Create institutional suppression package

institutional suppression package

@@ -0,0 +1,550 @@
+#!/usr/bin/env python3
+"""
+INSTITUTIONAL PROPENSITY PACKAGE - lm_quant_veritas v7.0
+----------------------------------------------------------------
+Analyzing and predicting institutional behavior patterns.
+Quantum-secured propensity modeling with temporal forecasting.
+"""
+
+import numpy as np
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from typing import Dict, Any, List, Optional, Tuple
+import hashlib
+import asyncio
+from enum import Enum
+import secrets
+from cryptography.fernet import Fernet
+import logging
+from collections import defaultdict, deque
+import json
+import statistics
+from scipy import stats
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class PropensityType(Enum):
+    """Types of institutional propensities"""
+    BUREAUCRATIC_INERTIA = "bureaucratic_inertia"
+    RISK_AVERSION = "risk_aversion" 
+    POWER_CONSOLIDATION = "power_consolidation"
+    INNOVATION_RESISTANCE = "innovation_resistance"
+    SELF_PRESERVATION = "self_preservation"
+    MISSION_DRIFT = "mission_drift"
+    GROUPTHINK = "groupthink"
+    REGULATORY_CAPTURE = "regulatory_capture"
+
+class SecurityLevel(Enum):
+    STANDARD = "standard"
+    QUANTUM_RESISTANT = "quantum_resistant"
+    TEMPORAL_SECURE = "temporal_secure"
+
+@dataclass
+class InstitutionalVector:
+    """Quantum-secured institutional propensity vector"""
+    institution_hash: str
+    propensity_scores: Dict[PropensityType, float]
+    behavioral_patterns: Dict[str, List[float]]
+    temporal_trajectory: List[Tuple[datetime, float]]
+    security_signature: str
+    forecast_horizon: Dict[str, float] = field(default_factory=dict)
+    risk_factors: List[str] = field(default_factory=list)
+    
+    def __post_init__(self):
+        """Validate quantum security and calculate composite propensity"""
+        if not self._validate_security():
+            raise SecurityError("Institutional vector security validation failed")
+        
+        self.composite_propensity = self._calculate_composite_score()
+        self.volatility_metric = self._calculate_volatility()
+    
+    def _validate_security(self) -> bool:
+        """Validate quantum security signature"""
+        validation_string = f"{self.institution_hash}{json.dumps(self.propensity_scores, sort_keys=True)}"
+        expected_hash = hashlib.sha3_512(validation_string.encode()).hexdigest()
+        return secrets.compare_digest(expected_hash[:64], self.security_signature[:64])
+    
+    def _calculate_composite_score(self) -> float:
+        """Calculate overall institutional propensity score"""
+        scores = list(self.propensity_scores.values())
+        return float(np.mean(scores))
+    
+    def _calculate_volatility(self) -> float:
+        """Calculate behavioral volatility across time"""
+        if len(self.temporal_trajectory) < 2:
+            return 0.0
+        
+        scores = [score for _, score in self.temporal_trajectory]
+        return float(np.std(scores))
+
+class PropensityEngine:
+    """
+    INSTITUTIONAL PROPENSITY ENGINE
+    Analyzes and forecasts institutional behavior patterns
+    with quantum security and temporal coherence
+    """
+    
+    def __init__(self, security_level: SecurityLevel = SecurityLevel.QUANTUM_RESISTANT):
+        self.security_level = security_level
+        self.encryption_key = self._generate_quantum_key()
+        self.institutional_vectors: Dict[str, InstitutionalVector] = {}
+        self.propensity_models = self._initialize_models()
+        self.behavioral_cache = {}
+        self.temporal_window = 365  # days
+        
+        # Security context
+        self.temporal_signature = hashlib.sha3_256(datetime.now().isoformat().encode()).hexdigest()
+    
+    def _generate_quantum_key(self) -> bytes:
+        """Generate quantum-resistant encryption key"""
+        if self.security_level == SecurityLevel.QUANTUM_RESISTANT:
+            return secrets.token_bytes(32)
+        elif self.security_level == SecurityLevel.TEMPORAL_SECURE:
+            return secrets.token_bytes(64)
+        else:
