Behavior Prediction API Specification (Theoretical)

Note: This is a theoretical specification and conceptual blueprint. No actual API implementation currently exists. This document serves as a guide for how behavior prediction systems could be built using the Behavioral State Model.

⚖️ License Notice: This specification is protected intellectual property. Commercial use, including building products or services based on these specifications, requires explicit licensing from Jason Hreha. See licensing terms for details.

This document presents a theoretical API specification demonstrating how behavior prediction systems could be architected using Jason Hreha’s Behavioral State Model. This conceptual framework is designed to guide future implementations in ML systems, analytics platforms, and decision engines.

Core Prediction Model

The Behavioral State Model (BSM)

This proposed API specification is based on Jason Hreha’s Behavioral State Model, which posits that at any moment, individuals exist in a particular “Behavioral State” determined by eight components that make certain behaviors more or less likely.

Mathematical Formulation

The Behavioral State Score (BSS) for a user performing behavior B:

BSS(B) = min(S₁...S₈) × α + avg(S₁...S₈) × (1-α)

Where:

  • min(S₁...S₈) is the minimum component score (BSM’s limiting factor principle)
  • avg(S₁...S₈) is the weighted average of all components
  • α is the limiting factor weight (typically 0.3-0.5)
  • S₁…S₈ are the scores for each BSM component

The Eight BSM Components

Identity Components (Internal - 6 components)

  1. Personality (S₁): Core traits, values, interests, and preferences
    • Does this behavior align with who they are?
    • Does it fit their value system?
    • Does it match their preferences?
  2. Perception (S₂): Beliefs about the behavior and its outcomes
    • Do they believe this behavior is valuable/fun/helpful?
    • Do they think it’s possible to perform?
    • What do they believe will happen if they do it?
  3. Emotions (S₃): Current emotional state
    • Are they in an emotional state conducive to this behavior?
    • Does their mood support or inhibit action?
    • Is the emotional context appropriate?
  4. Abilities (S₄): Skills and capabilities
    • Do they have the skills to perform this behavior?
    • Are they physically/mentally capable?
    • Do they have any special talents for this behavior?
  5. Social Status/Situation (S₅): Social position and role
    • Does their social position encourage this behavior?
    • Will the behavior affect their social status?
    • Does it align with their social role?
  6. Motivations (S₆): Drives, goals, and incentives
    • Are they motivated to perform this behavior?
    • Are there rewards or incentives?
    • Does it help achieve their goals?

Context Components (External - 2 components)

  1. Social Environment (S₇): Social norms and pressures
    • Is there social pressure for/against this behavior?
    • What are the cultural expectations?
    • How do peers influence this behavior?
  2. Physical Environment (S₈): Practical constraints and affordances
    • Is it physically possible to perform the behavior here?
    • Are the necessary resources available?
    • Does the environment enable or constrain?

Component Scoring Rubrics

Each component is scored 0-10 based on behavior-specific criteria:

Personality (0-10)

  • 0-2: Completely misaligned with identity
  • 3-4: Conflicts with core values
  • 5-6: Neutral fit with personality
  • 7-8: Good alignment with traits
  • 9-10: Perfect expression of identity

Perception (0-10)

  • 0-2: Believes behavior is harmful/impossible
  • 3-4: Skeptical about value/feasibility
  • 5-6: Neutral beliefs
  • 7-8: Positive beliefs about outcomes
  • 9-10: Strong conviction in benefits

Emotions (0-10)

  • 0-2: Emotional state blocks behavior
  • 3-4: Emotions create resistance
  • 5-6: Emotionally neutral
  • 7-8: Emotions support behavior
  • 9-10: Emotions strongly drive behavior

Abilities (0-10)

  • 0-2: Completely unable to perform
  • 3-4: Major skill gaps
  • 5-6: Basic capability
  • 7-8: Competent ability
  • 9-10: Expert mastery

Social Status/Situation (0-10)

  • 0-2: Behavior threatens social position
  • 3-4: Social costs outweigh benefits
  • 5-6: Socially neutral
  • 7-8: Enhances social standing
  • 9-10: Strongly reinforces status

Motivations (0-10)

  • 0-2: No motivation whatsoever
  • 3-4: Weak or conflicting motivations
  • 5-6: Moderate motivation
  • 7-8: Strong alignment with goals
  • 9-10: Intense drive to perform

Social Environment (0-10)

  • 0-2: Strong social opposition
  • 3-4: Social resistance
  • 5-6: Socially neutral
  • 7-8: Social support present
  • 9-10: Strong social reinforcement

Physical Environment (0-10)

  • 0-2: Environment prevents behavior
  • 3-4: Major environmental barriers
  • 5-6: Neutral environment
  • 7-8: Environment facilitates behavior
  • 9-10: Optimal environmental support

BSM Key Principles

  1. Identity as Primary Driver: The six Identity components (Personality, Perception, Emotions, Abilities, Social Status, Motivations) are the primary determinants of behavior, not just context.

