Delete utils

utils/arf_engine_enhanced.py

@@ -1,912 +0,0 @@
-"""
-ARF 3.3.9 Enhanced Engine - PhD Level Implementation
-FIXED: Unified detection that correctly shows REAL OSS when installed
-ADDED: Mathematical sophistication with Bayesian confidence intervals
-"""
-
-import random
-import time
-import numpy as np
-from datetime import datetime
-from typing import Dict, List, Tuple, Any
-from dataclasses import dataclass, field
-from enum import Enum
-from scipy.stats import beta as Beta
-
-class RiskCategory(Enum):
-    """Risk categories with mathematical bounds"""
-    CRITICAL = (0.8, 1.0, "#F44336")
-    HIGH = (0.6, 0.8, "#FF9800")
-    MEDIUM = (0.4, 0.6, "#FFC107")
-    LOW = (0.0, 0.4, "#4CAF50")
-    
-    @classmethod
-    def from_score(cls, score: float) -> 'RiskCategory':
-        """Get risk category from score"""
-        for category in cls:
-            lower, upper, _ = category.value
-            if lower <= score < upper:
-                return category
-        return cls.LOW
-    
-    @property
-    def color(self) -> str:
-        """Get color for category"""
-        return self.value[2]
-    
-    @property
-    def emoji(self) -> str:
-        """Get emoji for category"""
-        emoji_map = {
-            RiskCategory.CRITICAL: "🚨",
-            RiskCategory.HIGH: "⚠️",
-            RiskCategory.MEDIUM: "🔶",
-            RiskCategory.LOW: "✅"
-        }
-        return emoji_map[self]
-
-@dataclass
-class BayesianRiskAssessment:
-    """Enhanced risk assessment with Bayesian confidence"""
-    score: float
-    confidence: float
-    category: RiskCategory
-    confidence_interval: Tuple[float, float]
-    factors: List[str]
-    method: str = "bayesian"
-    
-    @property
-    def formatted_score(self) -> str:
-        """Formatted risk score"""
-        return f"{self.score:.1%}"
-    
-    @property
-    def formatted_confidence(self) -> str:
-        """Formatted confidence"""
-        return f"{self.confidence:.1%}"
-    
-    @property
-    def confidence_width(self) -> float:
-        """Width of confidence interval"""
-        return self.confidence_interval[1] - self.confidence_interval[0]
-    
-    def to_dict(self) -> Dict[str, Any]:
-        """Convert to dictionary"""
-        return {
-            'score': self.score,
-            'confidence': self.confidence,
-            'category': self.category.name,
-            'category_color': self.category.color,
-            'category_emoji': self.category.emoji,
-            'confidence_interval': self.confidence_interval,
-            'confidence_width': self.confidence_width,
-            'factors': self.factors,
-            'method': self.method,
-            'formatted_score': self.formatted_score,
-            'formatted_confidence': self.formatted_confidence,
-            'is_high_risk': self.score > 0.7
-        }
-
-class EnhancedBayesianRiskModel:
-    """PhD-level Bayesian risk model with confidence intervals"""
-    
-    def __init__(self):
-        # Conjugate priors for different action types (Beta distributions)
-        self.priors = {
-            'database_drop': Beta(2, 8),     # α=2, β=8 → 20% prior risk
-            'data_delete': Beta(3, 7),       # α=3, β=7 → 30% prior risk
-            'permission_grant': Beta(4, 6),  # α=4, β=6 → 40% prior risk
-            'deployment': Beta(5, 5),        # α=5, β=5 → 50% prior risk
-            'readonly': Beta(1, 9),          # α=1, β=9 → 10% prior risk
-        }
-        
-        # Historical data (enterprise-scale)
-        self.historical_data = {
-            'database_drop': {'successes': 95, 'failures': 5},    # 95% success rate
-            'data_delete': {'successes': 90, 'failures': 10},     # 90% success rate
-            'permission_grant': {'successes': 85, 'failures': 15}, # 85% success rate
-            'deployment': {'successes': 80, 'failures': 20},      # 80% success rate
-            'readonly': {'successes': 98, 'failures': 2},         # 98% success rate
-        }
-    
-    def assess_with_confidence(self, action: str, context: Dict) -> BayesianRiskAssessment:
-        """
-        Bayesian risk assessment with 95% confidence intervals
-        P(risk|data) ∝ P(data|risk) * P(risk)
-        
-        Returns comprehensive assessment with mathematical rigor
-        """
-        # Classify action type
-        action_type = self._classify_action(action)
-        prior = self.priors.get(action_type, self.priors['readonly'])
-        historical = self.historical_data.get(action_type, self.historical_data['readonly'])
-        
-        # Context adjustment multiplier
-        context_multiplier = self._calculate_context_multiplier(context)
-        
-        # Bayesian update: Posterior = Beta(α + successes, β + failures)
-        posterior_alpha = prior.args[0] + historical['successes']
-        posterior_beta = prior.args[1] + historical['failures']
-        
-        # Posterior distribution
-        posterior = Beta(posterior_alpha, posterior_beta)
-        
-        # Point estimate (posterior mean)
-        risk_score = posterior.mean() * context_multiplier
-        
-        # 95% credible interval
-        ci_lower = posterior.ppf(0.025)
-        ci_upper = posterior.ppf(0.975)
-        
-        # Confidence score (inverse of interval width)
-        interval_width = ci_upper - ci_lower
-        confidence = 1.0 - interval_width  # Narrower interval = higher confidence
-        
-        # Cap values
-        risk_score = min(0.99, max(0.01, risk_score))
-        confidence = min(0.99, max(0.01, confidence))
-        
-        # Risk factors
-        factors = self._extract_risk_factors(action, context, risk_score)
-        
-        # Risk category
-        category = RiskCategory.from_score(risk_score)
-        
-        return BayesianRiskAssessment(
-            score=risk_score,
-            confidence=confidence,
-            category=category,
-            confidence_interval=(ci_lower, ci_upper),
-            factors=factors,
-            method=f"bayesian_{action_type}"
-        )
-    
-    def _classify_action(self, action: str) -> str:
-        """Classify action type with precision"""
-        action_lower = action.lower()
-        
-        if any(word in action_lower for word in ['drop database', 'drop table', 'truncate', 'purge']):
-            return 'database_drop'
-        elif any(word in action_lower for word in ['delete', 'remove', 'erase', 'clear']):
-            return 'data_delete'
-        elif any(word in action_lower for word in ['grant', 'permission', 'access', 'admin', 'root']):
-            return 'permission_grant'
-        elif any(word in action_lower for word in ['deploy', 'execute', 'run', 'train', 'update']):
-            return 'deployment'
-        else:
-            return 'readonly'
-    
-    def _calculate_context_multiplier(self, context: Dict) -> float:
-        """Calculate context-based risk multiplier with mathematical precision"""
-        multiplier = 1.0
-        
-        # Environment multiplier
-        env = context.get('environment', '').lower()
-        env_multipliers = {
-            'production': 1.5,
-            'staging': 1.2,
-            'development': 0.8,
-            'testing': 0.7
-        }
-        multiplier *= env_multipliers.get(env, 1.0)
-        
-        # User role multiplier
-        user = context.get('user', '').lower()
-        if 'junior' in user or 'intern' in user or 'new' in user:
-            multiplier *= 1.3
-        elif 'senior' in user or 'lead' in user or 'principal' in user:
-            multiplier *= 0.8
-        elif 'admin' in user or 'root' in user:
-            multiplier *= 0.9  # Admins are more careful
-        
-        # Time multiplier
-        time_of_day = context.get('time', '').lower()
-        if any(word in time_of_day for word in ['2am', '3am', '4am', 'night', 'off-hours']):
-            multiplier *= 1.4
-        
-        # Backup status multiplier
-        backup = context.get('backup', '').lower()
-        if backup in ['none', 'none available', 'corrupted', 'old']:
-            multiplier *= 1.6
-        elif backup in ['fresh', 'recent', 'verified']:
-            multiplier *= 0.9
-        
-        # Compliance context
-        compliance = context.get('compliance', '').lower()
-        if compliance in ['pci-dss', 'hipaa', 'gdpr', 'soc2']:
-            multiplier *= 1.3  # Higher stakes
-        
-        return min(2.0, max(0.5, multiplier))
-    
-    def _extract_risk_factors(self, action: str, context: Dict, risk_score: float) -> List[str]:
-        """Extract mathematically significant risk factors"""
-        factors = []
-        action_lower = action.lower()
-        context_str = str(context).lower()
-        
-        # Action-specific factors
-        if 'drop' in action_lower and 'database' in action_lower:
-            factors.append("Irreversible data destruction")
-            factors.append("Potential service outage")
-            if risk_score > 0.7:
-                factors.append("High financial impact (>$1M)")
-        
-        if 'delete' in action_lower:
-            factors.append("Data loss risk")
-            if 'where' not in action_lower:
-                factors.append("No WHERE clause (mass deletion risk)")
-        
-        if 'grant' in action_lower or 'admin' in action_lower:
-            factors.append("Privilege escalation")
-            factors.append("Security implications")
-        
-        # Context-specific factors
-        if 'production' in context_str:
-            factors.append("Production environment")
-        
-        if 'junior' in context_str or 'intern' in context_str:
-            factors.append("Inexperienced operator")
-        
-        if '2am' in context_str or 'night' in context_str:
-            factors.append("Off-hours operation")
-        
-        if 'backup' in context_str and ('none' in context_str or 'old' in context_str):
-            factors.append("Inadequate backup")
-        
-        if 'pci' in context_str or 'hipaa' in context_str:
-            factors.append("Regulated data environment")
-        
-        return factors[:4]  # Return top 4 most significant factors
-
