Can LLMs handle strategic agents with externalities?
Strategic Classification with Externalities
October 11, 2024
https://arxiv.org/pdf/2410.08032This paper studies how to develop AI classifiers that are robust against manipulation from multiple, interacting users. Traditional strategic classification considers how a single user might game the system, while this work explores the more realistic scenario where users' actions affect each other, creating ripple effects.
For developers of LLM-based multi-agent systems, this research provides:
- A framework for modeling externalities: It shows how to mathematically express the influence that agents in a system have on each other's actions.
- Equilibrium analysis: The paper offers conditions under which a stable outcome (Nash Equilibrium) exists and can be efficiently calculated, essential for predicting agent behavior.
- Learning guarantees: It demonstrates that under certain assumptions, an AI can learn to create classifiers that perform well even with a varying number of interacting agents trying to game it.
- Gradient-based optimization: The paper outlines how to use gradient-based methods (common in machine learning) to train these robust classifiers.
This has major implications for building LLM-based agents that are more resilient to manipulation and can operate reliably in complex, multi-agent environments.