Cognitive Ecosystem Modeling Framework

A comprehensive framework for modeling cognitive ecosystems using active_inference, integrated with docs/guides/obsidian_linking for knowledge management.

Overview

This project combines cognitive modeling with knowledge management to create a powerful framework for:

• Modeling agent behaviors using active_inference
• Managing complex knowledge_organization
• Visualizing and analyzing knowledge_base/cognitive/cognitive_phenomena
• Simulating multi-agent interactions

Project Structure

See ai_folder_structure for comprehensive directory organization.

📁 docs/ # Documentation (See docs/guides/documentation_standards) 📁 tests/ # Test suite (See docs/guides/unit_testing) 📁 data/ # Data storage

Features

Knowledge Management

• docs/guides/obsidian_linking
• docs/guides/linking_completeness
• docs/templates/ai_concept_template
• docs/guides/ai_validation_framework

Cognitive Modeling

• knowledge_base/cognitive/active_inference
• knowledge_base/mathematics/belief_updating
• knowledge_base/cognitive/action_selection
• knowledge_base/cognitive/predictive_processing

Analysis & Visualization

• docs/tools/network_analysis
• docs/concepts/quality_metrics
• docs/tools/visualization
• docs/guides/simulation

Knowledge Integration Architecture

Bidirectional Knowledge Graph

The framework leverages docs/guides/obsidian_linking to create a living knowledge graph that:

• Enforces docs/guides/validation
• Enables docs/guides/ai_validation_framework of relationships
• Supports docs/guides/machine_learning of dependencies
• Facilitates docs/guides/research

Link Types and Semantics

See docs/guides/linking_completeness for comprehensive linking patterns.

1. Theoretical Dependencies

   [[knowledge_base/mathematics/measure_theory]] → [[knowledge_base/mathematics/probability_theory]] → [[knowledge_base/cognitive/stochastic_processes]]
   

   • Enforces prerequisite knowledge
   • Validates theoretical foundations
   • Ensures consistent notation

2. Implementation Dependencies

   [[knowledge_base/cognitive/active_inference]] → [[knowledge_base/mathematics/belief_updating]] → [[knowledge_base/cognitive/action_selection]]
   

   • Tracks computational requirements
   • Maintains implementation consistency
   • Documents design decisions

3. Validation Links

   [[docs/guides/unit_testing]] → [[docs/guides/validation]] → [[docs/concepts/quality_metrics]]
   

   • Ensures rigorous testing
   • Maintains quality standards
   • Documents validation procedures

Meta-Programming Capabilities

Code Generation

def generate_model_code(spec_file: Path) -> str:
    """Generate implementation from specifications.
    See [[docs/guides/ai_documentation_style]] for code generation patterns.
    """
    # Parse markdown specifications
    spec = parse_markdown_spec(spec_file)
    
    # Extract probabilistic model
    model = extract_probabilistic_model(spec)
    
    # Generate implementation
    return generate_implementation(model)

Validation Rules

def check_probabilistic_consistency():
    """Verify probabilistic consistency.
    See [[docs/guides/validation]] for validation rules.
    """
    # Check matrix constraints
    verify_stochastic_matrices()
    
    # Validate probability measures
    verify_measure_consistency()
    
    # Check inference specifications
    verify_inference_methods()

Benefits

1. Theoretical Consistency

   • docs/guides/ai_validation_framework of mathematical relationships
   • Enforcement of probabilistic constraints
   • Verification of implementation patterns

2. Learning Support

   • docs/guides/research of concepts
   • Clear dependency tracking
   • Interactive knowledge discovery

3. Implementation Quality

   • docs/guides/ai_documentation_style
   • Consistent design patterns
   • docs/guides/unit_testing

4. Documentation Integration

   • docs/guides/documentation_standards
   • docs/guides/package_documentation
   • docs/guides/ai_validation_framework