Enterprise behavioral simulation requires more than producing a plausible response. Many decisions depend on the shape of a population under a proposed action: which segments accept, defect, hesitate, or move into risk-sensitive states. This paper introduces Posterior Twins, a memory-grounded digital-twin approach that represents likely behavior as an updated distribution under a specific decision context. We evaluate a family of Twinning Labs behavioral-model operating points on a 226-example held-out behavioral-response benchmark and report both modal accuracy and Wasserstein-1 distance. The results show that modal accuracy and distributional fidelity identify different operating regimes. TL-Twin Alpha achieves the lowest observed Wasserstein-1 distance in the reported result set ($W_1 = 1.16$), while TL-Twin Delta and TL-Twin Gamma provide balanced operating points near the modal-accuracy frontier. The paper frames these results as a systems result: governed memory, behavioral model routing, scenario orchestration, distributional aggregation, and auditability are necessary for turning simulated behavior into reusable enterprise decision evidence.

翻译：企业行为模拟不仅需要生成合理的响应。许多决策依赖于给定行动方案下群体的分布形态：哪些细分群体接受、拒绝、犹豫或进入风险敏感状态。本文提出后验孪生（Posterior Twins）——一种基于记忆存储的数字孪生方法，将特定决策环境下可能的行为表征为更新的分布形式。我们基于包含226个保留样本的行为响应基准测试，对Twinning Labs系列行为模型运行点进行评估，并报告模态准确率与Wasserstein-1距离。结果表明，模态准确率与分布保真度对应不同的运行区间。在报告结果集中，TL-Twin Alpha实现最低的Wasserstein-1距离（$W_1 = 1.16$），而TL-Twin Delta与TL-Twin Gamma在模态准确率前沿附近提供均衡的运行点。本文将此类结果定位为系统级成果：受控记忆、行为模型路由、场景编排、分布聚合与可审计性，是将模拟行为转化为可复用企业决策证据的必要条件。