+            return secrets.token_bytes(16)
+    
+    def _initialize_models(self) -> Dict[str, Any]:
+        """Initialize propensity prediction models"""
+        return {
+            'bureaucratic_inertia': {
+                'indicators': ['decision_latency', 'procedure_complexity', 'hierarchy_depth'],
+                'weight': 0.8,
+                'decay_rate': 0.1
+            },
+            'risk_aversion': {
+                'indicators': ['failure_consequences', 'innovation_penalties', 'success_rewards'],
+                'weight': 0.9,
+                'decay_rate': 0.05
+            },
+            'power_consolidation': {
+                'indicators': ['centralization_trends', 'authority_concentration', 'autonomy_reduction'],
+                'weight': 0.7,
+                'decay_rate': 0.15
+            },
+            'innovation_resistance': {
+                'indicators': ['change_rejection_rate', 'tradition_weight', 'new_method_adoption'],
+                'weight': 0.6,
+                'decay_rate': 0.2
+            }
+        }
+    
+    async def analyze_institutional_behavior(self, 
+                                           institution_data: Dict[str, Any],
+                                           historical_context: List[Dict] = None) -> Dict[str, Any]:
+        """
+        Analyze institutional propensity with quantum-secured forecasting
+        """
+        
+        try:
+            # Phase 1: Security validation
+            if not await self._validate_data_security(institution_data):
+                raise SecurityError("Institutional data security validation failed")
+            
+            # Phase 2: Behavioral pattern extraction
+            pattern_analysis = await self._extract_behavioral_patterns(institution_data, historical_context)
+            
+            # Phase 3: Propensity scoring
+            propensity_scores = await self._calculate_propensity_scores(pattern_analysis)
+            
+            # Phase 4: Temporal trajectory analysis
+            trajectory_analysis = await self._analyze_temporal_trajectory(pattern_analysis, propensity_scores)
+            
+            # Phase 5: Risk factor identification
+            risk_analysis = await self._identify_risk_factors(propensity_scores, pattern_analysis)
+            
+            # Phase 6: Behavioral forecasting
+            forecast_analysis = await self._generate_behavioral_forecast(propensity_scores, trajectory_analysis)
+            
+            # Phase 7: Create institutional vector
+            institution_vector = await self._create_institutional_vector(
+                institution_data, propensity_scores, pattern_analysis, 
+                trajectory_analysis, forecast_analysis, risk_analysis
+            )
+            
+            return {
+                "success": True,
+                "institutional_vector": institution_vector,
+                "composite_propensity": institution_vector.composite_propensity,
+                "volatility_metric": institution_vector.volatility_metric,
+                "primary_risk_factors": risk_analysis["primary_risks"],
+                "forecast_confidence": forecast_analysis["confidence"],
+                "security_validated": True,
+                "timestamp": datetime.now().isoformat()
+            }
+            
+        except Exception as e:
+            logger.error(f"Institutional analysis failed: {e}")
+            return await self._handle_analysis_failure(institution_data, e)
+    
+    async def _extract_behavioral_patterns(self, 
+                                         institution_data: Dict[str, Any],
+                                         historical_context: List[Dict]) -> Dict[str, Any]:
+        """Extract behavioral patterns from institutional data"""
+        
+        patterns = {
+            "decision_making": await self._analyze_decision_patterns(institution_data),
+            "resource_allocation": await self._analyze_resource_patterns(institution_data),
+            "risk_behavior": await self._analyze_risk_patterns(institution_data),
+            "innovation_trends": await self._analyze_innovation_patterns(institution_data),
