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

arXiv: https://arxiv.org/abs/2210.00563 · HTML: https://mlsanity.com/paper/2210.00563/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 80 references that are in the map.
- First posted on arXiv 2 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [Neural Symbolic Regression that Scales](https://mlsanity.com/paper/2106.06427/) (2021)
- [A Bayesian machine scientist to aid in the solution of challenging scientific problems](https://mlsanity.com/paper/2004.12157/) (2020)
- [Scale-free networks are rare](https://mlsanity.com/paper/1801.03400/) (2018)
- [A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge](https://mlsanity.com/paper/2209.06257/) (2022)

## Built on by (most cited first, 6 of 6)

- [Deep Generative Symbolic Regression](https://mlsanity.com/paper/2401.00282/) (2023)
- [A Survey of AI Scientists](https://mlsanity.com/paper/2510.23045/) (2025)
- [A Transformer Model for Symbolic Regression towards Scientific Discovery](https://mlsanity.com/paper/2312.04070/) (2023)
- [OccamLLM: Fast and Exact Language Model Arithmetic in a Single Step](https://mlsanity.com/paper/2406.06576/) (2024)
- [auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation](https://mlsanity.com/paper/2606.26460/) (2026)
- [Reimagining Urban Science: Scaling Causal Inference with Large Language Models](https://mlsanity.com/paper/2504.12345/) (2025)

## Abstract (arXiv)

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.