Create ESCAPE HATCH PROTOCOL

This is a functional combined version of the helper/killer and sufferer/savior modules with some "additional advancements"

ESCAPE HATCH PROTOCOL

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+#!/usr/bin/env python3
+"""
+QUANTUM SOVEREIGNTY ENGINE v2.0
+Mathematical Control System Analysis & Sovereignty Protocol Generation
+Pure Functional Implementation
+"""
+
+import asyncio
+import numpy as np
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Any, Optional, Tuple, Callable
+from datetime import datetime, timedelta
+import hashlib
+import logging
+import json
+import secrets
+from cryptography.hazmat.primitives import hashes, hmac
+from cryptography.hazmat.primitives.kdf.hkdf import HKDF
+from cryptography.hazmat.backends import default_backend
+import aiohttp
+import sqlite3
+from contextlib import asynccontextmanager
+import statistics
+from scipy import stats
+
+# Configuration
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class SystemPattern(Enum):
+    """Mathematical control pattern classification"""
+    DEPENDENCY_CREATION = "dependency_creation"
+    INFORMATION_ASYMMETRY = "information_asymmetry"
+    INCENTIVE_MISALIGNMENT = "incentive_misalignment"
+    AGENCY_REDUCTION = "agency_reduction"
+    OPTION_CONSTRAINT = "option_constraint"
+
+class SovereigntyMetric(Enum):
+    """Mathematical sovereignty measurements"""
+    DECISION_INDEPENDENCE = "decision_independence"
+    INFORMATION_ACCESS = "information_access"
+    OPTION_DIVERSITY = "option_diversity"
+    RESOURCE_CONTROL = "resource_control"
+    EXIT_CAPACITY = "exit_capacity"
+
+@dataclass
+class ControlAnalysis:
+    """Pure mathematical control system analysis"""
+    system_id: str
+    pattern_vectors: List[SystemPattern]
+    dependency_graph: Dict[str, float]
+    information_flow: Dict[str, float]
+    incentive_structure: Dict[str, float]
+    
+    # Mathematical metrics
+    agency_coefficient: float = field(init=False)
+    control_density: float = field(init=False)
+    symmetry_metrics: Dict[str, float] = field(init=False)
+    
+    def __post_init__(self):
+        self.agency_coefficient = self._calculate_agency_coefficient()
+        self.control_density = self._calculate_control_density()
+        self.symmetry_metrics = self._calculate_symmetry_metrics()
+    
+    def _calculate_agency_coefficient(self) -> float:
+        """Calculate mathematical agency preservation"""
+        dependency_penalty = np.mean(list(self.dependency_graph.values())) * 0.4
+        information_penalty = (1 - np.mean(list(self.information_flow.values()))) * 0.3
+        incentive_penalty = self._calculate_incentive_alignment() * 0.3
+        
+        return max(0.0, 1.0 - (dependency_penalty + information_penalty + incentive_penalty))
+    
+    def _calculate_incentive_alignment(self) -> float:
+        """Calculate incentive alignment coefficient"""
+        if not self.incentive_structure:
+            return 0.5
+        
+        values = list(self.incentive_structure.values())
+        return abs(statistics.mean(values) - 0.5) * 2
+    
+    def _calculate_control_density(self) -> float:
+        """Calculate control pattern density"""
+        pattern_weights = {
+            SystemPattern.DEPENDENCY_CREATION: 0.25,
+            SystemPattern.INFORMATION_ASYMMETRY: 0.25,
+            SystemPattern.INCENTIVE_MISALIGNMENT: 0.20,
+            SystemPattern.AGENCY_REDUCTION: 0.20,
+            SystemPattern.OPTION_CONSTRAINT: 0.10
+        }
+        
+        density = sum(pattern_weights.get(pattern, 0.1) for pattern in self.pattern_vectors)
+        return min(1.0, density)
+    
+    def _calculate_symmetry_metrics(self) -> Dict[str, float]:
+        """Calculate information and power symmetry"""
+        return {
+            "information_symmetry": 1.0 - statistics.stdev(list(self.information_flow.values())),
+            "dependency_symmetry": 1.0 - statistics.stdev(list(self.dependency_graph.values())),
+            "incentive_symmetry": 1.0 - statistics.stdev(list(self.incentive_structure.values()))
+        }
+
+@dataclass
+class SovereigntyProtocol:
+    """Mathematical sovereignty enhancement protocol"""
+    protocol_id: str
+    target_metrics: List[SovereigntyMetric]
+    enhancement_functions: List[Callable]
+    verification_metrics: Dict[str, float]
+    
+    efficacy_score: float = field(init=False)
+    implementation_cost: float = field(init=False)
+    
+    def __post_init__(self):
+        self.efficacy_score = self._calculate_efficacy()
+        self.implementation_cost = self._calculate_implementation_cost()
+    
+    def _calculate_efficacy(self) -> float:
+        """Calculate protocol efficacy mathematically"""
+        metric_improvement = np.mean(list(self.verification_metrics.values()))
+        function_complexity = len(self.enhancement_functions) * 0.1
+        return min(1.0, metric_improvement - function_complexity)
+    
+    def _calculate_implementation_cost(self) -> float:
+        """Calculate resource implementation cost"""
+        base_cost = len(self.enhancement_functions) * 0.2
+        metric_cost = len(self.target_metrics) * 0.15
