Version 1
: Received: 20 July 2024 / Approved: 22 July 2024 / Online: 23 July 2024 (16:44:40 CEST)
How to cite:
Bai, J. Which Machine-Learning Model Do You Want In Your Ocean’s Eleven: A Computational Prisoner’s Dilemma Simulation. Preprints2024, 2024071759. https://doi.org/10.20944/preprints202407.1759.v1
Bai, J. Which Machine-Learning Model Do You Want In Your Ocean’s Eleven: A Computational Prisoner’s Dilemma Simulation. Preprints 2024, 2024071759. https://doi.org/10.20944/preprints202407.1759.v1
Bai, J. Which Machine-Learning Model Do You Want In Your Ocean’s Eleven: A Computational Prisoner’s Dilemma Simulation. Preprints2024, 2024071759. https://doi.org/10.20944/preprints202407.1759.v1
APA Style
Bai, J. (2024). Which Machine-Learning Model Do You Want In Your Ocean’s Eleven: A Computational Prisoner’s Dilemma Simulation. Preprints. https://doi.org/10.20944/preprints202407.1759.v1
Chicago/Turabian Style
Bai, J. 2024 "Which Machine-Learning Model Do You Want In Your Ocean’s Eleven: A Computational Prisoner’s Dilemma Simulation" Preprints. https://doi.org/10.20944/preprints202407.1759.v1
Abstract
Which machine-learning model is the best at winning the prisoner’s dilemma? Which models create the best cumulative outcomes? Is there a model that perfectly captures both winning and cumulative points? These are questions generated from a simple 2 x 2 payoff matrix of the prisoner’s dilemma. Imagine you and your partner in crime are caught and sent independently into questioning. You can either collaborate or defect, but you don’t know what your partner will do. If you both collaborate, cumulatively, you’ll each get one year in jail. If you defect and your partner collaborates, you’ll serve no time and they will serve 10 years, and vice versa. If you both defect, you’ll both serve 5 years in jail. Placing AIs against each other to play just one round doesn’t reveal much about their code, strategy, and end goal. So the Reinforcement Learning, Pattern Learning, Tit For Tat, and other models were put up against each other in a 100 round game where their behavior, convergence, and learning were analyzed to reveal the most effective ways strategies to beat the prisoner’s dilemma. All code and data is open sourced here.
Keywords
Game Theory; Prisoner's Dilemma; Machine Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
