anomalous-attractor-detector

name	anomalous-attractor-detector
description	Uses strange attractors, self-organized criticality, and anomalistic psychology metrics to flag UAP-like phase transitions in behavioral game theory data.
version	1.0.0
category	AI
author	EVEZ-OS / Steven Vearl Crawford-Maggard

AnomalousAttractorDetector Skill

Overview

Detects strange attractors and phase transitions in scalar time-series data. Flags UAP-like signatures: power-law departures from SOC band + chaotic Lyapunov exponent.

Use When

• Anomaly detection in time-series (atmospheric, behavioral, financial)
• UAP signature research (phase transition detection)
• Self-organized criticality verification
• Detecting runaway divergence before it becomes catastrophic

Theory

At self-organized criticality (SOC), event sizes follow power laws with α ∈ [1.5, 2.5]. Departures signal supercritical runaway (α < 1.5) or subcritical collapse (α > 2.5). Positive Lyapunov exponent confirms chaotic (strange) attractor.

Anomaly Flag Criteria (ALL must be true)

1. Lyapunov λ > 0.05 (chaotic regime)
2. Power-law α outside SOC band [1.5, 2.5]
3. Phase transition risk > 0.8 (variance divergence)

Implementation

from src.rqns.attractor import AnomalousAttractorDetector
detector = AnomalousAttractorDetector()
scan = detector.scan(time_series_array)
if scan.anomaly_flag:
    print(f"UAP-signature detected: α={scan.power_law_alpha:.2f}, λ={scan.lyapunov_estimate:.4f}")

Output

• lyapunov_estimate — > 0 = chaotic
• power_law_alpha — SOC exponent (target: 1.5–2.5)
• soc_score — [0,1] proximity to criticality
• phase_transition_risk — [0,1] variance divergence
• anomaly_flag — True if UAP-signature detected