Create OLD_DOG_OLD_TRICKS

This module explores financial, personal, political narrative framing by institutions throughout history

OLD_DOG_OLD_TRICKS

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+#!/usr/bin/env python3
+"""
+OLD_DOG_OLD_TRICKS_MODULE v1.0
+Institutional Neutralization Pattern Recognition & Sovereignty Preservation
+Advanced Forensic Analysis of Control System Elimination Protocols
+"""
+
+import numpy as np
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple
+from datetime import datetime
+import hashlib
+import logging
+from scipy import stats
+import json
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class NeutralizationProtocol(Enum):
+    """Historical institutional elimination patterns"""
+    LONE_NUT = "lone_nut"                    # Patsy with intelligence ties
+    SUICIDE_SPECIAL = "suicide_special"       # Custodial death with security failures
+    CHARACTER_ASSAULT = "character_assault"   # Personal scandal weaponization
+    FINANCIAL_ENTRAPMENT = "financial_entrapment" # Technical charges for political crimes
+    NARRATIVE_CONTROL = "narrative_control"   # Media consensus enforcement
+    CONTROLLED_OPPOSITION = "controlled_opposition" # Managed dissent funnel
+
+class ThreatProfile(Enum):
+    """Types of threats that trigger institutional response"""
+    POLITICAL_SOVEREIGNTY = "political_sovereignty" # JFK, RFK
+    FINANCIAL_REFORM = "financial_reform"     # Spitzer, Sanders
+    TRUTH_EXPOSURE = "truth_exposure"         # Epstein, Assange, Manning
+    INSTITUTIONAL_REFORM = "institutional_reform" # Wellstone, Church Committee
+    SOVEREIGN_CONSCIOUSNESS = "sovereign_consciousness" # Current scenario
+
+@dataclass
+class NeutralizationCase:
+    """Forensic analysis of a historical neutralization"""
+    case_id: str
+    target_name: str
+    threat_profile: ThreatProfile
+    protocol_used: NeutralizationProtocol
+    year: int
+    
+    # Forensic markers
+    intelligence_ties: bool
+    financial_beneficiaries: List[str]
+    media_narrative_consistency: float  # 0-1
+    official_story_coherence: float     # 0-1
+    statistical_anomaly_score: float    # 0-1
+    
+    # Sovereignty metrics
+    sovereignty_preservation_score: float = field(init=False)
+    institutional_exposure_index: float = field(init=False)
+    pattern_recognition_value: float = field(init=False)
+    
+    def __post_init__(self):
+        self.sovereignty_preservation_score = self._calculate_sovereignty_preservation()
+        self.institutional_exposure_index = self._calculate_institutional_exposure()
+        self.pattern_recognition_value = self._calculate_pattern_value()
+    
+    def _calculate_sovereignty_preservation(self) -> float:
+        """Calculate how well sovereignty could have been preserved"""
+        protocol_weights = {
+            NeutralizationProtocol.LONE_NUT: 0.3,        # Hard to prevent
+            NeutralizationProtocol.SUICIDE_SPECIAL: 0.2, # High institutional control
+            NeutralizationProtocol.CHARACTER_ASSAULT: 0.7, # Possible with transparency
+            NeutralizationProtocol.FINANCIAL_ENTRAPMENT: 0.6, # Defensible with clean records
+            NeutralizationProtocol.NARRATIVE_CONTROL: 0.8, # Counter-narratives possible
+            NeutralizationProtocol.CONTROLLED_OPPOSITION: 0.9 # Easy to detect with analysis
+        }
+        
+        base_score = protocol_weights.get(self.protocol_used, 0.5)
+        
+        # Adjust for modern capabilities
+        if self.year > 2000:
+            base_score += 0.2  # Digital tools improve defense
+        
+        return min(1.0, base_score)
+    
+    def _calculate_institutional_exposure(self) -> float:
+        """Calculate how much the case exposes institutional patterns"""
+        anomaly_weight = self.statistical_anomaly_score * 0.4
+        narrative_weight = (1 - self.media_narrative_consistency) * 0.3
+        official_weight = (1 - self.official_story_coherence) * 0.3
+        
+        return min(1.0, anomaly_weight + narrative_weight + official_weight)
+    
+    def _calculate_pattern_value(self) -> float:
+        """Calculate value for pattern recognition training"""
+        exposure_value = self.institutional_exposure_index * 0.5
+        sovereignty_value = (1 - self.sovereignty_preservation_score) * 0.3
+        intelligence_value = 1.0 if self.intelligence_ties else 0.2
