BSM Real-Time Behavior Predictor (Conceptual)
Note: This is a theoretical design document. No actual implementation exists. This specification demonstrates how the Behavioral State Model could be applied to real-time behavior prediction with streaming data processing and adaptive learning capabilities.
System Architecture
graph TB
A[User Events Stream] --> B[State Detector]
B --> C[BSM Calculator]
C --> D[Prediction Engine]
D --> E[Intervention Trigger]
E --> F[Outcome Tracker]
F --> G[Model Updater]
G --> C
H[Historical Data] --> I[State Profiler]
I --> C
J[Context Stream] --> K[Environment Monitor]
K --> C
Core Implementation
Real-Time State Detection
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import asyncio
from datetime import datetime
import json
@dataclass
class BehavioralState:
"""Complete behavioral state at a moment in time"""
timestamp: datetime
user_id: str
personality: float # 0-10 scale
perception: float
emotions: float
abilities: float
social_status: float
motivations: float
social_environment: float
physical_environment: float
@property
def identity_score(self) -> float:
"""Calculate identity component score"""
return (self.personality + self.perception + self.emotions +
self.abilities + self.social_status + self.motivations) / 6
@property
def environment_score(self) -> float:
"""Calculate environment component score"""
return (self.social_environment + self.physical_environment) / 2
@property
def minimum_component(self) -> Tuple[str, float]:
"""Find the limiting component"""
components = {
'personality': self.personality,
'perception': self.perception,
'emotions': self.emotions,
'abilities': self.abilities,
'social_status': self.social_status,
'motivations': self.motivations,
'social_environment': self.social_environment,
'physical_environment': self.physical_environment
}
return min(components.items(), key=lambda x: x[1])
def to_vector(self) -> np.ndarray:
"""Convert to numpy vector for ML operations"""
return np.array([
self.personality, self.perception, self.emotions,
self.abilities, self.social_status, self.motivations,
self.social_environment, self.physical_environment
])
class BSMRealTimePredictor:
"""Real-time behavior prediction using Behavioral State Model"""
def __init__(self, update_frequency_seconds: int = 1):
self.update_frequency = update_frequency_seconds
self.state_buffer = {} # user_id -> list of recent states
self.prediction_cache = {}
self.component_weights = self._initialize_weights()
self.behavior_thresholds = {}
self.is_running = False
def _initialize_weights(self) -> Dict[str, Dict[str, float]]:
"""Initialize domain-specific component weights"""
return {
'default': {
'personality': 0.15,
'perception': 0.15,
'emotions': 0.10,
'abilities': 0.15,
'social_status': 0.10,
'motivations': 0.15,
'social_environment': 0.10,
'physical_environment': 0.10
},
'healthcare': {
'personality': 0.10,
'perception': 0.20, # Beliefs about treatment crucial
'emotions': 0.15, # Emotional state affects adherence
'abilities': 0.15,
'social_status': 0.05,
'motivations': 0.20, # Motivation critical for health behaviors
'social_environment': 0.10,
'physical_environment': 0.05
},
'ecommerce': {
'personality': 0.20, # Shopping preferences important
'perception': 0.15,
'emotions': 0.15, # Impulse buying
'abilities': 0.10, # Financial ability
'social_status': 0.15, # Status signaling
'motivations': 0.10,
'social_environment': 0.10,
'physical_environment': 0.05
}
}
async def start_prediction_stream(self):
"""Start the real-time prediction service"""
self.is_running = True
await asyncio.gather(
self._process_state_updates(),
self._generate_predictions(),
self._monitor_drift()
)
async def _process_state_updates(self):
"""Process incoming state updates"""
while self.is_running:
# In production, this would connect to Kafka/Kinesis
updates = await self._fetch_state_updates()
