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On possible outputs of group decision making with interval uncertainties based on simulation techniques

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Abstract

Interval uncertainties are very common in group decision making (GDM), especially with the increasing complexity of decision-making systems. The aggregation approach is a widely used method for integrating interval uncertain information in a single interval output that is the basis for ranking alternatives. The ranking results are usually presented in absolute form, that is, an alternative has a 100% probability of being superior to the alternative immediately behind it. However, it seems inadequate and unreasonable to deduce this type of absolute ranking solely since overlap is common among interval outputs, that is an alternative is not always absolutely superior (or inferior) to another one. To this problem, this paper tries to explore other types of outputs for interval GDM problems using stochastic simulation, including ranking of alternatives with probabilities, competition for each ordered position, pairwise priority comparison of alternatives, and overall advantage of alternatives. All of these outputs provide us with more information to form a complete understanding of alternatives from different aspects. Finally, a numerical example regarding the policy selection about a company expanding into a new market is introduced to illustrate the obtainment of these possible outputs.

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Acknowledgements

The authors are very grateful to the Managing Editor and the anonymous reviewers for their insightful and constructive comments and suggestions that have led to an improved version of this paper.

Funding

This study was funded by the National Natural Science Foundation of China (Grant Nos. 71671031, 71701040, 71803073), the Humanities and Social Sciences Foundation of Chinese Ministry of Education (Grant Nos. 17YJC630067, 18YJC790211).

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Correspondence to Weiwei Li.

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Communicated by V. Loia.

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Yi, P., Li, W. & Zhang, D. On possible outputs of group decision making with interval uncertainties based on simulation techniques. Soft Comput 24, 9205–9213 (2020). https://doi.org/10.1007/s00500-019-04447-9

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