Emergence-focused design in complex system simulation
This work addresses the problem of understanding and simulating emergence in complex systems for researchers in computational biology and artificial life, but it appears incremental as it builds on existing principles without claiming major breakthroughs.
The authors tackled the challenge of modeling multilevel emergence in complex systems by developing an artificial genetic evolution model, which successfully reproduced genetic dynamics such as genotype-phenotype divergence and gene duplication.
Emergence is a phenomenon taken for granted in science but also still not well understood. We have developed a model of artificial genetic evolution intended to allow for emergence on genetic, population and social levels. We present the details of the current state of our environment, agent, and reproductive models. In developing our models we have relied on a principle of using non-linear systems to model as many systems as possible including mutation and recombination, gene-environment interaction, agent metabolism, agent survival, resource gathering and sexual reproduction. In this paper we review the genetic dynamics that have emerged in our system including genotype-phenotype divergence, genetic drift, pseudogenes, and gene duplication. We conclude that emergence-focused design in complex system simulation is necessary to reproduce the multilevel emergence seen in the natural world.