Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents

2026-06-08

AI Summary

This study addresses the challenges of “herd mentality” and opaque decision-making arising from strategic convergence in autonomous agent economies. To mitigate these issues, the authors propose the Behavioral Protocol Framework (BPF), a closed-loop architecture that integrates Mentalizing-based Social Intelligence (MbSI), Entropy-Controlled Pluralistic Alignment (PA) mechanisms, and a Verifiable Execution Kernel (VEK). This integration preserves strategic diversity while enabling auditable, transparent decision processes. Evaluated through Python-based simulations with a Streamlit interface, the framework demonstrates significant improvements in system stability, operational efficiency, and trustworthiness, alongside providing a complete, traceable audit trail of agent decisions.

Abstract

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.

Problem

• hivemind
• strategic convergence
• autonomous agents
• decision-making transparency
• agent economics

Innovation

• entropy-controlled alignment
• pluralistic alignment
• verifiable execution kernel
• Theory of Mind
• autonomous agent economy