提出四条原则，让城市衰败分析更科学地建模因果关系。
Four Guiding Principles for Modeling Causal Domain Knowledge: A Case Study on Brainstorming Approaches for Urban Blight Analysis
- 基于认知地图，提炼出四条因果建模准则。
- 发现现有分析中因果关系建模存在显著偏差。
- 适合城市规划与政策研究者参考使用。
城市衰败是规划与政策制定中的重要议题。研究人员常提出关于城市衰败指标间关系的理论，侧重于反映因果性的关联。本文通过引入四条有效建模因果领域知识的原则，改进了城市衰败分析中领域知识的整合方式。研究通过考察为城市衰败分析构建的认知地图，揭示了现有方法在因果建模方面存在显著偏离指导原则的现象。这些发现为未来城市衰败研究提供了宝贵洞见，有助于深化对城市衰败复杂交互机制的理解。
原文摘要 · Abstract (English)
Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing on relationships reflecting causality. In this paper, we improve on the integration of domain knowledge in the analysis of urban blight by introducing four rules for effective modeling of causal domain knowledge. The findings of this study reveal significant deviation from causal modeling guidelines by investigating cognitive maps developed for urban blight analysis. These findings provide valuable insights that will inform future work on urban blight, ultimately enhancing our understanding of urban blight complex interactions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。