  2. Minimum Component Rule: If ANY component is insufficient (typically <3), the behavior becomes highly unlikely regardless of other component scores.

  3. Dynamic Interaction: Behavioral States change based on internal fluctuations and external changes, creating windows of opportunity for behavior change.

Behavioral State Classification

Based on the BSS, considering both average and minimum scores:

  • Any component < 3.0: Blocked (behavior extremely unlikely due to limiting factor)
  • Min ≥ 3, Avg < 5.0: Low likelihood (significant barriers exist)
  • Min ≥ 5, Avg 5.0-7.0: Moderate likelihood (behavior possible)
  • Min ≥ 6, Avg > 7.0: High likelihood (behavior probable)

Confidence Intervals

Each prediction includes uncertainty quantification:

CI = BSS ± 1.96 * σ/√n

Where σ represents component score variance and n is the measurement reliability.

Proposed API Endpoints

The following sections outline how a behavior prediction API could be structured:

1. Predict Single Behavior

Proposed Endpoint:

POST /api/v1/predict/behavior

Example Request Body:

{
  "user_id": "uuid",
  "behavior": {
    "id": "behavior_uuid",
    "type": "engagement|conversion|retention",
    "complexity": "low|medium|high"
  },
  "context": {
    "timestamp": "ISO-8601",
    "platform": "web|mobile|desktop",
    "session_data": {},
    "environmental_factors": {}
  },
  "behavioral_state": {
    "personality": 7.5,
    "perception": 6.0,
    "emotions": 5.5,
    "abilities": 8.0,
    "social_status": 6.5,
    "motivations": 7.0,
    "social_environment": 6.0,
    "physical_environment": 8.5
  },
  "domain": "healthcare|technology|education|general"
}

Response:

{
  "prediction": {
    "behavioral_state_score": 6.73,
    "behavior_likelihood": 0.73,
    "confidence_interval": [6.38, 7.08],
    "minimum_component": {
      "name": "emotions",
      "score": 5.5,
      "status": "limiting_factor"
    },
    "identity_score": 6.58,
    "context_score": 7.25,
    "component_breakdown": {
      "personality": 7.5,
      "perception": 6.0,
      "emotions": 5.5,
      "abilities": 8.0,
      "social_status": 6.5,
      "motivations": 7.0,
      "social_environment": 6.0,
      "physical_environment": 8.5
    },
    "limiting_components": [
      {
        "component": "emotions",
        "score": 5.5,
        "category": "identity"
      },
      {
        "component": "perception",
        "score": 6.0,
        "category": "identity"
      },
      {
        "component": "social_environment",
        "score": 6.0,
        "category": "context"
      }
    ]
  },
  "recommendations": [
    {
      "component": "emotions",
      "current_score": 5.5,
      "target_score": 7.0,
      "intervention_type": "emotional_alignment",
      "strategies": [
        "Create positive emotional associations",
        "Address anxiety or negative feelings",
        "Build emotional rewards into behavior"
      ]
    },
    {
      "component": "perception",
      "current_score": 6.0,
      "target_score": 7.5,
      "intervention_type": "belief_modification",
      "strategies": [
        "Demonstrate clear value and benefits",
        "Show feasibility through examples",
        "Address misconceptions"
      ]
    }
  ],
  "metadata": {
    "model": "behavioral_state_model",
    "version": "1.0.0",
    "attribution": "Jason Hreha's Behavioral State Model",
    "reference": "https://www.thebehavioralscientist.com/articles/the-behavioral-state-model",
    "computation_time_ms": 52
  }
}

2. Behavior Chain Analysis 

POST /api/v1/analyze/behavior-chain

Analyzes sequences of behaviors and their dependencies:

Request Body:

{
  "user_id": "uuid",
  "target_behavior": "behavior_uuid",
  "time_window": {
    "start": "ISO-8601",
    "end": "ISO-8601"
  },
  "include_prerequisites": true
}

Response:

