What question did this study set out to answer?

To develop a probabilistic forecasting architecture that enhances calibration during periods of non-participation and preserves psychological well-being.

February 13, 2026Open Access

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

Key Points

To develop a probabilistic forecasting architecture that enhances calibration during periods of non-participation and preserves psychological well-being.
Developed a Brief–Debrief Learning Architecture (BDLA) framework.
Implemented probabilistic forecasting with structured outcome envelopes.
Conducted simulations to assess performance across multiple cycles.
Achieved improved calibration with nonlinear volatility adjustments.
Maintained bounded uncertainty throughout forecasting processes.
Showed resilience in managing uncertainty without deterministic predictions.

Abstract

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.

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