Twelve Published Studies Subconscious Uses to Check Simulated Experiments Against Real Human Behavior
A research leader deciding whether to trust a simulated experiment for a live product, pricing, or policy call needs one thing first: proof that the simulation reproduces what real people chose. Subconscious checks this by replaying published, peer-reviewed discrete-choice and conjoint studies inside its own simulated markets and comparing the result to the original human data.
Why does the validation check matter before a decision ships?
A synthetic method that quietly diverges from real human choice behavior only becomes visible after a launch, price, or policy decision has shipped. A validation check run beforehand, against studies whose outcomes are already known, catches that divergence before it shows up in market results.
How the replication check works
Our best configuration reaches 87% of the measured human ceiling on one study: 0.832 rank correlation against the published human result, where two independent samples of real humans reach 0.959. Across all 43 studies that pass design filters the mean is 0.73. A number without its limits attached is marketing. It is a validation result, not a guarantee for a new market. The full methodology and results are published in the causal fidelity paper, drawn from a validation corpus of roughly 300 replicated studies across 9 domains. Prior academic work shows conjoint designs track real-world behavior, including Hainmueller, Hangartner, and Yamamoto's PNAS study comparing stated preferences from survey experiments against real-world referendum voting behavior.
Publishing the scope of a metric next to the metric is what lets a buyer check it instead of taking it on faith. This is an aggregate metric, not a guarantee for any single study or a claim of universal predictive accuracy for a decision that has never been tested.
Which published studies does Subconscious use as checks?
The examples below are a subset of the corpus: named, peer-reviewed discrete-choice and conjoint studies that Subconscious replicates as an independent check against real human choice data, not illustrative marketing examples. Each row is a distinct check, not a ranking.
| Study | Domain tested |
|---|---|
| Hainmueller (Immigration Policy) | Attitudes toward immigrants, using the conjoint design from [Hainmueller and Hopkins, "The Hidden American Immigration Consensus"](https://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12138) |
| Adida (Immigration Policy) | Attitudes toward immigrants, a second published design |
| Kreps (COVID Vaccine Acceptance) | Willingness to accept a COVID-19 vaccine |
| Duch (COVID Vaccine Acceptance) | Willingness to accept a COVID-19 vaccine, a second published design |
| Skreli (Organic Tomatoes Product Design) | Product design preference for organic tomatoes |
| Wu (Subcompact Car Product Design) | Product design preference for subcompact cars |
| Bechtel (International Carbon Tax Policy) | Support for carbon tax policy as environmental mitigation |
| Luthi (Wind Energy Policy) | Support for wind energy policy |
| Adam (Patient Preferences in Medicine) | Preference between complementary and conventional medicine |
| Rao (Rural Clinician Job Preferences) | Job preferences under rural clinician scarcity |
| Ares (Yogurt Consumer Choice) | Consumer product choice for yogurt |
| Claret (Consumer Choice for Fish) | Consumer product choice for fish |
What can a team do after seeing the validation results?
A team that wants more than an aggregate accuracy figure can move from a simulated study to testing or validating with real human participants, on the same population and causal question, confirming a specific result without redesigning the underlying study.
Limitations
Naming where a method stops working is what makes its accuracy number usable for a buying decision. Replication accuracy is an aggregate validation metric, not a guarantee for any single study or decision. It does not establish universal predictive accuracy for a study outside the validation corpus. Audience or data-access claims tied to a specific third-party tool referenced in older materials are not carried forward here without current confirmation.
Next step
Read the validation methodology and full study list before treating any single simulated result as decision-grade. Teams comparing this approach against other case evidence or checking current standing on the replication leaderboard can also review how Subconscious's research is structured or book a walkthrough of a specific study design.