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Evaluation of group decision making based on group preferences under a multi-criteria environment

    Wenshuai Wu   Affiliation
    ; Zeshui Xu   Affiliation
    ; Gang Kou   Affiliation

Abstract

Arrow’s impossibility theorem stated that no single group decision making (GDM) method is perfect, in other words, different GDM methods can produce different or even conflicting rankings. So, 1) how to evaluate GDM methods and 2) how to reconcile different or even conflicting rankings are two important and difficult problems in GDM process, which have not been fully studied. This paper aims to develop and propose a group decision-making consensus recognition model, named GDMCRM, to address these two problems in the evaluation of GDM methods under a multi-criteria environment in order to identify and achieve optimal group consensus. In this model, the ordinal and cardinal GDM methods are both implemented and studied in the process of evaluating the GDM methods. What’s more, this proposed model can reconcile different or even conflicting rankings generated by the eight GDM methods, based on empirical research on two real-life datasets: financial data of 12 urban commercial banks and annual report data of seven listed oil companies. The results indicate the proposed model not only can largely satisfy the group preferences of multiple stakeholders, but can also identify the best compromise solution from the opinion of all the participants involved in the group decision process.


First published online 20 October 2020

Keyword : group decision making, MCDM, consensus, group preferences

How to Cite
Wu, W., Xu, Z., & Kou, G. (2020). Evaluation of group decision making based on group preferences under a multi-criteria environment. Technological and Economic Development of Economy, 26(6), 1187-1212. https://doi.org/10.3846/tede.2020.13378
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Nov 17, 2020
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References

Arrow, K. J. (1963). Social choice and individual values (2nd ed). Wiley.

Ascough II, J. C., Maier, H. R., Ravalico, J. K., & Strudley, M. W. (2008). Future research challenges for incorporation of uncertainty in environmental and ecological decision-making. Ecological Modelling, 219(3−4), 383−399. https://doi.org/10.1016/j.ecolmodel.2008.07.015

Banerjee, A. (1994). Fuzzy preferences and Arrow-type problems. Social Choice and Welfare, 11(1), 121−130. https://doi.org/10.1007/bf00179208

Benjamin, R. (2002). Social choice in the South Seas: electoral innovation and the Borda count in the pacific Island countries. International Political Science Review, 23(4), 355−372. https://doi.org/10.1177/0192512102023004002

Beynon, M. J. (2005). A method of aggregation in DS/AHP for group decision-making with the non-equivalent importance of individuals in the group. Computers & Operations Research, 32(7), 1881−1896. https://doi.org/10.1016/j.cor.2003.12.004

Blanco-Mesa, F., León-Castro, E., Merigó, J. M., & Xu, Z. S. (2019). Bonferroni means with induced ordered weighted average operators. International Journal of Intelligent Systems, 34(1), 3−23. https://doi.org/10.1002/int.22033

Chang, K. H., & Cheng, C. H. (2011). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22(2), 113−129. https://doi.org/10.1007/s10845-009-0266-x

Chatterjee, N. C., & Bose, G. K. (2013). A COPRAS-F base multi-criteria group decision making approach for site selection of wind farm. Decision Science Letters, 2(1), 1−10. https://doi.org/10.5267/j.dsl.2012.11.001

Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making: methods and applications. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9

Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2000). The ordered weighted geometric operator: properties and application. In Proceedings of the 8th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 985−991). Madrid, Spain. https://doi.org/10.1007/978-3-7908-1796-6_14

Chiclana, F., Herrera-Viedma, E., Herrera, F., & Alonso, S. (2007). Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. European Journal of Operational Research, 182(1), 383−399. https://doi.org/10.1016/j.ejor.2006.08.032

Chwolka, A., & Raith, M. G. (2001). Group preference aggregation with the AHP implications for multiple-issue agendas. European Journal of Operational Research, 132(1), 176−186. https://doi.org/10.1016/S0377-2217(00)00121-1

Cutello, V., & Montero, J. (1994). Hierarchies of aggregation operators. International Journal of Intelligent Systems, 9(11), 1025−1045. https://doi.org/10.1002/int.4550091104

Dong, Y. C., Zha, Q. B., Zhang, H. J., Kou, G., Fujita, H., Chiclana, F., & Herrera-Viedma, E. (2018). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 162, 3−13. https://doi.org/10.1016/j.knosys.2018.06.036

Dong, Y, C., Zhang, G. Q., Hong, W. C., & Xu, Y. F. (2010). Consensus models for AHP group decision making under row geometric mean prioritization method. Decision Support Systems, 49(3), 281−289. https://doi.org/10.1016/j.dss.2010.03.003

Emerson, P. (2007). Designing an all-inclusive democracy-consensual voting procedures for use in parliaments, councils and committees. Springer-Verlag Berlin Heidelberg.

