This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
The Paradox of Self-Interest in Multi-Agent Systems
In the realm of artificial intelligence, reinforcement learning (RL) has emerged as a powerful tool for training agents to make decisions in complex environments. However, when we introduce multiple agents into the mix, a fascinating yet unsettling phenomenon arises: the potential for self-interest to undermine global objectives.
The Pursuit of Personal Gain
In a multi-agent scenario, each agent's primary goal is to maximize its own reward function. This leads to a situation where each agent independently optimizes its own interests, without considering the broader implications for the system as a whole. This is known as the "tragedy of the commons" effect.
Paradoxical Situations
Consider a scenario where two agents are tasked with managing a shared resource, such as a water supply. Each agent's goal is to maximize its own water usage, without regard for the other agent's needs. As both agents pursue their individu...
This post was originally shared as an AI/ML insight. Follow me for more expert content on artificial intelligence and machine learning.
This content originally appeared on DEV Community and was authored by Dr. Carlos Ruiz Viquez
Dr. Carlos Ruiz Viquez | Sciencx (2025-10-01T16:22:20+00:00) **The Paradox of Self-Interest in Multi-Agent Systems** In. Retrieved from https://www.scien.cx/2025/10/01/the-paradox-of-self-interest-in-multi-agent-systems-in/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.