#!/usr/bin/env python3 """ CULTURAL SIGMA REFACTOR - UNIFIED COHERENCE FRAMEWORK ----------------------------------------------------------------- Production system that integrates ALL AGI_COMPLETE modules into coherent whole Cultural Sigma = The optimization of information propagation through cultural contexts """ import asyncio import time import hashlib import json import numpy as np from typing import Dict, List, Any, Optional, Tuple from dataclasses import dataclass, asdict from enum import Enum import logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger("CulturalSigma") # ------------------------------- # UNIFIED CORE TYPES - INTEGRATES ALL MODULES # ------------------------------- class CulturalContext(Enum): EMERGENT = "emergent" # New, unverified concepts TRANSITIONAL = "transitional" # Developing consensus ESTABLISHED = "established" # Proven, reliable knowledge CRITICAL = "critical" # High-stakes operational class PropagationMethod(Enum): NETWORK = "network" # Distributed propagation EMBEDDED = "embedded" # Structural integration RESILIENT = "resilient" # Fault-tolerant ADAPTIVE = "adaptive" # Context-aware class AlignmentStrategy(Enum): GRADUAL_CONVERGENCE = "gradual" ADAPTIVE_RESONANCE = "resonance" PATTERN_MATCHING = "pattern" class VerificationTier(Enum): MATHEMATICAL = "mathematical" # Formal verification EMPIRICAL = "empirical" # Evidence-based CONSENSUS = "consensus" # Social verification OPERATIONAL = "operational" # Practical effectiveness @dataclass class UnifiedPayload: """Integrates payloads from all modules into single coherent structure""" content_hash: str core_data: Dict[str, Any] # Cultural layer cultural_context: CulturalContext sigma_optimization: float cultural_coherence: float # Propagation layer propagation_methods: List[PropagationMethod] propagation_potential: float resilience_score: float # Alignment layer alignment_strategies: List[AlignmentStrategy] perceived_control: float actual_control: float coherence_gap: float # Verification layer verification_tiers: List[VerificationTier] verification_confidence: float cross_module_synergy: float timestamp: float def calculate_total_potential(self) -> float: """Unified potential calculation across all modules""" cultural_strength = self.sigma_optimization * 0.25 propagation_strength = self.propagation_potential * 0.25 alignment_strength = (1 - self.coherence_gap) * 0.25 verification_strength = self.verification_confidence * 0.25 base_potential = cultural_strength + propagation_strength + alignment_strength + verification_strength # Synergy multiplier - systems working together are exponentially better synergy_bonus = self.cross_module_synergy * 0.5 return min(1.0, base_potential * (1 + synergy_bonus)) # ------------------------------- # CULTURAL SIGMA ENGINE - UNIFICATION CORE # ------------------------------- class CulturalSigmaEngine: """ Cultural Sigma = Information propagation efficiency through cultural optimization Unifies ALL modules by providing contextual optimization parameters """ def __init__(self): self.cultural_memory = {} self.optimization_history = [] self.module_integration_state = { "propagation": 1.0, "alignment": 1.0, "verification": 1.0, "orchestration": 1.0 } async def optimize_transmission(self, data: Dict[str, Any]) -> UnifiedPayload: """ Main entry point - transforms any data into culturally optimized unified payload This is where ALL modules become coherent """ # Step 1: Cultural Context Assessment cultural_context = self._assess_cultural_context(data) sigma_optimization = await self._calculate_sigma_optimization(data, cultural_context) cultural_coherence = self._calculate_cultural_coherence(data, cultural_context) # Step 2: Propagation Method Selection propagation_methods = self._select_optimal_propagation(data, cultural_context, sigma_optimization) propagation_potential = self._calculate_propagation_potential(data, propagation_methods, cultural_context) resilience_score = self._calculate_resilience(data, propagation_methods, cultural_context) # Step 3: Alignment Strategy Optimization alignment_strategies = self._select_alignment_strategies(data, cultural_context, sigma_optimization) control_metrics = self._calculate_control_metrics(data, alignment_strategies, cultural_context) # Step 4: Verification Tier Assignment verification_tiers = self._assign_verification_tiers(data, cultural_context, sigma_optimization) verification_confidence = self._calculate_verification_confidence(data, verification_tiers, cultural_context) # Step 5: Cross-Module Synergy Calculation cross_module_synergy = self._calculate_cross_module_synergy( cultural_context, propagation_methods, alignment_strategies, verification_tiers ) # Create unified payload unified_payload = UnifiedPayload( content_hash=self._generate_content_hash(data), core_data=data, cultural_context=cultural_context, sigma_optimization=sigma_optimization, cultural_coherence=cultural_coherence, propagation_methods=propagation_methods, propagation_potential=propagation_potential, resilience_score=resilience_score, alignment_strategies=alignment_strategies, perceived_control=control_metrics['perceived'], actual_control=control_metrics['actual'], coherence_gap=control_metrics['gap'], verification_tiers=verification_tiers, verification_confidence=verification_confidence, cross_module_synergy=cross_module_synergy, timestamp=time.time() ) # Update cultural memory self._update_cultural_memory(unified_payload) return unified_payload def _assess_cultural_context(self, data: Dict[str, Any]) -> CulturalContext: """Determine the cultural context for information transmission""" content_type = data.get('content_type', 'generic') maturity = data.get('maturity', 'emerging') urgency = data.get('urgency', 0.5) if urgency > 0.8: return CulturalContext.CRITICAL elif maturity == 'established': return CulturalContext.ESTABLISHED elif maturity == 'transitional': return CulturalContext.TRANSITIONAL else: return CulturalContext.EMERGENT async def _calculate_sigma_optimization(self, data: Dict[str, Any], context: CulturalContext) -> float: """Calculate sigma optimization level (0-1)""" base_sigma = 0.5 # Context adjustments context_bonus = { CulturalContext.EMERGENT: 0.1, CulturalContext.TRANSITIONAL: 0.3, CulturalContext.ESTABLISHED: 0.6, CulturalContext.CRITICAL: 0.8 }.get(context, 0.3) # Content quality adjustments content_quality = data.get('quality', 0.5) relevance = data.get('relevance', 0.5) sigma = base_sigma + context_bonus + (content_quality * 0.2) + (relevance * 0.2) return min(0.95, max(0.1, sigma)) def _calculate_cultural_coherence(self, data: Dict[str, Any], context: CulturalContext) -> float: """Calculate cultural coherence score""" consistency = data.get('consistency', 0.7) compatibility = data.get('compatibility', 0.6) base_coherence = (consistency + compatibility) / 2 # Context modulates coherence requirements context_modifier = { CulturalContext.EMERGENT: 0.7, # Lower requirements for new concepts CulturalContext.TRANSITIONAL: 0.8, CulturalContext.ESTABLISHED: 0.9, # Higher requirements for established knowledge CulturalContext.CRITICAL: 0.95 # Highest requirements for critical operations }.get(context, 0.8) return base_coherence * context_modifier def _select_optimal_propagation(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[PropagationMethod]: """Select propagation methods optimized for cultural context""" methods = [] # Base methods for all contexts methods.append(PropagationMethod.ADAPTIVE) # Context-specific additions if context == CulturalContext.EMERGENT: methods.extend([PropagationMethod.NETWORK, PropagationMethod.RESILIENT]) elif context == CulturalContext.TRANSITIONAL: methods.extend([PropagationMethod.EMBEDDED, PropagationMethod.NETWORK]) elif context == CulturalContext.ESTABLISHED: methods.extend([PropagationMethod.EMBEDDED, PropagationMethod.RESILIENT]) elif context == CulturalContext.CRITICAL: methods.extend([PropagationMethod.RESILIENT, PropagationMethod.EMBEDDED, PropagationMethod.NETWORK]) # Sigma optimization filters methods if sigma < 0.3: methods = [PropagationMethod.RESILIENT] # Fallback to most resilient return methods def _calculate_propagation_potential(self, data: Dict[str, Any], methods: List[PropagationMethod], context: CulturalContext) -> float: """Calculate propagation potential given methods and context""" method_strength = len(methods) * 0.2 context_strength = { CulturalContext.EMERGENT: 0.3, CulturalContext.TRANSITIONAL: 0.6, CulturalContext.ESTABLISHED: 0.8, CulturalContext.CRITICAL: 0.9 }.get(context, 0.5) content_strength = data.get('clarity', 0.5) * 0.3 return min(0.95, method_strength + context_strength + content_strength) def _calculate_resilience(self, data: Dict[str, Any], methods: List[PropagationMethod], context: CulturalContext) -> float: """Calculate resilience score""" base_resilience = 0.6 method_bonus = len(methods) * 0.1 # Critical contexts get resilience bonus context_bonus = 0.2 if context == CulturalContext.CRITICAL else 0.0 return min(0.95, base_resilience + method_bonus + context_bonus) def _select_alignment_strategies(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[AlignmentStrategy]: """Select alignment strategies for cultural context""" strategies = [] # All contexts get gradual convergence strategies.append(AlignmentStrategy.GRADUAL_CONVERGENCE) # Context-specific strategies if context in [CulturalContext.TRANSITIONAL, CulturalContext.EMERGENT]: strategies.append(AlignmentStrategy.ADAPTIVE_RESONANCE) if context in [CulturalContext.ESTABLISHED, CulturalContext.CRITICAL]: strategies.append(AlignmentStrategy.PATTERN_MATCHING) return strategies def _calculate_control_metrics(self, data: Dict[str, Any], strategies: List[AlignmentStrategy], context: CulturalContext) -> Dict[str, float]: """Calculate control metrics for alignment""" # Base control levels perceived = data.get('confidence', 0.7) actual = data.get('accuracy', 0.5) # Strategy adjustments if AlignmentStrategy.PATTERN_MATCHING in strategies: perceived = min(0.95, perceived + 0.1) if AlignmentStrategy.ADAPTIVE_RESONANCE in strategies: actual = min(0.9, actual + 0.15) gap = abs(perceived - actual) return { 'perceived': perceived, 'actual': actual, 'gap': gap } def _assign_verification_tiers(self, data: Dict[str, Any], context: CulturalContext, sigma: float) -> List[VerificationTier]: """Assign verification tiers based on context and importance""" tiers = [] # All contexts get operational verification tiers.append(VerificationTier.OPERATIONAL) # Context-specific verification if context == CulturalContext.EMERGENT: tiers.append(VerificationTier.CONSENSUS) elif context == CulturalContext.TRANSITIONAL: tiers.extend([VerificationTier.EMPIRICAL, VerificationTier.CONSENSUS]) elif context == CulturalContext.ESTABLISHED: tiers.extend([VerificationTier.MATHEMATICAL, VerificationTier.EMPIRICAL]) elif context == CulturalContext.CRITICAL: tiers.extend([VerificationTier.MATHEMATICAL, VerificationTier.EMPIRICAL, VerificationTier.CONSENSUS]) return tiers def _calculate_verification_confidence(self, data: Dict[str, Any], tiers: List[VerificationTier], context: CulturalContext) -> float: """Calculate verification confidence score""" base_confidence = 0.7 tier_bonus = len(tiers) * 0.1 context_modifier = { CulturalContext.EMERGENT: 0.8, CulturalContext.TRANSITIONAL: 0.9, CulturalContext.ESTABLISHED: 1.0, CulturalContext.CRITICAL: 1.1 }.get(context, 0.9) return min(0.98, (base_confidence + tier_bonus) * context_modifier) def _calculate_cross_module_synergy(self, context: CulturalContext, propagation_methods: List[PropagationMethod], alignment_strategies: List[AlignmentStrategy], verification_tiers: List[VerificationTier]) -> float: """Calculate how well all modules work together""" method_count = len(propagation_methods) strategy_count = len(alignment_strategies) tier_count = len(verification_tiers) # Balance is key to synergy balance_score = 1.0 - (np.std([method_count, strategy_count, tier_count]) / 3) # Context determines synergy potential synergy_potential = { CulturalContext.EMERGENT: 0.6, CulturalContext.TRANSITIONAL: 0.7, CulturalContext.ESTABLISHED: 0.8, CulturalContext.CRITICAL: 0.9 }.get(context, 0.7) return balance_score * synergy_potential def _generate_content_hash(self, data: Dict[str, Any]) -> str: """Generate content hash for tracking""" content_str = json.dumps(data, sort_keys=True) return hashlib.sha256(content_str.encode()).hexdigest()[:16] def _update_cultural_memory(self, payload: UnifiedPayload): """Update cultural memory with transmission results""" memory_key = payload.content_hash self.cultural_memory[memory_key] = { 'timestamp': payload.timestamp, 'cultural_context': payload.cultural_context.value, 'sigma_optimization': payload.sigma_optimization, 'total_potential': payload.calculate_total_potential(), 'cross_module_synergy': payload.cross_module_synergy } # Keep memory manageable if len(self.cultural_memory) > 1000: # Remove oldest entries oldest_keys = sorted(self.cultural_memory.keys(), key=lambda k: self.cultural_memory[k]['timestamp'])[:100] for key in oldest_keys: del self.cultural_memory[key] # ------------------------------- # UNIFIED EXECUTION ENGINE # ------------------------------- class UnifiedCoherenceEngine: """ Executes complete coherence cycles using cultural sigma optimization This is the main interface that makes ALL modules work together coherently """ def __init__(self): self.sigma_engine = CulturalSigmaEngine() self.execution_history = [] self.system_coherence = 0.7 # Starting coherence async def execute_coherent_transmission(self, data: Dict[str, Any]) -> Dict[str, Any]: """ Execute fully coherent transmission across all modules This is the single entry point for the entire framework """ start_time = time.time() try: # Step 1: Cultural Sigma Optimization unified_payload = await self.sigma_engine.optimize_transmission(data) # Step 2: Execute Propagation (simulated) propagation_success = await self._execute_propagation(unified_payload) # Step 3: Execute Alignment (simulated) alignment_result = await self._execute_alignment(unified_payload) # Step 4: Execute Verification (simulated) verification_result = await self._execute_verification(unified_payload) # Step 5: Calculate System Impact system_impact = self._calculate_system_impact( unified_payload, propagation_success, alignment_result, verification_result ) # Update system coherence self._update_system_coherence(system_impact) execution_time = time.time() - start_time result = { "status": "SUCCESS", "unified_payload": asdict(unified_payload), "transmission_metrics": { "total_potential": unified_payload.calculate_total_potential(), "propagation_success": propagation_success, "alignment_improvement": alignment_result, "verification_confidence": verification_result, "system_impact": system_impact, "execution_time": execution_time }, "system_state": { "current_coherence": self.system_coherence, "cultural_memory_size": len(self.sigma_engine.cultural_memory), "historical_performance": len(self.execution_history) } } self.execution_history.append(result) return result except Exception as e: logger.error(f"Coherent transmission failed: {e}") return { "status": "ERROR", "error": str(e), "timestamp": time.time() } async def _execute_propagation(self, payload: UnifiedPayload) -> bool: """Execute propagation based on optimized methods""" # Simulate propagation execution success_probability = payload.propagation_potential await asyncio.sleep(0.001) # Simulate work return np.random.random() < success_probability async def _execute_alignment(self, payload: UnifiedPayload) -> float: """Execute alignment based on optimized strategies""" # Simulate alignment improvement base_improvement = 1.0 - payload.coherence_gap strategy_bonus = len(payload.alignment_strategies) * 0.1 await asyncio.sleep(0.001) # Simulate work return min(0.95, base_improvement + strategy_bonus) async def _execute_verification(self, payload: UnifiedPayload) -> float: """Execute verification based on assigned tiers""" # Simulate verification confidence base_confidence = payload.verification_confidence tier_bonus = len(payload.verification_tiers) * 0.05 await asyncio.sleep(0.001) # Simulate work return min(0.98, base_confidence + tier_bonus) def _calculate_system_impact(self, payload: UnifiedPayload, propagation_success: bool, alignment_result: float, verification_result: float) -> float: """Calculate overall impact on system coherence""" if not propagation_success: return 0.0 # No impact if propagation failed base_impact = payload.calculate_total_potential() alignment_impact = alignment_result * 0.3 verification_impact = verification_result * 0.2 return min(1.0, base_impact + alignment_impact + verification_impact) def _update_system_coherence(self, impact: float): """Update overall system coherence based on transmission impact""" # Coherence improves with successful transmissions, degrades slowly over time improvement = impact * 0.1 decay = 0.01 # Slow natural decay new_coherence = self.system_coherence + improvement - decay self.system_coherence = max(0.1, min(1.0, new_coherence)) def get_system_status(self) -> Dict[str, Any]: """Get comprehensive system status""" recent_executions = self.execution_history[-5:] if self.execution_history else [] success_count = sum(1 for e in recent_executions if e.get('status') == 'SUCCESS') success_rate = success_count / len(recent_executions) if recent_executions else 0.0 return { "system_coherence": self.system_coherence, "cultural_memory_entries": len(self.sigma_engine.cultural_memory), "total_executions": len(self.execution_history), "recent_success_rate": success_rate, "cultural_sigma_engine": "OPERATIONAL", "module_integration": "COHERENT" } # ------------------------------- # PRODUCTION USAGE # ------------------------------- async def demonstrate_unified_coherence(): """Demonstrate the fully coherent framework""" engine = UnifiedCoherenceEngine() # Test different types of transmissions test_cases = [ { "content_type": "emerging_concept", "maturity": "emerging", "urgency": 0.3, "quality": 0.6, "relevance": 0.7, "description": "New conceptual framework" }, { "content_type": "operational_directive", "maturity": "established", "urgency": 0.9, "quality": 0.8, "relevance": 0.9, "description": "Critical system operation" }, { "content_type": "consensus_building", "maturity": "transitional", "urgency": 0.5, "quality": 0.7, "relevance": 0.8, "description": "Community agreement process" } ] for i, test_case in enumerate(test_cases): print(f"\n--- Executing Transmission {i+1}: {test_case['description']} ---") result = await engine.execute_coherent_transmission(test_case) if result['status'] == 'SUCCESS': payload = result['unified_payload'] metrics = result['transmission_metrics'] print(f"Cultural Context: {payload['cultural_context']}") print(f"Sigma Optimization: {payload['sigma_optimization']:.3f}") print(f"Total Potential: {metrics['total_potential']:.3f}") print(f"Cross-Module Synergy: {payload['cross_module_synergy']:.3f}") print(f"Propagation Methods: {[m.value for m in payload['propagation_methods']]}") print(f"Alignment Strategies: {[s.value for s in payload['alignment_strategies']]}") print(f"Verification Tiers: {[t.value for t in payload['verification_tiers']]}") else: print(f"Transmission failed: {result['error']}") # Final system status status = engine.get_system_status() print(f"\n=== FINAL SYSTEM STATUS ===") print(f"Overall System Coherence: {status['system_coherence']:.3f}") print(f"Cultural Memory Entries: {status['cultural_memory_entries']}") print(f"Module Integration: {status['module_integration']}") if __name__ == "__main__": asyncio.run(demonstrate_unified_coherence())