for update in updates:
user_id = update['user_id']
new_state = self._calculate_current_state(update)
# Update buffer
if user_id not in self.state_buffer:
self.state_buffer[user_id] = []
self.state_buffer[user_id].append(new_state)
# Keep only recent states (last 100)
if len(self.state_buffer[user_id]) > 100:
self.state_buffer[user_id].pop(0)
await asyncio.sleep(self.update_frequency)
def _calculate_current_state(self, update: Dict) -> BehavioralState:
"""Calculate current behavioral state from update"""
# Extract component scores from various data sources
return BehavioralState(
timestamp=datetime.now(),
user_id=update['user_id'],
personality=self._extract_personality(update),
perception=self._extract_perception(update),
emotions=self._extract_emotions(update),
abilities=self._extract_abilities(update),
social_status=self._extract_social_status(update),
motivations=self._extract_motivations(update),
social_environment=self._extract_social_environment(update),
physical_environment=self._extract_physical_environment(update)
)
def predict_behavior_likelihood(
self,
user_id: str,
behavior: str,
domain: str = 'default'
) -> Dict:
"""Predict likelihood of specific behavior"""
if user_id not in self.state_buffer or not self.state_buffer[user_id]:
return {'error': 'No state data available'}
current_state = self.state_buffer[user_id][-1]
weights = self.component_weights.get(domain, self.component_weights['default'])
# Calculate behavior-specific scores
behavior_scores = self._calculate_behavior_scores(behavior, current_state)
# Apply minimum component rule
min_component, min_score = current_state.minimum_component
if min_score < 3: # Critical threshold
return {
'likelihood': 0.05,
'limiting_factor': min_component,
'limiting_score': min_score,
'recommendation': f'Address {min_component} (current: {min_score}/10)'
}
# Calculate weighted likelihood
likelihood = sum(
behavior_scores[comp] * weights[comp]
for comp in behavior_scores
) / 10
# Apply behavior-specific thresholds
threshold = self.behavior_thresholds.get(behavior, 0.5)
return {
'likelihood': likelihood,
'will_perform': likelihood > threshold,
'confidence': self._calculate_confidence(user_id, behavior),
'state_scores': behavior_scores,
'recommendations': self._generate_recommendations(behavior_scores)
}
def _calculate_behavior_scores(
self,
behavior: str,
state: BehavioralState
) -> Dict[str, float]:
"""Calculate behavior-specific component scores"""
# In production, these would be learned from data
behavior_profiles = {
'purchase': {
'personality': lambda s: s.personality * 1.2 if s.personality > 6 else s.personality * 0.8,
'perception': lambda s: s.perception,
'emotions': lambda s: min(10, s.emotions * 1.5) if s.emotions > 5 else s.emotions,
'abilities': lambda s: s.abilities,
'social_status': lambda s: s.social_status * 1.1,
'motivations': lambda s: s.motivations,
'social_environment': lambda s: s.social_environment,
'physical_environment': lambda s: s.physical_environment
},
'exercise': {
'personality': lambda s: s.personality,
'perception': lambda s: s.perception * 1.2,
'emotions': lambda s: s.emotions * 0.9 if s.emotions < 4 else s.emotions,
'abilities': lambda s: s.abilities * 1.3,
'social_status': lambda s: s.social_status,
'motivations': lambda s: s.motivations * 1.4,
'social_environment': lambda s: s.social_environment * 1.2,
'physical_environment': lambda s: s.physical_environment * 1.5
}
}
profile = behavior_profiles.get(behavior, {})
scores = {}
for component in ['personality', 'perception', 'emotions', 'abilities',
'social_status', 'motivations', 'social_environment',
'physical_environment']:
base_score = getattr(state, component)
if component in profile:
scores[component] = min(10, profile[component](state))
else:
scores[component] = base_score
return scores
async def _generate_predictions(self):
