Robustness and complexity

TL;DR

This paper explores how biological systems' robustness to errors influences component reliability, genetic variability, and complexity, linking biological and engineering systems through a unified framework.

Contribution

It introduces a unified framework connecting robustness, complexity, and system architecture across biological and engineered systems.

Findings

Robustness reduces direct pressure on component performance.

Biological systems exhibit increased genetic variability and neutral drift.

Connections between biological controls and neural networks highlight common principles.

Abstract

When a biological system robustly corrects component-level errors, the direct pressure on component performance declines. Components may become less reliable, maintain more genetic variability, or drift neutrally in design, creating the basis for new forms of organismal complexity. This article links the protection-decay dynamic to other aspects of robust and complex systems. Examples include the hourglass pattern of biological development and Doyle's hourglass architecture for robustly complex systems in engineering. The deeply and densely connected wiring architecture in biology's cellular controls and in machine learning's computational neural networks provide another link. By unifying these seemingly different aspects into a unified framework, we gain a new perspective on robust and complex systems.