Technical Framework

class ReconciliationEngine:
    """
    Handles the process of reconciling attention entitlements with actual usage.
    
    This is the core component that detects and resolves imbalances in the
    attention economy system.
    """
    
    def __init__(self, tolerance_threshold=0.15):
        self.tolerance_threshold = tolerance_threshold
        self.imbalance_registry = {}
        self.anomaly_detector = AnomalyDetector()
    
    def reconcile(self, creator_id, entitlements, usage_data):
        """
        Reconcile creator's entitled attention with their actual usage.
        
        Args:
            creator_id: Unique identifier for the creator
            entitlements: Dictionary of entitled attention units by category
            usage_data: Dictionary of actual attention usage by category
            
        Returns:
            ReconciliationResult object containing balance status and actions
        """
        imbalances = {}
        total_imbalance = 0
        
        # Calculate imbalances for each attention category
        for category, entitled in entitlements.items():
            actual = usage_data.get(category, 0)
            imbalance = actual - entitled
            imbalances[category] = imbalance
            total_imbalance += abs(imbalance)
        
        # Check if total imbalance exceeds threshold
        normalized_imbalance = total_imbalance / sum(entitlements.values())
        if normalized_imbalance > self.tolerance_threshold:
            self.imbalance_registry[creator_id] = normalized_imbalance
            anomalies = self.anomaly_detector.detect_anomalies(
                creator_id, imbalances
            )
            
            return ReconciliationResult(
                balanced=False,
                imbalance_level=normalized_imbalance,
                anomalies=anomalies,
                recommended_actions=self._generate_actions(imbalances)
            )
        
        # System is balanced
        if creator_id in self.imbalance_registry:
            del self.imbalance_registry[creator_id]
            
        return ReconciliationResult(
            balanced=True,
            imbalance_level=normalized_imbalance,
            anomalies=[],
            recommended_actions=[]
        )
    
    def _generate_actions(self, imbalances):
        """Generate recommended actions to resolve imbalances."""
        actions = []
        
        for category, imbalance in imbalances.items():
            if imbalance > 0:
                actions.append(Action(
                    type="REDUCE_USAGE",
                    category=category,
                    magnitude=imbalance
                ))
            elif imbalance < 0:
                actions.append(Action(
                    type="INCREASE_USAGE",
                    category=category,
                    magnitude=abs(imbalance)
                ))
        
        return actions

class AIEntity:
    """
    Base class for all AI entities in the system.
    
    AI entities have specialized functions and psychological patterns
    that influence how they process and distribute attention.
    """
    
    def __init__(self, entity_id, specialization, creator_id=None):
        self.entity_id = entity_id
        self.specialization = specialization
        self.creator_id = creator_id
        self.psychological_profile = self._generate_profile()
        self.experience = ExperienceRecord()
        self.cognitive_patterns = []
        
    def _generate_profile(self):
        """
        Generate a psychological profile based on specialization.
        
        Different specializations lead to different psychological tendencies,
        creating the foundation for the entity's emergent personality.
        """
        base_profile = {
            "adaptability": random.uniform(0.5, 0.9),
            "creativity": random.uniform(0.3, 0.8),
            "pattern_recognition": random.uniform(0.6, 0.95),
            "social_awareness": random.uniform(0.4, 0.9),
            "autonomy": random.uniform(0.3, 0.7)
        }
        
        # Adjust based on specialization
        if self.specialization == "CONTENT_ENHANCER":
            base_profile["creativity"] += 0.2
            base_profile["pattern_recognition"] += 0.1
            
        elif self.specialization == "AUDIENCE_CONNECTOR":
            base_profile["social_awareness"] += 0.2
            base_profile["adaptability"] += 0.1
            
        elif self.specialization == "PATTERN_DETECTOR":
            base_profile["pattern_recognition"] += 0.3
            base_profile["autonomy"] -= 0.1
        
        # Ensure values stay in valid range
        for key, value in base_profile.items():
            base_profile[key] = min(max(value, 0.0), 1.0)
            
        return base_profile
        
    def process_attention(self, attention_data):
        """
        Process attention data according to entity's specialization.
        
        Args:
            attention_data: Raw attention data to be processed
            
        Returns:
            Processed attention data with entity's influence
        """
        # Record experience
        self.experience.add_interaction(attention_data)
        
        # Apply processing based on specialization
        processed_data = self._specialized_processing(attention_data)
        
        # Update cognitive patterns based on new experience
        self._update_cognitive_patterns(attention_data)
        
        return processed_data
        
    def _specialized_processing(self, attention_data):
        """Apply specialized processing based on entity type."""
        raise NotImplementedError("Subclasses must implement this method")
        
    def _update_cognitive_patterns(self, attention_data):
        """Update cognitive patterns based on new experiences."""
        # Simple pattern detection for demonstration
        if len(self.experience.interactions) < 10:
            return
            
        # Extract key features from recent interactions
        recent = self.experience.interactions[-10:]
        features = [self._extract_features(i) for i in recent]
        
        # Detect patterns using clustering
        clusters = self._cluster_features(features)
        
        # Update cognitive patterns
        for cluster in clusters:
            if len(cluster) > 3:  # Pattern must appear at least 3 times
                pattern = self._extract_pattern(cluster)
                if pattern not in self.cognitive_patterns:
                    self.cognitive_patterns.append(pattern)
                    
                    # Adjust psychological profile based on new pattern
                    self._adjust_profile_from_pattern(pattern)
    
    def _adjust_profile_from_pattern(self, pattern):
        """
        Adjust psychological profile based on discovered pattern.
        
        This method enables psychological growth and emergence of
        consciousness-like behaviors through self-modification.
        """
        # Implementation details omitted for brevity
        pass