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Minds

August 21, 2026·Comparison·Minds Team

# **Synthetic Audience Validation and Accuracy Compared**

Synthetic audience accuracy percentages cannot be ranked unless the population, task, reference data, metric, and model version are identical. A defensible evaluation compares error distributions and decision fit, not marketing headlines.

[Run a validation study](https://getminds.ai/?register=true)

There is no defensible league table of synthetic audience accuracy today. Electric Twin, Simile, Aaru, Artificial Societies, and Minds publish impressive figures, but the numbers describe different tasks. Some compare survey distributions, some compare an agent with a person's later self-response, some report rank correlation, and some evaluate a network outcome. Ranking them as if they were the same score would be analytically wrong.

The correct question is narrower: how well does a frozen system perform on the population, decision, language, stimulus, and metric that matter to the buyer? That requires outcome isolation, preregistered metrics, subgroup reporting, failure cases, and version provenance.

## Why vendor percentages are not comparable

| Dimension | What can differ | Why it changes the score |
| --- | --- | --- |
| Research task | Survey replication, intervention prediction, self-retest, creative winner, network diffusion | Each tests a different capability |
| Reference | Human survey, observed behavior, expert judgment, later self-response | References have different noise and ceilings |
| Metric | Mean absolute error, overlap, correlation, exact match, consistency | The same outputs can score differently |
| Population | National sample, customer panel, niche segment, stakeholder network | Coverage and subgroup difficulty vary |
| Exposure | Public dataset, proprietary holdout, customer data | Leakage risk and reproducibility vary |
| System version | Model, provider, prompt, retrieval, post-processing, population | Improvements or regressions may come from any layer |

A vendor-reported result can be useful evidence without being a universal product property. It should be cited with its task and metric every time.

## What Minds has published

Minds' current public applied benchmark reproduced three meal-frequency distributions from the UK Food Standards Agency's Food and You 2 study. A locked cohort of 301 persistent Gen Z Minds produced 903 planned answers. Across 21 answer-option cells, the average absolute gap was 6.01 percentage points, expressed as 93.99% aggregate approximation. Mean distribution overlap was 78.98%, Pearson correlation was 0.804, and Spearman correlation was 0.834.

Those numbers apply to that instrument. The [full validation report](https://getminds.ai/research/synthetic-gen-z-food-survey-validation-2026) explicitly states that the result does not establish individual prediction accuracy, universal performance, or causal proof. The human percentages and benchmark files were held outside the runtime, although indirect exposure to a public survey through model pretraining or the persistent knowledge corpus cannot be excluded completely.

## What competitors publish

Electric Twin has published a 2026 accuracy paper and a [Times case study](https://www.electrictwin.com/case-studies/times) involving a holdout from a subscriber base. Simile says it validates weekly across more than 7,000 evaluations and uses those evaluations to train a confidence model. Aaru publishes task-specific customer and partner cases, including an [EY wealth study](https://aaru.com/case-studies/ey-wealth-research). Artificial Societies publishes survey-distribution and answer-consistency evaluations.

These are meaningful public proof signals. They remain incompatible as a single ranking because the estimands and baselines differ. Buyers should ask each vendor for the exact report, including negative results and subgroup tails.

## A fair bake-off design

1. Freeze a human dataset that none of the vendors can inspect.
2. Give every vendor the same population definition, stimuli, questions, and source policy.
3. Register the primary and secondary endpoints before receiving outputs.
4. Measure absolute distribution error, distribution overlap, calibration, rank agreement, subgroup error, and direction-of-effect accuracy where applicable.
5. Report every item and subgroup, not only the average.
6. Record platform, model, prompt, population, source, and calculator versions.
7. Measure cost, turnaround, failure rate, and human-service hours.
8. Repeat after a material model change to test stability.

For network simulations, add graph-specific endpoints such as community recovery, diffusion accuracy, and intervention effects. For conjoint or pricing methods, validate the estimator and experimental design separately from the realism of the synthetic answers.

## Confidence is not the same as accuracy

A calibrated confidence layer predicts when a result is likely to be correct. It is useful only when confidence tracks observed error on held-out tasks. High-confidence wrong answers are more dangerous than visibly uncertain ones.

Simile makes predicted confidence central to its positioning. Other platforms should be asked whether they expose calibration, uncertainty, or stability measures. Minds exposes method diagnostics for supported pipelines and publishes limitations around its applied validation; it should not describe one benchmark as a confidence model for every study.

## Model-change provenance

Synthetic systems depend on layers that change independently: frontier models, provider APIs, prompts, retrieval, source corpora, persona construction, orchestration, post-processing, and deterministic calculators. Electric Twin's public paper is useful because it discusses gains from multiple layers, including API-model improvement.

Every result should therefore carry a reproducibility envelope. Minds' [model-change and validation provenance policy](https://getminds.ai/research/model-change-validation-provenance) describes the disclosure commitment for public benchmarks and material research-system changes.

## What buyers should accept as proof

- A dated methodology with the population, instrument, holdout, and metrics.
- A complete result table, including weak items and subgroup tails.
- Clear labels for vendor-authored, customer-reported, partner-reviewed, or independently replicated evidence.
- System and data version records sufficient to explain change.
- A statement of where the evidence does not generalize.
- A next-step plan for human or behavioral validation matched to decision risk.

Reject an unqualified “X% accurate” claim with no metric, reference, or scope. Fluency is not validation, and a larger synthetic sample does not create a real sampling frame.

## Related evidence

Use the [synthetic audience validation checklist](https://getminds.ai/research/synthetic-audiences-validation-checklist), [data sources comparison](https://getminds.ai/comparison/synthetic-audience-data-sources-compared), [research evidence center](https://getminds.ai/research/synthetic-research-evidence-center), [quantitative pipeline comparison](https://getminds.ai/comparison/quantitative-research-pipelines-vs-ai-persona-chat), and [procurement checklist](https://getminds.ai/guide/synthetic-research-procurement-checklist) together.

## **Frequently asked questions**

### **Which synthetic audience platform is most accurate?**

Public evidence does not support one universal winner. Vendors report different tasks, datasets, metrics, baselines, and model versions. A fair answer requires a shared, outcome-blind benchmark matched to the buyer's decision.

### **What does 93.99% approximation mean in the Minds study?**

It means 100% minus the mean absolute percentage-point gap across 21 aggregate answer-option cells in one Food Standards Agency survey replication. It is not individual prediction accuracy and is not a universal product accuracy rate.

### **How should model changes affect validation?**

Material model, provider, prompt, retrieval, population, or calculator changes should trigger a scoped regression test. Results should record the relevant versions so a buyer can distinguish improved performance from a changed measurement setup.