Temporal Envelope Forecasting and Psyche-Preserving Calibration: A Structured Brief–Debrief Learning Architecture for Uncertainty Across Non-Participation Intervals

This paper presents a bounded-outcome probabilistic forecasting architecture designed to improve calibration across temporal non-participation intervals while preserving psychological stability. The proposed Brief–Debrief Learning Architecture (BDLA) formalizes structured outcome envelopes, probability band calibration, and volatility-sensitive adaptation without collapsing into deterministic prediction. Simulation across multiple cycles demonstrates improved calibration under nonlinear volatility adjustments while maintaining bounded uncertainty. The framework reframes forecasting from future prediction toward structured possibility stewardship and resilience engineering.