Jalali Mohammad S, Beaulieu Elizabeth
Harvard Medical School, Boston, MA.
MIT Sloan School of Management, Cambridge, MA.
Syst Dyn Rev. 2023 Nov;40(4). doi: 10.1002/sdr.1753.
Transparency is a critical aspect of systems science. While transparency of quantitative models has been assessed, transparency of their qualitative structures has been less scrutinized. We assess the transparency of causal loop diagrams (CLDs), a key qualitative visualization tool in system dynamics. We evaluate publications and a sample of most-cited comparable articles in other journals. We assess the inclusion of a plain language methods statement, overall discernibility of the methods, and identification of causal link sources. Reviewing 72 articles (: 36; other journals: 36), only 44%, 38%, and 25% fully satisfy each criterion, respectively. articles are characterized by higher transparency in the clarity of CLD development method and communication of causal link sources, yet the potential for enhancement is evident. We provide specific recommendations to increase the transparency of CLDs. Transparent reporting benefits original research authors, future expansion of CLDs, and the systems science community.
透明度是系统科学的一个关键方面。虽然定量模型的透明度已得到评估，但其定性结构的透明度却较少受到审视。我们评估因果回路图（CLD）的透明度，因果回路图是系统动力学中的一种关键定性可视化工具。我们评估了相关出版物以及其他期刊中最常被引用的可比文章样本。我们评估了是否包含通俗易懂的方法说明、方法的整体可辨别性以及因果联系来源的识别。在审查的72篇文章（：36篇；其他期刊：36篇）中，分别只有44%、38%和25%完全满足每个标准。文章的特点是在CLD开发方法的清晰度和因果联系来源的传达方面具有较高的透明度，但仍有明显的提升空间。我们提供了提高CLD透明度的具体建议。透明报告有利于原创研究作者、CLD的未来扩展以及系统科学界。
