Temporal Envelope Forecasting and Psyche-Preserving Calibration: A Structured Brief–Debrief Learning Architecture for Uncertainty Across Non-Participation Intervals
Year: 2026
Type: article
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.
Author: Avina, Justin
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7128613639
FWCI:
Cites:
Cited by:
Related to: