User Tutorials and How-To Guides

Welcome to the comprehensive tutorial series for the Ergodic Insurance Framework. These hands-on guides will walk you through everything from basic setup to advanced insurance optimization strategies.

Note

New to the framework? Start with Getting Started with Ergodic Insurance Framework to get up and running in minutes!

Tutorial Overview

Our tutorials are designed to take you from beginner to expert:

1. Getting Started - Installation, setup, and your first simulation

2. Basic Simulation - Understanding the simulation engine and parameters

3. Configuring Insurance - Setting up single and multi-layer insurance programs

4. Optimization Workflow - Finding optimal insurance strategies

5. Analyzing Results - Interpreting metrics and making decisions

6. Advanced Scenarios - Real-world applications and complex strategies

Quick Start Path

Depending on your role and objectives, follow these recommended paths:

For Actuaries and Risk Managers:

1. Getting Started with Ergodic Insurance Framework

2. Configuring Insurance Tutorial

3. Analyzing Results Tutorial

For Financial Analysts:

1. Getting Started with Ergodic Insurance Framework

2. Optimization Workflow Tutorial

3. Analyzing Results Tutorial

For Developers and Researchers:

1. Basic Simulation Tutorial

2. Optimization Workflow Tutorial

3. Advanced Scenarios Tutorial

Tutorials

Step-by-Step Tutorials:

• 1. Getting Started with Ergodic Insurance Framework
  • 1.1. What You’ll Learn
  • 1.2. Prerequisites
  • 1.3. Installation
  • 1.4. Your First Simulation
  • 1.5. Understanding the Results
  • 1.6. Common Patterns You’ll See in Future Simulations
  • 1.7. Next Steps
  • 1.8. Quick Tips
  • 1.9. Getting Help
  • 1.10. Summary
• 2. Basic Simulation Tutorial
  • 2.1. Learning Objectives
  • 2.2. Setting Up a Manufacturer
  • 2.3. Generating Losses
  • 2.4. Running Simulations
  • 2.5. Visualizing Results
  • 2.6. Advanced Simulation Features
  • 2.7. Best Practices
  • 2.8. Troubleshooting Common Issues
  • 2.9. Next Steps
  • 2.10. Summary
• 3. Configuring Insurance Tutorial
  • 3.1. Learning Objectives
  • 3.2. Insurance Fundamentals
  • 3.3. Single-Layer Insurance
  • 3.4. Multi-Layer Insurance Programs
  • 3.5. Premium Calculation Methods
  • 3.6. Optimizing Coverage Structure
  • 3.7. Real-World Considerations
  • 3.8. Best Practices
  • 3.9. Next Steps
  • 3.10. Summary
• 4. Optimization Workflow Tutorial
  • 4.1. Learning Objectives
  • 4.2. Understanding Optimization Goals
  • 4.3. Single-Objective Optimization
  • 4.4. Multi-Objective Optimization
  • 4.5. Constrained Optimization
  • 4.6. Dynamic Optimization
  • 4.7. Interpreting Optimization Results
  • 4.8. Best Practices
  • 4.9. Next Steps
  • 4.10. Summary
• 5. Analyzing Results Tutorial
  • 5.1. Learning Objectives
  • 5.2. Understanding Key Metrics
  • 5.3. Ergodic vs Ensemble Analysis
  • 5.4. Statistical Analysis
  • 5.5. Decision Making Framework
  • 5.6. Communicating Results
  • 5.7. Next Steps
  • 5.8. Summary
• 6. Advanced Scenarios Tutorial
  • 6.1. Learning Objectives
  • 6.2. Multi-Peril Insurance Programs
  • 6.3. Correlated Risks and Dependencies
  • 6.4. Dynamic Insurance Strategies
  • 6.5. Industry-Specific Applications
  • 6.6. Regulatory and Capital Optimization
  • 6.7. Portfolio Optimization
  • 6.8. Summary
  • 6.9. Next Steps

Support Resources

Help & Support:

• Troubleshooting Guide

Learning Objectives

By completing these tutorials, you will be able to:

• Set up and run insurance simulations for your specific use case

• Configure complex multi-layer insurance programs

• Optimize insurance parameters for maximum growth while controlling risk

• Analyze results using ergodic theory principles

• Apply the framework to real-world business scenarios

• Troubleshoot common issues and optimize performance

Prerequisites

Required:

• Python 3.12 or higher

• Basic Python knowledge (running scripts, installing packages)

Helpful but not required:

• Understanding of insurance concepts (retention, limits, premiums)

• Basic statistics knowledge (distributions, averages)

• Familiarity with financial metrics (ROE, ROA)

Interactive Learning

All tutorials include:

• Working code examples that you can run immediately

• Visualizations to understand concepts intuitively

• Best practices from real-world applications

• Common pitfalls and how to avoid them

• Exercises to test your understanding

Code Examples

All code examples in these tutorials are:

• ✅ Tested - Every example has been verified to work

• ✅ Complete - Full working code, not just snippets

• ✅ Documented - Clear comments explaining each step

• ✅ Practical - Based on real-world use cases

Getting Help

If you encounter issues:

1. Check the Troubleshooting Guide guide for common problems

2. Review the example notebooks in ergodic_insurance/notebooks/

3. Consult the ergodic_insurance for detailed function documentation

4. File an issue on GitHub

Tutorial Notebooks

Jupyter notebook versions of these tutorials are available in: ergodic_insurance/notebooks/tutorials/

These notebooks allow you to:

• Run code interactively

• Modify parameters and see results immediately

• Save your own experiments and notes

• Share analyses with colleagues

Next Steps

Ready to begin? Start with Getting Started with Ergodic Insurance Framework to install the framework and run your first simulation!

For those who prefer learning by example, check out our Examples section for complete working demonstrations.

Note

Time Investment: Each tutorial takes approximately 30-45 minutes to complete. The entire series can be completed in about 4 hours.

For Help

• Check the Frequently Asked Questions for common questions

• Consult the Glossary for term definitions

• Review example notebooks in ergodic_insurance/notebooks/

• Open an issue on GitHub

• Contact: Alex Filiakov (alexfiliakov@gmail.com)

Happy Learning!

We’re excited to help you master insurance optimization using ergodic theory. These tutorials represent the culmination of extensive research and real-world application.

Let’s transform insurance from a cost center to a growth enabler! 🚀