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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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使用网络分析来识别基于因果循环图的杆点,导致错误推断.

Loes Crielaard1,2, Rick Quax3,4, Alexia D M Sawyer5,6

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概括

在因果循环图 (CLD) 中识别杆点的网络分析指标是不可靠的. 这些方法可能会导致错误的政策建议,需要采用替代方法.

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科学领域:

  • 系统科学 系统科学
  • 网络理论 网络理论
  • 因果推理因果推理

背景情况:

  • 因果循环图 (CLD) 是表示系统关系的心理模型.
  • 网络分析越来越多地用于识别CLD中的杆点.
  • 杆点的定量识别旨在避免复杂的计算建模.

研究的目的:

  • 评估使用网络分析指标 (中间度和接近度中心度) 来识别CLD中的杆点的合理性.
  • 评估这些指标的可靠性,当CLDs代表具有不同的因果结构的相同系统时.
  • 确定网络分析指标的假设与CLD的性质之间的冲突.

主要方法:

  • 网络分析指标的比较分析 (中间度和接近度的中心性) 应用于CLD.
  • 对具有各种推断因果结构的CLD的度量表现的评估.
  • 识别和分析度量假设和CLD特征之间的冲突.

主要成果:

  • 网络分析指标在应用于代表相同系统但具有不同的因果结构的CLD时提供了不可靠的结果.
  • 在网络分析指标的基础假设与CLD捕获的信息之间发现了六个关键冲突.
  • 这些指标无法充分考虑CLD中描绘的因果关系的细微差别.

结论:

  • 应用距离和距离的中心性来识别来自CLD的杆点是过早的,可能是不正确的.
  • 错误地确定杆点可能导致有缺陷的政策建议.
  • 需要采用更符合系统动态观点的替代定量或定性方法.