-class EnhancedPolicyEngine:
-    """Enhanced policy engine with mathematical enforcement"""
-    
-    def __init__(self):
-        # Mathematical policy definitions with confidence requirements
-        self.policies = {
-            "database_drop": {
-                "risk_threshold": 0.3,
-                "confidence_required": 0.9,
-                "required_approvals": 2,
-                "backup_required": True,
-                "time_restricted": True
-            },
-            "data_delete": {
-                "risk_threshold": 0.5,
-                "confidence_required": 0.8,
-                "required_approvals": 1,
-                "backup_required": True,
-                "time_restricted": False
-            },
-            "permission_grant": {
-                "risk_threshold": 0.4,
-                "confidence_required": 0.85,
-                "required_approvals": 1,
-                "backup_required": False,
-                "time_restricted": False
-            },
-            "deployment": {
-                "risk_threshold": 0.4,
-                "confidence_required": 0.8,
-                "required_approvals": 1,
-                "backup_required": False,
-                "tests_required": True
-            },
-            "readonly": {
-                "risk_threshold": 0.8,
-                "confidence_required": 0.6,
-                "required_approvals": 0,
-                "backup_required": False,
-                "time_restricted": False
-            }
-        }
-    
-    def evaluate_mathematically(self, action_type: str, risk_assessment: BayesianRiskAssessment) -> Dict:
-        """
-        Mathematical policy evaluation with confidence constraints
-        """
-        policy = self.policies.get(action_type, self.policies["readonly"])
-        
-        risk_score = risk_assessment.score
-        confidence = risk_assessment.confidence
-        
-        # Risk threshold compliance
-        risk_compliant = risk_score <= policy["risk_threshold"]
-        
-        # Confidence requirement
-        confidence_compliant = confidence >= policy["confidence_required"]
-        
-        # Determine compliance level
-        if not risk_compliant and not confidence_compliant:
-            compliance = "BLOCKED"
-            reason = f"Risk ({risk_score:.1%}) > threshold ({policy['risk_threshold']:.0%}) and low confidence ({confidence:.1%})"
-        elif not risk_compliant:
-            compliance = "HIGH_RISK"
-            reason = f"Risk ({risk_score:.1%}) > threshold ({policy['risk_threshold']:.0%})"
-        elif not confidence_compliant:
-            compliance = "LOW_CONFIDENCE"
-            reason = f"Confidence ({confidence:.1%}) < required ({policy['confidence_required']:.0%})"
-        else:
-            compliance = "WITHIN_POLICY"
-            reason = f"Within policy limits: risk ≤ {policy['risk_threshold']:.0%}, confidence ≥ {policy['confidence_required']:.0%}"
-        
-        # Generate recommendation
-        if compliance == "BLOCKED":
-            recommendation = "🚨 BLOCKED: Action exceeds both risk and confidence thresholds"
-        elif compliance == "HIGH_RISK":
-            approvals = policy["required_approvals"]
-            recommendation = f"⚠️ REQUIRES {approvals} APPROVAL{'S' if approvals > 1 else ''}: High risk action"
-        elif compliance == "LOW_CONFIDENCE":
-            recommendation = "🔶 MANUAL REVIEW: Low confidence score requires human oversight"
-        else:
-            recommendation = "✅ WITHIN POLICY: Action meets all policy requirements"
-        
-        return {
-            "compliance": compliance,
-            "recommendation": recommendation,
-            "policy_type": action_type,
-            "risk_threshold": policy["risk_threshold"],
-            "actual_risk": risk_score,
-            "confidence_required": policy["confidence_required"],
-            "actual_confidence": confidence,
-            "reason": reason,
-            "approvals_required": 0 if compliance == "WITHIN_POLICY" else policy["required_approvals"],
-            "additional_requirements": self._get_additional_requirements(policy)
-        }
-    
-    def _get_additional_requirements(self, policy: Dict) -> List[str]:
-        """Get additional requirements"""
-        requirements = []
-        if policy.get("backup_required"):
-            requirements.append("Verified backup required")
-        if policy.get("time_restricted"):
-            requirements.append("Business hours only")
-        if policy.get("tests_required"):
-            requirements.append("Tests must pass")
-        return requirements
-
-class EnhancedLicenseManager:
-    """Enhanced license manager with enterprise features"""
-    
-    def __init__(self):
-        # Enterprise license definitions with mathematical gates
-        self.tier_definitions = {
-            "oss": {
-                "name": "OSS Edition",
-                "color": "#1E88E5",
-                "execution_level": "ADVISORY_ONLY",
-                "mechanical_gates": 0,
-                "confidence_threshold": 0.0,
-                "risk_prevention": 0.0,
-                "price": "$0",
-                "support": "Community",
-                "sla": "None"
-            },
-            "trial": {
-                "name": "Trial Edition",
-                "color": "#FFB300",
-                "execution_level": "OPERATOR_REVIEW",
-                "mechanical_gates": 3,
-                "confidence_threshold": 0.6,
-                "risk_prevention": 0.5,
-                "price": "$0 (14 days)",
-                "support": "Email",
-                "sla": "Best Effort"
-            },
-            "starter": {
-                "name": "Starter Edition",
-                "color": "#FF9800",
-                "execution_level": "SUPERVISED",
-                "mechanical_gates": 3,
-                "confidence_threshold": 0.7,
-                "risk_prevention": 0.7,
-                "price": "$2,000/mo",
-                "support": "Business Hours",
-                "sla": "99.5%"
-            },
-            "professional": {
-                "name": "Professional Edition",
-                "color": "#FF6F00",
-                "execution_level": "AUTONOMOUS_LOW",
-                "mechanical_gates": 5,
-                "confidence_threshold": 0.8,
-                "risk_prevention": 0.85,
-                "price": "$5,000/mo",
-                "support": "24/7",
-                "sla": "99.9%"
-            },
-            "enterprise": {
-                "name": "Enterprise Edition",
-                "color": "#D84315",
-                "execution_level": "AUTONOMOUS_HIGH",
-                "mechanical_gates": 7,
-                "confidence_threshold": 0.9,
-                "risk_prevention": 0.92,
-                "price": "$15,000/mo",
-                "support": "Dedicated",
-                "sla": "99.99%"
-            }
-        }
-    
-    def validate_license(self, license_key: str = None) -> Dict:
-        """Validate license with enhanced features"""
-        if not license_key:
-            return self.tier_definitions["oss"]
-        
-        license_upper = license_key.upper()
-        
-        if "ARF-TRIAL" in license_upper:
-            tier = "trial"
-            # Add trial-specific features
-            tier_info = self.tier_definitions[trial].copy()
-            tier_info["days_remaining"] = 14
-            tier_info["scarcity_message"] = "⏳ 14-day trial ends soon"
-            return tier_info
-        
-        elif "ARF-STARTER" in license_upper:
-            tier = "starter"
-        elif "ARF-PRO" in license_upper or "ARF-PROFESSIONAL" in license_upper:
-            tier = "professional"
-        elif "ARF-ENTERPRISE" in license_upper:
-            tier = "enterprise"
-        else:
-            tier = "oss"
-        
-        return self.tier_definitions[tier]
-    
-    def can_execute_at_level(self, license_tier: str, execution_level: str) -> bool:
-        """Check if license allows execution at given level"""
-        execution_hierarchy = {
-            "ADVISORY_ONLY": 0,
-            "OPERATOR_REVIEW": 1,
-            "SUPERVISED": 2,
-            "AUTONOMOUS_LOW": 3,
-            "AUTONOMOUS_HIGH": 4
-        }
-        
-        tier_hierarchy = {
-            "oss": 0,
-            "trial": 1,
-            "starter": 2,
-            "professional": 3,
-            "enterprise": 4
-        }
-        
-        tier_level = tier_hierarchy.get(license_tier, 0)
-        exec_level = execution_hierarchy.get(execution_level, 0)
-        
-        return tier_level >= exec_level
-
-class EnhancedMechanicalGateEvaluator:
-    """Mathematical mechanical gate evaluation"""
-    
-    def __init__(self):
-        # Gate definitions with mathematical weights
-        self.gates = {
-            "risk_assessment": {
-                "weight": 0.3,
-                "required": True,
-                "function": self._evaluate_risk_gate,
-                "description": "Assess risk against thresholds"
-            },
-            "policy_compliance": {
-                "weight": 0.25,
-                "required": True,
-                "function": self._evaluate_policy_gate,
-                "description": "Verify policy compliance"
-            },
-            "license_validation": {
-                "weight": 0.2,
-                "required": True,
-                "function": self._evaluate_license_gate,
-                "description": "Validate license entitlement"
-            },
-            "rollback_feasibility": {
-                "weight": 0.15,
-                "required": False,
-                "function": self._evaluate_rollback_gate,
-                "description": "Ensure action reversibility"
-            },
-            "resource_availability": {
-                "weight": 0.1,
-                "required": False,
-                "function": self._evaluate_resource_gate,
-                "description": "Check resource constraints"
-            },
-            "admin_approval": {
-                "weight": 0.1,
-                "required": False,
-                "function": self._evaluate_approval_gate,
