Miniature Insurance Economic Simulator - MIES¶

Source: genedan/MIES: Miniature Insurance Economic Simulator

No. 143 MIES – Endowments

Contents¶

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
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¶

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:

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:

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:

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:

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)

Backlinks:

list from [[Miniature Insurance Economic Simulator]] AND -"Changelog"