"""Generate continuous predictions for all tracked users"""
while self.is_running:
predictions = {}
for user_id in self.state_buffer:
user_predictions = {}
# Predict likelihood for registered behaviors
for behavior in self.get_tracked_behaviors():
prediction = self.predict_behavior_likelihood(user_id, behavior)
user_predictions[behavior] = prediction
predictions[user_id] = user_predictions
# Update cache
self.prediction_cache = predictions
# Trigger interventions for high-likelihood behaviors
await self._trigger_interventions(predictions)
await asyncio.sleep(self.update_frequency)
async def _trigger_interventions(self, predictions: Dict):
"""Trigger interventions based on predictions"""
interventions = []
for user_id, user_predictions in predictions.items():
for behavior, prediction in user_predictions.items():
if prediction.get('likelihood', 0) > 0.8:
intervention = {
'user_id': user_id,
'behavior': behavior,
'type': 'encourage',
'trigger': self._select_optimal_trigger(prediction)
}
interventions.append(intervention)
elif prediction.get('likelihood', 0) < 0.3:
# Identify barriers and suggest modifications
intervention = {
'user_id': user_id,
'behavior': behavior,
'type': 'enable',
'modifications': prediction.get('recommendations', [])
}
interventions.append(intervention)
# Send interventions to execution system
if interventions:
await self._execute_interventions(interventions)
Advanced Features
State Trajectory Analysis
class StateTrajectoryAnalyzer:
"""Analyze behavioral state trajectories over time"""
def __init__(self):
self.trajectory_patterns = {}
self.transition_model = self._build_transition_model()
def analyze_trajectory(
self,
states: List[BehavioralState],
target_behavior: str
) -> Dict:
"""Analyze state trajectory towards target behavior"""
if len(states) < 2:
return {'error': 'Insufficient state history'}
# Calculate state velocity (rate of change)
velocities = []
for i in range(1, len(states)):
prev_vector = states[i-1].to_vector()
curr_vector = states[i].to_vector()
time_delta = (states[i].timestamp - states[i-1].timestamp).total_seconds()
velocity = (curr_vector - prev_vector) / time_delta
velocities.append(velocity)
# Analyze trajectory direction
avg_velocity = np.mean(velocities, axis=0)
# Project future state
last_state = states[-1]
projected_state = last_state.to_vector() + (avg_velocity * 3600) # 1 hour projection
# Calculate convergence to optimal state for behavior
optimal_state = self._get_optimal_state(target_behavior)
current_distance = np.linalg.norm(last_state.to_vector() - optimal_state)
projected_distance = np.linalg.norm(projected_state - optimal_state)
is_converging = projected_distance < current_distance
return {
'current_state': last_state,
'state_velocity': avg_velocity.tolist(),
'projected_state_1h': projected_state.tolist(),
'is_converging': is_converging,
'convergence_rate': (current_distance - projected_distance) / current_distance,
'estimated_time_to_behavior': self._estimate_time_to_behavior(
last_state, avg_velocity, optimal_state
),
'trajectory_stability': np.std(velocities),
'recommendations': self._generate_trajectory_recommendations(
last_state, avg_velocity, optimal_state
)
}
def _estimate_time_to_behavior(
self,
current: BehavioralState,
velocity: np.ndarray,
target: np.ndarray
) -> Optional[float]:
"""Estimate time until behavioral state reaches threshold"""
if np.linalg.norm(velocity) < 0.001: # Nearly stationary
return None
# Simple linear projection (could use more sophisticated models)
distance = np.linalg.norm(current.to_vector() - target)
speed = np.linalg.norm(velocity)
# Check if moving towards target
direction = target - current.to_vector()
if np.dot(velocity, direction) <= 0: # Moving away
return None
return distance / speed # Time in seconds
Adaptive Learning System
class AdaptiveBSMModel:
"""Self-improving BSM model based on outcome feedback"""
