Computational Behavioral Strategy

Reference specifications and design patterns for implementing Behavioral Strategy in AI/ML systems and computational environments.

Overview

Note: This section documents reference specs and pseudocode patterns. Treat it as implementation guidance (not a hosted product or API endpoint).

This section provides detailed specifications and computational models for applying Behavioral Strategy, including:

  • Prediction API specifications: REST and streaming interface patterns for behavior prediction
  • Scoring algorithms: quantitative measurement rubrics for behavioral fit
  • System architectures: real-time behavioral state monitoring and segmentation patterns
  • Integration patterns: ontologies and frameworks for system integration

Available Resources

Behavior Prediction API Specification

Reference API spec showing how the Behavioral State Model can be used for behavior prediction with endpoint patterns and example integrations.

BSM Real-Time Predictor

Reference architecture for real-time behavior prediction with streaming data processing and adaptive updates.

BSM-Behavioral Strategy Ontology

Visual and computational ontology mapping the Behavioral State Model to Behavioral Strategy concepts with RDF/OWL representations and integration algorithms.

Key Features of These Specifications

  • Explicitness: Clear formulas, definitions, and calibration notes where applicable
  • Implementation Guidance: Example architectures and deployment patterns
  • Language Examples: Pseudocode in Python, JavaScript/TypeScript, and R
  • System Design Patterns: Streaming and real-time processing architectures
  • ML Integration Ideas: Integration patterns for common ML workflows

Example usage (pseudocode)

The following example demonstrates how a BSM-based prediction system might work in practice:

# Pseudocode example showing how BSM-style scoring can be applied

# Example behavioral state assessment (normalized scores)
behavioral_state = {
    "personality": 7.5,      # Aligns with user's values
    "perception": 6.0,       # Believes behavior is valuable
    "emotions": 5.5,         # Neutral emotional state
    "abilities": 8.0,        # Has necessary skills
    "social_status": 6.5,    # Social position supports
    "motivations": 7.0,      # Motivated by goals
    "social_environment": 6.0,  # Moderate social support
    "physical_environment": 8.5  # Environment enables
}

# Example prediction calculation
min_component = min(behavioral_state.values())  # 5.5 (emotions)
avg_component = sum(behavioral_state.values()) / len(behavioral_state)  # 6.81

# BSM principle: behavior unlikely if any component < 3
if min_component < 3:
    prediction = "Behavior blocked by limiting factor"
else:
    # Weighted calculation considering minimum component rule
    likelihood = (min_component * 0.3) + (avg_component * 0.7)
    prediction = f"Behavior likelihood: {likelihood:.1%}"

Integration patterns

How BSM Could Work with Machine Learning Pipelines 

# Pseudocode example showing one integration pattern

# Example BSM feature extraction
def extract_bsm_features(user_data):
    """Extract the 8 BSM components from user data"""
    return {
        'personality_score': analyze_personality_traits(user_data),
        'perception_score': assess_beliefs_and_attitudes(user_data),
        'emotion_score': evaluate_emotional_state(user_data),
        'ability_score': measure_capabilities(user_data),
        'social_status_score': analyze_social_position(user_data),
        'motivation_score': quantify_motivations(user_data),
        'social_env_score': assess_social_context(user_data),
        'physical_env_score': evaluate_environment(user_data)
    }

# Could be used in ML pipelines for behavior prediction

Potential LLM Integration 

# Example prompt enhancement using BSM
def enhance_prompt_with_bsm(base_prompt, behavioral_state):
    """
    Enhance LLM prompts with BSM context for better behavior prediction
    """
    bsm_context = f"""
    Current Behavioral State:
    - Identity factors: {behavioral_state['identity_score']}/10
    - Context factors: {behavioral_state['context_score']}/10
    - Limiting component: {behavioral_state['min_component']}
    
    Consider how these factors affect behavior likelihood.
    """
    return base_prompt + bsm_context

Performance and evaluation

Do not set arbitrary “accuracy targets” up front. Define evaluation criteria based on the decision you are making, the base rate, and the cost of false positives/negatives, then validate on held-out data.

Next Steps

← Back to Behavioral Strategy

See also

Table of contents