边际保真度并不能确立人口统计合成调查面板中的用户模拟：响应契约、支持坍缩与条件失效
Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure
- Minds AI Labs, Inc.（Minds AI实验室公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文通过多国多工具实验证明，人口统计合成面板的边际保真度主要反映响应契约和直接估计，而非个体用户模拟，并挑战了现有验证标准。
AI中文摘要：
人口统计合成调查面板通常通过将聚合答案与已发布的调查进行匹配来验证。我们测试了这种验证证书在三个国家的四个调查组织的六个多选电池组中确立了什么。主要分析仅限于三个工具，其合成队列和人类目标共享所述的人口框架；其他三个电池组仍作为敏感性分析。响应契约主导了测量的保真度。在对齐的工具中，在多达500名受访者的面板中，承诺集合在128个模型-电池选项槽中留下66个为空，而在每选项概率启发下则为0/128。在八个无上限的模型-工具比较中，概率将选项边际平均绝对误差降低了4.53至7.30个百分点。有上限的工具在两个模型上逆转了结果，直到向量投影到其规定的最大值。这些是测量效应：人类目标是实现的勾选所有适用项响应，而向量是潜在包含倾向。已发布的边际一致性也无法区分受访者模拟与直接人口估计。在九个对齐的模型-电池对上，无人物角色的人口患病率查询平均MAE为6.27，而承诺面板为12.39，并在所有九项比较中获胜。考虑约束的概率向量平均为5.34，并在九项中击败查询四次，因此基线挑战了验证标准，而不是证明直接估计普遍最优。在三个未发布的人口统计单元上，两种方法都不优于背诵国家分布。因此，人口边际一致性是关于响应契约和无需模拟受访者即可获得的估计量的证据，而不是个体模拟的证据。
英文摘要：
Demographic synthetic survey panels are often validated by matching aggregate answers to published surveys. We test what that certificate establishes across six multiselect batteries from four survey organisations in three countries. The headline analysis is restricted to three instruments whose synthetic cohort and human target share the stated population frame; three other batteries remain sensitivity analyses. The response contract dominates measured fidelity. In the aligned instruments, committed sets leave 66 of 128 model-battery option slots empty in panels of up to 500 respondents, versus 0 of 128 under per-option probability elicitation. Across eight uncapped model-instrument comparisons, probabilities reduce option-marginal MAE by 4.53 to 7.30 points. The capped instrument reverses on two models until the vectors are projected onto its stated maximum. These are measurement effects: human targets are realised check-all responses, whereas the vectors are latent inclusion propensities. Published marginal agreement also fails to discriminate respondent simulation from direct population estimation. On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons. Constraint-aware probability vectors average 5.34 and beat the query on four of nine, so the baseline challenges the validation criterion rather than proving direct estimation uniformly best. On three unpublished demographic cells, neither approach beats reciting the national distribution. Population-marginal agreement is therefore evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.