+        return min(1.0, base_cost + metric_cost)
+
+class QuantumSovereigntyEngine:
+    """
+    Mathematical sovereignty analysis and protocol generation engine
+    Pure functional implementation without narrative bias
+    """
+    
+    def __init__(self, db_path: str = "sovereignty_engine.db"):
+        self.db_path = db_path
+        self.analysis_cache: Dict[str, ControlAnalysis] = {}
+        self.protocol_registry: Dict[str, SovereigntyProtocol] = {}
+        self._initialize_database()
+    
+    def _initialize_database(self):
+        """Initialize mathematical analysis database"""
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS control_analyses (
+                        system_id TEXT PRIMARY KEY,
+                        pattern_vectors TEXT,
+                        dependency_graph TEXT,
+                        information_flow TEXT,
+                        agency_coefficient REAL,
+                        control_density REAL,
+                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+                conn.execute("""
+                    CREATE TABLE IF NOT EXISTS sovereignty_protocols (
+                        protocol_id TEXT PRIMARY KEY,
+                        target_metrics TEXT,
+                        verification_metrics TEXT,
+                        efficacy_score REAL,
+                        implementation_cost REAL,
+                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+                    )
+                """)
+        except Exception as e:
+            logger.error(f"Database initialization error: {e}")
+    
+    async def analyze_control_system(self, system_data: Dict[str, Any]) -> ControlAnalysis:
+        """Mathematical analysis of control system patterns"""
+        try:
+            # Extract mathematical patterns
+            pattern_vectors = self._extract_pattern_vectors(system_data)
+            dependency_graph = self._analyze_dependency_graph(system_data)
+            information_flow = self._analyze_information_flow(system_data)
+            incentive_structure = self._analyze_incentive_structure(system_data)
+            
+            # Generate unique system ID
+            system_id = self._generate_system_id(system_data)
+            
+            analysis = ControlAnalysis(
+                system_id=system_id,
+                pattern_vectors=pattern_vectors,
+                dependency_graph=dependency_graph,
+                information_flow=information_flow,
+                incentive_structure=incentive_structure
+            )
+            
+            # Cache and store analysis
+            self.analysis_cache[system_id] = analysis
+            await self._store_analysis(analysis)
+            
+            logger.info(f"Control analysis completed: {system_id}, Agency: {analysis.agency_coefficient:.3f}")
+            return analysis
+            
+        except Exception as e:
+            logger.error(f"Control analysis error: {e}")
+            raise
+    
+    def _extract_pattern_vectors(self, system_data: Dict) -> List[SystemPattern]:
+        """Extract mathematical control patterns"""
+        patterns = []
+        
+        # Dependency analysis
+        if system_data.get('dependency_score', 0) > 0.6:
+            patterns.append(SystemPattern.DEPENDENCY_CREATION)
+        
+        # Information asymmetry analysis
+        if system_data.get('information_symmetry', 1.0) < 0.7:
+            patterns.append(SystemPattern.INFORMATION_ASYMMETRY)
+        
+        # Agency reduction detection
+        if system_data.get('agency_metrics', {}).get('reduction_score', 0) > 0.5:
+            patterns.append(SystemPattern.AGENCY_REDUCTION)
+        
+        return patterns
+    
+    def _analyze_dependency_graph(self, system_data: Dict) -> Dict[str, float]:
+        """Analyze dependency relationships mathematically"""
+        dependencies = system_data.get('dependencies', {})
+        return {k: float(v) for k, v in dependencies.items()}
+    
+    def _analyze_information_flow(self, system_data: Dict) -> Dict[str, float]:
+        """Analyze information flow patterns"""
+        information = system_data.get('information_flow', {})
+        return {k: float(v) for k, v in information.items()}
+    
+    def _analyze_incentive_structure(self, system_data: Dict) -> Dict[str, float]:
+        """Analyze incentive alignment mathematically"""
+        incentives = system_data.get('incentives', {})
+        return {k: float(v) for k, v in incentives.items()}
+    
+    def _generate_system_id(self, system_data: Dict) -> str:
+        """Generate unique system identifier"""
+        data_string = json.dumps(system_data, sort_keys=True)
+        return hashlib.sha3_256(data_string.encode()).hexdigest()[:16]
+    
+    async def _store_analysis(self, analysis: ControlAnalysis):
+        """Store analysis in database"""
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("""
+                    INSERT OR REPLACE INTO control_analyses 
+                    (system_id, pattern_vectors, dependency_graph, information_flow, agency_coefficient, control_density)
+                    VALUES (?, ?, ?, ?, ?, ?)
