Methodology

How VektoraSim builds personas, conducts interviews, and generates reports.

1. How Personas Are Built

VektoraSim maintains a library of 1,000 simulated U.S. adult personas. Each persona's core demographic and economic profile is drawn from the American Community Survey (ACS) Public Use Microdata Sample (PUMS), published by the U.S. Census Bureau.

The PUMS-grounded attributes include age, sex, race, Hispanic origin, household income, employment status, occupation, industry, state, education level, disability status, and homeownership. These are real data distributions—not invented archetypes.

On top of these hard demographics, each persona receives synthetic behavioral and attitudinal attributes generated by large language models. These include OCEAN personality scores (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), shopping habits, media consumption, decision-making style, and other soft attributes. These are modeled, not measured—they add texture but should be treated as approximations.

2. How Interviews Work

When you run a study, VektoraSim presents your questions to each selected persona via a structured prompt sent to a large language model. The prompting strategy uses a prevention-first approach: the system prompt establishes the persona's identity and constraints before presenting your questions.

To measure internal consistency, each interview is run multiple times (replications) at a controlled temperature setting. The system compares responses across replications and computes a consistency score for each question. Higher consistency scores indicate that the persona gave similar answers across independent runs, which increases confidence in the simulated response.

Responses are collected, scored, and passed to the report generator. The raw interview transcripts are stored alongside the aggregated results.

3. How Reports Are Generated

Reports include several standard sections:

  • Frequency counts: How many personas chose each option or expressed each sentiment.
  • Theme clustering: Open-ended responses are grouped into themes using LLM-based analysis, with each theme labeled and accompanied by representative verbatim quotes.
  • Verbatim quotes: Direct excerpts from persona responses, selected to illustrate key themes.
  • Replication scores: Per-question consistency metrics showing how stable each finding is across repeated interviews.
  • Panel composition: A demographic breakdown of the personas who responded, compared against ACS national benchmarks.

Every report is generated with mandatory disclaimer sections that cannot be removed or modified. These ensure that anyone reading the report understands the synthetic nature of the data.

4. What We Don't Do

VektoraSim is transparent about the boundaries of synthetic simulation:

  • We do not calculate confidence intervals or margins of error. There is no probability sampling design that would make such calculations valid.
  • We do not claim statistical significance. Our consistency scores measure internal replication stability, not inferential significance.
  • We do not simulate non-response. Every persona provides a response, unlike real-world data collection where non-response is a significant source of potential bias.
  • We do not call our outputs "research." VektoraSim is a simulation sandbox for early-stage idea exploration, not a replacement for professional studies with human participants.

5. Limitations

LIMITATIONS AND RECOMMENDED REAL-WORLD VALIDATION

This report is generated from a synthetic simulation tool. The personas are statistically calibrated to ACS PUMS distributions for core demographics and economics, with additional behavioral and attitudinal details modeled via large language models. No probability sampling design was used, and no design-based variance or confidence intervals can be calculated.


Key limitations include:

  • Potential residual biases from LLM training data or prompt construction.
    • Absence of true human cognitive processes, interviewer effects, or non-response.
      • Coverage gaps for certain hard-to-reach populations.
        • Every persona provides a response; real-world surveys experience non-response that can introduce bias.

        Recommendations: Treat these findings as hypotheses for further testing. Validate high-stakes results with a small real-world probability or quota survey (e.g., via Prolific, Lucid, or a professional firm). Compare key metrics (pricing sensitivity, feature priorities, objection frequency) between synthetic and real responses. Document any divergences before proceeding to build or invest.

MODE-EFFECT DISCLAIMER

This report presents results from a synthetic simulation exercise. Personas respond via large language models conditioned on public data sources. Unlike real surveys, there is no human respondent fatigue, social desirability bias from live interviewers, or true non-response. However, LLM responses may still reflect training data patterns or prompt sensitivities. These outputs are not equivalent to probability-based survey research and should be used only for early-stage idea exploration and scenario testing. We strongly recommend follow-up validation with real human respondents before making significant business decisions.

6. Validation

We are currently running validation studies comparing synthetic and real human responses via Prolific. Results will be published here when available.