Scholarlink

<p>This study addresses the decision-making challenges in cooperative alliances between institutional and individual investors in collective investments by proposing a three-dimensional multi-attribute group decision-making method based on intuitionistic fuzzy sets. Methodologically, this research achieves three significant breakthroughs: First, it upgrades the traditional two-dimensional decision model to a three-dimensional spatial structure, establishing an intuitionistic fuzzy set-based three-dimensional point-set grid framework that organically integrates three key dimensions: agents, attributes, and schemes. Cross-sectional transformation better handles dimension decomposition and integration in decision-making processes. Second, addressing coexistence of benefit allocation and cost sharing in actual investments, it proves cost sharing satisfies Shapley value properties. Third, under unknown attribute weights, it develops an optimal alliance selection algorithm by combining TOPSIS, fuzzy measures, and Choquet integral theory. Numerical example analysis shows this method enables investors to select optimal cooperative alliances under fuzzy willingness and limited data, with alliances of some agents achieving optimal decision-making efficiency. Simulation verification further confirms that under ± 1% parameter fluctuation and 95% confidence level, decision result sensitivity rate is below 2%, with consistent internal logic, and good model stability, robustness, and self-consistency. Compared with traditional methods, the new approach significantly improves data type and dimensional processing. It enriches intuitionistic fuzzy set applications in complex decision-making and provides operational decision-support tools for practical investment, offering important guidance for supply chain collaboration, cross-border investment evaluation, and other fields.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on the Choice of Cooperative Alliance Schemes between Institutional and Individual Investors Based on Fuzzy Willingness and Multiple Attributes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

查看全文
  • Qianbo Lai,
  • Jifa Wang,
  • Yang Wang,
  • Xiaowei Shi

摘要

This study addresses the decision-making challenges in cooperative alliances between institutional and individual investors in collective investments by proposing a three-dimensional multi-attribute group decision-making method based on intuitionistic fuzzy sets. Methodologically, this research achieves three significant breakthroughs: First, it upgrades the traditional two-dimensional decision model to a three-dimensional spatial structure, establishing an intuitionistic fuzzy set-based three-dimensional point-set grid framework that organically integrates three key dimensions: agents, attributes, and schemes. Cross-sectional transformation better handles dimension decomposition and integration in decision-making processes. Second, addressing coexistence of benefit allocation and cost sharing in actual investments, it proves cost sharing satisfies Shapley value properties. Third, under unknown attribute weights, it develops an optimal alliance selection algorithm by combining TOPSIS, fuzzy measures, and Choquet integral theory. Numerical example analysis shows this method enables investors to select optimal cooperative alliances under fuzzy willingness and limited data, with alliances of some agents achieving optimal decision-making efficiency. Simulation verification further confirms that under ± 1% parameter fluctuation and 95% confidence level, decision result sensitivity rate is below 2%, with consistent internal logic, and good model stability, robustness, and self-consistency. Compared with traditional methods, the new approach significantly improves data type and dimensional processing. It enriches intuitionistic fuzzy set applications in complex decision-making and provides operational decision-support tools for practical investment, offering important guidance for supply chain collaboration, cross-border investment evaluation, and other fields.