用于验证系统动力学模型中因果关系的模糊集定性比较分析(fsQCA)

Muhammad Shalahuddin, W. Sunindyo, Mohammad Ridwan Effendi, K. Surendro
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引用次数: 0

摘要

建模人员往往会根据自己的观点,为同一问题创建不同的系统动力学模型,这可能会降低利益相关者的信心。验证系统动力学可增强利益相关者的信心。本研究建议使用基于集合论方法的模糊集合定性比较分析(fsQCA)技术来验证因果循环图(CLD)模型中实体之间的因果联系。本案例研究利用有限的样本数据分析了印度尼西亚移动网络运营商的问题,利用fsQCA方法检验了CLD模型中需要验证的实体之间的因果联系。在通过系统动力学方法创建 CLD 模型后,利用 fsQCA 强化了之前形成的模型。fsQCA 方法融合了定性比较分析(QCA)和模糊集理论,允许部分成员关系,可以识别 CLD 模型中各实体之间的因果联系。它有助于利用有限的样本数据测试因果关系,增强利益相关者对 CLD 模型的信心。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy‐set qualitative comparative analysis (fsQCA) for validating causal relationships in system dynamics models
Modelers often create diverse system dynamics models for the same issue, depending on their viewpoints, which can decrease stakeholder assurance. Validating system dynamics may enhance stakeholder confidence. This study suggests using fuzzy‐set qualitative comparative analysis (fsQCA) as a technique based on a set theory approach to validate the causal connections between entities in causal loop diagram (CLD) models. This case study analyzed the issue of Indonesian mobile network operators with limited sample data, utilizing the fsQCA method to test causal connections between entities in the CLD model that require validation. Following the creation of the CLD model through the system dynamics methodology, fsQCA was employed to enhance the previously formed model. The fsQCA method fuses qualitative comparative analysis (QCA) with fuzzy set theory, permitting partial membership, and can identify causal links among entities in the CLD model. It assists in testing causal relationships using limited sample data and boosts stakeholder confidence in the CLD model.
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