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In social science, formal and quantitative models, such as ones describingeconomic growth and collective action, are used to formulate mechanisticexplanations, provide predictions, and uncover questions about observedphenomena. Here, we demonstrate the use of a machine learning system to aid thediscovery of symbolic models that capture nonlinear and dynamical relationshipsin social science datasets. By extending neuro-symbolic methods to find compactfunctions and differential equations in noisy and longitudinal data, we showthat our system can be used to discover interpretable models from real-worlddata in economics and sociology. Augmenting existing workflows with symbolicregression can help uncover novel relationships and explore counterfactualmodels during the scientific process. We propose that this AI-assistedframework can bridge parametric and non-parametric models commonly employed insocial science research by systematically exploring the space of nonlinearmodels and enabling fine-grained control over expressivity andinterpretability.

AI-Assisted Discovery of Quantitative and Formal Models in Social Science