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https://github.com/macr0nerd/project_dilemma

The prisoner's dilemma in python
https://github.com/macr0nerd/project_dilemma

good-first-issue goodfirstissue help-wanted open-source prisoner-dilemma prisoner-dilemma-simulation prisoners-dilemma python python-3 python3 simulation simulator

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The prisoner's dilemma in python

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README 

        
# Project Dilemma

Project Dilemma is a simulation tool for testing algorithms in the prisoner's dilemma.

It provides a standard interface to define both algorithm and simulation classes so that they may be easily tested.

Inspired by [this Veritasium](https://youtu.be/mScpHTIi-kM?si=7pe8XjmjjWLhMup6) video.



## Table of Contents

* [Installation](#installation)

  * [PyPi](#pypi)

  * [Manual](#manual)

* [Configuration](#configuration)

  * [Config File Location](#config-file-location)

  * [Config Format](#config-format)

  * [Dynamic Imports](#dynamic-imports)

* [Algorithms](#algorithms)

* [Simulations](#simulations)

* [Generational Simulations](#generational-simulations)



## Installation

### PyPi

1. `pip install project-dilemma`

### Manual

1. Download git repo

2. Change into repo root directory

3. `pip install .`



## Configuration

### Config File Location

Project Dilemma will automatically try to load the configuration from the user's and system's configuration directories,

usually set by `$XDG_CONFIG_DIRS`. For most Linux users, this will check `~/.config/project_dilemma` and them somewhere

in `/etc`.



This behaviour can be overridden by specifying the `--config` flag to the config file you want to use.

### Config Format

Project Dilemma uses the [TOML](https://toml.io/) format for configuration files.

This is a human-readable format that is easy to write.

The schema has been provided below:



```toml

simulation_id = "name of simulation"

algorithms_directory = "/path/to/algorithms/"

nodes = [ { node_id = "node_1", algorithm = { file = "foo.py", object = "Foo" }, quantity = 11 },

          { node_id = "node_2", algorithm = { file = "bar/baz.py", object = "Baz" } } ]

simulation = { file = "foobar.py", object = "GenerationalFooBar" }

generational_simulation = { file = "foobar.py", object = "FooBar" }

simulation_arguments = { foo = "bar" }

simulation_data = "path/to/round.json"

simulation_data_output = "path/to/round.json"

simulation_results_output = "path/to/results.json"

simulations_directory = "/path/to/simulations/"

```



* algorithms_directory

  * A path to the directory containing the algorithms files

* generational_simulation

  * The simulation to run for each generation in a generational simulation as a [Dynamic Import](#dynamic-imports)

* nodes

  * An array of tables that specify:

    * node id

    * algorithm, as defined in the [Dynamic Imports](#dynamic-imports)

    * quantity, if not specified then 1 node is assumed

      * Note: the node index will be appended to the node_id

section

* simulation

  * The simulation to run as a [Dynamic Import](#dynamic-imports)

* simulation_id

  * The name of the simulation

* simulation_arguments

  * Arguments to pass into the simulation

* simulation_data

  * Path to a JSON file containing previous simulation data

* simulation_data_output

  * Path to write the simulation data as a JSON

* simulation_results_output

  * Path to write the simulation results

* simulations_directory

  * A path to the directory containing additional simulation files

  * Required for user provided simulations



### Dynamic Imports

Because a lot of the objects, such as the algorithms and simulations, can or must be provided by the user, this data

must be imported dynamically.

In order to easily import these objects without importing every simulation and algorithm, the following format can be

used to tell the program where to look for the imports:



```toml

{ file = "path/to/file", object = "ObjectToImport" }

```



* file

  * A path to the file containing the object relative to the associated directory in the config

  * Required for algorithms and user provided simulations

* object

  * The object to import

    * For builtin simulations, specify the simulation class name here



## Algorithms

Algorithms can be defined very easily.

Only four things must be done to subclass the Algorithm interface:

1. Set class name

2. Set `algorithm_id`

3. Pass in the mutations to the interface's init (see template for example)

4. Implement the `decide` function

5. Set mutations (optional)



The `decide` function is what the simulation uses to run the algorithm.

It accepts a `project_dilemma.interfaces.base.Rounds` object which can be used to get the results of prior rounds.

The function should return `True` for cooperation, and `False` for defection.



If you want to add mutations, set the static mutation list *after* defining the class as to avoid circular imports.



A template has been provided in `templates/algorithm_template.py` for ease of use.



## Simulations

Simulations a more complicated to configure as compared to algorithms.

You only need to override the `run_simulation` and `process_simulation` methods, but these are incredibly important.



`run_simulation` returns a `project_dilemma.interfaces.base.Simulations` object that will be used by

`process_simulation` to get the results.



For example, the provided standard simulations process the rounds data to calculate scores for each node

A template can be found in `templates/simulation_template.py`.



### Generational Simulations

Generational Simulations are deceptively simple.

There is only one function to override: `generational_hook`.

However, this means that all the generational processing must be done in this function.



A template has been provided in `templates/generational_simulation_template.py`.



## License

Copyright 2023 Gabriele Ron



Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at



    http://www.apache.org/licenses/LICENSE-2.0



Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.

---

This project utilizes the [platformdirs](https://github.com/platformdirs/platformdirs) project which is licensed under the MIT License.

Copyright (c) 2010-202x The platformdirs developers