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.
@article{arxiv.2512.07306,
title = {Exact Synthetic Populations for Scalable Societal and Market Modeling},
author = {Thierry Petit and Arnault Pachot},
journal= {arXiv preprint arXiv:2512.07306},
year = {2025}
}