dastardi / gametheory Goto Github PK

An easy-to-use resource created by Luke Currier and Rory Smead for modeling replicator dynamics using Python!

1. Go to https://colab.google/.

2. In the upper right corner, click New Notebook.

3. Once in your notebook, click File -> Upload Notebook -> Github.

4. Search for "rorysmead/gametheory".

5. Select "Current Version". It should load automatically.

6. Once in the notebook, you need to run the first two cells before you can run the third cell containing run(). You can do that using Shift->Return or by pressing the play button in the top left corner of the cell.

7. Once you've done that, you're off to the races! You can create new matrices by using the same format as the others in the second code cell (make sure you type your brackets and commas carefully, we'll try to figure out a better way of doing this soon!).

8. Additionally, you can edit mutation and correlation levels as well as change how the output appears using the variables listed below and in the notebook.

Mutation: mu
Default value: 0.00
The chance of mutation, from 0 to 1.

Correlation: r
Default value: 0.00
The chance of correlation, from 0 to 1.

Number of Runs: runs
Default value: 10
The number of times the program runs a randomized simulation according to the provided matrix.
The program will average results from all runs to arrive at a percentage spread of strategies.

Maximum Number of Generations: maxgen
Default value: 1000
The maximum number of replications in a given simulation.
This number will not be reached by most traditional games.

Print Runs: printruns
Default value: True
A boolean (True or False) that determines if the simulation shows results for each individual run in addition to the averaged results.

Print Generations: printgen
Default value: True
A boolean (True or False) that determines if the simulation shows how many generations it took for the simulation to stabilize.

Random Matrix: randfill
Default value: True
A boolean (True or False) that determines whether the matrix has random or uniform distribution.

This is an original project created by Luke Currier, assisted by and based on code from Rory Smead. It is meant to be an easy-to-use companion for non-technical game theorists who want to observe replicator dynamics for assorted game matrix combinations, and an alternative to popular but more complicated game theory simulation tools such as NashPy. Meant to be easy to understand and interact with, as well as build on top of if desired, its notebook format provides easy interaction while keeping it simple - a full UI is something that would be an awesome project in the future.

The notebook is organized into four sections - variables and a user guide, the backend code which runs the replicator dynamics, the editable matrix window, and a last slot to run the program without having to worry about interacting with the matrices. Users should feel free to mess around with the second two as much as they'd like and experiment, but I only recommend changing the code in the second section if you know what you're doing! Changing things in google colab won't alter the github code, so you'll be able to switch things up as much as you'd like on your personal copy of the code.

Thank you for using our project, and we hope you enjoy!