y la Representación Democrática del Inmigrante. En ARENERE, J.: IV Jornadas Jurídicas de Albarracín (22 pp). Consejo General del Poder Judicial. TSJA, Memoria Judicial Anual de Aragón del año 2003, p. 66, Zaragoza.
Moreno-Jiménez, J. M. (2006). E-cognocracia: Nueva sociedad, nueva democracia. Estudios de Economía Aplicada, 24(1–2), 559–581.
Moreno-Jiménez, J. M., Aguarón, J., & Escobar, M. T. (2001). Metodología científica en valoración y selección ambiental. Pesquisa Operacional, 21, 3–18.
Moreno-Jiménez, J. M., Aguarón, J., & Escobar, M. T. (2008). The core of consistency in AHP-group decision making. Group Decision & Negotiation, 17, 249–265.
Moreno-Jiménez, J. M., Aguarón, J., Raluy, A., & Turón, A. (2005). A spreadsheet module for consistent AHP-consensus building. Group Decision & Negotiation, 14(2), 89–108.
Moreno-Jiménez, J. M., & Polasek, W. (2003). E-democracy and knowledge. A multicriteria framework for the new democratic era. Journal Multi-criteria Decision Analysis, 12, 163–176.
Moreno-Jiménez, J. M., & Vargas, L. G. (1993). A probabilistic study of preference structures in the analytic hierarchy process with Interval Judgments. Mathematical and Computer Modelling, 17(4–5), 73–81.
Peniwati, K. (2007). Criteria for evaluating group decision-making methods. Mathematical and Computer Modelling, 46(7–8), 935–947.
Ramanathan, R. (1997). Stochastic decision making using multiplicative AHP. European Journal of Operational Research, 97, 543–549.
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.
Ramsay, J. O. (1977). Maximum likelihood estimation in multidimensional scaling. Psychometrika, 42, 241–266.
Roy, B. (1985). Methodologie Multicritère d’Aide à la Décision. Paris: Gestion Economica.
Rubin, D. (1987). A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: the SIR algorithm. Journal of the American Statistical Association, 82, 543–546.
Saaty, T. L. (1972). An eigenvalue allocation model in contingency planning. University of Pennsylvania, 19, 72.
Saaty, T. L. (1980). Multicriteria decision making: The analytic hierarchy process. New York: Mc Graw-Hill. (2nd impression 1990, RSW Pub. Pittsburgh, PA).
Saaty, T. L., & Peniwati, K. (2008). Group decision making: Drawing out and reconciling differences. Pittsburgh, PA: RWS Publications.
Salvador, M., Gargallo, P., & Moreno-Jiménez, J.M. (2014). A Bayesian approach to maximizing inner compatibility in AHP-Systemic Decision Making. Group Decision & Negotiation. (Forthcoming).
Stam, A., & Silva, A. P. D. (1997). Stochastic judgements in the AHP: The measurement of rank reversal probabilities. Decision Science, 28(3), 655–688.
Van den Honert, R. C., & Lootsma, F. A. (2000). Assessing the quality of negotiated proposals using the REMBRANDT system. European Journal of Operational Research, 120(1), 162–173.
Van den Honert, R. C. (1998). Stochastic group preference modelling in the multiplicative AHP: A model of group consensus. European Journal of Operational Research, 110(1), 99–11.
Acknowledgments
This work was partially financed by the project “Social Cognocracy Network” (Ref. ECO2011-24181), supported by the Spanish Ministry of Science and Innovation.
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Moreno-Jiménez, J.M., Salvador, M., Gargallo, P. et al. Systemic decision making in AHP: a Bayesian approach. Ann Oper Res 245, 261–284 (2016). https://doi.org/10.1007/s10479-014-1637-z
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DOI: https://doi.org/10.1007/s10479-014-1637-z