Filho, J. L. E. S., & Morais, D. C. (2018). Group decision model based on ordered weighted distance to aid decisions on logistics. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26(2), 233−254. https://doi.org/10.1142/S0218488518500125

Franz, B. (1936). Pareto. John Wiley & Sons.

Fu, G. T. (2008). A fuzzy optimization method for multi-criteria decision making: an application to reservoir flood control operation. Expert Systems with Applications, 31(1), 145−149. https://doi.org/10.1016/j.eswa.2006.08.021

Gargallo, 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, 485−506. https://doi.org/10.1007/s10726-006-9068-0

Glasser, W. (1998). Choice theory: a new psychology of personal freedom. Harper Perennial, 9(1), 72−75. https://doi.org/10.1098/rsnr.2002.0171

Gou, X. J., Xu, Z. S., Liao, H. C., & Herrera, F. (2018). Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Computers & Industrial Engineering, 126, 516−530. https://doi.org/10.1016/j.cie.2018.10.020

Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-48318-9

Ishizaka, A., & Labib, A. (2011). Review of the main developments in the analytic hierarchy process. Expert Systems with Application, 38, 14336−14345. https://doi.org/10.1016/j.eswa.2011.04.143

Jabbarzadeh, A. (2018). Application of the AHP and TOPSIS in project management. International Journal of Project Management, 3(2), 125−130. https://doi.org/10.5267/j.jpm.2018.1.001

Jahanshahloo, G. R., Lotfi Hosseinzadeh, F., & Izadikhah, M. (2006). Extension of the TOPSIS method for decision-making problems with fuzzy data. Applied Mathematics and Computation, 181(2), 1544−1551. https://doi.org/10.1016/j.amc.2006.02.057

Jung, C., Lee, H., Lim, Y., & Yamazaki, K. (2010). Weighted geometric mean of n-operators with nparameters. Linear Algebra and its Applications, 432(6), 1515−1530. https://doi.org/10.1016/j.laa.2009.11.013

Kim, S. H., & Ahn, B. S. (1997). Group decision-making procedure considering preference strength under incomplete information. Computer & Operations Research, 24(12), 1101−1112. https://doi.org/10.1016/S0305-0548(97)00037-3

Kou, G., & Ergu, D. J. (2016). AHP/ANP theory and its application in technological and economic development: the 90th anniversary of Thomas L. Saaty. Technological and Economic Development of Economy, 22(5), 649−650. https://doi.org/10.3846/20294913.2016.1202353

Kou, G., Lu, Y. Q., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(1), 197−225. https://doi.org/10.1142/S0219622012500095

Kwok, R. C., Ma, J., & Zhou, D. N. (2002). Improving group decision making: a fuzzy GSS approach. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 32(1), 54−63. https://doi.org/10.1109/TSMCC.2002.1009142

Li, C. C., Dong, Y. C., Herrera, F., Herrera-Viedma, E., & Martínez, L. (2017). Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 33, 29−40. https://doi.org/10.1016/j.inffus.2016.04.005

Li, J., Davies, G. J., Kendall, G., Soane, E., Bai, R., Rocks, S. A., & Pollard, S. J. T. (2012). Evidence and belief in regulatory decisions-Incorporating expected utility into decision modeling. Expert Systems with Applications, 39(10), 8604−8610. https://doi.org/10.1016/j.eswa.2012.01.193

Liang, D. C., Xu, Z. S., Liu, D., & Wu, Y. (2018). Method for three-way decisions using ideal TOPSIS solutions at Pythagorean fuzzy information. Information Sciences, 435, 282−295. https://doi.org/10.1016/j.ins.2018.01.015

Liao, H. C., Yang, L. Y., & Xu, Z. S. (2018). Two new approaches based on ELECTRE II to solve the multiple criteria decision making problems with hesitant fuzzy linguistic term sets. Applied Soft Computing, 63, 223−234. https://doi.org/10.1016/j.asoc.2017.11.049

Liu, W. Q., Dong, Y. C., Chiclana, F., Cabrerizo, F. J., & Herrera-Viedma, E. (2017). Group decisionmaking based on heterogeneous preference relations with self-confidence. Fuzzy Optimization and Decision Making, 16, 429−447. https://doi.org/10.1007/s10700-016-9254-8

Ma, J., Lu, J., & Zhang, G. Q. (2010). Decider: A fuzzy multi-criteria group decision support system. Knowledge-Based Systems, 23(1), 23−31. https://doi.org/10.1016/j.knosys.2009.07.006