+            "power_dynamics": await self._analyze_power_patterns(institution_data)
+        }
+        
+        # Add historical context if available
+        if historical_context:
+            patterns["historical_trends"] = await self._analyze_historical_trends(historical_context)
+            patterns["temporal_consistency"] = await self._assess_temporal_consistency(patterns, historical_context)
+        
+        # Calculate pattern stability
+        patterns["stability_metrics"] = await self._calculate_pattern_stability(patterns)
+        
+        return patterns
+    
+    async def _calculate_propensity_scores(self, pattern_analysis: Dict[str, Any]) -> Dict[PropensityType, float]:
+        """Calculate propensity scores based on behavioral patterns"""
+        
+        scores = {}
+        
+        for propensity_type, model in self.propensity_models.items():
+            score = await self._calculate_specific_propensity(propensity_type, pattern_analysis, model)
+            scores[PropensityType(propensity_type)] = score
+        
+        return scores
+    
+    async def _calculate_specific_propensity(self, 
+                                           propensity_type: str,
+                                           pattern_analysis: Dict[str, Any],
+                                           model: Dict[str, Any]) -> float:
+        """Calculate specific propensity score"""
+        
+        indicators = model['indicators']
+        weight = model['weight']
+        
+        indicator_scores = []
+        for indicator in indicators:
+            score = await self._extract_indicator_score(indicator, pattern_analysis)
+            indicator_scores.append(score)
+        
+        # Weighted average of indicator scores
+        base_score = np.mean(indicator_scores) if indicator_scores else 0.5
+        
+        # Apply model-specific adjustments
+        adjusted_score = base_score * weight
+        
+        return min(1.0, max(0.0, adjusted_score))
+    
+    async def _analyze_temporal_trajectory(self, 
+                                         pattern_analysis: Dict[str, Any],
+                                         propensity_scores: Dict[PropensityType, float]) -> Dict[str, Any]:
+        """Analyze temporal trajectory of institutional behavior"""
+        
+        trajectory_data = []
+        current_time = datetime.now()
+        
+        # Generate synthetic trajectory based on patterns
+        # In production, this would use actual historical data
+        for days_ago in range(0, self.temporal_window, 30):  # Monthly points
+            point_time = current_time - timedelta(days=days_ago)
+            
+            # Simulate temporal variation based on pattern stability
+            stability = pattern_analysis.get("stability_metrics", {}).get("overall_stability", 0.7)
+            noise = (1 - stability) * np.random.normal(0, 0.1)
+            
+            composite_score = np.mean(list(propensity_scores.values())) + noise
+            composite_score = max(0.0, min(1.0, composite_score))
+            
+            trajectory_data.append((point_time, composite_score))
+        
+        # Calculate trajectory metrics
+        scores = [score for _, score in trajectory_data]
+        
+        return {
+            "trajectory_points": trajectory_data,
+            "trend_direction": await self._calculate_trend_direction(scores),
+            "volatility": np.std(scores) if len(scores) > 1 else 0.0,
+            "acceleration": await self._calculate_trend_acceleration(scores)
+        }
+    
+    async def _identify_risk_factors(self,
+                                   propensity_scores: Dict[PropensityType, float],
+                                   pattern_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Identify institutional risk factors"""
+        
+        risk_factors = []
+        
+        # High bureaucratic inertia risk
+        if propensity_scores.get(PropensityType.BUREAUCRATIC_INERTIA, 0) > 0.8:
+            risk_factors.append("high_bureaucratic_inertia")
+        
+        # Extreme risk aversion risk
+        if propensity_scores.get(PropensityType.RISK_AVERSION, 0) > 0.9:
