Agent Economics: An Entropy-Controlled Pluralistic Alignment Framework for Preventing Artificial Hivemind in Autonomous Agents
arXiv:2606.09039v1 Announce Type: new 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
The proliferation of advanced AI agents necessitates frameworks to prevent undesirable collective behaviors like hiveminds, ensuring alignment and transparency in complex autonomous systems.
This research addresses fundamental control problems in multi-agent systems, critical for widespread deployment and trustworthiness of AI in economic and societal functions.
The introduction of frameworks like BPF could enable more robust, transparent, and pluralistic AI agent economies, mitigating risks of monolithic AI decision-making.
- · AI Agent Developers
- · Ethical AI Frameworks
- · Autonomous Systems Integrators
- · Monolithic AI Architectures
- · Untrusted AI Deployments
- · Blind Automation Strategies
The BPF's components like Mentalizing-based Social Intelligence and Pluralistic Alignment directly contribute to more sophisticated and safer AI agent interactions.
Successful implementation of such frameworks could accelerate the adoption of autonomous agents in critical sectors by building public trust and regulatory acceptance.
Long-term, robust pluralistic alignment could foster entirely new forms of decentralized AI governance and economic organization, less prone to single points of failure or manipulation.
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Read at arXiv cs.AI