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
1Department of Public and Occupational Health, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands. l.crielaard@amsterdamumc.nl.
Scientific reports
|November 29, 2023
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.
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.
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