基于近似完美贝叶斯均衡的信念驱动多智能体协作：用于社会模拟
Belief-Driven Multi-Agent Collaboration via Approximate Perfect Bayesian Equilibrium for Social Simulation
AI总结：
本文提出BEACOF框架，通过动态博弈模型解决多智能体协作中的信念与能力估计循环依赖问题，验证其在对抗、开放和混合场景中的有效性，提升社会模拟的可靠性。
摘要：
高保真社会模拟对解决复杂网络社会挑战至关重要，但需要能真实复现人类互动动态光谱的智能体。当前基于大语言模型的多智能体框架主要采用静态交互拓扑，无法捕捉现实世界中协作知识合成与竞争批判推理之间的流动振荡。本文提出BEACOF，受完美贝叶斯均衡启发，将社会互动建模为动态不完全信息博弈，通过迭代更新智能体对同伴能力的信念并自主调节协作策略，确保在不确定性下的序列理性决策。在对抗、开放和混合场景中验证，BEACOF防止协调失败并促进向高质量解决方案的稳健收敛，展示出在可靠社会模拟中的优越潜力。源代码和数据集已公开发布：https://github.com/WUT-IDEA/BEACOF。
英文摘要：
High-fidelity social simulation is pivotal for addressing complex Web societal challenges, yet it demands agents capable of authentically replicating the dynamic spectrum of human interaction. Current LLM-based multi-agent frameworks, however, predominantly adhere to static interaction topologies, failing to capture the fluid oscillation between cooperative knowledge synthesis and competitive critical reasoning seen in real-world scenarios. This rigidity often leads to unrealistic ``groupthink'' or unproductive deadlocks, undermining the credibility of simulations for decision support. To bridge this gap, we propose \textit{BEACOF}, a \textit{belief-driven adaptive collaboration framework} inspired by Perfect Bayesian Equilibrium (PBE). By modeling social interaction as a dynamic game of incomplete information, BEACOF rigorously addresses the circular dependency between collaboration type selection and capability estimation. Agents iteratively refine probabilistic beliefs about peer capabilities and autonomously modulate their collaboration strategy, thereby ensuring sequentially rational decisions under uncertainty. Validated across adversarial (judicial), open-ended (social) and mixed (medical) scenarios, BEACOF prevents coordination failures and fosters robust convergence toward high-quality solutions, demonstrating superior potential for reliable social simulation. Source codes and datasets are publicly released at: https://github.com/WUT-IDEA/BEACOF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
