This paper develops a robustness framework for emotional criticality monitoring under adversarial or distorted governance signals. The Emotional Criticality Condition depends on observable indicators such as saturation, misalignment, and endurance, but these signals can be manipulated through metric gaming, delayed feedback, observation gaps, feedback suppression, cross-channel inconsistency, or direct adversarial pressure. The paper introduces a distortion field D (t) 2=zD⊤WDzDD (t) ²=zD^ WDzDD (t) 2=zD⊤WDzD, constructed from six observable distortion channels. This distortion field measures indicator-integrity degradation and enters the governance model through a distortion-corrected effective capacity. The resulting distortion-corrected emotional criticality index raises visible risk when monitoring signals are compromised. The paper classifies five canonical attack types against emotional criticality monitoring: endurance inflation, saturation suppression, misalignment masking, conflict fragmentation, and channel desynchronization. It argues that while any single governance indicator is manipulable, cross-channel divergence provides a second-order detection mechanism that is structurally harder to spoof. Experimental protocols evaluate false-negative reduction, lead-time recovery, null-window specificity, channel ablation, and adaptive adversary robustness. Synthetic adversarial tests show that distortion correction substantially recovers detection performance under attack while preserving low null-window false positives. The paper positions distortion-corrected emotional criticality as the adversarial robustness and indicator-integrity layer of the AGP/AGM series.
Bin Seol (Mon,) studied this question.