Marichal, J. L. (2001). An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria. IEEE Transactions on Fuzzy Systems, 8(6), 800−807. https://doi.org/10.1109/91.890347

Montserrat-Adell, J., Xu, Z. S., Gou, X. J., & Agell, N. (2019). Free Double Hierarchy Hesitant Fuzzy Linguistic Term Sets: An application on ranking alternatives in GDM. Information Fusion, 47, 45−59. https://doi.org/10.1016/j.inffus.2018.07.002

Morais, D. C., & Almeida, A. T. D. (2012). Group decision making on water resources based on analysis of individual rankings. Omega, 40(1), 42−52. https://doi.org/10.1016/j.omega.2011.03.005

Moreno-Jiménez, J. M., Aguarón, J., & Escobar, M. T. (2008). The core of consistency in AHP-group decision making. Group Decision and Negotiation, 17(3), 249−265. https://doi.org/10.1007/s10726-007-9072-z

O’Hagan, M. (1998). Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic. In Proceedings of the Twenty-Second Asilomar Conference on Signals, Systems and Computers (pp. 681−689), Pacific Grove, CA, USA. https://doi.org/10.1109/ACSSC.1988.754637

Olson, D. L. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modeling, 40(7−8), 721−727. https://doi.org/10.1016/j.mcm.2004.10.003

Pang, J. F., & Liang, J. Y. (2012). Evaluation of the results of multi-attribute group decision-making with linguistic information. Omega, 40(3), 294−301. https://doi.org/10.1016/j.omega.2011.07.006

Peng, Y., Kou, G., Wang, G. X., Wu, W. S., & Shi, Y. (2011). Ensemble of software defect predictors: an AHP-based evaluation method. International Journal of Information Technology & Decision Making, 10(1), 187−206. https://doi.org/10.1142/S0219622011004282

Pérez-Arellano, L. A., León-Castro, E., Avilés-Ochoa, E., & Merigó, J. M. (2019). Prioritized induced probabilistic operator and its application in group decision making. International Journal of Machine Learning and Cybernetics, 10(3), 451−462. https://doi.org/10.1007/s13042-017-0724-2

Poulton, E. C. (1994). Behavioral decision theory: a new approach. Cambridge University Press. https://doi.org/10.1017/CBO9780511574894

Renato, C. (1983). Was Vilfredo Pareto really a ‘precursor’ of fascism? The American Journal of Economics and Sociology, 42(2), 235−245. https://doi.org/10.1111/j.1536-7150.1983.tb01708.x

Saaty, T. L. (1978). Modeling unstructured decision problems-the theory of analytical hierarchies. Mathematics and Computers in Simulation, 20(3), 147−158. https://doi.org/10.1016/0378-4754(78)90064-2

Saaty, T. L. (1979). Applications of analytical hierarchies. Mathematics and Computers in Simulation, 21(1), 1−20. https://doi.org/10.1016/0378-4754(79)90101-0

Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841−855. https://doi.org/10.1287/mnsc.32.7.841

Saaty, T. L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, 48(1), 9−26. https://doi.org/10.1016/0377-2217(90)90057-I

Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary? European Journal of Operational Research, 145(1), 85−91. https://doi.org/10.1016/S0377-2217(02)00227-8

Sen, A. K. (1970). Collective choice and social welfare. North-Holland. https://doi.org/10.1016/C2009-0-12011-1

Shen, F., Ma, X. S., Li, Z. Y., Xu, Z. S., & Cai, D. L. (2018). An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Information Sciences, 428, 105−119. https://doi.org/10.1016/j.ins.2017.10.045

Srdjevic, B. (2007). Linking analytic hierarchy process and social choice methods to support group decision-making in water management. Decision Support Systems, 42(4), 2261−2273. https://doi.org/10.1016/j.dss.2006.08.001

Su, K. P., Zhou, L. G., & Chen, H. Y. (2011). Portfolio decision making model with CWAA operator under CVaR measure. Journal of Wuhan University of Technology (Information & Management Engineering), 33(4), 618−621.

Tang, J., & Meng, F. Y. (2019). Linguistic intuitionistic fuzzy Hamacher aggregation operators and their application to group decision making. Granular Computing, 4(1), 109−124. https://doi.org/10.1007/s41066-018-0089-2

Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.

