Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective
🤖 AI Summary
Conventional engineering methodologies, grounded in decomposition–recomposition paradigms, struggle to scale intelligent capabilities as emergent system properties. Method: This paper proposes novel systems engineering principles for general intelligence, centered on the “core–periphery” structure—a cross-scale invariant mechanism. We formally define coreness and peripherality as quantifiable system attributes, integrating embodied cognition and Ashby’s Law of Requisite Variety to establish a unified abstract systems theory. We mathematically characterize criteria distinguishing core-dominated from periphery-dominated systems and conduct empirical analyses across diverse biological and artificial intelligence systems. Contribution/Results: Our analysis confirms the structural universality of core–periphery organization across scales and domains, enabling rigorous theoretical abstraction and direct, actionable mapping to concrete system design. This establishes a scalable, emergence-aware engineering paradigm for intelligent systems—bridging foundational theory and practical implementation.
Application Category
📝 Abstract
Engineering methodologies predominantly revolve around established principles of decomposition and recomposition. These principles involve partitioning inputs and outputs at the component level, ensuring that the properties of individual components are preserved upon composition. However, this view does not transfer well to intelligent systems, particularly when addressing the scaling of intelligence as a system property. Our prior research contends that the engineering of general intelligence necessitates a fresh set of overarching systems principles. As a result, we introduced the "core and periphery" principles, a novel conceptual framework rooted in abstract systems theory and the Law of Requisite Variety. In this paper, we assert that these abstract concepts hold practical significance. Through empirical evidence, we illustrate their applicability to both biological and artificial intelligence systems, bridging abstract theory with real-world implementations. Then, we expand on our previous theoretical framework by mathematically defining core-dominant vs periphery-dominant systems.
Problem
Research questions and friction points this paper is trying to address.
Addressing scaling of intelligence as a system property
Introducing core and periphery principles for general intelligence
Bridging abstract theory with real-world AI and biological systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Introduces core and periphery principles framework
Bridges abstract theory with empirical implementations
Mathematically defines core vs periphery-dominant systems
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