{
  "behavior_chain": {
    "nodes": [
      {
        "id": "b1",
        "behavior": "problem_recognition",
        "likelihood": 0.85,
        "status": "completed"
      },
      {
        "id": "b2", 
        "behavior": "solution_search",
        "likelihood": 0.72,
        "status": "in_progress"
      },
      {
        "id": "b3",
        "behavior": "target_action",
        "likelihood": 0.41,
        "status": "predicted",
        "dependencies": ["b1", "b2"]
      }
    ],
    "edges": [
      {
        "from": "b1",
        "to": "b2",
        "strength": 0.83,
        "type": "enables"
      }
    ],
    "critical_path": ["b1", "b2", "b3"],
    "bottlenecks": ["b2"]
  }
}

3. Batch Predictions 

POST /api/v1/predict/batch

For processing multiple users/behaviors simultaneously:

Request Body:

{
  "predictions": [
    {
      "user_id": "uuid1",
      "behavior_id": "behavior1",
      "behavioral_state": {
        "problem_awareness": 7,
        "solution_awareness": 8,
        "solution_belief": 6,
        "motivation_intensity": 7,
        "prioritization": 6,
        "self_efficacy": 8,
        "resource_availability": 9,
        "trigger_presence": 5
      },
      "context": {}
    },
    {
      "user_id": "uuid2",
      "behavior_id": "behavior2",
      "behavioral_state": {
        "problem_awareness": 5,
        "solution_awareness": 4,
        "solution_belief": 3,
        "motivation_intensity": 6,
        "prioritization": 4,
        "self_efficacy": 5,
        "resource_availability": 6,
        "trigger_presence": 3
      },
      "context": {}
    }
  ],
  "options": {
    "parallel_processing": true,
    "include_explanations": true,
    "use_default_weights": true
  }
}

Integration Examples

The following examples demonstrate how developers could integrate with a BSM-based prediction system:

Hypothetical Python SDK 

# This is a conceptual example - no actual SDK exists
from behavioral_strategy import BehaviorPredictor, BehavioralState

# Initialize predictor (theoretical)
predictor = BehaviorPredictor(api_key="your_key")

# Create behavioral state assessment
behavioral_state = BehavioralState(
    problem_awareness=8,      # User clearly recognizes the problem
    solution_awareness=6,     # Knows our solution exists
    solution_belief=7,        # Believes it will help
    motivation_intensity=8,   # Strongly motivated
    prioritization=5,         # Medium priority
    self_efficacy=6,         # Moderate confidence
    resource_availability=7,  # Has most resources
    trigger_presence=4        # Few environmental triggers
)

# Single prediction with BSM
result = predictor.predict(
    user_id="user123",
    behavior="complete_purchase",
    behavioral_state=behavioral_state,
    context={
        "cart_value": 127.50,
        "items_count": 3,
        "time_on_site": 485
    }
)

# Calculate BSS manually
bss = behavioral_state.calculate_score()
print(f"Behavioral State Score: {bss:.2f}")
print(f"Classification: {behavioral_state.classify()}")
print(f"Bottlenecks: {behavioral_state.identify_bottlenecks()}")

# Analyze behavior chain
chain = predictor.analyze_chain(
    user_id="user123",
    target_behavior="become_subscriber",
    time_window=("2024-01-01", "2024-12-31")
)

# Batch processing with different behavioral states
results = predictor.batch_predict([...])

JavaScript/TypeScript 

import { BehaviorPredictor, BehavioralState } from '@behavioral-strategy/predictor';

const predictor = new BehaviorPredictor({ apiKey: 'your_key' });

// Create behavioral state with TypeScript typing
const behavioralState: BehavioralState = {
  problemAwareness: 8,
  solutionAwareness: 6,
  solutionBelief: 7,
  motivationIntensity: 8,
  prioritization: 5,
  selfEfficacy: 6,
  resourceAvailability: 7,
  triggerPresence: 4
};

// Async prediction with BSM
const prediction = await predictor.predict({
  userId: 'user123',
  behavior: {
    id: 'complete_purchase',
    type: 'conversion',
    complexity: 'medium'
  },
  behavioralState,
  context: {
    timestamp: new Date(),
    platform: 'mobile'
  }
});

// Calculate BSS and get recommendations
const bss = calculateBSS(behavioralState);
const bottlenecks = identifyBottlenecks(behavioralState);

// React hook example with BSM
const { prediction, loading, error } = useBehaviorPrediction(
  userId, 
  behaviorId,
  behavioralState
);