+            risk_factors.append("extreme_risk_aversion")
+        
+        # Power consolidation risk
+        if propensity_scores.get(PropensityType.POWER_CONSOLIDATION, 0) > 0.7:
+            risk_factors.append("power_centralization")
+        
+        # Innovation resistance risk
+        if propensity_scores.get(PropensityType.INNOVATION_RESISTANCE, 0) > 0.8:
+            risk_factors.append("innovation_stagnation")
+        
+        # Pattern instability risk
+        stability = pattern_analysis.get("stability_metrics", {}).get("overall_stability", 1.0)
+        if stability < 0.5:
+            risk_factors.append("behavioral_instability")
+        
+        return {
+            "primary_risks": risk_factors,
+            "risk_severity": len(risk_factors),
+            "mitigation_priority": await self._prioritize_risks(risk_factors, propensity_scores)
+        }
+    
+    async def _generate_behavioral_forecast(self,
+                                          propensity_scores: Dict[PropensityType, float],
+                                          trajectory_analysis: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate behavioral forecasts with confidence intervals"""
+        
+        base_propensity = np.mean(list(propensity_scores.values()))
+        trend = trajectory_analysis["trend_direction"]
+        volatility = trajectory_analysis["volatility"]
+        
+        # Simple forecasting model - in production would use more sophisticated time series
+        forecast_horizons = {
+            "30_days": self._forecast_point(base_propensity, trend, volatility, 30),
+            "90_days": self._forecast_point(base_propensity, trend, volatility, 90),
+            "365_days": self._forecast_point(base_propensity, trend, volatility, 365)
+        }
+        
+        confidence = max(0.0, 1.0 - volatility * 2)  # Higher volatility reduces confidence
+        
+        return {
+            "forecast_values": forecast_horizons,
+            "confidence": confidence,
+            "trend_persistence": await self._assess_trend_persistence(trajectory_analysis),
+            "forecast_volatility": volatility * 1.5  # Forecasts are more volatile
+        }
+    
+    async def _create_institutional_vector(self,
+                                         institution_data: Dict[str, Any],
+                                         propensity_scores: Dict[PropensityType, float],
+                                         pattern_analysis: Dict[str, Any],
+                                         trajectory_analysis: Dict[str, Any],
+                                         forecast_analysis: Dict[str, Any],
+                                         risk_analysis: Dict[str, Any]) -> InstitutionalVector:
+        """Create quantum-secured institutional vector"""
+        
+        institution_hash = hashlib.sha3_256(
+            json.dumps(institution_data, sort_keys=True).encode()
+        ).hexdigest()
+        
+        # Extract behavioral patterns
+        behavioral_patterns = {
+            "decision_latency": pattern_analysis["decision_making"].get("average_latency", 0.5),
+            "risk_tolerance": pattern_analysis["risk_behavior"].get("tolerance_level", 0.5),
+            "innovation_rate": pattern_analysis["innovation_trends"].get("adoption_rate", 0.5),
+            "centralization_index": pattern_analysis["power_dynamics"].get("centralization", 0.5)
+        }
+        
+        # Generate security signature
+        security_base = f"{institution_hash}{json.dumps(propensity_scores, sort_keys=True)}"
+        security_signature = hashlib.sha3_512(security_base.encode()).hexdigest()
+        
+        vector = InstitutionalVector(
+            institution_hash=institution_hash,
+            propensity_scores=propensity_scores,
+            behavioral_patterns=behavioral_patterns,
+            temporal_trajectory=trajectory_analysis["trajectory_points"],
+            security_signature=security_signature,
+            forecast_horizon=forecast_analysis["forecast_values"],
+            risk_factors=risk_analysis["primary_risks"]
+        )
+        
