# Core and Periphery as Closed-System Precepts for Engineering General Intelligence

arXiv: https://arxiv.org/abs/2208.02837 · HTML: https://mlsanity.com/paper/2208.02837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Open Ended Intelligence: The individuation of Intelligent Agents](https://mlsanity.com/paper/1505.06366/) (2015)
- [Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like](https://mlsanity.com/paper/1604.04660/) (2016)
- [Mesarovician Abstract Learning Systems](https://mlsanity.com/paper/2111.14766/) (2021)
- [Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure](https://mlsanity.com/paper/2101.12047/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Systems-Theoretical Formalization of Closed Systems](https://mlsanity.com/paper/2311.10786/) (2023)
- [The Systems Engineering Approach in Times of Large Language Models](https://mlsanity.com/paper/2411.09050/) (2024)
- [Exploring Core and Periphery Precepts in Biological and Artificial Intelligence: An Outcome-Based Perspective](https://mlsanity.com/paper/2507.04594/) (2025)

## Abstract (arXiv)

Engineering methods are centered around traditional notions of decomposition and recomposition that rely on partitioning the inputs and outputs of components to allow for component-level properties to hold after their composition. In artificial intelligence (AI), however, systems are often expected to influence their environments, and, by way of their environments, to influence themselves. Thus, it is unclear if an AI system's inputs will be independent of its outputs, and, therefore, if AI systems can be treated as traditional components. This paper posits that engineering general intelligence requires new general systems precepts, termed the core and periphery, and explores their theoretical uses. The new precepts are elaborated using abstract systems theory and the Law of Requisite Variety. By using the presented material, engineers can better understand the general character of regulating the outcomes of AI to achieve stakeholder needs and how the general systems nature of embodiment challenges traditional engineering practice.