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.
Highlights
Nowadays, data have become a torrent flowing into various area of the global economy
We can see that the best credit among urban commercial banks is that of the Taizhou Bank, the second best is the Bank of Shanghai, and the third best is the Bank of Hangzhou, while the worst is Jiaxing City Commercial Bank, followed by the Bank of Nanchang, which is consistent with the real banking industry
In Stage 1 of the evaluation process, the ranks obtained by the eight Group decision making (GDM) methods for the two datasets are different and even conflicting rankings, that is, no group decision-making method is perfect to satisfy every member of a group, which verifies the pointed out problem of Arrow’s impossibility theorem for greatly expanding our theoretical significance and theoretical implication
Summary
Data have become a torrent flowing into various area of the global economy. Wu et al Evaluation of group decision making based on group preferences under a multi-criteria. Makers are struggling to gain useful, valuable information, rules, patterns or business insights out of data for decision support. The reasons are as follows (Kim & Ahn, 1997; Zhang, 2016; Wang et al, 2016): 1) Decision-making is often produced under time pressure, usually lacks of knowledge and data scenarios. It is necessary to absorb group wisdom, based on the opinions of group members to minimize the impact of individual decision or personal prejudice
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