BFF: Simple explanations for complex phenomena

2026-07-01

AI Summary

This study investigates whether self-replicating programs can be efficiently discovered through random walks driven by simple mutations, without relying on pairwise interactions, and examines whether structural constraints on the depth and breadth of ancestral trees genuinely impede their emergence. To address this, we simulate mutation-driven random walks in program space, integrating state-of-the-art self-replicator detection methods with structural constraint strategies. Our results demonstrate that simple mutation mechanisms are at least as effective as pairwise interactions in discovering self-replicators. Moreover, while constraints on ancestral tree structure suppress the proliferation and dominance of self-replicators within populations, they do not prevent their initial emergence. These findings reveal a viable pathway for the spontaneous emergence of self-replication without complex interactions and clarify the precise limits of structural constraints in this context.

Abstract

The ''Computational Life'' paper (Agüera y Arcas et al., 2024) argues that paired interactions in a computational soup are an effective way to find self-replicators. In this work, aided by recent developments in self-replicator detection, we explore the alternate hypothesis that self-replicators can be found at least as easily using simple mutation random walks in program space. We also explore the claim that capping the maximum ''depth'' and ''width'' of the ancestry tree stops self-replicators from emerging, showing instead that it merely stops self-replicators from taking over the soup.

Problem

• self-replicators
• computational soup
• mutation random walks
• ancestry tree
• emergence

Innovation

• self-replicators
• mutation random walks
• program space
• ancestry tree constraints
• computational life