How can I predict agent behavior using short-sightedness?
Inferring Short-Sightedness in Dynamic Noncooperative Games
This paper addresses the problem of inferring how far ahead agents (e.g., humans or robots) plan in multi-agent interactions, specifically when their objectives are unknown. It introduces a method to estimate the "short-sightedness" or "myopia" of agents by modeling how much they discount future costs. This is done by formulating an inverse dynamic game and solving it using gradient descent on a differentiable Mixed Complementarity Problem (MiCP) representing the game's equilibrium conditions.
For LLM-based multi-agent systems, this research is relevant because it offers a way to model and reason about agents with varying levels of planning depth. This could be used to improve the accuracy of predicting agent behavior, particularly in scenarios with limited information or noisy observations, which is typical for LLMs interacting in a complex environment. The ability to estimate short-sightedness could be incorporated into LLM agents' decision-making processes, leading to more robust and effective interactions. Moreover, using a MiCP formulation could offer computational advantages for integrating these concepts into practical LLM-based multi-agent applications.