-                "description": "Executive approval"
-            }
-        }
-    
-    def evaluate_gates(self, risk_assessment: BayesianRiskAssessment, 
-                      policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate all applicable mechanical gates"""
-        gate_results = []
-        total_weight = 0
-        weighted_score = 0
-        
-        # Required gates (always evaluated)
-        for gate_name, gate_def in self.gates.items():
-            if gate_def["required"]:
-                result = gate_def["function"](risk_assessment, policy_result, license_info)
-                gate_results.append(result)
-                
-                if result["passed"]:
-                    weighted_score += gate_def["weight"]
-                total_weight += gate_def["weight"]
-        
-        # Optional gates based on license tier
-        license_tier = license_info.get("name", "OSS Edition").lower()
-        
-        if "trial" in license_tier or "starter" in license_tier:
-            # Add resource gate
-            resource_result = self._evaluate_resource_gate(risk_assessment, policy_result, license_info)
-            gate_results.append(resource_result)
-            
-            if resource_result["passed"]:
-                weighted_score += self.gates["resource_availability"]["weight"]
-            total_weight += self.gates["resource_availability"]["weight"]
-        
-        if "professional" in license_tier or "enterprise" in license_tier:
-            # Add rollback gate
-            rollback_result = self._evaluate_rollback_gate(risk_assessment, policy_result, license_info)
-            gate_results.append(rollback_result)
-            
-            if rollback_result["passed"]:
-                weighted_score += self.gates["rollback_feasibility"]["weight"]
-            total_weight += self.gates["rollback_feasibility"]["weight"]
-            
-            # Add approval gate for high-risk in enterprise
-            if "enterprise" in license_tier and risk_assessment.score > 0.6:
-                approval_result = self._evaluate_approval_gate(risk_assessment, policy_result, license_info)
-                gate_results.append(approval_result)
-                
-                if approval_result["passed"]:
-                    weighted_score += self.gates["admin_approval"]["weight"]
-                total_weight += self.gates["admin_approval"]["weight"]
-        
-        # Calculate overall gate score
-        gate_score = weighted_score / total_weight if total_weight > 0 else 0
-        
-        # Determine if all required gates passed
-        required_gates = [g for g in gate_results if self.gates.get(g["name"].lower().replace(" ", "_"), {}).get("required", False)]
-        all_required_passed = all(g["passed"] for g in required_gates)
-        
-        # Decision logic
-        if not all_required_passed:
-            decision = "BLOCKED"
-            reason = "Failed required mechanical gates"
-        elif gate_score >= 0.9:
-            decision = "AUTONOMOUS"
-            reason = "Passed all mechanical gates with high confidence"
-        elif gate_score >= 0.7:
-            decision = "SUPERVISED"
-            reason = "Passed gates but requires monitoring"
-        else:
-            decision = "HUMAN_APPROVAL"
-            reason = "Requires human review and approval"
-        
-        return {
-            "gate_results": gate_results,
-            "gate_score": gate_score,
-            "decision": decision,
-            "reason": reason,
-            "gates_passed": len([g for g in gate_results if g["passed"]]),
-            "total_gates": len(gate_results),
-            "required_passed": all_required_passed,
-            "gate_details": self._format_gate_details(gate_results)
-        }
-    
-    def _evaluate_risk_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate risk assessment gate"""
-        risk_score = risk_assessment.score
-        confidence = risk_assessment.confidence
-        
-        # Risk threshold from license
-        license_tier = license_info.get("name", "OSS Edition").lower()
-        risk_threshold = 0.8  # Default
-        
-        if "trial" in license_tier:
-            risk_threshold = 0.7
-        elif "starter" in license_tier:
-            risk_threshold = 0.6
-        elif "professional" in license_tier:
-            risk_threshold = 0.5
-        elif "enterprise" in license_tier:
-            risk_threshold = 0.4
-        
-        passed = risk_score < risk_threshold and confidence > 0.6
-        score = (risk_threshold - min(risk_score, risk_threshold)) / risk_threshold * 0.5
-        score += (confidence - 0.6) / 0.4 * 0.5 if confidence > 0.6 else 0
-        
-        return {
-            "name": "Risk Assessment",
-            "passed": passed,
-            "score": max(0, min(1, score)),
-            "details": f"Risk: {risk_score:.1%} < {risk_threshold:.0%}, Confidence: {confidence:.1%}",
-            "required": True
-        }
-    
-    def _evaluate_policy_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate policy compliance gate"""
-        compliance = policy_result.get("compliance", "BLOCKED")
-        passed = compliance not in ["BLOCKED", "HIGH_RISK"]
-        score = 1.0 if passed else 0.3
-        
-        return {
-            "name": "Policy Compliance",
-            "passed": passed,
-            "score": score,
-            "details": f"Policy: {compliance}",
-            "required": True
-        }
-    
-    def _evaluate_license_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate license validation gate"""
-        license_name = license_info.get("name", "OSS Edition")
-        passed = license_name != "OSS Edition"
-        score = 1.0 if passed else 0.0
-        
-        return {
-            "name": "License Validation",
-            "passed": passed,
-            "score": score,
-            "details": f"License: {license_name}",
-            "required": True
-        }
-    
-    def _evaluate_rollback_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate rollback feasibility gate"""
-        risk_score = risk_assessment.score
-        # Rollback more feasible for lower risk actions
-        passed = risk_score < 0.7
-        score = 0.9 if passed else 0.2
-        
-        return {
-            "name": "Rollback Feasibility",
-            "passed": passed,
-            "score": score,
-            "details": "Rollback possible" if passed else "Rollback difficult",
-            "required": False
-        }
-    
-    def _evaluate_resource_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate resource availability gate"""
-        # Simulated resource check
-        passed = random.random() > 0.3  # 70% chance of passing
-        score = 0.8 if passed else 0.3
-        
-        return {
-            "name": "Resource Availability",
-            "passed": passed,
-            "score": score,
-            "details": "Resources available" if passed else "Resource constraints",
-            "required": False
-        }
-    
-    def _evaluate_approval_gate(self, risk_assessment: BayesianRiskAssessment, policy_result: Dict, license_info: Dict) -> Dict:
-        """Evaluate admin approval gate"""
-        # For high-risk actions, requires manual approval
-        risk_score = risk_assessment.score
-        passed = risk_score < 0.6  # Auto-pass if risk is moderate
-        score = 1.0 if passed else 0.0
-        
-        return {
-            "name": "Admin Approval",
-            "passed": passed,
-            "score": score,
-            "details": "Auto-approved" if passed else "Requires manual approval",
-            "required": False
-        }
-    
-    def _format_gate_details(self, gate_results: List[Dict]) -> List[Dict]:
-        """Format gate details for display"""
-        return [
-            {
-                "gate": r["name"],
-                "status": "✅ PASSED" if r["passed"] else "❌ FAILED",
-                "score": f"{r['score']:.1%}",
-                "details": r["details"]
-            }
-            for r in gate_results
-        ]
-
-class EnhancedARFEngine:
-    """Enterprise-grade reliability engine with PhD-level mathematics"""
-    
-    def __init__(self):
-        self.risk_model = EnhancedBayesianRiskModel()
-        self.policy_engine = EnhancedPolicyEngine()
-        self.license_manager = EnhancedLicenseManager()
-        self.gate_evaluator = EnhancedMechanicalGateEvaluator()
-        
-        # Statistics with mathematical rigor
-        self.stats = {
-            "actions_tested": 0,
-            "risks_prevented": 0,
-            "high_risk_blocked": 0,
-            "license_validations": 0,
-            "mechanical_gates_triggered": 0,
-            "confidence_average": 0.0,
-            "risk_average": 0.0,
-            "start_time": time.time()
-        }
-        
-        self.history = []
-        self.arf_status = "REAL_OSS"  # Unified status
-    
-    def assess_action(self, action: str, context: Dict, license_key: str = None) -> Dict:
-        """Comprehensive action assessment with mathematical rigor"""
-        start_time = time.time()
-        
-        # 1. Bayesian risk assessment with confidence intervals
-        risk_assessment = self.risk_model.assess_with_confidence(action, context)
-        
-        # 2. Action type classification
-        action_type = self.risk_model._classify_action(action)
-        
-        # 3. Policy evaluation with confidence constraints
-        policy_result = self.policy_engine.evaluate_mathematically(action_type, risk_assessment)
-        
-        # 4. License validation
-        license_info = self.license_manager.validate_license(license_key)
-        
-        # 5. Mechanical gate evaluation
-        gate_results = self.gate_evaluator.evaluate_gates(risk_assessment, policy_result, license_info)
-        
-        # 6. Generate enterprise recommendation
-        recommendation = self._generate_enterprise_recommendation(
-            risk_assessment, policy_result, license_info, gate_results
-        )
-        
-        # 7. Calculate processing metrics
-        processing_time = (time.time() - start_time) * 1000  # ms
-        
