Entropy, Symmetry, and the Difficulty of Self-Replication

TL;DR

This paper explores how entropy and symmetry influence the complexity, disorder tolerance, and design principles of self-replicating systems, integrating concepts from multiple mathematical disciplines.

Contribution

It introduces a framework combining entropy and symmetry to analyze and improve the reliability of self-replication in disordered environments.

Findings

Symmetry reduces assembly errors in self-replicating systems.

Entropy measures help quantify disorder and replication success.

Mathematical integration offers new insights into self-replication design principles.

Abstract

The defining property of an artificial physical self-replicating system, such as a self-replicating robot, is that it has the ability to make copies of itself from basic parts. Three questions that immediately arises in the study of such systems are: 1) How complex is the whole robot in comparison to each basic part ? 2) How disordered can the parts be while having the robot successfully replicate ? 3) What design principles can enable complex self-replicating systems to function in disordered environments generation after generation ? Consequently, much of this article focuses on exploring different concepts of entropy as a measure of disorder, and how symmetries can help in reliable self replication, both at the level of assembly (by reducing the number of wrong ways that parts could be assembled), and also as a parity check when replicas manufacture parts generation after generation. The mathematics underpinning these principles that quantify artificial physical self-replicating systems are articulated here by integrating ideas from information theory, statistical mechanics, ergodic theory, group theory, and integral geometry.