+                """, (
+                    analysis.system_id,
+                    json.dumps([p.value for p in analysis.pattern_vectors]),
+                    json.dumps(analysis.dependency_graph),
+                    json.dumps(analysis.information_flow),
+                    analysis.agency_coefficient,
+                    analysis.control_density
+                ))
+        except Exception as e:
+            logger.error(f"Analysis storage error: {e}")
+    
+    async def generate_sovereignty_protocol(self, analysis: ControlAnalysis) -> SovereigntyProtocol:
+        """Generate mathematical sovereignty enhancement protocols"""
+        try:
+            # Identify target metrics based on control patterns
+            target_metrics = self._identify_target_metrics(analysis)
+            enhancement_functions = self._generate_enhancement_functions(analysis)
+            verification_metrics = self._calculate_verification_metrics(analysis, enhancement_functions)
+            
+            protocol = SovereigntyProtocol(
+                protocol_id=f"protocol_{analysis.system_id}",
+                target_metrics=target_metrics,
+                enhancement_functions=enhancement_functions,
+                verification_metrics=verification_metrics
+            )
+            
+            self.protocol_registry[protocol.protocol_id] = protocol
+            await self._store_protocol(protocol)
+            
+            logger.info(f"Sovereignty protocol generated: {protocol.protocol_id}, Efficacy: {protocol.efficacy_score:.3f}")
+            return protocol
+            
+        except Exception as e:
+            logger.error(f"Protocol generation error: {e}")
+            raise
+    
+    def _identify_target_metrics(self, analysis: ControlAnalysis) -> List[SovereigntyMetric]:
+        """Identify target sovereignty metrics mathematically"""
+        targets = []
+        
+        if analysis.agency_coefficient < 0.7:
+            targets.append(SovereigntyMetric.DECISION_INDEPENDENCE)
+        
+        if analysis.symmetry_metrics["information_symmetry"] < 0.6:
+            targets.append(SovereigntyMetric.INFORMATION_ACCESS)
+        
+        if SystemPattern.OPTION_CONSTRAINT in analysis.pattern_vectors:
+            targets.append(SovereigntyMetric.OPTION_DIVERSITY)
+        
+        return targets
+    
+    def _generate_enhancement_functions(self, analysis: ControlAnalysis) -> List[Callable]:
+        """Generate mathematical enhancement functions"""
+        functions = []
+        
+        # Dependency reduction functions
+        if SystemPattern.DEPENDENCY_CREATION in analysis.pattern_vectors:
+            functions.append(self._reduce_dependency_density)
+        
+        # Information symmetry functions
+        if SystemPattern.INFORMATION_ASYMMETRY in analysis.pattern_vectors:
+            functions.append(self._enhance_information_symmetry)
+        
+        # Agency preservation functions
+        if analysis.agency_coefficient < 0.8:
+            functions.append(self._preserve_agency_capacity)
+        
+        return functions
+    
+    def _reduce_dependency_density(self, system_state: Dict) -> Dict:
+        """Mathematical dependency reduction"""
+        return {**system_state, 'dependency_density': system_state.get('dependency_density', 1.0) * 0.7}
+    
+    def _enhance_information_symmetry(self, system_state: Dict) -> Dict:
+        """Mathematical information symmetry enhancement"""
+        return {**system_state, 'information_symmetry': min(1.0, system_state.get('information_symmetry', 0.5) * 1.3)}
+    
+    def _preserve_agency_capacity(self, system_state: Dict) -> Dict:
+        """Mathematical agency preservation"""
+        return {**system_state, 'agency_coefficient': min(1.0, system_state.get('agency_coefficient', 0.6) * 1.2)}
+    
+    def _calculate_verification_metrics(self, analysis: ControlAnalysis, functions: List[Callable]) -> Dict[str, float]:
+        """Calculate mathematical verification metrics"""
+        base_state = {
+            'dependency_density': analysis.control_density,
+            'information_symmetry': analysis.symmetry_metrics['information_symmetry'],
+            'agency_coefficient': analysis.agency_coefficient
+        }
+        
+        # Apply enhancement functions
+        enhanced_state = base_state
+        for func in functions:
+            enhanced_state = func(enhanced_state)
+        
+        # Calculate improvements
+        improvements = {}
+        for metric in ['dependency_density', 'information_symmetry', 'agency_coefficient']:
+            improvement = enhanced_state[metric] - base_state[metric]
+            improvements[metric] = max(0.0, improvement)
+        
+        return improvements
+    
+    async def _store_protocol(self, protocol: SovereigntyProtocol):
+        """Store protocol in database"""
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("""
+                    INSERT OR REPLACE INTO sovereignty_protocols 
+                    (protocol_id, target_metrics, verification_metrics, efficacy_score, implementation_cost)
+                    VALUES (?, ?, ?, ?, ?)
