Evaluation of group decision making based on group preferences under a multi-criteria environment
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
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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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
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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
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