用分布模拟企业行为，让决策更精准。
Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions
- 基于记忆的数字孪生，动态更新行为分布。
- 最低Wasserstein-1距离达1.16，分布拟合优。
- 适合需要可审计行为模拟的企业决策者。
企业行为模拟不仅需生成合理响应，还需刻画在特定决策下群体行为的分布形态：哪些用户接受、流失、犹豫或进入高敏感状态。本文提出后验孪生（Posterior Twins），一种基于记忆的数字孪生方法，将可能行为建模为特定决策情境下的更新分布。我们在226个独立行为响应样本上评估了一系列Twinning Labs模型配置，报告了模态准确率与Wasserstein-1距离。结果表明，模态准确率与分布保真度揭示了不同的运行区间：TL-Twin Alpha实现最低的Wasserstein-1距离（$W_1 = 1.16$），而TL-Twin Delta与Gamma在模态准确率前沿提供了平衡点。论文将这些成果视为系统性成果：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关键。
原文摘要 · Abstract (English)
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.
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