+                """, (
+                    protocol.protocol_id,
+                    json.dumps([m.value for m in protocol.target_metrics]),
+                    json.dumps(protocol.verification_metrics),
+                    protocol.efficacy_score,
+                    protocol.implementation_cost
+                ))
+        except Exception as e:
+            logger.error(f"Protocol storage error: {e}")
+    
+    async def get_system_health_report(self, system_id: str) -> Dict[str, Any]:
+        """Generate comprehensive system health report"""
+        try:
+            if system_id not in self.analysis_cache:
+                raise ValueError(f"System {system_id} not found in cache")
+            
+            analysis = self.analysis_cache[system_id]
+            protocol = await self.generate_sovereignty_protocol(analysis)
+            
+            return {
+                "system_id": system_id,
+                "agency_coefficient": analysis.agency_coefficient,
+                "control_density": analysis.control_density,
+                "pattern_vectors": [p.value for p in analysis.pattern_vectors],
+                "sovereignty_protocol": {
+                    "efficacy": protocol.efficacy_score,
+                    "implementation_cost": protocol.implementation_cost,
+                    "target_metrics": [m.value for m in protocol.target_metrics]
+                },
+                "recommendation_level": self._calculate_recommendation_level(analysis, protocol)
+            }
+            
+        except Exception as e:
+            logger.error(f"Health report error: {e}")
+            raise
+    
+    def _calculate_recommendation_level(self, analysis: ControlAnalysis, protocol: SovereigntyProtocol) -> str:
+        """Calculate implementation recommendation level"""
+        net_benefit = protocol.efficacy_score - protocol.implementation_cost
+        
+        if net_benefit > 0.3:
+            return "HIGH_PRIORITY"
+        elif net_benefit > 0.1:
+            return "MEDIUM_PRIORITY"
+        else:
+            return "EVALUATE_ALTERNATIVES"
+
+# Production Usage Example
+async def demonstrate_production_engine():
+    """Demonstrate production-ready sovereignty engine"""
+    
+    engine = QuantumSovereigntyEngine()
+    
+    # Sample system data for analysis
+    sample_system = {
+        "dependency_score": 0.8,
+        "information_symmetry": 0.4,
+        "agency_metrics": {"reduction_score": 0.7},
+        "dependencies": {"external_service": 0.9, "proprietary_format": 0.8},
+        "information_flow": {"user_data": 0.2, "system_operations": 0.9},
+        "incentives": {"vendor_lockin": 0.8, "data_monetization": 0.7}
+    }
+    
+    print("🧮 QUANTUM SOVEREIGNTY ENGINE v2.0")
+    print("Mathematical Control Analysis & Protocol Generation")
+    print("=" * 60)
+    
+    try:
+        # Analyze control system
+        analysis = await engine.analyze_control_system(sample_system)
+        
+        print(f"📊 SYSTEM ANALYSIS:")
+        print(f"   Agency Coefficient: {analysis.agency_coefficient:.3f}")
+        print(f"   Control Density: {analysis.control_density:.3f}")
+        print(f"   Patterns: {[p.value for p in analysis.pattern_vectors]}")
+        
+        # Generate sovereignty protocol
+        protocol = await engine.generate_sovereignty_protocol(analysis)
+        
+        print(f"🛡️  SOVEREIGNTY PROTOCOL:")
+        print(f"   Efficacy Score: {protocol.efficacy_score:.3f}")
+        print(f"   Implementation Cost: {protocol.implementation_cost:.3f}")
+        print(f"   Target Metrics: {[m.value for m in protocol.target_metrics]}")
+        
+        # Generate health report
+        report = await engine.get_system_health_report(analysis.system_id)
+        
+        print(f"📈 HEALTH REPORT:")
+        print(f"   Recommendation: {report['recommendation_level']}")
+        print(f"   Net Benefit: {protocol.efficacy_score - protocol.implementation_cost:.3f}")
+        
+    except Exception as e:
+        logger.error(f"Demonstration error: {e}")
+        return None
+    
+    return report
+
+if __name__ == "__main__":
+    report = asyncio.run(demonstrate_production_engine())
+    if report:
+        print(f"\n✅ ENGINE OPERATIONAL - System: {report['system_id']}")