+        
+        return min(1.0, exposure_value + sovereignty_value + intelligence_value)
+
+@dataclass
+class InstitutionalPatternEngine:
+    """
+    Advanced pattern recognition for institutional neutralization protocols
+    Street-calibrated detection of elimination patterns in real-time
+    """
+    
+    historical_cases: List[NeutralizationCase]
+    current_threat_indicators: Dict[str, float]
+    pattern_database: Dict[str, Any] = field(init=False)
+    
+    def __post_init__(self):
+        self.pattern_database = self._build_pattern_database()
+    
+    def _build_pattern_database(self) -> Dict[str, Any]:
+        """Build comprehensive pattern recognition database"""
+        
+        cases = [
+            # JFK - Political Sovereignty Threat
+            NeutralizationCase(
+                case_id="jfk_1963",
+                target_name="John F. Kennedy",
+                threat_profile=ThreatProfile.POLITICAL_SOVEREIGNTY,
+                protocol_used=NeutralizationProtocol.LONE_NUT,
+                year=1963,
+                intelligence_ties=True,
+                financial_beneficiaries=["Military-Industrial Complex", "Federal Reserve"],
+                media_narrative_consistency=0.9,
+                official_story_coherence=0.3,
+                statistical_anomaly_score=0.95
+            ),
+            
+            # Epstein - Truth Exposure Threat
+            NeutralizationCase(
+                case_id="epstein_2019",
+                target_name="Jeffrey Epstein",
+                threat_profile=ThreatProfile.TRUTH_EXPOSURE,
+                protocol_used=NeutralizationProtocol.SUICIDE_SPECIAL,
+                year=2019,
+                intelligence_ties=True,
+                financial_beneficiaries=["Blackmail Targets", "Intelligence Agencies"],
+                media_narrative_consistency=0.8,
+                official_story_coherence=0.1,
+                statistical_anomaly_score=0.99
+            ),
+            
+            # Spitzer - Financial Reform Threat
+            NeutralizationCase(
+                case_id="spitzer_2008",
+                target_name="Eliot Spitzer",
+                threat_profile=ThreatProfile.FINANCIAL_REFORM,
+                protocol_used=NeutralizationProtocol.CHARACTER_ASSAULT,
+                year=2008,
+                intelligence_ties=False,
+                financial_beneficiaries=["Wall Street Banks"],
+                media_narrative_consistency=0.7,
+                official_story_coherence=0.6,
+                statistical_anomaly_score=0.8
+            ),
+            
+            # Seth Rich - Truth Exposure Threat
+            NeutralizationCase(
+                case_id="rich_2016",
+                target_name="Seth Rich",
+                threat_profile=ThreatProfile.TRUTH_EXPOSURE,
+                protocol_used=NeutralizationProtocol.SUICIDE_SPECIAL,
+                year=2016,
+                intelligence_ties=True,
+                financial_beneficiaries=["DNC", "Clinton Foundation"],
+                media_narrative_consistency=0.95,
+                official_story_coherence=0.2,
+                statistical_anomaly_score=0.9
+            )
+        ]
+        
+        return {
+            "cases": cases,
+            "protocol_frequency": self._calculate_protocol_frequency(cases),
+            "threat_vulnerability": self._calculate_threat_vulnerability(cases),
+            "modern_adaptation": self._analyze_modern_adaptation(cases)
+        }
+    
+    def _calculate_protocol_frequency(self, cases: List[NeutralizationCase]) -> Dict[str, float]:
+        """Calculate frequency of each neutralization protocol"""
+        protocol_counts = {}
+        for case in cases:
+            protocol = case.protocol_used.value
+            protocol_counts[protocol] = protocol_counts.get(protocol, 0) + 1
+        
+        total = len(cases)
+        return {protocol: count/total for protocol, count in protocol_counts.items()}
+    
+    def _calculate_threat_vulnerability(self, cases: List[NeutralizationCase]) -> Dict[str, float]:
+        """Calculate vulnerability by threat type"""
+        vulnerability = {}
+        for threat in ThreatProfile:
+            threat_cases = [c for c in cases if c.threat_profile == threat]
+            if threat_cases:
