#!/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())