Rethinking Self-Replication: Detecting Distributed Selfhood in the Outlier Cellular Automaton

TL;DR

This paper provides formal evidence that spontaneous, distributed self-replication can emerge in cellular automata, challenging traditional views and highlighting complex multi-component self-replicating structures.

Contribution

It introduces a data-driven causal framework to identify and analyze self-replicating patterns in cellular automata, demonstrating their spontaneous and distributed nature.

Findings

Self-replicators in the Outlier CA are spontaneous and robust.

Self-replicators often consist of multiple disjoint clusters.

Distributed self-replication raises questions about individuality in artificial life.

Abstract

Spontaneous self-replication in cellular automata has long been considered rare, with most known examples requiring careful design or artificial initialization. In this paper, we present formal, causal evidence that such replication can emerge unassisted -- and that it can do so in a distributed, multi-component form. Building on prior work identifying complex dynamics in the Outlier rule, we introduce a data-driven framework that reconstructs the full causal ancestry of patterns in a deterministic cellular automaton. This allows us to rigorously identify self-replicating structures via explicit causal lineages. Our results show definitively that self-replicators in the Outlier CA are not only spontaneous and robust, but are also often composed of multiple disjoint clusters working in coordination, raising questions about some conventional notions of individuality and replication in artificial life systems.