User Guide

Welcome to the scikit-agent user guide! This comprehensive guide will help you get started with building, solving, and simulating economic models using scikit-agent.

Getting Started

If you’re new to scikit-agent, start here:

• Installation - Install scikit-agent and set up your environment

• Quickstart Guide - Build and simulate your first model in minutes

Core Concepts

Learn about the fundamental concepts and components:

• Models Guide - Understanding model structure and building custom models

• Algorithms Guide - Solution methods for solving your models

• Simulation Guide - Monte Carlo simulation and analysis

What is scikit-agent?

scikit-agent is a Python package for agent-based economic modeling that follows scikit-learn conventions. It provides:

🧱 Modular Architecture: Build models using composable “blocks” that represent different stages or aspects of economic behavior.

🔬 Multiple Solution Methods: Choose from value function iteration, neural networks, and other modern computational approaches.

📊 Rich Simulation Tools: Generate synthetic data with powerful Monte Carlo simulators that handle heterogeneity, aging, and complex dynamics.

🐍 Pythonic API: Familiar patterns for Python users with clear, readable code.

Key Design Principles

Block-Based Models

Models are composed of DBlocks (Dynamic Blocks) that capture:

• Shocks: Random variables affecting agents

• Dynamics: State transition equations

• Controls: Decision variables agents optimize

• Rewards: Objective functions to maximize

Flexible Calibration

Parameters can be:

• Simple numbers: "CRRA": 2.0

• Mathematical expressions: "utility": "c**(1-gamma)/(1-gamma)"

• Age-varying sequences: "income": [1.0, 1.2, 1.1, 0.8]

Topics Covered

• Installation
  • Requirements
  • Install from PyPI
  • Install from Source
  • Development Installation
  • Verify Installation
• Quickstart Guide
  • Installation
  • Core Concepts
  • Your First Model: Consumption-Saving
  • Working with Grids
  • Neural Network Solutions
  • Next Steps
  • Key Takeaways
• Models Guide
  • Understanding Model Structure
  • Building Custom Models
  • Working with Predefined Models
  • Advanced Model Features
  • Model Validation and Inspection
  • Best Practices
  • Next Steps
  • Common Patterns
• Algorithms Guide
  • Solution Methods
  • Choosing the Right Algorithm
  • Algorithm Configuration
  • Performance Considerations
• Simulation Guide
  • Running Simulations
  • Simulation Configuration
  • Data Collection
  • Analysis and Visualization

Need Help?

• Examples: Browse the Examples for complete working examples

• API Reference: See API Reference for detailed class and function documentation

• GitHub Issues: Report bugs or request features at github.com/scikit-agent/scikit-agent

Contributing

scikit-agent is an open-source project and welcomes contributions! Whether you’re:

• Reporting bugs

• Suggesting new features

• Contributing code

• Improving documentation

• Sharing examples

See our contribution guidelines in the repository for how to get involved.

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Ready to get started? Head to the Quickstart Guide guide!