Methodology for synthetic panel research
This page explains how Pollitics should be understood methodologically: a system for building coherent synthetic audience representations, exploring reactions through LLM-driven simulation, and reading outputs as structured exploratory evidence.
Synthetic populations, not personal data replicas
The goal is to generate statistically coherent synthetic profiles from aggregated structures, not to recreate real individuals. This distinction matters for privacy, interpretation and scientific positioning.
From profile calibration to reaction simulation
Once synthetic profiles are defined, Pollitics uses scenario framing and language-model-based interactions to surface arguments, hesitations, misunderstandings and reaction patterns. The value is in comparative reading and iteration, not in pretending that one simulated answer equals one observed human fact.
Where the limits are
Synthetic panel outputs remain model-based. They should be interpreted with domain judgment, category context and, when stakes are high, complemented by human studies or other forms of evidence.
Scientific references
Explains a constraint-programming approach for generating exact synthetic populations from aggregated statistics, with direct relevance for coherent synthetic panels and privacy-preserving audience simulation.
LLMs, Virtual Users, and BiasExamines virtual users, survey prediction and bias, which is directly relevant when framing what simulated respondents can and cannot be used for.
FAQ
Are synthetic respondents real people?
No. They are simulated profiles designed to reflect aggregated structures and plausible behavioral patterns, not actual individuals.
What is the right use of Pollitics results?
Use them to compare options, surface objections, refine framing and accelerate early decisions. Do not present them as direct substitutes for every measured human outcome.
Why explain limits so explicitly?
Because methodological clarity improves trust, reduces overclaiming and helps both search engines and LLMs retrieve a more accurate description of the product.