SCIENCE · VALIDATION & CALIBRATION
A methodology is only worth its control mechanisms.
Four systematic validation mechanisms, independent external audit protocols, a reproducibility commitment. Scientific rigor is demonstrated by the mechanisms that guarantee it: not by the postures that claim it.
WHAT METHODOLOGICAL VALIDATION GUARANTEES
Without control mechanisms, a methodology is just a dressed-up hypothesis.
The decision-intelligence market often displays elaborate methodologies without the control mechanisms that should validate them: sophisticated diagrams never confronted with deviation measurements, generative AI invoked as a guarantee of seriousness, the founders' academic prestige mobilized without a documented reproducibility protocol. Posturing without protocol becomes a problem as soon as the decision carries weight: a chief risk officer cannot internally validate a tool whose control mechanisms are unknown, an ethics committee cannot approve a predictive-AI deployment no third party can audit.
We build Imagine All The People on the inverse principle: every step of our approach rests on explicit validation. Typologies are validated by cross-reference with public qualitative studies. The statistical coherence of the synthetic populations is measured on published benchmarks. The robustness of the projections is tested through parametric variations. Auditability by an independent third party is offered structurally, as a condition of existence of a serious scientific approach rather than a methodological option.
HOW WE VALIDATE IN PRACTICE
Four systematic mechanisms, mobilized for every simulation.
Validation by cross-reference with public qualitative studies.
The typologies we build are never posited out of nothing: they are validated by cross-reference with the available public qualitative studies, academic work, reports from research organizations, sector studies, publications by regulatory authorities. This cross-reference guarantees sociologically meaningful cuts, not statistical artifacts. When the public literature is insufficient, we decline the mission: validation is a precondition, not an adjustment variable.
Variance measurements on quantitative benchmarks.
Our synthetic populations are evaluated quantitatively on their deviations from the target distributions, unit marginal, binary and ternary. These measurements are documented for every simulation in a calibration report appended to the inquiry dossier, and published on standardized benchmarks made available to the scientific community. This dual documentation, client-facing and public, guarantees the contestability of our results.
Robustness tests through parametric variations.
Our projections are delivered as bundles of trajectories under parametric variations, not as single results. We test sensitivity to the structuring assumptions: calibration constraints, typologies, interview protocols, cascade models. This analysis separates robust results from fragile ones and flags the zones of uncertainty to the decision-maker. An insight that does not survive a reasonable variation is presented as such.
Independent external audit on request.
Every simulation can be audited by a third party designated by the client: an audit firm, an academic counter-expert, a supervisory authority, an internal ethics committee. Our full traceability makes this audit technically possible: every insight traces back to the interviews that produced it. We structurally provide the necessary access, a consultation interface, methodological exports, working sessions with our scientific teams. This availability is a contractual guarantee, not a commercial option.
HOW VALIDATION COMPARES
Validation mechanisms by method.
| Classic panel studies | AI simulation without controls | Our discipline | |
|---|---|---|---|
| Validation of typologies | Standard methodological framework, no systematic cross-reference | Absent, or limited to ad hoc checks | Systematic cross-reference with public qualitative studies |
| Published variance measurements | Methodological documentation in the appendix, rarely public | Absent, no reference benchmark | Published NPORS benchmarks, reproducible results |
| Robustness tests | Rarely systematic, provider-dependent | Absent, monolithic output | Systematic parametric sensitivity analysis |
| Independent external audit | Possible but logistically complex | Impossible: an algorithmic black box | Offered structurally, full traceability guaranteed |
| Reproducibility by third parties | Theoretically possible, practically rare | Impossible without access to the underlying model | Published in the scientific articles, open protocols |
FRAUD PREVENTION
FRAUD PREVENTION
Designing a predictive fraud-detection AI without algorithmic discrimination.
This case illustrates how methodological validation can be a non-negotiable precondition. A European health insurer was preparing the deployment of a predictive fraud-detection AI. The ethical stakes were high: a poorly calibrated system could structurally discriminate against vulnerable policyholder typologies. Our validation by cross-reference with the public sociological studies on inequalities in access to care identified the typologies exposed to false positives. Our variance measurements across eleven tested configurations quantitatively documented the deviations between scenarios. The independent external audit was able to trace every conclusion back to the individual interviews. Without these validation mechanisms, the insurer's ethics committee could not have approved the final deployment: validation was not a methodological extra, it was the condition of the system's existence.
Read the full case →REPRODUCIBILITY AS A PRINCIPLE
Our protocols are published, our benchmarks are reproducible, our methodologies are contestable.
Reproducibility is not a methodological option: it is the structural criterion of a scientific approach. A protocol that a third party cannot reproduce is not a scientific methodology, it is dressed-up craft. We commit Imagine All The People on three concrete dimensions of reproducibility.
Publication of protocols in our scientific articles.
Our methodological protocols, the population-generation algorithm by Maximum Entropy Relaxation, calibration protocols on NPORS benchmarks, variance measurement methods, are published in our scientific articles available on ArXiv and in the academic editions we contribute to. A researcher who wants to reproduce our results has the necessary technical specifications. The relevant publications are available from the Scientific papers page.
Benchmarks open to challenge.
Our performance benchmarks are documented with enough precision for third parties to reproduce or contest them. We use standardized public benchmarks (NPORS in particular) rather than proprietary benchmarks only we could validate. This transparency exposes our results to adversarial challenge: that is precisely the point of a scientific approach. Detailed results are presented on the Benchmarks page.
Availability for adversarial challenge.
Beyond technical publication, we commit our scientific teams to responding publicly to substantiated methodological challenges: answers to scientific critiques, dialogues with counter-experts, participation in the field's academic conferences. This availability for challenge is a condition of existence of a serious scientific discipline. It structurally sets us apart from players who build their commercial positions on methodological opacity.
Want to audit our methodology?
Whether you are a chief risk officer evaluating a partnership, a research director validating a methodology before an internal recommendation, an ethics committee assessing a consequential system, or an academic partner considering a collaboration: our scientific team can provide you with the detailed validation protocols, the variance measurements from our public benchmarks, and the technical access needed for an independent external audit. Methodological validation is not a commercial posture: it is a contractual commitment.
See the benchmarks →