Welcome to the Prisoner's Dilemma simulation, where you'll immerse yourself in the classic game theory scenario using Python and Pygame. In this interactive environment, you'll explore decision-making strategies, analyze outcomes, and delve into the intricacies of cooperation and betrayal.
- Pygame Interface: Immerse yourself in a visually captivating environment crafted with Pygame.
- Decision Strategies: Experiment with various decision-making strategies, from calculated tit-for-tat to unpredictable randomness.
- Real-time Analytics: Observe live statistics and visualizations to understand the consequences of your choices.
- Customizable Parameters: Adjust simulation parameters such as rounds played, opponent strategies, and more.
- Educational Insights: Gain insights into game theory concepts and the dynamics of strategic interactions.
- Installation: Clone or download the repository to your local machine.
- Dependencies: Ensure you have Python and Pygame installed on your system.
- Execution: Run the
prisoners_dilemma.pyscript to start the simulation. - Strategy Selection: Choose your decision strategy and witness its impact on the outcome.
- Analysis: Explore numerical data and graphical representations to understand the implications of different strategies.
- Iterate and Learn: Experiment with different strategies, tweak parameters, and refine your understanding of strategic decision-making.
- Strategy: Start with cooperation and then mirror the opponent's previous move.
- Description: Forgiving but firm, as it retaliates against defection but forgives and returns to cooperation if the opponent cooperates again.
- Strategy: Start with cooperation. Continue cooperating as long as you win or draw; switch to defection if you lose.
- Description: Reactive and adaptive, adjusting based on the outcome of previous interactions.
- Strategy: Start with cooperation. Keep the same action if it resulted in the highest payoff in the previous round; switch actions otherwise.
- Description: Reinforces successful strategies while abandoning unsuccessful ones, promoting stability.
- Strategy: Choose cooperation or defection randomly, with equal probability, in each round.
- Description: Introduces unpredictability into the game, sometimes exploiting overly deterministic opponents.
- Strategy: Start with cooperation and continue cooperating unless the opponent defects; switch to defection permanently if the opponent defects.
- Description: Punishes defection severely, promoting long-term cooperation.
- Strategy: Cooperate unless the opponent defects twice in a row.
- Description: More forgiving than TFT, allowing for occasional mistakes by the opponent without triggering retaliation.
- Strategy: Mimic the opponent's previous move, but with a small probability of error.
- Description: Introduces noise into the decision-making process, making it less predictable.
- Strategy: Occasionally forgive the opponent's defection without immediate cooperation in return.
- Description: Similar to TFT but with occasional unconditional cooperation, promoting flexibility.
- Strategy: Start with defection and then mimic the opponent's previous move.
- Description: Opposite of TFT, adjusting based on the opponent's previous move.
- Strategy: Always cooperate regardless of the opponent's move.
- Description: Maintains cooperation and can exploit opponents who defect in the long run.
- Strategy: Always defect regardless of the opponent's move.
- Description: Maximizes personal payoff but often leads to mutual defection and lower overall payoffs in repeated interactions.
- Strategy: Defects to check how opponent reacts and tries taking advantage of them.
- Description: Tests opponents based on reaction.
- Advanced AI Models: Develop sophisticated AI algorithms to simulate human-like decision-making.
- Multiplayer Integration: Enable multiplayer functionality for interactive gameplay with friends or online opponents.
- Enhanced Visualization: Implement stunning graphical enhancements for a more engaging user experience.
- Statistical Insights: Integrate comprehensive statistical analysis tools to dissect gameplay patterns and outcomes.
Your contributions are valuable! Whether it's enhancing existing features, suggesting new ideas, or fixing bugs, your input is welcome. Feel free to open issues or submit pull requests to contribute to the project's growth.
This project is licensed under the MIT License - see the LICENSE file for details.