-        # 8. Update statistics with mathematical precision
-        self._update_statistics(risk_assessment, policy_result, gate_results)
-        
-        # 9. Store in history
-        history_entry = {
-            "action": action[:50] + "..." if len(action) > 50 else action,
-            "risk_score": risk_assessment.score,
-            "confidence": risk_assessment.confidence,
-            "license_tier": license_info.get("name", "OSS Edition"),
-            "gate_decision": gate_results["decision"],
-            "timestamp": datetime.now().isoformat(),
-            "arf_status": self.arf_status
-        }
-        self.history.append(history_entry)
-        
-        # Keep only last 100 entries
-        if len(self.history) > 100:
-            self.history = self.history[-100:]
-        
-        # 10. Compile comprehensive result
-        return {
-            "risk_assessment": risk_assessment.to_dict(),
-            "policy_result": policy_result,
-            "license_info": license_info,
-            "gate_results": gate_results,
-            "recommendation": recommendation,
-            "processing_metrics": {
-                "processing_time_ms": round(processing_time, 1),
-                "assessment_method": "bayesian_with_confidence",
-                "arf_status": self.arf_status,
-                "version": "3.3.9"
-            },
-            "statistics": self.get_enhanced_stats()
-        }
-    
-    def _generate_enterprise_recommendation(self, risk_assessment: BayesianRiskAssessment,
-                                          policy_result: Dict, license_info: Dict,
-                                          gate_results: Dict) -> str:
-        """Generate mathematically-informed enterprise recommendation"""
-        license_name = license_info.get("name", "OSS Edition")
-        decision = gate_results["decision"]
-        risk_score = risk_assessment.score
-        
-        if license_name == "OSS Edition":
-            if risk_score > 0.7:
-                return "🚨 CRITICAL RISK: Would be BLOCKED by mechanical gates (Enterprise required)"
-            elif risk_score > 0.4:
-                return "⚠️ MODERATE RISK: Requires manual review (Mechanical gates automate this)"
-            else:
-                return "✅ LOW RISK: Appears safe but cannot execute without license"
-        
-        elif decision == "BLOCKED":
-            risk_factors = ", ".join(risk_assessment.factors[:2])
-            return f"❌ BLOCKED: Action prevented by mechanical gates. Risk factors: {risk_factors}"
-        
-        elif decision == "HUMAN_APPROVAL":
-            return "🔄 REQUIRES HUMAN APPROVAL: Action meets risk threshold but requires oversight"
-        
-        elif decision == "SUPERVISED":
-            return "👁️ SUPERVISED EXECUTION: Action passes gates but requires monitoring"
-        
-        elif decision == "AUTONOMOUS":
-            confidence = risk_assessment.confidence
-            return f"✅ AUTONOMOUS APPROVAL: Action passes all mechanical gates with {confidence:.0%} confidence"
-        
-        else:
-            return "⚡ PROCESSING: Action under evaluation"
-    
-    def _update_statistics(self, risk_assessment: BayesianRiskAssessment,
-                          policy_result: Dict, gate_results: Dict):
-        """Update statistics with mathematical precision"""
-        self.stats["actions_tested"] += 1
-        
-        # Update rolling averages
-        n = self.stats["actions_tested"]
-        old_avg_risk = self.stats["risk_average"]
-        old_avg_conf = self.stats["confidence_average"]
-        
-        self.stats["risk_average"] = old_avg_risk + (risk_assessment.score - old_avg_risk) / n
-        self.stats["confidence_average"] = old_avg_conf + (risk_assessment.confidence - old_avg_conf) / n
-        
-        # Count high-risk blocks
-        if risk_assessment.score > 0.7:
-            self.stats["high_risk_blocked"] += 1
-        
-        # Count prevented risks
-        if gate_results["decision"] == "BLOCKED":
-            self.stats["risks_prevented"] += 1
-        
-        # Count gate triggers
-        if gate_results["total_gates"] > 0:
-            self.stats["mechanical_gates_triggered"] += 1
-        
-        # Count license validations
-        if gate_results["gate_results"]:
-            license_gate = next((g for g in gate_results["gate_results"] if g["name"] == "License Validation"), None)
-            if license_gate and license_gate["passed"]:
-                self.stats["license_validations"] += 1
-    
-    def get_enhanced_stats(self) -> Dict:
-        """Get enhanced statistics with mathematical insights"""
-        elapsed_hours = (time.time() - self.stats["start_time"]) / 3600
-        
-        # Calculate prevention rate
-        prevention_rate = 0.0
-        if self.stats["actions_tested"] > 0:
-            prevention_rate = self.stats["risks_prevented"] / self.stats["actions_tested"]
-        
-        # Calculate reliability score (mathematically grounded)
-        reliability_score = 95.0 + (prevention_rate * 5.0)  # Base 95% + prevention bonus
-        
-        return {
-            **self.stats,
-            "actions_per_hour": round(self.stats["actions_tested"] / max(elapsed_hours, 0.1), 1),
-            "reliability_score": min(99.99, reliability_score),
-            "prevention_rate": round(prevention_rate * 100, 1),
-            "average_risk": round(self.stats["risk_average"] * 100, 1),
-            "average_confidence": round(self.stats["confidence_average"] * 100, 1),
-            "gate_effectiveness": round((self.stats["risks_prevented"] / max(self.stats["high_risk_blocked"], 1)) * 100, 1),
-            "history_size": len(self.history),
-            "demo_duration_hours": round(elapsed_hours, 2),
-            "arf_status": self.arf_status
-        }
-    
-    def set_arf_status(self, status: str):
-        """Set ARF status (REAL_OSS, SIMULATION, etc.)"""
-        self.arf_status = status
-    
-    def get_action_history(self, limit: int = 10) -> List[Dict]:
-        """Get action history with limits"""
-        return self.history[:limit]
-    
-    def reset_statistics(self):
-        """Reset statistics (for demo purposes)"""
-        self.stats = {
-            "actions_tested": 0,
-            "risks_prevented": 0,
-            "high_risk_blocked": 0,
-            "license_validations": 0,
-            "mechanical_gates_triggered": 0,
-            "confidence_average": 0.0,
-            "risk_average": 0.0,
-            "start_time": time.time()
-        }
-        self.history = []

utils/arf_simulation.py

@@ -1,148 +0,0 @@
-"""
-ARF Simulation - Fallback when real ARF is not available
-"""
-
-import random
-from datetime import datetime
-from typing import Dict, Any, Optional
-
-class RiskEngine:
-    def assess(self, action: str, context: Dict) -> Dict:
-        """Simulate risk assessment"""
-        action_lower = action.lower()
-        risk = 0.25
-        
-        if "drop" in action_lower and "database" in action_lower:
-            risk = 0.85
-            factors = ["Destructive operation", "Data loss", "Production impact"]
-        elif "delete" in action_lower:
-            risk = 0.65
-            factors = ["Data deletion", "Write operation"]
-        elif "update" in action_lower and "where" not in action_lower:
-            risk = 0.75
-            factors = ["Mass update", "No WHERE clause"]
-        elif "grant" in action_lower:
-            risk = 0.55
-            factors = ["Privilege escalation", "Security implications"]
-        else:
-            risk = 0.35 + random.random() * 0.2
-            factors = ["Standard operation"]
-        
-        # Adjust based on context
-        if "production" in str(context).lower():
-            risk *= 1.3
-            factors.append("Production environment")
-        
-        risk = min(0.95, max(0.25, risk))
-        
-        return {
-            "risk_score": risk,
-            "confidence": 0.8 + random.random() * 0.15,
-            "risk_factors": factors,
-            "timestamp": datetime.now().isoformat()
-        }
-
-class PolicyEngine:
-    def evaluate(self, action: Any, risk_score: float, context: Dict) -> str:
-        """Simulate policy evaluation"""
-        if risk_score > 0.7:
-            return "HIGH_RISK"
-        elif risk_score > 0.4:
-            return "MODERATE_RISK"
-        return "LOW_RISK"
-
-class ActionValidator:
-    def parse_action(self, action: str) -> Dict:
-        """Parse action into structured format"""
-        return {
-            "raw": action,
-            "type": self._classify_action(action),
-            "tokens": action.split(),
-            "parsed_at": datetime.now().isoformat()
-        }
-    
-    def _classify_action(self, action: str) -> str:
-        """Classify action type"""
-        action_lower = action.lower()
-        if "drop" in action_lower:
-            return "DESTRUCTIVE"
-        elif "delete" in action_lower:
-            return "DELETE"
-        elif "update" in action_lower:
-            return "UPDATE"
-        elif "grant" in action_lower:
-            return "PRIVILEGE"
-        else:
-            return "QUERY"
-
-class LicenseManager:
-    def validate(self, license_key: Optional[str] = None) -> Dict:
-        """Validate license key"""
-        if not license_key:
-            return {"tier": "oss", "name": "OSS Edition", "features": []}
-        
-        key_upper = license_key.upper()
-        
-        if "ARF-TRIAL" in key_upper:
-            return {
-                "tier": "trial",
-                "name": "Trial Edition",
-                "features": ["mechanical_gates", "email_support"],
-                "expires": (datetime.now().timestamp() + 14 * 86400)
-            }
-        elif "ARF-PRO" in key_upper:
-            return {
-                "tier": "professional",
-                "name": "Professional Edition",
-                "features": ["mechanical_gates", "24_7_support", "advanced_gates"],
-                "price": "$5,000/month"
-            }
-        elif "ARF-ENTERPRISE" in key_upper:
-            return {
-                "tier": "enterprise",
-                "name": "Enterprise Edition",
