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<p>Identifying key influencing factors of meta-action unit (MAU) fault heterogeneity is a typical multi-attribute group decision-making (MAGDM) problem. However, non-cooperative behaviors among decision-makers (DMs) often lead to inconsistencies in decision information, thereby undermining consensus. To tackle this issue, this study proposes a novel consensus approach based on a three-dimensional house of trust (3D HOT) model within the MAGDM framework. First, new distance measures and aggregation operators for Fermatean fuzzy sets (FFSs) are introduced to enhance the accuracy and realism of information processing. Subsequently, a 3D HOT model is developed to quantify trust levels among DMs and determine their weights, while a mixed consensus process, which integrates both cardinal and ranking consensus, is proposed to evaluate the coherence of DMs’ preferences. Furthermore, three non-cooperative behavior functions are constructed to identify unruly, unreliable, and dissimilar behaviors based on individual personality traits. The practicality and effectiveness of the proposed approach are demonstrated through a real-world case study about extracting key influencing factors of MAU fault heterogeneity, complemented by simulation experiments for further validation. This research provides both theoretical and practical insights into handling non-cooperation and enhancing consensus in complex MAGDM scenarios.</p>

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A Novel Consensus Approach Based on the 3D House of Trust in MAGDM: Application to Identifying Key Influencing Factors of MAU Fault Heterogeneity

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Chuanxi Jin,
  • Ershun Pan,
  • Genbao Zhang

摘要

Identifying key influencing factors of meta-action unit (MAU) fault heterogeneity is a typical multi-attribute group decision-making (MAGDM) problem. However, non-cooperative behaviors among decision-makers (DMs) often lead to inconsistencies in decision information, thereby undermining consensus. To tackle this issue, this study proposes a novel consensus approach based on a three-dimensional house of trust (3D HOT) model within the MAGDM framework. First, new distance measures and aggregation operators for Fermatean fuzzy sets (FFSs) are introduced to enhance the accuracy and realism of information processing. Subsequently, a 3D HOT model is developed to quantify trust levels among DMs and determine their weights, while a mixed consensus process, which integrates both cardinal and ranking consensus, is proposed to evaluate the coherence of DMs’ preferences. Furthermore, three non-cooperative behavior functions are constructed to identify unruly, unreliable, and dissimilar behaviors based on individual personality traits. The practicality and effectiveness of the proposed approach are demonstrated through a real-world case study about extracting key influencing factors of MAU fault heterogeneity, complemented by simulation experiments for further validation. This research provides both theoretical and practical insights into handling non-cooperation and enhancing consensus in complex MAGDM scenarios.