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.

翻译：在社会科学中，形式化与定量模型（例如描述经济增长和集体行动的模型）被用于构建机制性解释、提供预测以及揭示关于所观察现象的问题。在此，我们展示了一种机器学习系统的应用，以辅助发现捕捉社会科学数据集中非线性与动态关系的符号模型。通过扩展神经符号方法，在含噪声和纵向数据中寻找简洁函数与微分方程，我们证明该系统可用于从经济学和社会学的真实世界数据中发现可解释模型。将符号回归增强至现有工作流程中，有助于在科学探索过程中揭示新颖关系并探索反事实模型。我们提出，这种人机协同框架能够通过系统性地探索非线性模型空间并实现对表达力与可解释性的细粒度控制，从而弥合社会科学研究中常用的参数模型与非参数模型之间的鸿沟。