https://github.com/genedan/MIES

Miniature Insurance Economic Simulator
https://github.com/genedan/MIES

actuarial economics insurance

Last synced: about 1 month ago
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Miniature Insurance Economic Simulator

• Host: GitHub
• URL: https://github.com/genedan/MIES
• Owner: genedan
• License: gpl-3.0
• Created: 2017-12-31T00:19:03.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-04-16T23:10:30.000Z (over 3 years ago)
• Last Synced: 2024-08-01T16:16:23.964Z (about 2 months ago)
• Topics: actuarial, economics, insurance
• Language: Python
• Homepage:
• Size: 658 KB
• Stars: 22
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# MIES

[![PyPI version](https://badge.fury.io/py/mies.svg)](https://badge.fury.io/py/mies)

Miniature Insurance Economic Simulator

![](docs/claim_schema.png)

## Introduction

The purpose of MIES is to simulate, with the appropriate balance between detail and abstraction, various economic equilibria that result between the interactions between insurance firms, policyholders, and regulators.

The goal is to create an explanatory model that links economic theory with actuarial science.

## Development Blog

Additional examples can be found in a series of blog posts, here:

https://genedan.com/category/mies/

## Documentation

Documentation can be found here:

https://genedan.com/mies/docs/

## Concepts Modeled

* [Adverse Selection](https://genedan.com/no-135-mies-simulating-an-insurance-market/)

* Availability of insurance

* Predatory pricing

* Impact of insolvency on insureds

## Project Goals

* Should be consistent with both macro and microeconomic theory

* Should be consistent with actuarial theory

## Installation

```

pip install mies

```

## Example Simulation

```python

import pandas as pd

import datetime as dt

from entities.god import God

from entities.broker import Broker

from entities.insurer import Insurer

```

Set up the environment and relative entities - A broker, a population of insureds, and two insurers with 4B in capital:

```python

ahura = God()

ahura.make_population(1000)

rayon = Broker()

company_1 = Insurer(4000000, 'company_1')

company_2 = Insurer(4000000, 'company_2')

```

Set up the pricing strategy for each company:

```python

company_1_formula = 'incurred_loss ~ age_class + profession + health_status + education_level'

company_2_formula = 'incurred_loss ~ age_class'

```

The broker can now be used to place business, given each insurer's pricing strategy:

```python

rayon.place_business(

        pricing_date,

        company_1,

        company_2

)

event_date = pricing_date + dt.timedelta(days=1)

```    

Generate losses, report claims, and then reprice and renew policies:

```python

event_date = pricing_date + dt.timedelta(days=1)

ahura.smite(event_date)

rayon.report_claims(event_date)

company_1.price_book(company_1_formula)

company_2.price_book(company_2_formula)

pricing_date = pricing_date.replace(pricing_date.year + 1)

```