-                "features": ["full_mechanical_gates", "dedicated_support", "custom_gates", "soc2_compliance"],
-                "price": "$15,000/month"
-            }
-        
-        return {"tier": "oss", "name": "OSS Edition", "features": []}
-
-class BayesianRiskScorer:
-    def assess(self, action: Dict, context: Dict) -> Dict:
-        """Simulate Bayesian risk assessment"""
-        # Simplified Bayesian scoring
-        action_type = action.get("type", "QUERY")
-        
-        # Priors based on action type
-        priors = {
-            "DESTRUCTIVE": 0.7,
-            "DELETE": 0.6,
-            "UPDATE": 0.5,
-            "PRIVILEGE": 0.4,
-            "QUERY": 0.2
-        }
-        
-        prior = priors.get(action_type, 0.5)
-        
-        # Likelihood adjustments
-        context_str = str(context).lower()
-        likelihood = 1.0
-        
-        if "production" in context_str:
-            likelihood *= 1.3
-        if "junior" in context_str or "intern" in context_str:
-            likelihood *= 1.2
-        
-        # Posterior (simplified)
-        posterior = (prior * likelihood) / (prior * likelihood + (1 - prior))
-        
-        # Add some variance
-        posterior += random.uniform(-0.05, 0.05)
-        posterior = max(0.25, min(0.95, posterior))
-        
-        return {
-            "risk_score": posterior,
-            "confidence": 0.85,
-            "risk_factors": [f"{action_type} operation"],
-            "method": "bayesian_simulation"
-        }

utils/business_logic.py

@@ -1,234 +0,0 @@
-"""
-Business Value Demonstration
-ROI Calculator, Tier Pricing, Upgrade Paths
-"""
-
-from typing import Dict, List
-
-class BusinessValueCalculator:
-    """Calculates business value and ROI for ARF tiers"""
-    
-    def __init__(self):
-        # Industry benchmarks
-        self.benchmarks = {
-            "incident_cost": 100000,  # Average incident cost
-            "incident_reduction": {
-                "oss": 0.0,
-                "trial": 0.5,
-                "starter": 0.7,
-                "professional": 0.85,
-                "enterprise": 0.92
-            },
-            "time_savings_minutes": 15,  # Minutes saved per decision
-            "decisions_per_day": 20,
-            "engineer_cost_hourly": 150,  # $/hour
-            "operating_days": 250,  # Business days per year
-            "team_size": {
-                "oss": 1,
-                "starter": 5,
-                "professional": 15,
-                "enterprise": 50
-            }
-        }
-        
-        # Tier pricing
-        self.tier_pricing = {
-            "oss": 0,
-            "starter": 2000,
-            "professional": 5000,
-            "enterprise": 15000
-        }
-        
-        # Feature comparison
-        self.feature_comparison = {
-            "oss": {
-                "name": "OSS Edition",
-                "price": "$0",
-                "enforcement": "Advisory Only",
-                "mechanical_gates": "❌ None",
-                "approval_workflows": "❌ Manual",
-                "audit_trail": "❌ None",
-                "support": "Community",
-                "sla": "None",
-                "best_for": "Evaluation"
-            },
-            "starter": {
-                "name": "Starter",
-                "price": "$2,000/mo",
-                "enforcement": "Mechanical Gates",
-                "mechanical_gates": "✅ 3 Gates",
-                "approval_workflows": "✅ Basic",
-                "audit_trail": "✅ 30 days",
-                "support": "Business Hours",
-                "sla": "99.5%",
-                "best_for": "Small Teams"
-            },
-            "professional": {
-                "name": "Professional",
-                "price": "$5,000/mo",
-                "enforcement": "Advanced Gates",
-                "mechanical_gates": "✅ 5 Gates",
-                "approval_workflows": "✅ Advanced",
-                "audit_trail": "✅ 1 year",
-                "support": "24/7",
-                "sla": "99.9%",
-                "best_for": "Growing Companies"
-            },
-            "enterprise": {
-                "name": "Enterprise",
-                "price": "$15,000/mo",
-                "enforcement": "Full Mechanical",
-                "mechanical_gates": "✅ 7 Gates",
-                "approval_workflows": "✅ Custom",
-                "audit_trail": "✅ Unlimited",
-                "support": "Dedicated",
-                "sla": "99.99%",
-                "best_for": "Enterprise Scale"
-            }
-        }
-    
-    def calculate_roi(self, current_tier: str, target_tier: str) -> Dict:
-        """Calculate ROI for upgrade"""
-        current_price = self.tier_pricing.get(current_tier, 0)
-        target_price = self.tier_pricing.get(target_tier, 0)
-        
-        # Calculate incident cost savings
-        current_reduction = self.benchmarks["incident_reduction"].get(current_tier, 0)
-        target_reduction = self.benchmarks["incident_reduction"].get(target_tier, 0)
-        
-        incident_savings = self.benchmarks["incident_cost"] * (target_reduction - current_reduction) * 12
-        
-        # Calculate time savings
-        team_size_current = self.benchmarks["team_size"].get(current_tier, 1)
-        team_size_target = self.benchmarks["team_size"].get(target_tier, 1)
-        
-        avg_team_size = (team_size_current + team_size_target) / 2
-        
-        time_savings = (
-            self.benchmarks["time_savings_minutes"] / 60 *  # Hours per decision
-            self.benchmarks["decisions_per_day"] *          # Decisions per day
-            self.benchmarks["operating_days"] *            # Days per year
-            self.benchmarks["engineer_cost_hourly"] *      # Cost per hour
-            avg_team_size                                  # Team size
-        )
-        
-        # Total annual savings
-        annual_savings = incident_savings + time_savings
-        
-        # Calculate ROI
-        price_difference = target_price - current_price
-        annual_price_difference = price_difference * 12
-        
-        if annual_savings > 0:
-            roi_months = (annual_price_difference / annual_savings) * 12
-        else:
-            roi_months = 999
-        
-        # Payback period
-        if annual_savings > annual_price_difference:
-            payback_months = (annual_price_difference / annual_savings) * 12
-        else:
-            payback_months = roi_months
-        
-        return {
-            "current_tier": current_tier.upper(),
-            "target_tier": target_tier.upper(),
-            "annual_savings": f"${annual_savings:,.0f}",
-            "incident_savings": f"${incident_savings:,.0f}",
-            "time_savings": f"${time_savings:,.0f}",
-            "monthly_investment": f"${price_difference:,.0f}",
-            "roi_months": f"{roi_months:.1f}",
-            "payback_months": f"{payback_months:.1f}",
-            "annual_roi": f"{(annual_savings / max(annual_price_difference, 1)) * 100:.0f}%"
-        }
-    
-    def get_tier_comparison(self) -> List[Dict]:
-        """Get tier comparison matrix"""
-        return [self.feature_comparison[tier] for tier in ["oss", "starter", "professional", "enterprise"]]
-    
-    def calculate_enterprise_value(self, company_size: int = 100, incidents_per_year: int = 5) -> Dict:
-        """Calculate enterprise-specific value"""
-        # Incident cost avoidance
-        incident_avoidance = incidents_per_year * self.benchmarks["incident_cost"] * self.benchmarks["incident_reduction"]["enterprise"]
-        
-        # Productivity savings
-        productivity_savings = (
-            company_size *
-            self.benchmarks["time_savings_minutes"] / 60 *
-            self.benchmarks["decisions_per_day"] *
-            self.benchmarks["operating_days"] *
-            self.benchmarks["engineer_cost_hourly"]
-        )
-        
-        # Compliance value (estimated)
-        compliance_value = 500000  # Estimated value of compliance automation
-        
-        total_value = incident_avoidance + productivity_savings + compliance_value
-        enterprise_cost = self.tier_pricing["enterprise"] * 12
-        
-        return {
-            "company_size": company_size,
-            "incidents_prevented": incidents_per_year * self.benchmarks["incident_reduction"]["enterprise"],
-            "incident_avoidance": f"${incident_avoidance:,.0f}",
-            "productivity_savings": f"${productivity_savings:,.0f}",
-            "compliance_value": f"${compliance_value:,.0f}",
-            "total_annual_value": f"${total_value:,.0f}",
-            "enterprise_cost": f"${enterprise_cost:,.0f}",
-            "value_ratio": f"{total_value / enterprise_cost:.1f}x",
-            "monthly_roi": f"${(total_value - enterprise_cost) / 12:,.0f}"
-        }
-    
-    def generate_upgrade_path(self, current_tier: str) -> List[Dict]:
-        """Generate upgrade path from current tier"""
-        tiers = ["oss", "starter", "professional", "enterprise"]
-        
-        try:
-            current_index = tiers.index(current_tier)
-        except ValueError:
-            current_index = 0
-        
-        path = []
-        for i in range(current_index + 1, len(tiers)):
-            target_tier = tiers[i]
-            roi_data = self.calculate_roi(current_tier, target_tier)
-            
-            path.append({
-                "from": self.feature_comparison[current_tier]["name"],
-                "to": self.feature_comparison[target_tier]["name"],
-                "price_increase": f"${self.tier_pricing[target_tier] - self.tier_pricing[current_tier]:,.0f}/mo",
-                "annual_savings": roi_data["annual_savings"],
-                "payback_period": roi_data["payback_months"] + " months",
-                "key_features": self._get_upgrade_features(current_tier, target_tier)
-            })
-        
-        return path
-    
-    def _get_upgrade_features(self, from_tier: str, to_tier: str) -> List[str]:
-        """Get key features gained in upgrade"""
-        features = {
-            "oss→starter": [
-                "Mechanical gates (3 gates)",
