# Exact Synthetic Populations for Scalable Societal and Market Modeling

arXiv: https://arxiv.org/abs/2512.07306 · HTML: https://mlsanity.com/paper/2512.07306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 8 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data](https://mlsanity.com/paper/2402.04823/) (2024)
- [Prediction of rare feature combinations in population synthesis: Application of deep generative modelling](https://mlsanity.com/paper/1909.07689/) (2019)
- [Copula-based transferable models for synthetic population generation](https://mlsanity.com/paper/2302.09193/) (2023)
- [Llms, Virtual Users, and Bias: Predicting Any Survey Question Without Human Data](https://mlsanity.com/paper/2503.16498/) (2025)
- [Efficient Implementation of the Global Cardinality Constraint with Costs](https://mlsanity.com/paper/2502.02688/) (2025)

## Built on by (most cited first, 1 of 1)

- [Maximum Entropy Relaxation of Multi-Way Cardinality Constraints for Synthetic Population Generation](https://mlsanity.com/paper/2603.22558/) (2026)

## Abstract (arXiv)

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.