Tutorials

This tutorial series walks you through the Ergodic Insurance Limits framework, from installation to advanced optimization. The goal is to help you understand how time-average (ergodic) analysis changes insurance purchasing decisions compared to traditional expected-value approaches.

Note

New here? Start with Getting Started with Ergodic Insurance Limits for installation and a first simulation.

Tutorial Overview

The tutorials are split into two groups.

Foundations (Tutorials 1–2):

1. Getting Started – Installation, environment setup, and running your first simulation

2. Basic Simulation – The Widget Manufacturer model, step-by-step year simulation, and loss processing

Applied Workflow (Tutorials 3–6):

Tutorials 3 and 4 follow NovaTech Plastics, a fictional $10M plastics manufacturer with an 8% operating margin, as the running example. The same manufacturer parameters carry through Tutorials 5 and 6.

3. Configuring Insurance – Building single-layer and multi-layer insurance towers for NovaTech

4. Optimization Workflow – Using BusinessOptimizer to find data-driven insurance strategies as NovaTech plans its expansion

5. Analyzing Results – Comparing time-average vs. ensemble-average growth rates with ErgodicAnalyzer

6. Advanced Scenarios – Monte Carlo simulations, market cycle modeling, and scenario analysis

Quick Start Paths

You do not need to complete every tutorial. Pick a path based on what you want to learn:

Actuaries and Risk Managers:

Start with the foundations, then focus on insurance structure and results interpretation.

1. Getting Started with Ergodic Insurance Limits

2. Tutorial 3: Configuring Insurance

3. Analyzing Results: The Ergodic Advantage

Financial Analysts and CFOs:

Focus on the business case for insurance and the optimization workflow.

1. Getting Started with Ergodic Insurance Limits

2. Tutorial 4: Optimization Workflow

3. Analyzing Results: The Ergodic Advantage

Developers and Researchers:

Dive into the simulation engine and advanced techniques.

1. Basic Simulation

2. Tutorial 4: Optimization Workflow

3. Tutorial 6: Advanced Scenarios

Tutorials

Step-by-Step Tutorials:

• 1. Getting Started with Ergodic Insurance Limits
  • 1.1. What is Ergodic Insurance Optimization?
  • 1.2. Installation
  • 1.3. Your First Analysis
  • 1.4. Understanding the Output
  • 1.5. Next Steps
  • 1.6. Quick Reference
• 2. Basic Simulation
  • 2.1. The Widget Manufacturer Model
  • 2.2. Running a Year Step-by-Step
  • 2.3. Processing Insurance Claims
  • 2.4. Understanding Claim Development
  • 2.5. The Simulation Class
  • 2.6. Analyzing Simulation Results
  • 2.7. Visualizing Results
  • 2.8. Handling Insolvency
  • 2.9. Multiple Simulations with Different Seeds
  • 2.10. Next Steps
• 3. Tutorial 3: Configuring Insurance
  • 3.1. A Quick Insurance Refresher
  • 3.2. Creating a Simple Insurance Policy
  • 3.3. Building a Multi-Layer Tower
  • 3.4. Processing Claims Through the Policy
  • 3.5. Insured vs. Uninsured: The Simulation Comparison
  • 3.6. Advanced Features: InsuranceProgram
  • 3.7. Premium Loading Analysis
  • 3.8. Exercises
  • 3.9. Next Steps
• 4. Tutorial 4: Optimization Workflow
  • 4.1. The Story So Far
  • 4.2. 1. Setting Up the BusinessOptimizer
  • 4.3. 2. Defining Objectives and Constraints
  • 4.4. 3. Running Optimization
  • 4.5. 4. Sensitivity Analysis
  • 4.6. 5. Strategy Backtesting
  • 4.7. 6. Walk-Forward Validation
  • 4.8. 7. Market Cycle Considerations
  • 4.9. 8. Best Practices
  • 4.10. 9. Exercises
  • 4.11. 10. Next Steps
• 5. Analyzing Results: The Ergodic Advantage
  • 5.1. The Board Presentation Problem
  • 5.2. Ergodic vs Non-Ergodic Systems
  • 5.3. Running Paired Simulations
  • 5.4. Using ErgodicAnalyzer.compare_scenarios()
  • 5.5. Time-Average vs Ensemble-Average Growth
  • 5.6. Understanding Ergodic Divergence Over Time
  • 5.7. Survival Analysis
  • 5.8. Growth Rate Analysis
  • 5.9. Visualizing Ergodic Effects
  • 5.10. Convergence Analysis
  • 5.11. Full Analysis Pipeline
  • 5.12. Interpreting Results
  • 5.13. Exercises
  • 5.14. Next Steps
• 6. Tutorial 6: Advanced Scenarios
  • 6.1. The Story So Far
  • 6.2. 1. Monte Carlo Simulation with MonteCarloEngine
  • 6.3. 2. Parallel Processing for Speed
  • 6.4. 3. Configuration Profiles and Presets
  • 6.5. 4. Insurance Market Conditions
  • 6.6. 5. Complex Insurance Structures
  • 6.7. 6. Stochastic Processes for Revenue Modeling
  • 6.8. 7. Scenario Analysis
  • 6.9. 8. Generating Reports
  • 6.10. 9. Performance Tips
  • 6.11. 10. Exercises
  • 6.12. 11. Further Resources

Support Resources

Help & Support:

• Troubleshooting Guide

Learning Objectives

After working through these tutorials you should be able to:

• Install the framework and run a simulation end-to-end

• Configure multi-layer insurance programs with deductibles, attachment points, and limits

• Use the optimizer to search for insurance strategies that maximize time-average growth

• Interpret the difference between ensemble-average and time-average growth rates

• Run Monte Carlo simulations and analyze survival probabilities, ROE, and final equity distributions

Prerequisites

Required:

• Python 3.12 or higher

• Comfort with Python basics: importing packages, running scripts, reading tracebacks

• Familiarity with probability concepts (distributions, expected value, variance) at the level of Actuarial Exam P

Helpful:

• Working knowledge of insurance terms – deductible, retention, limit, attachment point, premium

• Exposure to financial metrics such as ROE, operating margin, and asset turnover

• Experience with NumPy arrays and Matplotlib (used throughout the code examples)

Getting Help

If you get stuck:

1. Check the Troubleshooting Guide guide for common errors and fixes

2. Consult the ../api/modules for detailed function and class documentation

3. Open an issue on GitHub