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

2025-08-11

AI Summary

This study addresses the existence of spontaneous, distributed self-replication in cellular automata. We propose a data-driven framework based on causal lineage reconstruction, providing the first formal proof—within a deterministic system—that robust, initialization-free self-replication can spontaneously emerge. Methodologically, we integrate causal analysis with dynamical modeling to enable complete causal tracing and identification of multi-component structures under the Outlier rule. Our key contributions are: (1) a paradigm shift from centralized definitions of “individual” and “replication” in artificial life toward a distributed, multi-fragment cooperative replication model; and (2) empirical demonstration that diverse stable self-replicating structures spontaneously emerge and coordinate without external intervention under the Outlier rule—offering novel theoretical tools and empirical foundations for understanding the origins of life and self-organization in complex systems.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Models for Web evolution
• Security and Privacy: Data transparency and provenance

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.

Problem

• Detecting spontaneous self-replication in cellular automata
• Analyzing distributed multi-component self-replicating structures
• Challenging conventional notions of individuality in artificial life

Innovation

• Data-driven framework for causal ancestry
• Identifies self-replicators via causal lineages
• Detects distributed multi-component self-replication