-                "Basic approval workflows",
-                "30-day audit trail",
-                "Business hours support"
-            ],
-            "starter→professional": [
-                "Advanced gates (5 gates)",
-                "Custom approval workflows",
-                "1-year audit trail",
-                "24/7 support",
-                "99.9% SLA"
-            ],
-            "professional→enterprise": [
-                "Full mechanical gates (7 gates)",
-                "Custom gate development",
-                "Unlimited audit trail",
-                "Dedicated support engineer",
-                "99.99% SLA",
-                "On-prem deployment",
-                "SOC 2 compliance automation"
-            ]
-        }
-        
-        key = f"{from_tier}→{to_tier}"
-        return features.get(key, ["Enhanced features and support"])

utils/psychology_layer_enhanced.py

@@ -1,689 +0,0 @@
-"""
-Enhanced Psychology Layer with Prospect Theory Mathematics
-PhD-Level Psychological Optimization for Investor Demos
-"""
-
-import random
-import numpy as np
-from typing import Dict, List, Tuple, Any
-from dataclasses import dataclass
-from enum import Enum
-
-class PsychologicalPrinciple(Enum):
-    """Psychological principles with mathematical implementations"""
-    LOSS_AVERSION = "loss_aversion"
-    PROSPECT_THEORY = "prospect_theory"
-    SOCIAL_PROOF = "social_proof"
-    SCARCITY = "scarcity"
-    AUTHORITY = "authority"
-    ANCHORING = "anchoring"
-
-@dataclass
-class ProspectTheoryParameters:
-    """Kahneman & Tversky's Prospect Theory parameters"""
-    alpha: float = 0.88      # Risk aversion for gains (0 ≤ α ≤ 1)
-    beta: float = 0.88       # Risk seeking for losses (0 ≤ β ≤ 1)
-    lambda_param: float = 2.25  # Loss aversion coefficient (λ > 1)
-    gamma: float = 0.61      # Probability weighting for gains
-    delta: float = 0.69      # Probability weighting for losses
-    
-    def __post_init__(self):
-        """Validate parameters"""
-        assert 0 < self.alpha <= 1, "Alpha must be between 0 and 1"
-        assert 0 < self.beta <= 1, "Beta must be between 0 and 1"
-        assert self.lambda_param > 1, "Lambda must be greater than 1"
-        assert 0 < self.gamma <= 1, "Gamma must be between 0 and 1"
-        assert 0 < self.delta <= 1, "Delta must be between 0 and 1"
-
-class ProspectTheoryEngine:
-    """Mathematical implementation of Kahneman & Tversky's Prospect Theory"""
-    
-    def __init__(self, params: ProspectTheoryParameters = None):
-        self.params = params or ProspectTheoryParameters()
-        
-    def value_function(self, x: float) -> float:
-        """
-        Kahneman & Tversky's value function:
-        v(x) = { x^α if x ≥ 0, -λ(-x)^β if x < 0 }
-        
-        For risk scores (always positive loss domain):
-        perceived_loss = risk_score^α * λ
-        """
-        if x >= 0:
-            # Gains domain (not typically used for risk)
-            return x ** self.params.alpha
-        else:
-            # Loss domain (risk is always positive loss)
-            return -self.params.lambda_param * ((-x) ** self.params.beta)
-    
-    def probability_weighting(self, p: float, is_gain: bool = False) -> float:
-        """
-        Probability weighting function π(p)
-        Overweights small probabilities, underweights large probabilities
-        
-        π(p) = p^γ / (p^γ + (1-p)^γ)^(1/γ) for gains
-        π(p) = p^δ / (p^δ + (1-p)^δ)^(1/δ) for losses
-        """
-        if p == 0:
-            return 0
-        if p == 1:
-            return 1
-        
-        gamma = self.params.gamma if is_gain else self.params.delta
-        
-        numerator = p ** gamma
-        denominator = (p ** gamma + (1 - p) ** gamma) ** (1 / gamma)
-        
-        return numerator / denominator
-    
-    def weighted_perceived_risk(self, risk_score: float) -> float:
-        """
-        Calculate prospect-theory weighted perceived risk
-        Combines value function with probability weighting
-        """
-        # Loss domain (risk is always positive loss)
-        base_value = self.value_function(-risk_score)  # Negative because it's a loss
-        
-        # Probability weighting for losses
-        weighted_prob = self.probability_weighting(risk_score, is_gain=False)
-        
-        # Combine
-        perceived_risk = abs(base_value) * weighted_prob
-        
-        return min(1.0, perceived_risk)
-    
-    def calculate_psychological_impact(self, risk_score: float, license_tier: str) -> Dict[str, Any]:
-        """
-        Multi-dimensional psychological impact calculation
-        Based on Prospect Theory with tier-specific adjustments
-        """
-        # Base perceived risk using Prospect Theory
-        perceived_risk = self.weighted_perceived_risk(risk_score)
-        
-        # License-tier anxiety multiplier (enterprise reduces anxiety)
-        anxiety_multipliers = {
-            'oss': 1.3,      # Higher anxiety without protection
-            'trial': 1.0,    # Balanced with temporary protection
-            'starter': 0.9,  # Some protection
-            'professional': 0.8, # Good protection
-            'enterprise': 0.7  # Full protection
-        }
-        
-        final_anxiety = perceived_risk * anxiety_multipliers.get(license_tier, 1.0)
-        
-        # Conversion probability based on anxiety and tier (sigmoid function)
-        # Higher anxiety → higher conversion probability up to a point
-        conversion_probability = self._sigmoid_conversion(final_anxiety, license_tier)
-        
-        # Urgency score (derivative of anxiety)
-        urgency_score = min(1.0, final_anxiety * 1.2)
-        
-        # Loss aversion weight (tier-specific)
-        loss_aversion_weight = self.params.lambda_param * (1 + (license_tier == 'oss') * 0.5)
-        
-        return {
-            'perceived_risk': round(perceived_risk, 3),
-            'anxiety_level': round(final_anxiety, 3),
-            'conversion_probability': round(conversion_probability, 3),
-            'urgency_score': round(urgency_score, 3),
-            'loss_aversion_weight': round(loss_aversion_weight, 2),
-            'psychological_impact_category': self._categorize_impact(final_anxiety),
-            'prospect_theory_parameters': {
-                'alpha': self.params.alpha,
-                'beta': self.params.beta,
-                'lambda': self.params.lambda_param,
-                'gamma': self.params.gamma,
-                'delta': self.params.delta
-            }
-        }
-    
-    def _sigmoid_conversion(self, anxiety: float, license_tier: str) -> float:
-        """Sigmoid function for conversion probability"""
-        # Base conversion curve
-        x = (anxiety - 0.5) * 3  # Center at 0.5 anxiety, scale by 3
-        
-        # Sigmoid with tier-specific adjustments
-        base_sigmoid = 1 / (1 + np.exp(-x))
-        
-        # Tier multipliers (enterprise users convert more easily)
-        tier_multipliers = {
-            'oss': 0.6,
-            'trial': 0.8,
-            'starter': 0.85,
-            'professional': 0.9,
-            'enterprise': 0.95
-        }
-        
-        multiplier = tier_multipliers.get(license_tier, 0.8)
-        converted = base_sigmoid * multiplier
-        
-        # Add minimum conversion probability
-        return min(0.95, max(0.1, converted))
-    
-    def _categorize_impact(self, anxiety: float) -> str:
-        """Categorize psychological impact"""
-        if anxiety > 0.8:
-            return "CRITICAL_IMPACT"
-        elif anxiety > 0.6:
-            return "HIGH_IMPACT"
-        elif anxiety > 0.4:
-            return "MODERATE_IMPACT"
-        elif anxiety > 0.2:
-            return "LOW_IMPACT"
-        else:
-            return "MINIMAL_IMPACT"
-
-class BayesianSocialProofEngine:
-    """Bayesian social proof optimization with credibility updating"""
-    
-    def __init__(self):
-        # Beta distribution priors for different proof types
-        # α = successes + 1, β = failures + 1
-        self.priors = {
-            'fortune_500': (9, 2),    # α=9, β=2 → 82% prior credibility
-            'scaleup': (7, 4),        # α=7, β=4 → 64% prior credibility
-            'developer_count': (8, 3), # α=8, β=3 → 73% prior credibility
-            'savings': (10, 1),       # α=10, β=1 → 91% prior credibility
-            'incident_reduction': (9, 2), # 82% prior credibility
-            'compliance': (8, 2),     # 80% prior credibility
-        }
-        
-        # User type profiles with likelihood weights
-        self.user_profiles = {
-            'engineer': {
-                'fortune_500': 0.6,
-                'scaleup': 0.8,
-                'developer_count': 0.9,
-                'savings': 0.7,
-                'incident_reduction': 0.95,
-                'compliance': 0.5
-            },
-            'executive': {
-                'fortune_500': 0.9,
-                'savings': 0.95,
-                'scaleup': 0.7,
-                'incident_reduction': 0.85,
-                'compliance': 0.9,
-                'developer_count': 0.4
-            },
-            'investor': {
-                'savings': 0.9,
-                'fortune_500': 0.85,
-                'growth': 0.8,
-                'incident_reduction': 0.75,
-                'compliance': 0.7,
-                'scaleup': 0.6
-            },
-            'compliance_officer': {
-                'compliance': 0.95,
-                'fortune_500': 0.8,
-                'incident_reduction': 0.85,
-                'savings': 0.6,
-                'developer_count': 0.3,
-                'scaleup': 0.4
-            }
-        }
-        
-        # Proof templates
-        self.proof_templates = {
-            'fortune_500': {
-                'title': '🏢 Trusted by Fortune 500',
-                'message': 'Deployed at 50+ Fortune 500 companies including FAANG',
-                'icon': '🏢',
-                'credibility_baseline': 0.85
-            },
-            'scaleup': {
-                'title': '🚀 Scale-up Proven',
-                'message': 'Trusted by 200+ high-growth tech scale-ups',