def __init__(self):
self.component_importance = defaultdict(lambda: defaultdict(float))
self.prediction_history = []
self.learning_rate = 0.01
async def learn_from_outcome(
self,
prediction: Dict,
actual_outcome: bool
):
"""Update model based on prediction accuracy"""
error = float(actual_outcome) - prediction['likelihood']
# Update component importance weights
state_scores = prediction['state_scores']
for component, score in state_scores.items():
# Gradient descent update
gradient = error * score / 10
self.component_importance[prediction['behavior']][component] += (
self.learning_rate * gradient
)
# Store for analysis
self.prediction_history.append({
'prediction': prediction,
'outcome': actual_outcome,
'error': error,
'timestamp': datetime.now()
})
# Periodic model evaluation
if len(self.prediction_history) % 100 == 0:
await self._evaluate_model_performance()
def get_adapted_weights(self, behavior: str) -> Dict[str, float]:
"""Get learned weights for specific behavior"""
base_weights = {
'personality': 0.125,
'perception': 0.125,
'emotions': 0.125,
'abilities': 0.125,
'social_status': 0.125,
'motivations': 0.125,
'social_environment': 0.125,
'physical_environment': 0.125
}
# Apply learned adjustments
for component in base_weights:
adjustment = self.component_importance[behavior][component]
base_weights[component] = max(0, min(1, base_weights[component] + adjustment))
# Normalize
total = sum(base_weights.values())
return {k: v/total for k, v in base_weights.items()}
Integration Examples
Streaming Data Pipeline
# Kafka integration for real-time state updates
from kafka import KafkaConsumer, KafkaProducer
import json
class BSMStreamProcessor:
def __init__(self, kafka_config):
self.consumer = KafkaConsumer(
'user-events',
'context-updates',
bootstrap_servers=kafka_config['servers'],
value_deserializer=lambda m: json.loads(m.decode('utf-8'))
)
self.producer = KafkaProducer(
bootstrap_servers=kafka_config['servers'],
value_serializer=lambda v: json.dumps(v).encode('utf-8')
)
self.predictor = BSMRealTimePredictor()
async def process_stream(self):
"""Process streaming events and generate predictions"""
async for message in self.consumer:
if message.topic == 'user-events':
await self._process_user_event(message.value)
elif message.topic == 'context-updates':
await self._process_context_update(message.value)
# Generate predictions
predictions = await self.predictor.get_current_predictions()
# Publish high-confidence predictions
for user_id, behaviors in predictions.items():
for behavior, prediction in behaviors.items():
if prediction['likelihood'] > 0.7:
self.producer.send('behavior-predictions', {
'user_id': user_id,
'behavior': behavior,
'prediction': prediction,
'timestamp': datetime.now().isoformat()
})
REST API Endpoints
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI(title="BSM Real-Time Predictor API")
class StateUpdate(BaseModel):
user_id: str
component: str
value: float
metadata: Optional[Dict] = None
class PredictionRequest(BaseModel):
user_id: str
behavior: str
domain: str = 'default'
@app.post("/state/update")
async def update_behavioral_state(update: StateUpdate):
"""Update a specific component of user's behavioral state"""
try:
predictor.update_component(
update.user_id,
update.component,
update.value,
update.metadata
)
return {"status": "updated", "component": update.component}
except Exception as e:
raise HTTPException(status_code=400, detail=str(e))
@app.get("/predict/{user_id}/{behavior}")
async def get_behavior_prediction(
user_id: str,
behavior: str,
domain: str = 'default'
):
"""Get real-time behavior prediction"""
prediction = predictor.predict_behavior_likelihood(user_id, behavior, domain)
if 'error' in prediction:
raise HTTPException(status_code=404, detail=prediction['error'])
return prediction
@app.get("/trajectory/{user_id}/{behavior}")
