Drivers of Replicator Organisation in the Nanopond Artificial Chemistry

Abstract

Automata chemistries are a field within Artificial Life that studies how populations of self-replicating programs control their own evolution. Nanopond is an automata chemistry that until now had no publications available to document its features, no studies on what its outputs are, and no comparisons with other systems. Yet it is relatively easy to set up and compile; it runs very quickly, and can be parallelised; and it produces graphical results that suggest a level of emergent complexity worthy of scientific study. In this work, the emergent complexity observable in Nanopond is characterised and placed in the context of similar systems, notably Tierra, Avida, Amoeba and Stringmol. Premliminary results are presented which show how several common self-replicating attractor states in the emergent patterns exhibited by Nanopond are related to the distribution of energy around the system, demonstrating important properties of emergent complexity.