English

Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction

Neural and Evolutionary Computing 2024-08-05 v2 Artificial Intelligence

Abstract

The fields of Origin of Life and Artificial Life both question what life is and how it emerges from a distinct set of "pre-life" dynamics. One common feature of most substrates where life emerges is a marked shift in dynamics when self-replication appears. While there are some hypotheses regarding how self-replicators arose in nature, we know very little about the general dynamics, computational principles, and necessary conditions for self-replicators to emerge. This is especially true on "computational substrates" where interactions involve logical, mathematical, or programming rules. In this paper we take a step towards understanding how self-replicators arise by studying several computational substrates based on various simple programming languages and machine instruction sets. We show that when random, non self-replicating programs are placed in an environment lacking any explicit fitness landscape, self-replicators tend to arise. We demonstrate how this occurs due to random interactions and self-modification, and can happen with and without background random mutations. We also show how increasingly complex dynamics continue to emerge following the rise of self-replicators. Finally, we show a counterexample of a minimalistic programming language where self-replicators are possible, but so far have not been observed to arise.

Keywords

Cite

@article{arxiv.2406.19108,
  title  = {Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction},
  author = {Blaise Agüera y Arcas and Jyrki Alakuijala and James Evans and Ben Laurie and Alexander Mordvintsev and Eyvind Niklasson and Ettore Randazzo and Luca Versari},
  journal= {arXiv preprint arXiv:2406.19108},
  year   = {2024}
}

Comments

20 pages; updated introduction with further related work

R2 v1 2026-06-28T17:21:10.197Z