White Paper on Anomalous Systems Analysis (ASA): A Framework for Cross-Domain Detection, Quantification, and Forecasting of Anomalous Phenomena Clusters

Anomalous Systems Analysis (ASA) is a newly proposed research discipline unifying quantitative and phenomenological methodologies for the study of emergent anomalies across cognitive, environmental, and data-centric domains. Grounded in the Unified Archetypal Bayesian Theory (UABT) and operationalized through the Adaptive APC Forecast Engine (AAFE), ASA introduces a scalable, probabilistic system for modeling what are herein defined as Anomalous Phenomena Clusters (APCs). These clusters represent statistically meaningful deviations or irregular co-occurrences across multiple observation layers—ranging from atmospheric measurements and sensor telemetry to sociocognitive reporting and institutional acknowledgment.