Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents
2026-06-08 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors propose a new system called the Behavioral Protocol Framework (BPF) to help groups of autonomous agents avoid all acting the same way, which can cause problems in agent economies. Their system includes three parts: one that helps agents understand each other’s intentions (Mentalizing-based Social Intelligence), one that maintains a mix of different strategies (Pluralistic Alignment), and one that keeps a clear record of decisions (Verifiable Execution Kernel). They plan to test their framework using simulations to see if it keeps agents diverse in their behavior and makes decision-making transparent. The goal is to improve how stable, efficient, and trustworthy these agent economies can be.
Autonomous agentsAgent economyHivemind effectTheory of MindMentalizingPluralistic AlignmentEntropy-controlVerifiable ExecutionAgent decision-makingSimulation
Authors
Cheonsu Jeong
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.