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Adaptive Conflict Resolution Model for Large-Group Decision-Making Based on Dynamic Trust Relationship Evolution and Weight Determination

Bing Yan et al.

IEEE Transactions on Systems, Man, and Cybernetics: Systems2026https://doi.org/10.1109/tsmc.2025.3647277article
AJG 3
Weight
0.37

What the paper says

Large group decision-making (LGDM) involves multiple decision-makers (DMs) and criteria, frequently resulting in conflicts and inconsistencies. This study proposes a novel conflict resolution model based on dynamic trust relationships to effectively identify and address potential disputes in LGDM. First, hesitant fuzzy 2-tuple linguistic sets (HF2TLSs) are utilized to accurately capture DMs’ preferences in uncertain environments. Then, a Markov trust state transition model is developed to capture the dynamic evolution of trust relationships. Next, an enhanced PageRank algorithm, built on trust networks, is employed to determine the weights of DMs and subgroups, thereby improving decision quality. Additionally, multidimensional conflict detection indicators are introduced to quantify cognitive and interest conflicts among subgroups. Finally, an adaptive conflict resolution mechanism is presented to balance heterogeneous interests and achieve agreement. An illustrative example validates the model, with comparative analyses demonstrating its rationality and superiority.

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https://doi.org/https://doi.org/10.1109/tsmc.2025.3647277

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@article{bing2026,
  title        = {{Adaptive Conflict Resolution Model for Large-Group Decision-Making Based on Dynamic Trust Relationship Evolution and Weight Determination}},
  author       = {Bing Yan et al.},
  journal      = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tsmc.2025.3647277},
}

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Adaptive Conflict Resolution Model for Large-Group Decision-Making Based on Dynamic Trust Relationship Evolution and Weight Determination

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.