论文标题
AI辅助发现社会科学中的定量和形式模型
AI-Assisted Discovery of Quantitative and Formal Models in Social Science
论文作者
论文摘要
在社会科学中，形式和定量模型（例如描述经济增长和集体行动的模型）用于制定机械解释，提供预测并发现有关观察到现象的问题。在这里，我们演示了使用机器学习系统来帮助发现符合社会科学数据集中非线性和动态关系的符号模型。通过扩展神经符号方法以在嘈杂和纵向数据中找到紧凑的功能和微分方程，我们表明我们的系统可用于从经济学和社会学中的现实世界中发现可解释的模型。通过符号回归来增强现有的工作流程可以帮助发现新的关系并在科学过程中探索反事实模型。我们建议，这个AI辅助框架可以通过系统地探索非线性模型的空间并实现对表现力和可解释性的细粒度控制，可以弥合社会科学研究中常用的参数和非参数模型。
In social science, formal and quantitative models, such as ones describing economic growth and collective action, are used to formulate mechanistic explanations, provide predictions, and uncover questions about observed phenomena. Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture nonlinear and dynamical relationships in social science datasets. By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. Augmenting existing workflows with symbolic regression can help uncover novel relationships and explore counterfactual models during the scientific process. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research by systematically exploring the space of nonlinear models and enabling fine-grained control over expressivity and interpretability.
