将系统动力学与结构方程模型融合，构建可比较的因果建模框架。
Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
- 提出统一数学框架整合两类不同假设的因果建模方法。
- 支持从分布生成系统、开发新方法并比较结果以指导数据科学。
- 适合关注负责任AI和因果推理的科研人员使用。
AI/ML模型在解决复杂问题方面迅速崛起，但也放大了人类偏见等意外后果。推动负责任AI/ML的发展需要借助更丰富的系统动态因果模型。然而，主要障碍在于不同方法基于不同假设（即达娜·梅多的“不可避免先验”），难以整合。本文将系统动力学与结构方程模型纳入同一数学框架，实现系统生成、方法开发与结果比较，为数据科学与AI/ML中的系统动态认知基础提供依据。
原文摘要 · Abstract (English)
AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow's "the unavoidable a priori"). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.
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