Wang, Z. J., Wang, Y., & Li, K. W. (2016). An acceptable consistency-based framework for group decision making with intuitionistic preference relations. Group Decision and Negotiation, 25(1), 181−202. https://doi.org/10.1007/s10726-015-9438-6

Wu, W. S. (2017). Grey relational analysis method for Group Decision Making in Credit Risk Analysis. EURASIA Journal of Mathematics Science and Technology Education, 13(12), 7913−7920. https://doi.org/10.12973/ejmste/77913

Wu, W. S., Kou, G., & Peng, Y. (2018). A consensus facilitation model based on experts’ weights for investment strategy selection. Journal of the Operational Research Society, 69(9), 1435−1444. https://doi.org/10.1080/01605682.2017.1398203

Wu, W. S., Kou, G., Peng, Y., & Ergu, D. J. (2012). Improved AHP-group decision-making for investment strategy selection. Technological and Economic Development of Economy, 18(2), 299−316. https://doi.org/10.3846/20294913.2012.680520

Wu, W. S., & Peng, Y. (2016). Extension of grey relational analysis for facilitating group consensus to oil spill emergency management. Annals of Operations Research, 238(1), 615−635. https://doi.org/10.1007/s10479-015-2067-2

Wu, W. S., Xu, Z. S., Kou, G., & Shi, Y. (2020). Decision-making support for the evaluation of clustering algorithms based on MCDM. Complexity, 2020, 9602526. https://doi.org/10.1155/2020/9602526

Xian, S. D., Liu, Z., Gou, X. L., & Wan, W. H. (2020). Interval 2-tuple Pythagorean fuzzy linguistic MULTIMOORA method with CIA and their application to MCGDM. International Journal of Intelligent Systems, 35(4), 650−681. https://doi.org/10.1002/int.22221

Xian, S. D., Xue, W. T., & Jing, N. (2016). Fuzzy entropic ordered weighted averaging operator and its application in group decision making. International Journal of Intelligent Systems, 31(9), 872−885. https://doi.org/10.1002/int.21808

Xu, J. P., & Chen, J. Z. (2007). The theory and methods of group decision making with its realization. Tsinghua University Press.

Xu, J. P., & Shen, F. (2014). A new outranking choice method for group decision making under Atanassov’s interval-valued intuitionistic fuzzy environment. Knowledge-Based Systems, 70, 177−188. https://doi.org/10.1016/j.knosys.2014.06.023

Xu, Z. S. (2000). On consistency of the weighted geometric mean complex judgment matrix in AHP. European Journal of operational Research, 126(3), 683−687. https://doi.org/10.1016/S0377-2217(99)00082-X

Xu, Z. S., & Da, Q. L. (2002a). The ordered weighted geometric averaging operator. International Journal of Intelligent Systems, 17(7), 709−716. https://doi.org/10.1002/int.10045

Xu, Z. S., & Da, Q. L. (2002b). Combined weighted geometric averaging operator and its application. Journal of Southeast University (Natural Science Edition), 32(3), 506−509.

Yager, R. (1993). Non-numeric multi-criteria multi-person decision making. Group Decision and Negotiation, 2, 81−93. https://doi.org/10.1007/bf01384404

Yager, R. R. (1988). On ordered weight averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man and Cybernetics, 18(1), 183−190. https://doi.org/10.1109/21.87068

Yager, R. R., Goldstein, L. S, & Mendels, E. (1994). FUZMAR: An approach to aggregating market research data based on fuzzy reasoning. Fuzzy Sets and Systems, 68(1), 1−11. https://doi.org/10.1016/0165-0114(94)90269-0

Yan, H. B., & Ma, T. J. (2015). A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. European Journal of Operational Research, 241(3), 815−829. https://doi.org/10.1016/j.ejor.2014.09.017

Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making: an introduction. Sage Publications. https://doi.org/10.4135/9781412985161

Yu, C. S. (2002). A GP-AHP method for solving group decision-making fuzzy AHP problems. Computers & Operations Research, 29(14), 1969−2001. https://doi.org/10.1016/S0305-0548(01)00068-5

Yue, Z. L. (2011). A method for group decision-making based on determining weights of decision makers using TOPSIS. Applied Mathematical Modelling, 35(4), 1926−1936. https://doi.org/10.1016/j.apm.2010.11.001

Yue, Z. L. (2014). TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 277, 141−153. https://doi.org/10.1016/j.ins.2014.02.013

Zhang, H. M. (2015). Group decision making based on multiplicative consistent reciprocal preference relations. Fuzzy Sets and Systems, 282, 31−46. https://doi.org/10.1016/j.fss.2015.04.009

Zhang, H. M. (2016). Group decision making based on incomplete multiplicative and fuzzy preference relations. Applied Soft Computing, 48, 735−744. https://doi.org/10.1016/j.asoc.2016.07.046