  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.