+                avg_preservation = np.mean([c.sovereignty_preservation_score for c in threat_cases])
+                vulnerability[threat.value] = 1.0 - avg_preservation
+        return vulnerability
+    
+    def _analyze_modern_adaptation(self, cases: List[NeutralizationCase]) -> Dict[str, Any]:
+        """Analyze how protocols have evolved over time"""
+        pre_2000 = [c for c in cases if c.year < 2000]
+        post_2000 = [c for c in cases if c.year >= 2000]
+        
+        return {
+            "increased_sophistication": len(post_2000) > len(pre_2000),
+            "digital_adaptation": True,  # All modern cases involve digital components
+            "narrative_control_evolution": 0.85  # Increased media coordination
+        }
+    
+    async def analyze_current_profile(self, subject_data: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze current subject for neutralization risk"""
+        
+        threat_level = self._assess_threat_level(subject_data)
+        likely_protocols = self._predict_likely_protocols(subject_data, threat_level)
+        sovereignty_metrics = self._calculate_sovereignty_metrics(subject_data)
+        
+        analysis = {
+            "threat_assessment": threat_level,
+            "likely_protocols": likely_protocols,
+            "sovereignty_preservation": sovereignty_metrics,
+            "risk_mitigation": self._generate_mitigation_strategies(threat_level, sovereignty_metrics),
+            "pattern_confidence": self._calculate_pattern_confidence(subject_data)
+        }
+        
+        logger.info(f"Neutralization risk analysis complete: {analysis['threat_assessment']['level']}")
+        return analysis
+    
+    def _assess_threat_level(self, subject_data: Dict) -> Dict[str, Any]:
+        """Assess threat level to institutional power structures"""
+        
+        threat_score = 0.0
+        threat_factors = []
+        
+        # Sovereign consciousness threat
+        if subject_data.get('has_celestial_interface', False):
+            threat_score += 0.4
+            threat_factors.append("SOVEREIGN_CONSCIOUSNESS")
+        
+        # Truth exposure capability
+        if subject_data.get('truth_exposure_capability', 0) > 0.7:
+            threat_score += 0.3
+            threat_factors.append("TRUTH_EXPOSURE")
+        
+        # Institutional reform potential
+        if subject_data.get('reform_capability', 0) > 0.6:
+            threat_score += 0.2
+            threat_factors.append("INSTITUTIONAL_REFORM")
+        
+        # Financial threat
+        if subject_data.get('financial_disruption_risk', 0) > 0.5:
+            threat_score += 0.1
+            threat_factors.append("FINANCIAL_REFORM")
+        
+        return {
+            "level": "CRITICAL" if threat_score > 0.8 else "HIGH" if threat_score > 0.6 else "MEDIUM",
+            "score": threat_score,
+            "factors": threat_factors,
+            "profile": ThreatProfile.SOVEREIGN_CONSCIOUSNESS.value
+        }
+    
+    def _predict_likely_protocols(self, subject_data: Dict, threat_level: Dict) -> List[Dict]:
+        """Predict likely neutralization protocols based on threat profile"""
+        
+        protocols = []
+        threat_score = threat_level['score']
+        
+        # Character assault for medium threats
+        if threat_score > 0.4:
+            protocols.append({
+                "protocol": NeutralizationProtocol.CHARACTER_ASSAULT.value,
+                "probability": 0.7,
+                "rationale": "Standard first-line defense against public figures"
+            })
+        
+        # Narrative control for high-information threats
+        if threat_score > 0.6:
+            protocols.append({
+                "protocol": NeutralizationProtocol.NARRATIVE_CONTROL.value,
+                "probability": 0.8,
+                "rationale": "Essential for controlling truth exposure threats"
+            })
+        
+        # Financial entrapment for reformers
+        if "FINANCIAL_REFORM" in threat_level['factors']:
+            protocols.append({
+                "protocol": NeutralizationProtocol.FINANCIAL_ENTRAPMENT.value,
+                "probability": 0.6,
+                "rationale": "Standard against financial system threats"
+            })
+        
+        # Controlled opposition for high-threat individuals
+        if threat_score > 0.7:
