用LLM增强的多智能体框架模拟人类策略推理，表现优于传统模型。
Approximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames
- 基于超博弈的层级信念模型，实现角色化多智能体策略互动
- 在单次两人猜谜游戏中，人工推理者逼近人类行为并达最优解
- 适合研究博弈论、社会模拟与大模型战略能力的学者
基于LLM的多智能体仿真在博弈论与社会模拟中日益流行。现有方法多采用弱代理概念和简化架构。本文构建了面向复杂递归推理的角色化多智能体框架，支持系统性开发与评估策略推理。游戏由裁判负责匹配、走步验证与环境管理。玩家采用前沿大语言模型，依赖形式化的超博弈层级信念模型进行决策。我们通过单次、两人制猜谜游戏评估最新LLMs的递归推理能力，并与经济学基准模型及人类实验数据对比。此外，提出一种新的语义化推理度量方式，用于k级理论分析。实验表明，人工推理者在逼近人类行为与达成最优解方面均优于基准模型。
原文摘要 · Abstract (English)
LLM-driven multi-agent-based simulations have been gaining traction with applications in game-theoretic and social simulations. While most implementations seek to exploit or evaluate LLM-agentic reasoning, they often do so with a weak notion of agency and simplified architectures. We implement a role-based multi-agent strategic interaction framework tailored to sophisticated recursive reasoners, providing the means for systematic in-depth development and evaluation of strategic reasoning. Our game environment is governed by the umpire responsible for facilitating games, from matchmaking through move validation to environment management. Players incorporate state-of-the-art LLMs in their decision mechanism, relying on a formal hypergame-based model of hierarchical beliefs. We use one-shot, 2-player beauty contests to evaluate the recursive reasoning capabilities of the latest LLMs, providing a comparison to an established baseline model from economics and data from human experiments. Furthermore, we introduce the foundations of an alternative semantic measure of reasoning to the k-level theory. Our experiments show that artificial reasoners can outperform the baseline model in terms of both approximating human behaviour and reaching the optimal solution.
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