Summary of Sequential Three-way Group Decision-making For Double Hierarchy Hesitant Fuzzy Linguistic Term Set, by Nanfang Luo et al.
Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set
by Nanfang Luo, Qinghua Zhang, Qin Xie, Yutai Wang, Longjun Yin, Guoyin Wang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) to address limitations in existing research on fusing information quickly and interpreting decision results. The S3W-GDM method is constructed using granular computing, considering the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS). A novel multi-level expert information fusion method is proposed, utilizing concepts like expert decision tables and decision-leveled information extraction/aggregation. The neighborhood theory, outranking relation, and regret theory are used to redesign calculations of conditional probability and relative loss function. The granular structure of DHHFLTS based on S3WD is defined to improve decision-making efficiency, with a proposed decision-making strategy and interpretation for each decision-level. An illustrative example of diagnosis is presented, along with comparative and sensitivity analysis with other methods verifying the efficiency and rationality of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to help groups make decisions when there’s uncertainty and complexity involved. They used “granular computing” to develop a method called S3W-GDM that considers different levels of vagueness, hesitation, and variation in group decision-making problems. The method combines information from multiple experts and uses different theories to calculate the probability of certain outcomes and how much regret is felt when a particular choice is made. This new approach can help improve the efficiency of group decision-making and provide better interpretations of the results. |
Keywords
» Artificial intelligence » Loss function » Probability
