AI-Assisted Discovery of Quantitative and Formal Models in Social Science
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.
Code (1)
Tasks
counterfactualSociologySymbolic RegressionSimilar Papers 제목 키워드 기반
A Survey in Mathematical Language Processing
Informal mathematical text underpins real-world quantitative reasoning and communication. Developing sophisticated methods of retrieval and abstraction from this dual modality is crucial in the pursuit of the vision of a…
RetrievalSurveyLLM-Assisted Replication for Quantitative Social Science
The replication crisis, the failure of scientific claims to be validated by further research, is one of the most pressing issues for empirical research. This is partly an incentive problem: replication is costly and less…
Code GenerationDecentralized Causal Discovery using Judo Calculus
We describe a theory and implementation of an intuitionistic decentralized framework for causal discovery using judo calculus, which is formally defined as j-stable causal inference using j-do-calculus in a topos of shea…
Computational EfficiencyCausal InferenceIntelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models
The recent wave of artificial intelligence, epitomized by large language models (LLMs), has presented opportunities and challenges for methodological innovation in political science, sparking discussions on a potential p…
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structure…
Causal InferenceDomain AdaptationIn-Context LearningKnowledge Graph Completion+5