Exact Synthetic Populations for Scalable Societal and Market Modeling
This work addresses the need for scalable and privacy-preserving societal and market modeling, enabling reproducible decision-grade insights without personal data, though it appears incremental as it builds on existing constraint-programming and synthetic data methods.
The paper tackles the problem of generating synthetic populations that precisely match target statistics and enforce individual consistency, introducing a constraint-programming framework that achieves exact control without requiring microdata. It validates the approach on official demographic sources and studies the impact of distributional deviations on downstream analyses.
We introduce a constraint-programming framework for generating synthetic populations that reproduce target statistics with high precision while enforcing full individual consistency. Unlike data-driven approaches that infer distributions from samples, our method directly encodes aggregated statistics and structural relations, enabling exact control of demographic profiles without requiring any microdata. We validate the approach on official demographic sources and study the impact of distributional deviations on downstream analyses. This work is conducted within the Pollitics project developed by Emotia, where synthetic populations can be queried through large language models to model societal behaviors, explore market and policy scenarios, and provide reproducible decision-grade insights without personal data.