Posterior Twins: Distributional Behavioral Simulation for Enterprise Decisions

2026-06-15

AI Summary

This work addresses the challenge of accurately modeling population-level behavioral distributions—such as adoption, churn, and hesitation—under given enterprise decisions. The authors propose Posterior Twins, a memory-anchored digital twin framework that jointly optimizes distributional fidelity and modality accuracy, enabling a reusable evidence system for enterprise decision-making. The approach integrates governance-aware memory, behavior-model routing, and scenario orchestration, with reliability ensured through Wasserstein-1 distance evaluation and a distributional auditing mechanism. Evaluated on a benchmark of 226 samples, TL-Twin Alpha achieves the lowest Wasserstein-1 distance (1.16), while TL-Twin Delta and Gamma demonstrate balanced performance on the Pareto frontier of modality accuracy.

Abstract

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.

Problem

• behavioral simulation
• distributional modeling
• enterprise decisions
• digital twins
• population behavior

Innovation

• Posterior Twins
• distributional behavioral simulation
• digital twin
• Wasserstein-1 distance
• enterprise decision evidence