cognitive/docs/guides/simulation.md

---
title: Simulation Guide
type: guide
status: draft
created: 2024-02-12
tags:
  - simulation
  - modeling
  - framework
semantic_relations:
  - type: implements
    links: [[model_implementation]]
  - type: relates
    links:
      - [[implementation_guides]]
      - [[ai_validation_framework]]
---

# Simulation Framework Guide

## Overview

This guide provides comprehensive documentation for running simulations in the cognitive modeling framework. It covers simulation setup, execution, analysis, and visualization.

## Simulation Components

### Core Elements
1. Model Configuration
   - Parameter settings
   - Initial conditions
   - Environment setup
   - Agent definitions

2. Execution Pipeline
   - Simulation steps
   - State updates
   - Event handling
   - Data collection

3. Analysis Tools
   - Data processing
   - Statistical analysis
   - Performance metrics
   - Result validation

### Configuration

```yaml
simulation:
  name: cognitive_simulation
  duration: 1000  # timesteps
  agents: 10
  environment:
    type: dynamic
    dimensions: [100, 100]
  parameters:
    learning_rate: 0.01
    noise_level: 0.1
    update_interval: 5
```

## Running Simulations

### Basic Usage
```python
from cognitive.simulation import Simulator

# Create simulator
sim = Simulator(config_path="config.yaml")

# Run simulation
results = sim.run()

# Analyze results
analysis = sim.analyze(results)

# Visualize
sim.visualize(analysis)
```

### Advanced Features

1. Batch Processing
   ```python
   # Run multiple simulations
   batch_results = sim.run_batch(
       num_runs=10,
       parallel=True
   )
   ```

2. Parameter Sweeps
   ```python
   # Test different parameters
   param_results = sim.parameter_sweep(
       parameter="learning_rate",
       values=[0.01, 0.05, 0.1]
   )
   ```

3. Custom Callbacks
   ```python
   # Add custom monitoring
   @sim.on_step
   def monitor_state(state):
       log_metrics(state)
   ```

## Analysis Tools

### Data Processing
- Time series analysis
- State space analysis
- Agent behavior analysis
- Environment dynamics

### Visualization
- State trajectories
- Agent interactions
- Performance metrics
- Network dynamics

### Metrics
- Convergence rates
- Stability measures
- Efficiency metrics
- Error analysis

## Best Practices

### Performance
- Use vectorized operations
- Enable parallel processing
- Optimize memory usage
- Profile critical sections

### Reproducibility
- Set random seeds
- Version configurations
- Document parameters
- Archive results

### Validation
- Unit test components
- Verify constraints
- Check conservation laws
- Validate outputs

## Advanced Topics

### Custom Models
- Extending base classes
- Adding new behaviors
- Custom environments
- Specialized metrics

### Distributed Simulation
- Multi-node execution
- Load balancing
- Data synchronization
- Result aggregation

### Real-time Analysis
- Live monitoring
- Interactive visualization
- Dynamic adjustment
- Event handling

## Integration Points

### Data Pipeline
- Input preprocessing
- Result postprocessing
- Data storage
- Export formats

### External Tools
- Visualization libraries
- Analysis packages
- Storage backends
- Monitoring systems

## Related Documentation
- [[model_implementation]]
- [[implementation_guides]]
- [[ai_validation_framework]]