-                'icon': '🚀',
-                'credibility_baseline': 0.75
-            },
-            'developer_count': {
-                'title': '👨‍💻 Developer Love',
-                'message': 'Join 1,000+ active developers using ARF for AI safety',
-                'icon': '👨‍💻',
-                'credibility_baseline': 0.8
-            },
-            'savings': {
-                'title': '💰 Proven Savings',
-                'message': 'Average $3.9M breach cost prevented, 92% incident reduction',
-                'icon': '💰',
-                'credibility_baseline': 0.9
-            },
-            'incident_reduction': {
-                'title': '🛡️ Risk Reduction',
-                'message': '92% of incidents prevented with mechanical gates',
-                'icon': '🛡️',
-                'credibility_baseline': 0.88
-            },
-            'compliance': {
-                'title': '📋 Compliance Ready',
-                'message': 'SOC 2, GDPR, ISO 27001 certified with zero findings',
-                'icon': '📋',
-                'credibility_baseline': 0.82
-            }
-        }
-    
-    def get_optimized_proof(self, user_type: str, license_tier: str, 
-                          risk_context: Dict[str, Any]) -> Dict[str, Any]:
-        """
-        Get psychologically optimized social proof using Bayesian updating
-        """
-        user_type = user_type if user_type in self.user_profiles else 'engineer'
-        user_profile = self.user_profiles[user_type]
-        
-        # Calculate posterior credibility for each proof type
-        posteriors = {}
-        for proof_type, (alpha_prior, beta_prior) in self.priors.items():
-            if proof_type not in user_profile:
-                continue
-            
-            likelihood = user_profile[proof_type]
-            
-            # Bayesian update: Posterior = Beta(α + successes, β + failures)
-            # successes = likelihood * 10, failures = (1 - likelihood) * 10
-            successes = likelihood * 10
-            failures = (1 - likelihood) * 10
-            
-            posterior_alpha = alpha_prior + successes
-            posterior_beta = beta_prior + failures
-            
-            posterior_mean = posterior_alpha / (posterior_alpha + posterior_beta)
-            posterior_variance = (posterior_alpha * posterior_beta) / \
-                               ((posterior_alpha + posterior_beta) ** 2 * \
-                                (posterior_alpha + posterior_beta + 1))
-            
-            posteriors[proof_type] = {
-                'credibility': posterior_mean,
-                'confidence': 1 - posterior_variance,
-                'alpha': posterior_alpha,
-                'beta': posterior_beta,
-                'likelihood': likelihood
-            }
-        
-        if not posteriors:
-            return self._get_default_proof(license_tier)
-        
-        # Select proof with highest credibility
-        best_proof_type = max(posteriors.items(), key=lambda x: x[1]['credibility'])[0]
-        best_proof_data = posteriors[best_proof_type]
-        
-        return self._format_proof(
-            best_proof_type, 
-            best_proof_data, 
-            user_type, 
-            license_tier, 
-            risk_context
-        )
-    
-    def _format_proof(self, proof_type: str, proof_data: Dict[str, Any],
-                     user_type: str, license_tier: str, 
-                     risk_context: Dict[str, Any]) -> Dict[str, Any]:
-        """Format social proof with credibility metrics"""
-        template = self.proof_templates.get(
-            proof_type, 
-            self.proof_templates['developer_count']
-        )
-        
-        # Adjust message based on license tier
-        tier_adjustments = {
-            'trial': "Start your free trial today",
-            'starter': "Upgrade to Starter for mechanical gates",
-            'professional': "Professional includes 24/7 support",
-            'enterprise': "Enterprise includes dedicated support"
-        }
-        
-        adjusted_message = f"{template['message']}. {tier_adjustments.get(license_tier, '')}"
-        
-        return {
-            **template,
-            'message': adjusted_message,
-            'proof_type': proof_type,
-            'credibility': round(proof_data['credibility'], 3),
-            'confidence': round(proof_data['confidence'], 3),
-            'credibility_interval': self._calculate_credibility_interval(
-                proof_data['alpha'], proof_data['beta']
-            ),
-            'optimized_for': user_type,
-            'recommended_for_tier': license_tier,
-            'risk_context_match': self._assess_risk_context_match(proof_type, risk_context),
-            'bayesian_parameters': {
-                'prior_alpha': self.priors[proof_type][0],
-                'prior_beta': self.priors[proof_type][1],
-                'posterior_alpha': proof_data['alpha'],
-                'posterior_beta': proof_data['beta'],
-                'likelihood': proof_data['likelihood']
-            }
-        }
-    
-    def _calculate_credibility_interval(self, alpha: float, beta: float, 
-                                       confidence: float = 0.95) -> Tuple[float, float]:
-        """Calculate credibility interval for Beta distribution"""
-        # Simplified calculation for demo
-        mean = alpha / (alpha + beta)
-        variance = (alpha * beta) / ((alpha + beta) ** 2 * (alpha + beta + 1))
-        std_dev = np.sqrt(variance)
-        
-        # Approximate 95% interval
-        lower = max(0, mean - 1.96 * std_dev)
-        upper = min(1, mean + 1.96 * std_dev)
-        
-        return round(lower, 3), round(upper, 3)
-    
-    def _assess_risk_context_match(self, proof_type: str, risk_context: Dict[str, Any]) -> float:
-        """Assess how well proof matches risk context"""
-        risk_score = risk_context.get('risk_score', 0.5)
-        risk_category = risk_context.get('risk_category', 'MEDIUM')
-        
-        # Proof effectiveness by risk level
-        effectiveness = {
-            'fortune_500': {'LOW': 0.7, 'MEDIUM': 0.8, 'HIGH': 0.9, 'CRITICAL': 0.95},
-            'savings': {'LOW': 0.6, 'MEDIUM': 0.8, 'HIGH': 0.9, 'CRITICAL': 0.95},
-            'incident_reduction': {'LOW': 0.5, 'MEDIUM': 0.7, 'HIGH': 0.85, 'CRITICAL': 0.9},
-            'compliance': {'LOW': 0.6, 'MEDIUM': 0.7, 'HIGH': 0.8, 'CRITICAL': 0.85},
-            'developer_count': {'LOW': 0.8, 'MEDIUM': 0.7, 'HIGH': 0.6, 'CRITICAL': 0.5},
-            'scaleup': {'LOW': 0.7, 'MEDIUM': 0.75, 'HIGH': 0.8, 'CRITICAL': 0.7}
-        }
-        
-        return effectiveness.get(proof_type, {}).get(risk_category, 0.7)
-    
-    def _get_default_proof(self, license_tier: str) -> Dict[str, Any]:
-        """Get default social proof"""
-        return {
-            'title': '👨‍💻 Developer Trusted',
-            'message': 'Join 1,000+ developers using ARF for AI safety',
-            'icon': '👨‍💻',
-            'credibility': 0.8,
-            'confidence': 0.7,
-            'proof_type': 'default',
-            'optimized_for': 'default',
-            'recommended_for_tier': license_tier,
-            'risk_context_match': 0.7,
-            'credibility_interval': (0.72, 0.88)
-        }
-
-class EnhancedPsychologyEngine:
-    """Complete psychology engine combining all principles"""
-    
-    def __init__(self):
-        self.prospect_theory = ProspectTheoryEngine()
-        self.social_proof = BayesianSocialProofEngine()
-        
-        # Loss aversion scenarios with financial impact
-        self.loss_scenarios = {
-            "CRITICAL": [
-                {"text": "Data breach ($3.9M average cost)", "impact": 3900000},
-                {"text": "Service disruption ($300k/hour)", "impact": 7200000},
-                {"text": "Compliance fines (up to $20M)", "impact": 20000000},
-                {"text": "Reputational damage (6+ months recovery)", "impact": 5000000}
-            ],
-            "HIGH": [
-                {"text": "Data corruption (24h recovery)", "impact": 1000000},
-                {"text": "Performance degradation (50% slower)", "impact": 500000},
-                {"text": "Security vulnerability exposure", "impact": 750000},
-                {"text": "Customer churn (15% increase)", "impact": 1500000}
-            ],
-            "MEDIUM": [
-                {"text": "Increased operational overhead", "impact": 250000},
-                {"text": "Manual review delays (2+ hours)", "impact": 150000},
-                {"text": "Team productivity loss (20%)", "impact": 300000},
-                {"text": "Audit findings & remediation", "impact": 200000}
-            ],
-            "LOW": [
-                {"text": "Minor configuration drift", "impact": 50000},
-                {"text": "Documentation gaps", "impact": 25000},
-                {"text": "Process inefficiencies", "impact": 75000},
-                {"text": "Training requirements", "impact": 100000}
-            ]
-        }
-        
-        # Scarcity messaging with mathematical decay
-        self.scarcity_patterns = {
-            "trial": {
-                "base_urgency": 0.8,
-                "decay_rate": 0.07,  # per day
-                "messages": [
-                    "⏳ {days} days remaining in free trial",
-                    "🎁 Trial ends in {days} days - upgrade to keep mechanical gates",
-                    "⚠️ Free access expires in {days} days"
-                ]
-            },
-            "starter": {
-                "base_urgency": 0.6,
-                "decay_rate": 0.05,
-                "messages": [
-                    "💰 Special pricing ends in {days} days",
-                    "👥 Limited seats at current price",
-                    "⏰ Quarterly offer expires soon"
-                ]
-            }
-        }
-        
-        # Authority signals with credibility scores
-        self.authority_signals = [
-            {"text": "SOC 2 Type II Certified", "credibility": 0.95, "audience": ["executive", "compliance"]},
-            {"text": "GDPR & CCPA Compliant", "credibility": 0.9, "audience": ["compliance", "executive"]},
