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https://github.com/Axelrod-Python/Axelrod

A research tool for the Iterated Prisoner's Dilemma
https://github.com/Axelrod-Python/Axelrod

computer-science evolutionary-game-theory game-theory mathematics prisoners-dilemma python reproducible-research

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A research tool for the Iterated Prisoner's Dilemma

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README 

        
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Axelrod

=======



Goals

-----



A Python library with the following principles and goals:



1. Enabling the reproduction of previous Iterated Prisoner's Dilemma research

   as easily as possible.

2. Creating the de-facto tool for future Iterated Prisoner's Dilemma

   research.

3. Providing as simple a means as possible for anyone to define and contribute

   new and original Iterated Prisoner's Dilemma strategies.

4. Emphasizing readability along with an open and welcoming community that

   is accommodating for developers and researchers of a variety of skill levels.



Features

--------



With Axelrod you:



- have access `to over 200 strategies

  `_, including original and classics like Tit

  For Tat and Win Stay Lose Shift. These are extendable through parametrization

  and a collection of strategy transformers.

- can create `head to head matches

  `_ between pairs of strategies.

- can create `tournaments

  `_ over a number of strategies.

- can study population dynamics through `Moran processes

  `_ and an `infinite

  population model

  `_.

- can analyse detailed `results of tournaments

  `_ and matches.

- can `visualise results

  `_ of tournaments.



  .. image:: http://axelrod.readthedocs.io/en/stable/_images/demo_strategies_boxplot.svg

     :height: 300 px

     :align: center



- can reproduce a number of contemporary research topics such as `fingerprinting `_ of

  strategies and `morality metrics

  `_.



  .. image:: https://github.com/Axelrod-Python/Axelrod-fingerprint/raw/master/assets/Tricky_Defector.png

     :height: 300 px

     :align: center



The library has 100% test coverage and is extensively documented. See the

documentation for details and examples of all the features:

http://axelrod.readthedocs.org/



`An open reproducible framework for the study of the iterated prisoner's

dilemma `_:

a peer reviewed paper introducing the library (22 authors).



Installation

------------



The library is tested on Python versions 3.8, 3.9, and 3.10.



The simplest way to install is::



    $ pip install axelrod



To install from source::



    $ git clone https://github.com/Axelrod-Python/Axelrod.git

    $ cd Axelrod

    $ python setup.py install



Quick Start

-----------



The following runs a basic tournament::



    >>> import axelrod as axl

    >>> players = [s() for s in axl.demo_strategies]  # Create players

    >>> tournament = axl.Tournament(players, seed=1)  # Create a tournament

    >>> results = tournament.play()  # Play the tournament

    >>> results.ranked_names

    ['Defector', 'Grudger', 'Tit For Tat', 'Cooperator', 'Random: 0.5']



Examples

--------



- https://github.com/Axelrod-Python/tournament is a tournament pitting all the

  strategies in the repository against each other. These results can be easily

  viewed at http://axelrod-tournament.readthedocs.org.

- https://github.com/Axelrod-Python/Axelrod-notebooks contains a set of example

  Jupyter notebooks.

- https://github.com/Axelrod-Python/Axelrod-fingerprint contains fingerprints

  (data and plots) of all strategies in the library.



Contributing

------------



All contributions are welcome!



You can find helpful instructions about contributing in the

documentation:

https://axelrod.readthedocs.io/en/latest/how-to/contributing/index.html



Publications

------------



You can find a list of publications that make use of or cite the library

on the `citations `_ page.



Contributors

------------



The library has had many awesome contributions from many `great

contributors `_.

The Core developers of the project are:



- `drvinceknight `_

- `gaffney2010 `_

- `marcharper `_

- `meatballs `_

- `nikoleta-v3 `_



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