# User-Centric Behavior Logging - OOP Design for Evidence Graphs

## Paradigm Shift: From System-Centric to User-Centric

Traditional logging focuses on **what the system did**. User-Centric Behavior Logging (UCBL) focuses on **why the user did it** and **what it means in context**. This fundamental shift requires rethinking our object model to capture behavioral evidence rather than technical events.

## Core OOP Principles for Evidence Graphs

### 1. Behavioral Entities as First-Class Objects

```python
class UserIntent:
    """Captures the inferred intent behind user actions"""
    def __init__(self, intent_type, confidence_score, evidence_chain):
        self.intent_type = intent_type  # "search", "explore", "troubleshoot"
        self.confidence_score = confidence_score
        self.evidence_chain = evidence_chain
        self.temporal_context = None
        self.emotional_indicators = []

class BehaviorPattern:
    """Represents recurring user behavior patterns"""
    def __init__(self, pattern_id, frequency, context_triggers):
        self.pattern_id = pattern_id
        self.frequency = frequency
        self.context_triggers = context_triggers
        self.deviation_threshold = 0.3
        self.learning_weight = 0.1

class CognitiveLoad:
    """Measures user cognitive burden during interactions"""
    def __init__(self, complexity_score, time_pressure, error_frequency):
        self.complexity_score = complexity_score
        self.time_pressure = time_pressure
        self.error_frequency = error_frequency
        self.frustration_indicators = []
```

### 2. Evidence Graph Nodes as Behavioral Objects

```python
class BehaviorNode:
    """Base class for all behavioral evidence nodes"""
    def __init__(self, node_id, timestamp, user_context):
        self.node_id = node_id
        self.timestamp = timestamp
        self.user_context = user_context
        self.confidence_score = 1.0
        self.relationships = []
        self.evidence_weight = 1.0
    
    def add_relationship(self, target_node, relationship_type, strength):
        """Creates weighted edges in the evidence graph"""
        pass

class DecisionPoint(BehaviorNode):
    """Captures moments where users make choices"""
    def __init__(self, node_id, timestamp, user_context, available_options, chosen_option):
        super().__init__(node_id, timestamp, user_context)
        self.available_options = available_options
        self.chosen_option = chosen_option
        self.decision_time = None
        self.hesitation_indicators = []

class EmotionalState(BehaviorNode):
    """Infers emotional context from behavioral signals"""
    def __init__(self, node_id, timestamp, user_context, emotion_type, intensity):
        super().__init__(node_id, timestamp, user_context)
        self.emotion_type = emotion_type  # "frustrated", "confident", "confused"
        self.intensity = intensity
        self.triggers = []
        self.duration = None
```

### 3. Contextual Behavior Collectors

```python
class BehaviorCollector:
    """Abstract base for collecting behavioral evidence"""
    def __init__(self, collection_strategy):
        self.collection_strategy = collection_strategy
        self.behavior_buffer = []
        self.pattern_detector = PatternDetector()
    
    def collect_evidence(self, user_action, system_response, context):
        """Transforms raw events into behavioral evidence"""
        pass

class InteractionCollector(BehaviorCollector):
    """Collects evidence from user-system interactions"""
    def collect_evidence(self, user_action, system_response, context):
        # Infer intent from action sequence
        intent = self._infer_intent(user_action, context)
        
        # Measure cognitive load
        cognitive_load = self._measure_cognitive_load(user_action, system_response)
        
        # Detect emotional indicators
        emotional_state = self._detect_emotional_state(user_action, context)
        
        return BehaviorEvidence(intent, cognitive_load, emotional_state)

class NavigationCollector(BehaviorCollector):
    """Collects evidence from user navigation patterns"""
    def collect_evidence(self, navigation_path, dwell_times, backtrack_events):
        # Analyze exploration vs goal-directed behavior
        exploration_score = self._calculate_exploration_score(navigation_path)
        
        # Detect confusion indicators
        confusion_indicators = self._detect_confusion(backtrack_events, dwell_times)
        
        return NavigationEvidence(exploration_score, confusion_indicators)
```

## Outside-the-Box Ideas for Evidence Graphs

### 1. Temporal Behavior Clustering

```python
class TemporalBehaviorCluster:
    """Groups behaviors by temporal proximity and semantic similarity"""
    def __init__(self, time_window, semantic_threshold):
        self.time_window = time_window
        self.semantic_threshold = semantic_threshold
        self.behavior_clusters = []
    
    def cluster_behaviors(self, behavior_stream):
        """Creates temporal-semantic clusters for evidence graph nodes"""
        # Group behaviors that occur within time windows
        # and share semantic similarity (intent, context, outcome)
        pass

class BehaviorMomentum:
    """Captures the 'flow' of user behavior over time"""
    def __init__(self):
        self.velocity = 0.0  # Speed of task completion
        self.acceleration = 0.0  # Change in velocity
        self.direction_changes = []  # Pivot points in behavior
    
    def calculate_momentum(self, behavior_sequence):
        """Measures behavioral momentum for evidence weighting"""
        pass
```

### 2. Predictive Behavior Modeling

```python
class BehaviorPredictor:
    """Predicts likely next behaviors based on current evidence"""
    def __init__(self, model_type="markov_chain"):
        self.model_type = model_type
        self.transition_probabilities = {}
        self.confidence_thresholds = {}
    
    def predict_next_behavior(self, current_context, evidence_history):
        """Predicts next likely user behavior with confidence scores"""
        pass
    
    def generate_counterfactual_evidence(self, actual_behavior, predicted_behavior):
        """Creates evidence nodes for 'what didn't happen' - valuable for anomaly detection"""
        pass

class BehaviorAnomaly:
    """Detects and models behavioral anomalies as special evidence nodes"""
    def __init__(self, anomaly_type, deviation_score, baseline_behavior):
        self.anomaly_type = anomaly_type
        self.deviation_score = deviation_score
        self.baseline_behavior = baseline_behavior
        self.potential_causes = []
```

