Behavioral Universe Network (BUN): A Behavioral Information-Based Framework for Complex Systems
Wei Zhou, Ailiya Borjigin, Cong He
TL;DR
The paper addresses the challenge of modeling and governing complex, cross-domain interactions by introducing the Behavioral Universe Network (BUN), a framework rooted in the Agent-Interaction-Behavior (AIB) formalism. BUN unifies subjects, objects, and behaviors as first-class entities within a shared Behavioral Information Base (BIB), enabling information-driven triggers, semantic enrichment, and adaptive rules to coordinate multi-agent systems. The approach formalizes Behavior = S : f(O) with policy constraints $P_1$, $P_2$, and $P_3$, and centralizes histories, semantics, models, and rules in the BIB to achieve deep coordination. The proposed framework offers tangible benefits in behavior analysis, adaptability, and cross-domain interoperability, positioning BUN as a foundational construct for next-generation digital governance and intelligent applications.
Abstract
Modern digital ecosystems feature complex, dynamic interactions among autonomous entities across diverse domains. Traditional models often separate agents and objects, lacking a unified foundation to capture their interactive behaviors. This paper introduces the Behavioral Universe Network (BUN), a theoretical framework grounded in the Agent-Interaction-Behavior (AIB) formalism. BUN treats subjects (active agents), objects (resources), and behaviors (operations) as first-class entities, all governed by a shared Behavioral Information Base (BIB). We detail the AIB core concepts and demonstrate how BUN leverages information-driven triggers, semantic enrichment, and adaptive rules to coordinate multi-agent systems. We highlight key benefits: enhanced behavior analysis, strong adaptability, and cross-domain interoperability. We conclude by positioning BUN as a promising foundation for next-generation digital governance and intelligent applications.