Computational Life: How Well-formed, Self-replicating Programs Emerge from Simple Interaction
This addresses a foundational question in Origin of Life and Artificial Life about the emergence of life-like properties from computational rules, but it is incremental as it builds on existing hypotheses and computational studies.
The paper tackled the problem of how self-replicating programs emerge from simple computational substrates without explicit fitness landscapes, showing that self-replicators tend to arise from random interactions and self-modification, with increasingly complex dynamics following their emergence, and provided a counterexample where they do not arise.
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.