# Cognitive Ecosystem Modeling Framework

A comprehensive framework for modeling cognitive ecosystems using [[active_inference|Active Inference]], integrated with [[docs/guides/obsidian_linking|Obsidian]] for knowledge management.

## Overview

This project combines cognitive modeling with knowledge management to create a powerful framework for:
- Modeling agent behaviors using [[active_inference|Active Inference]]
- Managing complex [[knowledge_organization|knowledge structures]]
- Visualizing and analyzing [[knowledge_base/cognitive/cognitive_phenomena|cognitive networks]]
- Simulating multi-agent interactions

## Project Structure

See [[ai_folder_structure]] for comprehensive directory organization.

📁 docs/                   # Documentation (See [[docs/guides/documentation_standards|Documentation Standards]])
📁 tests/                  # Test suite (See [[docs/guides/unit_testing|Unit Testing Guide]])
📁 data/                   # Data storage

## Features

### Knowledge Management
- [[docs/guides/obsidian_linking|Obsidian-compatible markdown files]]
- [[docs/guides/linking_completeness|Bidirectional linking]]
- [[docs/templates/ai_concept_template|Template-based node creation]]
- [[docs/guides/ai_validation_framework|Automated relationship tracking]]

### Cognitive Modeling
- [[knowledge_base/cognitive/active_inference|Active Inference implementation]]
- [[knowledge_base/mathematics/belief_updating|Belief updating mechanisms]]
- [[knowledge_base/cognitive/action_selection|Policy selection algorithms]]
- [[knowledge_base/cognitive/predictive_processing|State estimation tools]]

### Analysis & Visualization
- [[docs/tools/network_analysis|Network analysis]]
- [[docs/concepts/quality_metrics|Performance metrics]]
- [[docs/tools/visualization|Interactive visualizations]]
- [[docs/guides/simulation|Simulation frameworks]]

## Knowledge Integration Architecture

### Bidirectional Knowledge Graph
The framework leverages [[docs/guides/obsidian_linking|Obsidian's linking capabilities]] to create a living knowledge graph that:
- Enforces [[docs/guides/validation|mathematical and theoretical consistency]]
- Enables [[docs/guides/ai_validation_framework|automated validation]] of relationships
- Supports [[docs/guides/machine_learning|dynamic discovery]] of dependencies
- Facilitates [[docs/guides/research|learning through exploration]]

#### Link Types and Semantics
See [[docs/guides/linking_completeness]] for comprehensive linking patterns.

1. Theoretical Dependencies
   ```markdown
   [[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
   ```markdown
   [[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
   ```markdown
   [[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
```python
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
```python
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|Automated validation]] of mathematical relationships
   - Enforcement of probabilistic constraints
   - Verification of implementation patterns

2. **Learning Support**
   - [[docs/guides/research|Guided exploration]] of concepts
   - Clear dependency tracking
   - Interactive knowledge discovery

3. **Implementation Quality**
   - [[docs/guides/ai_documentation_style|Automated code generation]]
   - Consistent design patterns
   - [[docs/guides/unit_testing|Rigorous testing framework]]

4. **Documentation Integration**
   - [[docs/guides/documentation_standards|Living documentation]]
   - [[docs/guides/package_documentation|Executable specifications]]
   - [[docs/guides/ai_validation_framework|Automated validation]]