Cognitive Big Data Architecture for Daily Operational Jamming Transition Detection with Low-Latency Inference in Infrastructure-Constrained Financial Markets: The MERI Framework

We introduce the MERI (Market Evolutionary Resilience Index), a cognitive big data framework operationalising jamming transition physics into a daily operational regime detector (low-latency inference, 8 ms per observation). Opaque models cannot be deployed in regulated environments because every automated alert must decompose into auditable feature contributions. The MERI addresses this by treating the market as a complex adaptive system whose metabolic state constitutes the primary observable. Three cognitive layers fuse heterogeneous streaming data: an EGARCH-GED econometric baseline, a Random Forest classifier on a 15-dimensional physics-derived feature space, and a TreeSHAP Gini attribution audit ensuring full prediction-level transparency. Fisher Information Gain epistemic gating restricts automated intervention to predictions exceeding 2.5 nats certainty. Evaluated on South African financial markets (2015–2025, N=2870 trading days, Eskom load-shedding as exogenous forcing), the MERI achieves 97.3% accuracy (AUC = 0.9973, recall = 1.000), statistically equivalent to Temporal Fusion Transformers (Model Confidence Set, 90% confidence) while delivering 85.7% high-certainty predictions versus 23.4% for deep learning. A Granger-validated 48-h early warning lead (F=62.003, p<0.001), 7.78× recovery hysteresis (Cohen’s d=2.13), and infrastructure dominance of 78.0% (Gini) confirm that the framework is operationally feasible for daily monitoring in the South African JSE–Eskom setting. Cross-domain portability is proposed as a theoretical extension pending empirical validation.