Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nikoleta-v3/evolution-of-cooperation-with-limited-updating-payoff-memory

A repository for working on the topic ``The Prisoner's Dilemma with stochastic payoffs'.'
https://github.com/nikoleta-v3/evolution-of-cooperation-with-limited-updating-payoff-memory

Last synced: 4 days ago
JSON representation

A repository for working on the topic ``The Prisoner's Dilemma with stochastic payoffs'.'

Awesome Lists containing this project

README 

        
# Evolution of cooperation among individuals with limited updating payoff memory



[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)



A repository for the project "Evolution of cooperation among individuals with

limited payoff memory," a collaboration with [@chilbe3](https://twitter.com/chilbe3)

and Alex McAvoy, at the [Max Planck Research Group Dynamics of Social Behavior](https://www.evolbio.mpg.de/socialdynamics).



Preprint: https://arxiv.org/abs/2311.02365.



## Software



The code for simulating the evolutionary process described in the paper has been

implemented in Matlab. The code used for the results can be found in the folder

`matlab`.



As Matlab is not an open-source language, we have also implemented the process

in Python. Note that the Python code is slower.



### Installation



To use the source code (Python or Matlab), you will first need to clone the

repository locally to your computer. You just need to run the following command

in the terminal:



```shell

$ git clone [email protected]:Nikoleta-v3/evolution-of-cooperation-with-limited-updating-payoff-memory.git

```



## Analysis



The simulations have been run on the supercomputer. We acknowledge and are

thankful to the Scientific Compute Cluster at GWDG, the joint data center of the

Max Planck Society for the Advancement of Science (MPG), and the University of

Göttingen.



For the results, the evolutionary process when the updating considers the two

last rounds with two interactions and the results for higher mutation have the

longest running times. We recommend running them on a remote computer.



The data generated from running the processes have been archived. However, if

you want to reproduce the work, you can use the Matlab scripts (see [README.md](matlab/README.md)).



The scripts output a `.csv` files with the residents at different time steps of

the evolutionary process. You need to run the script `clean_data.py`, which can

be found in the folder `scripts`, before running the analysis.



## Figures



The analysis and figures have been created using Jupyter Notebooks. Before

running the analysis, you have to download the simulation data. They are

archived in Zenodo: [link here](https://zenodo.org/record/7664286#.Y_YDWS0w2hk).



The notebooks for the figures can be found in the folder `nbs`.



The analysis only uses basic Python packages that should be installed with your

Python. However, we have created an environment file. To install it, navigate to

the project using the terminal and run the command:



```shell

$ conda env create -f environment.yml

```



This installs all the dependencies on a `conda` environment. You can activate

the environment by running:



```shell

$ conda activate stochastic-payoffs

```



The environment can also appear in your Jupyter Notebook by running the

following command:



```shell

$ python -m ipykernel install --user --name stochastic-payoffs --display-name stochastic-payoffs

```



## Paper



The paper and supplementary information are written in LaTeX. All the necessary

files to compile the documents can be found in the folder `paper`.



## License



The repository is under an MIT license.



## Contributions



All contributions are welcome, whether they are in the form of code, feedback, or reporting issues.



## Python



As a reminder, the Python code for the evolutionary process is rather slow, so

we recommend using the Matlab code. We are working on creating a more efficient

Python code for such processes.



To install the Python package, navigate to the repository using the terminal.

Once you are there, run the following command:



```shell

$ python setup.py install

```



### Tests



The Python code for the project has been developed using test-driven

development.



To run the test suite and the test suite's coverage, run the following command

(while the environment is activated):



```shell

$ pytest --cov=src tests/

```