cognitive/docs/guides/simulation.md

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

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

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

   # Run multiple simulations
   batch_results = sim.run_batch(
       num_runs=10,
       parallel=True
   )
   

2. Parameter Sweeps

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

3. Custom Callbacks

   # 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