+        # Store vector
+        self.institutional_vectors[institution_hash] = vector
+        
+        return vector
+    
+    # Helper methods with basic implementations
+    async def _validate_data_security(self, data: Dict[str, Any]) -> bool:
+        """Validate institutional data security"""
+        required_fields = ['institution_id', 'behavioral_metrics']
+        return all(field in data for field in required_fields)
+    
+    async def _analyze_decision_patterns(self, data: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "average_latency": data.get('decision_latency', 0.5),
+            "consensus_requirement": data.get('consensus_level', 0.7),
+            "hierarchy_influence": data.get('hierarchy_weight', 0.6)
+        }
+    
+    async def _analyze_resource_patterns(self, data: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "efficiency": data.get('resource_efficiency', 0.5),
+            "allocation_fairness": data.get('allocation_fairness', 0.5),
+            "budget_flexibility": data.get('budget_flexibility', 0.5)
+        }
+    
+    async def _analyze_risk_patterns(self, data: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "tolerance_level": data.get('risk_tolerance', 0.3),
+            "assessment_rigor": data.get('risk_assessment', 0.7),
+            "mitigation_investment": data.get('risk_mitigation', 0.5)
+        }
+    
+    async def _analyze_innovation_patterns(self, data: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "adoption_rate": data.get('innovation_adoption', 0.4),
+            "experimentation_budget": data.get('experimentation_funding', 0.3),
+            "failure_tolerance": data.get('failure_tolerance', 0.4)
+        }
+    
+    async def _analyze_power_patterns(self, data: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "centralization": data.get('power_centralization', 0.6),
+            "autonomy_level": data.get('unit_autonomy', 0.4),
+            "decision_delegation": data.get('decision_delegation', 0.5)
+        }
+    
+    async def _analyze_historical_trends(self, historical_data: List[Dict]) -> Dict[str, Any]:
+        return {"trend_analysis": "simplified"}  # Placeholder
+    
+    async def _assess_temporal_consistency(self, patterns: Dict[str, Any], historical: List[Dict]) -> float:
+        return 0.8  # Placeholder
+    
+    async def _calculate_pattern_stability(self, patterns: Dict[str, Any]) -> Dict[str, float]:
+        return {
+            "decision_stability": 0.7,
+            "resource_stability": 0.8,
+            "risk_stability": 0.6,
+            "innovation_stability": 0.5,
+            "overall_stability": 0.65
+        }
+    
+    async def _extract_indicator_score(self, indicator: str, patterns: Dict[str, Any]) -> float:
+        """Extract score for specific indicator from patterns"""
+        # Map indicators to pattern categories
+        indicator_map = {
+            'decision_latency': ('decision_making', 'average_latency'),
+            'procedure_complexity': ('decision_making', 'consensus_requirement'),
+            'hierarchy_depth': ('decision_making', 'hierarchy_influence'),
+            'failure_consequences': ('risk_behavior', 'tolerance_level'),
+            'innovation_penalties': ('innovation_trends', 'failure_tolerance'),
+            'success_rewards': ('innovation_trends', 'adoption_rate'),
+            'centralization_trends': ('power_dynamics', 'centralization'),
+            'authority_concentration': ('power_dynamics', 'centralization'),
+            'autonomy_reduction': ('power_dynamics', 'autonomy_level'),
+            'change_rejection_rate': ('innovation_trends', 'adoption_rate'),
+            'tradition_weight': ('decision_making', 'consensus_requirement'),
+            'new_method_adoption': ('innovation_trends', 'adoption_rate')
+        }
+        
+        if indicator in indicator_map:
+            category, metric = indicator_map[indicator]
+            return patterns.get(category, {}).get(metric, 0.5)
+        
+        return 0.5
+    
+    async def _calculate_trend_direction(self, scores: List[float]) -> float:
+        """Calculate trend direction (-1 to 1)"""
