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A consensus facilitation model based on experts’ weights for investment strategy selection

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  • Wenshuai Wu
  • Gang Kou
  • Yi Peng

Abstract

In this paper, we investigate group decision-making in a multi-criteria complex environment using an analytical hierarchy process (AHP) seeking to solve the problem of how to effectively aggregate individual preferences to reach a group consensus. Currently, the two methods considered most useful for aggregating individual preferences are the aggregation of individual judgments (AIJ) and the aggregation of individual priorities (AIP). These two aggregation methods involve two independent stages, which disconnect the processes from each other, and the relationship between the two has not yet been researched. Besides, the authorities of different experts can vary, as they are restricted by the individuals’ expertise and experiences as well as the complexity of real-world applications. In this article, a new consensus facilitation model for AHP group decision-making that takes into account experts’ weights, called EWAHP-GDM, is established to address these aspects. In this model, two aggregation methods are unified and integrated in group decision-making with AHP. We present the quantification steps for the experts’ weights and conduct an empirical analysis to verify our model by comparing it with four classic models/methods. The numerical results demonstrate that our model can provide effective investment strategy selection in investment decision-making.

Suggested Citation

  • Wenshuai Wu & Gang Kou & Yi Peng, 2018. "A consensus facilitation model based on experts’ weights for investment strategy selection," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 69(9), pages 1435-1444, September.
  • Handle: RePEc:taf:tjorxx:v:69:y:2018:i:9:p:1435-1444
    DOI: 10.1080/01605682.2017.1398203
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    Cited by:

    1. Yingli Wu & Xin Li & Qingquan Liu & Guangji Tong, 2022. "The Analysis of Credit Risks in Agricultural Supply Chain Finance Assessment Model Based on Genetic Algorithm and Backpropagation Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1269-1292, December.

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