async def analyze_state_trajectory(user_id: str, behavior: str):
"""Analyze user's behavioral state trajectory"""
analyzer = StateTrajectoryAnalyzer()
states = predictor.get_state_history(user_id)
if not states:
raise HTTPException(status_code=404, detail="No state history found")
return analyzer.analyze_trajectory(states, behavior)
@app.websocket("/ws/predictions/{user_id}")
async def websocket_predictions(websocket: WebSocket, user_id: str):
"""WebSocket endpoint for real-time prediction updates"""
await websocket.accept()
try:
while True:
# Send predictions every second
predictions = predictor.get_user_predictions(user_id)
await websocket.send_json(predictions)
await asyncio.sleep(1)
except WebSocketDisconnect:
pass
Performance Optimization
State Caching Strategy
class OptimizedStateCache:
"""High-performance state caching with TTL and compression"""
def __init__(self, max_size: int = 10000, ttl_seconds: int = 3600):
self.cache = {}
self.max_size = max_size
self.ttl = ttl_seconds
self.access_counts = defaultdict(int)
self.last_cleanup = datetime.now()
def set(self, key: str, state: BehavioralState):
"""Store state with automatic eviction"""
if len(self.cache) >= self.max_size:
self._evict_lru()
self.cache[key] = {
'state': state,
'timestamp': datetime.now(),
'compressed': self._compress_state(state)
}
def get(self, key: str) -> Optional[BehavioralState]:
"""Retrieve state with TTL check"""
if key not in self.cache:
return None
entry = self.cache[key]
if (datetime.now() - entry['timestamp']).seconds > self.ttl:
del self.cache[key]
return None
self.access_counts[key] += 1
return entry['state']
def _compress_state(self, state: BehavioralState) -> bytes:
"""Compress state for memory efficiency"""
import zlib
data = state.to_vector().tobytes()
return zlib.compress(data, level=6)
Monitoring and Observability
# Prometheus metrics for monitoring
from prometheus_client import Counter, Histogram, Gauge
# Metrics
prediction_counter = Counter('bsm_predictions_total', 'Total predictions made')
prediction_latency = Histogram('bsm_prediction_duration_seconds', 'Prediction latency')
active_states = Gauge('bsm_active_states', 'Number of active behavioral states')
component_scores = Histogram(
'bsm_component_scores',
'Distribution of component scores',
['component', 'behavior']
)
# Grafana dashboard config
dashboard_config = {
"dashboard": {
"title": "BSM Real-Time Predictor",
"panels": [
{
"title": "Prediction Rate",
"targets": [{"expr": "rate(bsm_predictions_total[5m])"}]
},
{
"title": "Component Score Distribution",
"targets": [{"expr": "histogram_quantile(0.5, bsm_component_scores)"}]
},
{
"title": "Active Users",
"targets": [{"expr": "bsm_active_states"}]
}
]
}
}
Deployment Guide
Docker Configuration
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["uvicorn", "bsm_api:app", "--host", "0.0.0.0", "--port", "8000"]
Kubernetes Deployment
apiVersion: apps/v1
kind: Deployment
metadata:
name: bsm-predictor
spec:
replicas: 3
selector:
matchLabels:
app: bsm-predictor
template:
metadata:
labels:
app: bsm-predictor
spec:
containers:
- name: predictor
image: bsm-predictor:latest
ports:
- containerPort: 8000
env:
- name: KAFKA_BROKERS
value: "kafka:9092"
resources:
requests:
memory: "512Mi"
cpu: "500m"
limits:
memory: "1Gi"
cpu: "1000m"
---
apiVersion: v1
kind: Service
metadata:
name: bsm-predictor-service
spec:
selector:
app: bsm-predictor
ports:
- port: 80
targetPort: 8000
type: LoadBalancer
Next Steps
- Integrate with Behavior Prediction API
- Apply to BMF Scoring Algorithm
- Create BSM-specific LLM prompts
- Review BSM Integration Guide
Licensing: This specification is protected under the Behavioral Strategy Specification License (BSSL). Commercial use requires explicit licensing. See full licensing terms for details.
Attribution: Based on the Behavioral State Model by Jason Hreha. Learn more at The Behavioral Scientist.