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https://github.com/jmiszczak/matthew_reduction_game

Implementation of games on networks for controlling the inequalities in the capital distribution
https://github.com/jmiszczak/matthew_reduction_game

agent-based-modeling agent-based-simulation econophysics game-theory network-analysis simulation sociophysics strategic-game

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Implementation of games on networks for controlling the inequalities in the capital distribution

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README 

        
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5541170.svg)](https://doi.org/10.5281/zenodo.5541170)



# matthew_reduction_game



This software provides implementation of games on networks for controlling the inequalities in the capital

distribution (a.k.a. Janosik model).



Software and data in this repository was used in the manuscript:



J.A. Miszczak, *Constructing games on networks for controlling the inequalities in the capital distribution*, Physica A: Statistical Mechanics and its Applications, Volume 594, 15 May 2022, 126997 DOI:[10.1016/j.physa.2022.126997](https://doi.org/10.1016/j.physa.2022.126997), arXiv:[2201.10913](https://arxiv.org/abs/2201.10913).



## Quick start



Main functionality is contained within `src/model` folder. To run a simulation of the base model with capital

distribution initialized using constant difference of capital, run



    python Janosik_grid2d_batch.py uniform



Here the last parameter can be `uniform` or `boltzmann`.



Example of the model with Parrondo's game used for policy selection can be run as



    python JanosikParrondo_grid2d_batch.py nagents



where the parameter `nagents` is used to define a number of agents in the network. Please note tha only equal difference

methods (i.e. uniform) is used for this case.



In all case simulation is executed using parallel capabilities of Mesa library. The number of cored ussed in the

simulation is controlled by the `nr_processes` parameter of `BatchRunnerMP` class.



## Package organization and installation



The provided files can be used to reproduce all the results presented in the accompanying manuscript. They can be also

used to extend the models and run simulations using random graphs models.



### Description of subdirectories



* `src/model` includes model used in the manuscript,

* `src/scripts` contains some extra scripts used in the manuscript,

* `data` is used to store simulation results and can be used to store graphs generated for running simulations,

* `plots` is used to store final plots.



### Anaconda environment



You can use to install most of the required packages

	

	conda env create -f matthew_reduction_game.yml



and 



	pip install -e git+https://github.com/projectmesa/mesa#egg=mesa



to install the latest version of Mesa library.



This software was tested with



* Python 3.9.7

* Mesa 0.8.9

* NetworkX 2.6.3

* Pandas 1.3.3

* conda 4.10.3

* Ubuntu 20.04.3 LTS



## Publication



The presented results were published in



J.A. Miszczak, *Constructing games on networks for controlling the inequalities in the capital distribution*, Physica A: Statistical Mechanics and its Applications, Vol. 594 (2022), 126997, DOI:[10.1016/j.physa.2022.126997](https://doi.org/10.1016/j.physa.2022.126997), arXiv:[2201.10913](https://arxiv.org/abs/2201.10913) 



```

@article{miszczak2022constructing,

  title = {Constructing games on networks for controlling the inequalities in the capital distribution},

  author = {Miszczak, Jaros{\l}aw Adam},

  journal = {Physica A: Statistical Mechanics and its Applications},

  volume = {594},

  pages = {126997},

  year = {2022},

  doi = {10.1016/j.physa.2022.126997}

}

```



### Contact information



Responsible person: Jarosław Miszczak