### 3. Multi-Dimensional Behavior Spaces

```python
class BehaviorSpace:
    """N-dimensional space where each dimension represents a behavioral aspect"""
    def __init__(self, dimensions):
        self.dimensions = dimensions  # ["efficiency", "exploration", "confidence", "expertise"]
        self.behavior_vectors = []
        self.cluster_centers = []
    
    def map_behavior_to_space(self, behavior_evidence):
        """Maps behavioral evidence to coordinates in behavior space"""
        pass
    
    def find_behavior_neighbors(self, target_behavior, similarity_threshold):
        """Finds similar behaviors in the space for evidence graph connections"""
        pass

class BehaviorPersona:
    """Dynamic user persona that evolves based on behavioral evidence"""
    def __init__(self, user_id):
        self.user_id = user_id
        self.expertise_levels = {}  # Domain-specific expertise
        self.behavior_preferences = {}
        self.adaptation_rate = 0.1
        self.confidence_patterns = []
    
    def update_persona(self, new_evidence):
        """Updates persona based on new behavioral evidence"""
        pass
```

### 4. Collaborative Behavior Networks

```python
class BehaviorInfluence:
    """Models how user behaviors influence each other"""
    def __init__(self, influencer_id, influenced_id, influence_type, strength):
        self.influencer_id = influencer_id
        self.influenced_id = influenced_id
        self.influence_type = influence_type  # "mimicry", "avoidance", "learning"
        self.strength = strength
        self.temporal_decay = 0.95
    
class CollectiveBehavior:
    """Captures emergent behaviors from user groups"""
    def __init__(self, group_id, behavior_type):
        self.group_id = group_id
        self.behavior_type = behavior_type
        self.emergence_threshold = 0.6
        self.participants = []
        self.synchronization_score = 0.0
```

### 5. Contextual Behavior Adaptation

```python
class ContextualBehaviorAdapter:
    """Adapts behavior interpretation based on situational context"""
    def __init__(self):
        self.context_weights = {}
        self.adaptation_rules = []
        self.context_history = []
    
    def adapt_behavior_interpretation(self, raw_behavior, context):
        """Adjusts behavior interpretation based on context"""
        # Same action can mean different things in different contexts
        # e.g., rapid clicking might indicate urgency or frustration
        pass

class BehaviorContext:
    """Rich context object that influences behavior interpretation"""
    def __init__(self):
        self.temporal_context = {}  # Time of day, day of week, season
        self.social_context = {}    # Presence of others, social pressure
        self.task_context = {}      # Task complexity, urgency, importance
        self.environmental_context = {}  # Device, location, network conditions
        self.emotional_context = {}     # Stress level, mood, energy
```

## Evidence Graph Integration Patterns

### 1. Behavior-Driven Graph Construction

```python
class BehaviorGraphBuilder:
    """Constructs evidence graphs from behavioral data"""
    def __init__(self, graph_strategy="temporal_semantic"):
        self.graph_strategy = graph_strategy
        self.node_factory = BehaviorNodeFactory()
        self.edge_calculator = BehaviorEdgeCalculator()
    
    def build_evidence_graph(self, behavior_stream):
        """Builds evidence graph optimized for behavioral analysis"""
        nodes = self._create_behavior_nodes(behavior_stream)
        edges = self._calculate_behavior_relationships(nodes)
        return EvidenceGraph(nodes, edges)

class BehaviorQuery:
    """Query interface optimized for behavioral evidence retrieval"""
    def __init__(self, graph):
        self.graph = graph
        self.query_optimizer = BehaviorQueryOptimizer()
    
    def find_behavior_patterns(self, pattern_template, time_range):
        """Finds recurring behavioral patterns in the evidence graph"""
        pass
    
    def trace_decision_path(self, decision_point, max_depth=5):
        """Traces the behavioral path leading to a decision"""
        pass
    
    def detect_behavior_anomalies(self, baseline_period, anomaly_threshold):
        """Detects anomalous behavioral patterns"""
        pass
```

### 2. Real-Time Behavior Streaming

```python
class BehaviorStream:
    """Real-time stream of behavioral evidence"""
    def __init__(self, stream_processor):
        self.stream_processor = stream_processor
        self.behavior_buffer = CircularBuffer(max_size=10000)
        self.pattern_matcher = RealTimePatternMatcher()
    
    def process_behavior_event(self, event):
        """Processes behavioral events in real-time"""
        behavior_evidence = self._extract_behavior_evidence(event)
        self.behavior_buffer.add(behavior_evidence)
        
        # Real-time pattern detection
        patterns = self.pattern_matcher.match_patterns(behavior_evidence)
        
        # Update evidence graph incrementally
        self._update_evidence_graph(behavior_evidence, patterns)
```

## Key Insights for Evidence Graph Design

1. **Behavior as Primary Entity**: Instead of logging system events, log behavioral evidence with rich context
2. **Temporal Clustering**: Group related behaviors by time and semantic similarity
3. **Predictive Evidence**: Include "what didn't happen" as valuable evidence nodes
4. **Multi-Dimensional Mapping**: Map behaviors to n-dimensional spaces for similarity analysis
5. **Dynamic Personas**: Build evolving user models from behavioral evidence
6. **Contextual Adaptation**: Same behavior means different things in different contexts
7. **Collaborative Networks**: Model how users influence each other's behaviors
8. **Real-Time Processing**: Stream behavioral evidence for immediate insights

This OOP design transforms UCBLLogger from a system-centric tool into a behavioral intelligence platform that captures the rich context of human-computer interaction for evidence graph analysis.