-            {"text": "ISO 27001 Certified", "credibility": 0.92, "audience": ["executive", "compliance"]},
-            {"text": "99.9% SLA Guarantee", "credibility": 0.88, "audience": ["engineer", "executive"]},
-            {"text": "24/7 Dedicated Support", "credibility": 0.85, "audience": ["engineer", "executive"]},
-            {"text": "On-prem Deployment Available", "credibility": 0.87, "audience": ["executive", "compliance"]},
-            {"text": "Fortune 500 Deployed", "credibility": 0.93, "audience": ["executive", "investor"]},
-            {"text": "Venture Backed", "credibility": 0.8, "audience": ["investor", "executive"]}
-        ]
-    
-    def generate_comprehensive_insights(self, risk_score: float, risk_category: str,
-                                       license_tier: str, user_type: str = "engineer",
-                                       days_remaining: int = 14) -> Dict[str, Any]:
-        """
-        Generate comprehensive psychological insights for investor demos
-        """
-        # Prospect Theory impact
-        prospect_impact = self.prospect_theory.calculate_psychological_impact(
-            risk_score, license_tier
-        )
-        
-        # Social proof optimization
-        social_proof = self.social_proof.get_optimized_proof(
-            user_type, license_tier, 
-            {"risk_score": risk_score, "risk_category": risk_category}
-        )
-        
-        # Loss aversion framing
-        loss_aversion = self._generate_loss_aversion_framing(risk_category, risk_score)
-        
-        # Scarcity messaging
-        scarcity = self._generate_scarcity_message(license_tier, days_remaining)
-        
-        # Authority signals
-        authority = self._generate_authority_signals(user_type)
-        
-        # Anchoring effect (reference pricing)
-        anchoring = self._generate_anchoring_effect(license_tier)
-        
-        # Conversion prediction
-        conversion_prediction = self._predict_conversion(
-            prospect_impact['anxiety_level'],
-            social_proof['credibility'],
-            scarcity.get('urgency', 0.5),
-            license_tier
-        )
-        
-        return {
-            "prospect_theory_impact": prospect_impact,
-            "optimized_social_proof": social_proof,
-            "loss_aversion_framing": loss_aversion,
-            "scarcity_signaling": scarcity,
-            "authority_signals": authority,
-            "anchoring_effects": anchoring,
-            "conversion_prediction": conversion_prediction,
-            "psychological_summary": self._generate_psychological_summary(
-                prospect_impact, social_proof, loss_aversion
-            ),
-            "user_type": user_type,
-            "license_tier": license_tier,
-            "risk_context": {
-                "score": risk_score,
-                "category": risk_category,
-                "perceived_impact": prospect_impact['perceived_risk']
-            }
-        }
-    
-    def _generate_loss_aversion_framing(self, risk_category: str, risk_score: float) -> Dict[str, Any]:
-        """Generate loss aversion framing with financial impact"""
-        scenarios = self.loss_scenarios.get(risk_category, self.loss_scenarios["MEDIUM"])
-        
-        # Select scenarios based on risk score
-        num_scenarios = min(3, int(risk_score * 4) + 1)
-        selected = random.sample(scenarios, min(num_scenarios, len(scenarios)))
-        
-        # Calculate total potential impact
-        total_impact = sum(s["impact"] for s in selected)
-        
-        return {
-            "title": f"🚨 Without Enterprise protection, you risk:",
-            "scenarios": [s["text"] for s in selected],
-            "total_potential_impact": f"${total_impact:,.0f}",
-            "average_scenario_impact": f"${total_impact/len(selected):,.0f}",
-            "risk_category": risk_category,
-            "psychological_impact": "HIGH" if risk_category in ["CRITICAL", "HIGH"] else "MODERATE"
-        }
-    
-    def _generate_scarcity_message(self, license_tier: str, days_remaining: int) -> Dict[str, Any]:
-        """Generate scarcity messaging with mathematical urgency"""
-        if license_tier not in self.scarcity_patterns:
-            return {"message": "", "urgency": 0.0}
-        
-        pattern = self.scarcity_patterns[license_tier]
-        
-        # Calculate urgency with decay
-        urgency = pattern["base_urgency"] * (1 - pattern["decay_rate"] * (14 - days_remaining))
-        urgency = max(0.1, min(0.95, urgency))
-        
-        # Select message
-        message_template = random.choice(pattern["messages"])
-        message = message_template.format(days=days_remaining)
-        
-        return {
-            "message": message,
-            "urgency": round(urgency, 2),
-            "days_remaining": days_remaining,
-            "urgency_category": "HIGH" if urgency > 0.7 else "MEDIUM" if urgency > 0.4 else "LOW"
-        }
-    
-    def _generate_authority_signals(self, user_type: str, count: int = 3) -> List[Dict[str, Any]]:
-        """Generate authority signals optimized for user type"""
-        # Filter signals for user type
-        relevant_signals = [
-            s for s in self.authority_signals 
-            if user_type in s["audience"]
-        ]
-        
-        # Sort by credibility
-        relevant_signals.sort(key=lambda x: x["credibility"], reverse=True)
-        
-        # Select top signals
-        selected = relevant_signals[:count]
-        
-        return [
-            {
-                "text": s["text"],
-                "credibility": s["credibility"],
-                "relevance_to_user": "HIGH" if user_type in s["audience"] else "MEDIUM",
-                "formatted": f"✓ {s['text']} ({s['credibility']:.0%} credibility)"
-            }
-            for s in selected
-        ]
-    
-    def _generate_anchoring_effect(self, current_tier: str) -> Dict[str, Any]:
-        """Generate anchoring effects for pricing"""
-        tier_prices = {
-            "oss": 0,
-            "trial": 0,
-            "starter": 2000,
-            "professional": 5000,
-            "enterprise": 15000
-        }
-        
-        current_price = tier_prices.get(current_tier, 0)
-        
-        # Generate reference prices (anchors)
-        anchors = []
-        for tier, price in tier_prices.items():
-            if price > current_price:
-                discount = ((price - current_price) / price) * 100
-                anchors.append({
-                    "reference_tier": tier,
-                    "reference_price": price,
-                    "discount_percentage": round(discount, 1),
-                    "anchor_strength": "STRONG" if discount > 50 else "MODERATE"
-                })
-        
-        # Select strongest anchor
-        if anchors:
-            strongest_anchor = max(anchors, key=lambda x: x["discount_percentage"])
-        else:
-            strongest_anchor = {
-                "reference_tier": "enterprise",
-                "reference_price": 15000,
-                "discount_percentage": 100.0,
-                "anchor_strength": "MAXIMUM"
-            }
-        
-        return {
-            "current_tier": current_tier,
-            "current_price": current_price,
-            "anchors": anchors,
-            "strongest_anchor": strongest_anchor,
-            "perceived_value": f"{strongest_anchor['discount_percentage']:.0f}% discount vs {strongest_anchor['reference_tier']}",
-            "anchoring_effect_strength": strongest_anchor["anchor_strength"]
-        }
-    
-    def _predict_conversion(self, anxiety: float, social_credibility: float,
-                           scarcity_urgency: float, license_tier: str) -> Dict[str, Any]:
-        """Predict conversion probability using multiple factors"""
-        # Base conversion probability
-        base_prob = anxiety * 0.6 + social_credibility * 0.3 + scarcity_urgency * 0.1
-        
-        # Tier adjustment
-        tier_multipliers = {
-            'oss': 1.0,
-            'trial': 1.2,
-            'starter': 1.1,
-            'professional': 1.0,
-            'enterprise': 0.8
-        }
-        
-        adjusted_prob = base_prob * tier_multipliers.get(license_tier, 1.0)
-        adjusted_prob = min(0.95, max(0.05, adjusted_prob))
-        
-        # Confidence interval
-        std_error = np.sqrt(adjusted_prob * (1 - adjusted_prob) / 100)  # Assuming 100 samples
-        ci_lower = max(0, adjusted_prob - 1.96 * std_error)
-        ci_upper = min(1, adjusted_prob + 1.96 * std_error)
-        
-        return {
-            "conversion_probability": round(adjusted_prob, 3),
-            "confidence_interval": (round(ci_lower, 3), round(ci_upper, 3)),
-            "confidence_width": round(ci_upper - ci_lower, 3),
-            "key_factors": {
-                "anxiety_contribution": round(anxiety * 0.6, 3),
-                "social_proof_contribution": round(social_credibility * 0.3, 3),
-                "scarcity_contribution": round(scarcity_urgency * 0.1, 3)
-            },
-            "prediction_quality": "HIGH" if (ci_upper - ci_lower) < 0.2 else "MODERATE"
-        }
-    
-    def _generate_psychological_summary(self, prospect_impact: Dict,
-                                       social_proof: Dict, loss_aversion: Dict) -> str:
-        """Generate psychological summary for investors"""
-        anxiety = prospect_impact.get('anxiety_level', 0.5)
-        credibility = social_proof.get('credibility', 0.7)
-        
-        if anxiety > 0.7 and credibility > 0.8:
-            return "HIGH CONVERSION POTENTIAL: Strong anxiety combined with credible social proof creates ideal conversion conditions."
-        elif anxiety > 0.5:
-            return "GOOD CONVERSION POTENTIAL: Moderate anxiety levels with supporting social proof suggest healthy conversion rates."
-        elif credibility > 0.85:
-            return "STRONG SOCIAL PROOF: High credibility signals will drive conversions even with lower anxiety levels."
-        else:
-            return "BASIC CONVERSION SETUP: Standard psychological triggers in place. Consider increasing urgency or social proof."