Finnish social security as an OpenAI Gym-environment

This library implements life-cycle of an individual s an OpenAI Gym-environmen. can be used in a life-cycle model to predict, e.g., employment rate.

The library depends on the benefits library that implements Finnish social security a

Description of the environment can be found from articles (Tanskanen, 2019; Tanskanen, 2020).

Versions

v0 - minimal

Minimal environment with only three states: working, unemployed, retired. This is the version used in article Deep reinforced learning enables solving discrete-choice life cycle models to analyze social security reforms (2020)

v1 - baseline

A more realistic environment with only ten states. This is the version used in article Ty{"o}llisyysvaikutuksien arviointia teko{"a}lyll{"a}: Unelmoivatko robotit ansiosidonnaisesta sosiaaliturvasta.

v2 - improved

A version of the model with 15 states. This is the version used in article Tanskanen (2020) Includes fixes to version v1.

v3 - the last model with single individuals

A more realistic environment with 15 states. Includes fixes to versions v1 and v2.

v4 - couples

A still more realistic environment with 16 states. It this version, couples are implemented. Both partners in a couple make their decisions individually. This is the one that should be used.

Rewards

Reward is log utility of net income minus a constant representing the free time lost.

Installation

Clone the repository and run command

pip install -e .

in the parent directory of gym_unemployment, where you can find setup.py file.

Adding a new environment

An example file that can be modified is saved as gym_unemployment/envs/test_environment.py

To add a new environment consists of a few steps: (1) copy test_environment.py, (2) registering the new environment with Gym, (3) Updating the library. Steps (2) and (3) can be done by replicating the way test environment is included:

Test environment is included in econogym module in file gym_unemployment/envs/init.py with line

from gym_unemployment.envs.test_environment import TestEnv

But it is not yet registered as a Gym environment. This is accomplished in file gym_unemployment/init.py with lines

register(
	id='test-v1',
	entry_point='gym_unemployment.envs:TestEnv',
)

Finally, to include a new environment in the module, one needs to install it with pip. Run command

pip install -e .

in the parent directory of gym_unemployment, where you can find setup.py file.

The model is written in Python 3.

References

@misc{lifecycle_rl_,
  author = {Antti J. Tanskanen},
  title = {Elinkaarimalli},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ajtanskanen/lifecycle_rl}},
}

Description of the lifecycle model can be found from articles Tanskanen (2020) and Tanskanen (2022).

@article{tanskanen2022deep,
  title={Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms},
  author={Tanskanen, Antti J},
  journal={Social Sciences & Humanities Open},
  volume={5},
  pages={100263},
  year={2022}
}

@article{tanskanen2020tyollisyysvaikutuksien,
  title={Ty{\"o}llisyysvaikutuksien arviointia teko{\"a}lyll{\"a}: Unelmoivatko robotit ansiosidonnaisesta sosiaaliturvasta},
  author={Tanskanen, Antti J},
  journal={Kansantaloudellinen aikakauskirja},
  volume={2},
  pages={292--321},
  year={2020}
}