institutional suppression package

#!/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())