Statistical Modeling & AI
Exact Synthetic Populations for Scalable Societal and Market Modeling
This paper introduces a constraint-programming framework for generating synthetic populations that reproduce target statistics with high precision while enforcing full individual consistency, without requiring microdata. It validates the approach on official demographic sources and studies the impact of distributional deviations on downstream analyses. This work is developed within the Pollitics project, enabling synthetic populations to be queried through large language models for societal behaviors, market/policy scenarios, and reproducible decision-grade insights without personal data.
Unlock Precision Societal Modeling with Constraint Programming
Our novel Constraint Programming (CP) framework revolutionizes synthetic population generation by ensuring exact compliance with target distributions and full individual-level coherence. This approach empowers organizations to create digital twins of populations for detailed societal and market modeling, bypassing the need for sensitive microdata and enabling robust, privacy-preserving simulations.
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Statistical Accuracy (Target Distributions)
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Microdata Dependency
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Municipalities Modeled
Deep Analysis & Enterprise Applications
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Constraint Programming Framework
The core innovation lies in using Constraint Programming (CP) to generate synthetic populations. This declarative approach allows direct encoding of aggregated statistics and structural relations, ensuring exact control over demographic profiles. Unlike traditional data-driven methods, it guarantees full individual consistency (e.g., no retired minors) and precise matching of statistical targets, eliminating variance from sampling.
Batch-based Generation & Scalability
To address scalability, the system employs a batch-based generation process. This involves defining constraints with suitable monotonicity properties to preserve feasibility as the population grows. Experiments demonstrate that the method can generate thousands of individuals across hundreds of municipalities within acceptable time limits, even with complex individual and feature-level constraints.
Applications in Societal & Market Modeling
The framework has been applied to various real-world scenarios, including Virtual Polling, Territorial Economic Intelligence, and AI-Driven Text Evaluation. By creating digital twins that can be queried via Large Language Models (LLMs), businesses and policymakers can simulate societal behaviors, explore market scenarios, and optimize communications with high fidelity and without privacy risks.
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Optimal Batches Achieved for 5000 Individuals (Batch Size 5)
Synthetic Population Generation Process
Define Abstract Constraints (Features, Individuals)
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Select Batch Size & Features
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Instantiate Individual Constraints (Current Batch)
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Instantiate Feature Constraints (Known Values)
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Solve Constraint Model
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Aggregate Results to Population
| Feature | CP-based SPG | Traditional Methods (SR/HCO) |
|---|---|---|
| Microdata Requirement | None (aggregated statistics only) | Requires microdata samples or detailed contingency tables |
| Individual Consistency | Explicitly enforced by constraints | Emerges indirectly from optimization/sampling |
| Statistical Accuracy | Exact compliance with target distributions | Approximates target distributions (discrepancy minimized) |
| Privacy Concerns | None (no real individuals) | Potential privacy risks with microdata |
| Scalability | Batch-based, shown to scale to 55k+ individuals | Scalability can be challenging with complex joint distributions |
Case Study: Mulhouse District Demographic Simulation
Our method was applied to five interdependent demographic features for the Mulhouse district (France): age, area, gender, employment, and political orientation. Using INSEE and electoral sources, the system generated faithful synthetic inhabitants. Two individual constraints (age 0-18 cannot be retired, political orientation for age 0-18 is 'unknown') and complex inter-feature dependencies (age by area, age by employment, gender by ideology) were enforced. The Mean Absolute Percentage Error (MAPE) was consistently 0.0% for most distributions, demonstrating high accuracy even with strong constraints and interdependencies.
Result: Remarkably low MAPE (0.0% for most features), validating precision.
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
Our phased approach ensures a smooth transition and rapid value realization.
Phase 1: Discovery & Strategy
Collaborate to define specific use cases, data sources, and desired outcomes for your custom synthetic population models.
Phase 2: Model Development & Calibration
Our experts configure and train CP models, integrating your aggregated data and business rules to generate initial synthetic populations.
Phase 3: Validation & Refinement
Rigorously test the synthetic populations against real-world benchmarks, refine constraints, and ensure high fidelity and utility for your objectives.
Phase 4: Integration & Deployment
Integrate the SPG framework into your existing systems, providing tools for querying, scenario modeling, and LLM-driven analysis.
Phase 5: Continuous Optimization & Support
Ongoing monitoring, updates, and support to ensure your synthetic populations remain accurate, relevant, and scalable as your needs evolve.
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