Model Training & Customization

Domain-Specific Weight Calibration 

{
  "domain": "healthcare",
  "weights": {
    "problem_awareness": 0.18,
    "solution_awareness": 0.12,
    "solution_belief": 0.15,
    "motivation_intensity": 0.15,
    "prioritization": 0.08,
    "self_efficacy": 0.12,
    "resource_availability": 0.10,
    "trigger_presence": 0.10
  },
  "calibration_data": {
    "n_samples": 50000,
    "accuracy": 0.84,
    "f1_score": 0.81,
    "model_type": "Behavioral State Model (BSM)"
  }
}

BSM Calculation Example 

# Example: E-commerce Purchase Behavior
behavioral_state = {
    "problem_awareness": 9,      # Urgent need for product
    "solution_awareness": 8,     # Found your product
    "solution_belief": 6,        # Reviews are mixed
    "motivation_intensity": 7,   # Want it but not desperate
    "prioritization": 4,         # Other expenses this month
    "self_efficacy": 9,         # Easy to buy online
    "resource_availability": 8,  # Has payment method
    "trigger_presence": 3        # No urgency cues
}

# Calculate weighted BSS
weights = [0.15, 0.12, 0.13, 0.15, 0.10, 0.12, 0.13, 0.10]
scores = list(behavioral_state.values())

bss = sum(w * s for w, s in zip(weights, scores)) / sum(weights)
# BSS = 6.5 (Likely Performer)

# Identify intervention opportunities
bottlenecks = [
    ("trigger_presence", 3),      # Add urgency: "Only 2 left!"
    ("prioritization", 4),        # Offer payment plan
    ("solution_belief", 6)        # Show testimonials
]

Custom BSM Component Extensions

The API supports extending the BSM with domain-specific components:

# Register custom BSM component
predictor.register_bsm_component(
    name="social_proof",
    description="Peer influence on behavior",
    scorer=lambda user, context: calculate_social_influence_score(user),
    weight=0.08,
    rubric={
        "0-2": "No social influence",
        "3-4": "Minimal peer activity",
        "5-6": "Moderate social proof",
        "7-8": "Strong peer influence",
        "9-10": "Overwhelming social pressure"
    }
)

# Use extended BSM in prediction
result = predictor.predict_with_extensions(
    user_id="user123",
    behavior="adopt_feature",
    behavioral_state=behavioral_state,
    extensions={"social_proof": 7}
)

Performance Benchmarks

Operation Latency (p50) Latency (p99) Throughput
Single Prediction 12ms 47ms 8,000 req/s
Batch (100 items) 89ms 215ms 800 req/s
Chain Analysis 34ms 98ms 2,500 req/s

Error Handling

Error Response Format 

{
  "error": {
    "code": "INVALID_BEHAVIOR_ID",
    "message": "Behavior ID 'xyz' not found in behavior registry",
    "details": {
      "valid_behaviors": ["behavior1", "behavior2"],
      "suggestion": "Use /api/v1/behaviors to list valid IDs"
    }
  },
  "request_id": "req_abc123",
  "timestamp": "2024-01-15T10:30:00Z"
}

Common Error Codes

  • INVALID_USER_ID: User not found
  • MISSING_CONTEXT: Required context fields missing
  • MODEL_UNAVAILABLE: Prediction model temporarily unavailable
  • RATE_LIMIT_EXCEEDED: API rate limit reached
  • INVALID_TIME_RANGE: Time window exceeds allowed range

Webhooks & Streaming

Real-time Behavior Events 

// WebSocket connection for real-time predictions
const ws = new WebSocket('wss://api.behavioralstrategy.com/stream');

ws.on('behavior_detected', (event) => {
  console.log(`User ${event.userId} performed ${event.behavior}`);
  // Trigger real-time prediction update
});

Webhook Configuration 

{
  "webhook_url": "https://your-app.com/behavioral-webhook",
  "events": [
    "prediction.completed",
    "behavior.detected",
    "threshold.exceeded"
  ],
  "filters": {
    "min_likelihood": 0.7,
    "behavior_types": ["conversion", "retention"]
  }
}

Security & Authentication

API Key Authentication 

curl -X POST https://api.behavioralstrategy.com/v1/predict/behavior \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"user_id": "123", "behavior": {...}}'

OAuth 2.0 Flow

For user-specific predictions:

1. Redirect to: https://auth.behavioralstrategy.com/oauth/authorize
2. Exchange code for token
3. Use token for API requests

Rate Limits

Tier Requests/min Burst Batch Size
Free 60 100 10
Pro 600 1000 100
Enterprise Custom Custom 1000

Next Steps

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 behavioralstrategy.com

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