[Paper] Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play
Source: arXiv - 2606.19308v1
Overview
Large language model (LLM)-based multi-agent systems (MAS) have demonstrated great potential in solving tasks with execution complexity, by distributing subtasks across cooperative agents. However, this divide-and-conquer paradigm falls short on decision-making tasks that are also prevalent in the real world. These tasks require simultaneous reasoning from the stances of all involved stakeholders whose decisions are mutually dependent and thus cannot be solved in isolation. We characterize this challenge as stance entanglement, a form of decision complexity distinct from execution complexity. To address it, we propose Multi-Agent Fictitious Play (MAFP), a novel MAS paradigm that represents stakeholder stances as agents and formulates decision-making as an equilibrium-seeking process. Built on the game-theoretic principle of fictitious play, MAFP iteratively updates each agent’s decision by best responding to the empirical mixture of other agents’ past decisions. This enables agents to expose and address one another’s weaknesses, progressively improving decision quality and robustness. We evaluate MAFP on challenging decision-making tasks that test the capability of deciding strategies for competitive scenarios prior to acting. MAFP outperforms both single-round and multi-round baselines on two complementary metrics, tournament strength and robustness, demonstrating its effectiveness in addressing stance entanglement.
Key Contributions
This paper presents research in the following areas:
- cs.CL
- cs.MA
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.CL.
Authors
- Leyang Shen
- Yang Zhang
- Xiaoyan Zhao
- Chun Kai Ling
- Tat-Seng Chua
Paper Information
- arXiv ID: 2606.19308v1
- Categories: cs.CL, cs.MA
- Published: June 17, 2026
- PDF: Download PDF