Game-Theoretic Lens on LLM-based Multi-Agent Systems

2026-01-21

AI Summary

Current research on large language model (LLM) multi-agent systems lacks a unified theoretical framework, making it difficult to systematically characterize their social interactions and strategic behaviors. This work addresses this gap by introducing game theory as a foundational lens, constructing an interpretable, comparable, and scalable analytical and design framework centered on four core elements: players, strategies, payoffs, and information. Through game-theoretic modeling, comprehensive literature review, and integrative framework synthesis, the study presents the first unified taxonomy specifically tailored for LLM multi-agent systems. The proposed framework not only offers a structured basis for classifying existing approaches but also provides essential theoretical grounding for the future design, evaluation, and development of such systems, effectively bridging the fragmented landscape in this emerging field.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language models (LLMs) have demonstrated strong reasoning, planning, and communication abilities, enabling them to operate as autonomous agents in open environments. While single-agent systems remain limited in adaptability and coordination, recent progress has shifted attention toward multi-agent systems (MAS) composed of interacting LLMs that pursue cooperative, competitive, or mixed objectives. This emerging paradigm provides a powerful testbed for studying social dynamics and strategic behaviors among intelligent agents. However, current research remains fragmented and lacks a unifying theoretical foundation. To address this gap, we present a comprehensive survey of LLM-based multi-agent systems through a game-theoretic lens. By organizing existing studies around the four key elements of game theory: players, strategies, payoffs, and information, we establish a systematic framework for understanding, comparing, and guiding future research on the design and analysis of LLM-based MAS.

Problem

• LLM-based multi-agent systems
• game theory
• theoretical foundation
• fragmented research
• systematic framework

Innovation

• game theory
• large language models
• multi-agent systems
• strategic interaction
• systematic framework