How many Nash equilibria exist in LQ games?
Nash equilibria in scalar discrete-time linear quadratic games
This paper examines finding "Nash Equilibria" in systems where multiple AI agents interact, specifically focusing on a simple scenario with two agents and linear dynamics. A Nash Equilibrium is a state where no agent can improve its outcome by changing its strategy alone.
The key takeaway for LLM-based multi-agent systems is the use of "Gröbner bases," a mathematical tool, to analyze and predict how many different Nash Equilibria exist within these systems and even calculate them directly. This has implications for designing robust LLM-based agents that can anticipate and navigate complex interactions, potentially leading to more predictable and stable multi-agent applications. However, the paper also highlights that these methods become computationally much harder as the number of agents increase, indicating limitations for complex, real-world multi-agent LLM systems.