Metadata-Version: 2.1
Name: mies
Version: 0.0.0
Summary: Miniature Insurance Economic Simulator
Home-page: https://github.com/genedan/MIES
Author: Gene Dan
Author-email: genedan@gmail.com
License: UNKNOWN
Description: # MIES
        Miniature Insurance Economic Simulator
        
        ![](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
        
        * 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
        
        ## 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)
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX :: Linux
Requires-Python: >=3.8.2
Description-Content-Type: text/markdown
