Rethinking Self-Replication: Detecting Distributed Selfhood in the Outlier Cellular Automaton
This challenges conventional notions of individuality and replication in artificial life systems, though it is incremental as it builds on prior work on complex dynamics.
The paper tackled the problem of spontaneous self-replication in cellular automata, showing that it can emerge unassisted in a distributed, multi-component form in the Outlier rule, with definitive evidence of robust replicators.
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.