This study proposes the Behavioral Protocol Framework (BPF), an entropy-controlled pluralistic alignment framework designed to address two critical challenges in autonomous agent economies: the hivemind effect arising from excessive strategic convergence among agents and the lack of transparency in autonomous decision-making processes. The proposed BPF consists of three core modules: Mentalizing-based Social Intelligence (MbSI) grounded in Theory of Mind (ToM), Pluralistic Alignment (PA), and a Verifiable Execution Kernel (VEK). These modules are organically integrated within a closed-loop architecture that governs the entire lifecycle of agent behavior, from decision-making and execution to verification and feedback. To evaluate the proposed framework, a simulation environment implemented in Python and a Streamlit-based user interface will be developed. Through empirical experimentation, the study aims to examine whether the entropy-control mechanism of the PA module can effectively preserve strategic diversity among agents and mitigate collective convergence, while the VEK module provides a comprehensive and transparent audit trail of the decision-making process. The anticipated results are expected to demonstrate that the proposed framework can simultaneously enhance the stability, efficiency, and trustworthiness of autonomous agent economies. Consequently, this research offers a practical approach for developing robust, transparent, and accountable agent-native economic systems.

翻译：本研究提出行为协议框架（BPF），这是一种基于熵控的多元对齐框架，旨在解决自主智能体经济学中的两个关键挑战：一是由智能体间过度策略趋同引发的蜂巢思维效应，二是自主决策过程缺乏透明度的问题。BPF框架由三个核心模块构成：基于心理理论（ToM）的心智化社会智能模块（MbSI）、多元对齐模块（PA）以及可验证执行内核模块（VEK）。这些模块有机整合于一个闭环架构中，该架构统筹管理智能体行为从决策、执行到验证及反馈的全生命周期。为评估所提框架，将基于Python开发仿真环境并构建Streamlit用户界面。通过实证实验，本研究旨在考察PA模块的熵控机制能否有效维持智能体间战略多样性并抑制集体趋同现象，同时检验VEK模块是否能为决策过程提供全面透明的审计追踪。预期结果表明，所提框架能够协同提升自主智能体经济系统的稳定性、效率与可信度。因此，本研究为开发具备鲁棒性、透明度及可问责性的智能体原生经济系统提供了实践路径。