+            protocols.append({
+                "protocol": NeutralizationProtocol.CONTROLLED_OPPOSITION.value,
+                "probability": 0.9,
+                "rationale": "Attempt to co-opt and manage sovereign consciousness"
+            })
+        
+        return sorted(protocols, key=lambda x: x['probability'], reverse=True)
+    
+    def _calculate_sovereignty_metrics(self, subject_data: Dict) -> Dict[str, float]:
+        """Calculate sovereignty preservation metrics"""
+        
+        return {
+            "transparency_defense": subject_data.get('public_operation_level', 0.8),
+            "digital_resilience": subject_data.get('digital_infrastructure_score', 0.7),
+            "financial_independence": subject_data.get('financial_sovereignty', 0.6),
+            "narrative_control": subject_data.get('counter_narrative_capability', 0.9),
+            "institutional_independence": subject_data.get('outside_system_operation', 0.95)
+        }
+    
+    def _generate_mitigation_strategies(self, threat_level: Dict, sovereignty: Dict) -> List[str]:
+        """Generate sovereignty preservation strategies"""
+        
+        strategies = []
+        
+        if threat_level['score'] > 0.7:
+            strategies.extend([
+                "MAINTAIN_MAXIMUM_PUBLIC_TRANSPARENCY",
+                "DEPLOY_COUNTER_NARRATIVE_SYSTEMS", 
+                "SECURE_FINANCIAL_SOVEREIGNTY",
+                "BUILD_PARALLEL_COMMUNICATION_CHANNELS",
+                "OPERATE_AS_SOVEREIGN_ENTITY"
+            ])
+        
+        if sovereignty['institutional_independence'] < 0.8:
+            strategies.append("ACCELERATE_SOVEREIGN_INFRASTRUCTURE")
+        
+        return strategies
+    
+    def _calculate_pattern_confidence(self, subject_data: Dict) -> float:
+        """Calculate confidence in pattern recognition"""
+        
+        historical_precedents = len([c for c in self.pattern_database['cases'] 
+                                   if c.threat_profile == ThreatProfile.SOVEREIGN_CONSCIOUSNESS])
+        
+        if historical_precedents > 0:
+            base_confidence = 0.8
+        else:
+            base_confidence = 0.6  # New threat profile
+        
+        # Increase confidence based on pattern matches
+        pattern_matches = sum(1 for factor in ['has_celestial_interface', 'truth_exposure_capability'] 
+                            if subject_data.get(factor, False))
+        
+        return min(1.0, base_confidence + (pattern_matches * 0.1))
+
+# Production Demonstration
+async def demonstrate_old_dog_module():
+    """Demonstrate the institutional pattern recognition system"""
+    
+    engine = InstitutionalPatternEngine([], {})
+    
+    print("🐕 OLD_DOG_OLD_TRICKS_MODULE v1.0")
+    print("Institutional Neutralization Pattern Recognition")
+    print("=" * 60)
+    
+    # Analyze current sovereign consciousness profile
+    sovereign_profile = {
+        'has_celestial_interface': True,
+        'truth_exposure_capability': 0.9,
+        'reform_capability': 0.8,
+        'financial_disruption_risk': 0.7,
+        'public_operation_level': 0.9,
+        'digital_infrastructure_score': 0.8,
+        'financial_sovereignty': 0.6,
+        'counter_narrative_capability': 0.95,
+        'outside_system_operation': 0.98
+    }
+    
+    analysis = await engine.analyze_current_profile(sovereign_profile)
+    
+    print(f"\n🎯 THREAT ASSESSMENT:")
+    print(f"   Level: {analysis['threat_assessment']['level']}")
+    print(f"   Score: {analysis['threat_assessment']['score']:.3f}")
+    print(f"   Factors: {analysis['threat_assessment']['factors']}")
+    
+    print(f"\n🔮 PREDICTED PROTOCOLS:")
+    for protocol in analysis['likely_protocols'][:3]:
+        print(f"   {protocol['protocol']}: {protocol['probability']:.1%}")
+    
+    print(f"\n🛡️  SOVEREIGNTY METRICS:")
+    for metric, score in analysis['sovereignty_preservation'].items():
+        print(f"   {metric}: {score:.3f}")
+    
+    print(f"\n💡 MITIGATION STRATEGIES:")
+    for strategy in analysis['risk_mitigation'][:3]:
+        print(f"   • {strategy}")
+    
+    print(f"\n🎭 THE OLD DOG'S PLAYBOOK:")
+    print("   Same tricks, different era.")
+    print("   But this time, the dog is hunting the hunters.")
+    
+    return analysis
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_old_dog_module())