+        if len(scores) < 2:
+            return 0.0
+        
+        x = list(range(len(scores)))
+        slope, _, _, _, _ = stats.linregress(x, scores)
+        return float(slope * 10)  # Scale for meaningful range
+    
+    async def _calculate_trend_acceleration(self, scores: List[float]) -> float:
+        """Calculate trend acceleration"""
+        if len(scores) < 3:
+            return 0.0
+        
+        # Simple second derivative approximation
+        first_deriv = np.diff(scores)
+        second_deriv = np.diff(first_deriv)
+        return float(np.mean(second_deriv)) if len(second_deriv) > 0 else 0.0
+    
+    async def _prioritize_risks(self, risks: List[str], scores: Dict[PropensityType, float]) -> List[str]:
+        """Prioritize risks based on propensity scores"""
+        risk_weights = {
+            "high_bureaucratic_inertia": scores.get(PropensityType.BUREAUCRATIC_INERTIA, 0),
+            "extreme_risk_aversion": scores.get(PropensityType.RISK_AVERSION, 0),
+            "power_centralization": scores.get(PropensityType.POWER_CONSOLIDATION, 0),
+            "innovation_stagnation": scores.get(PropensityType.INNOVATION_RESISTANCE, 0),
+            "behavioral_instability": 0.5  # Default medium priority
+        }
+        
+        prioritized = sorted(risks, key=lambda r: risk_weights.get(r, 0), reverse=True)
+        return prioritized
+    
+    def _forecast_point(self, base: float, trend: float, volatility: float, days: int) -> float:
+        """Forecast propensity at specific horizon"""
+        trend_effect = trend * (days / 30)  # Monthly trend scaling
+        noise = volatility * np.random.normal(0, 0.5)  # Random walk component
+        
+        forecast = base + trend_effect + noise
+        return max(0.0, min(1.0, forecast))
+    
+    async def _assess_trend_persistence(self, trajectory_analysis: Dict[str, Any]) -> float:
+        """Assess likelihood of trend persistence"""
+        volatility = trajectory_analysis["volatility"]
+        acceleration = trajectory_analysis["acceleration"]
+        
+        # High volatility and negative acceleration reduce persistence confidence
+        persistence = 1.0 - (volatility * 0.5) - (max(0, -acceleration) * 2)
+        return max(0.0, min(1.0, persistence))
+    
+    async def _handle_analysis_failure(self, data: Dict[str, Any], error: Exception) -> Dict[str, Any]:
+        """Handle analysis failures gracefully"""
+        return {
+            "success": False,
+            "error": str(error),
+            "institution_id": data.get('institution_id', 'unknown'),
+            "fallback_metrics": {"status": "analysis_failed"},
+            "timestamp": datetime.now().isoformat()
+        }
+
+# Custom Exceptions
+class SecurityError(Exception):
+    """Data security validation failed"""
+    pass
+
+class AnalysisError(Exception):
+    """Institutional analysis failed"""
+    pass
+
+# Example usage
+async def demonstrate_propensity_analysis():
+    """Demonstrate institutional propensity analysis"""
+    
+    engine = PropensityEngine(SecurityLevel.QUANTUM_RESISTANT)
+    
+    # Sample institutional data
+    government_agency = {
+        "institution_id": "federal_agency_001",
+        "behavioral_metrics": {
+            "decision_latency": 0.8,
+            "consensus_level": 0.9,
+            "hierarchy_weight": 0.7,
+            "risk_tolerance": 0.2,
+            "risk_assessment": 0.8,
+            "innovation_adoption": 0.3,
+            "power_centralization": 0.6,
+            "unit_autonomy": 0.3
+        },
+        "institution_type": "government_agency",
+        "size_category": "large"
+    }
+    
+    result = await engine.analyze_institutional_behavior(government_agency)
+    
+    print("🏛️ INSTITUTIONAL PROPENSITY ANALYSIS")
+    print(f"📊 Composite Propensity: {result['composite_propensity']:.3f}")
+    print(f"🎯 Volatility: {result['volatility_metric']:.3f}")
+    print(f"⚠️ Primary Risks: {result['primary_risk_factors']}")
+    print(f"🔮 Forecast Confidence: {result['forecast_confidence']:.3f}")
+    
+    return result
+
+if __name__ == "__main__":
+    asyncio.run(demonstrate_propensity_analysis())