Prisoner’s Dilemma Agent
This project explored how autonomous agents behave in a repeated Prisoner’s Dilemma environment, where each agent must decide whether to cooperate or defect based on strategy, incentives, and prior interactions.
The project connects game theory, multi-agent systems, and AI decision-making.
This was a group project based on a copied repository. My contribution focused on understanding the agent strategies, testing behavior, interpreting outcomes, and preparing the project for portfolio presentation.
Project Overview
The Prisoner’s Dilemma is a classic game theory problem used to study cooperation and competition. In this project, agents repeatedly interact and make decisions based on programmed strategies or learned behavior.
The simulation helps demonstrate how simple decision rules can lead to complex strategic patterns over time.
What I Worked On
- Tested agent behavior across repeated game rounds
- Compared cooperation and defection patterns
- Interpreted strategy outcomes using a game theory lens
- Reviewed the codebase and documented the agent workflow
- Prepared the project for public GitHub presentation
Technical Focus
What This Project Demonstrates
This project shows my interest in building AI systems that do more than generate text. It demonstrates how agents can be used to model strategic behavior, decision-making, cooperation, and competition in simulated environments.