title: Understanding how replication processes can maintain systems away from equilibrium using Algorithmic Information Theory.
abstract: Replication can be envisaged as a computational process that is able to generate and maintain order far-from-equilibrium. Replication processes, can self-regulate, as the drive to replicate can counter degradation processes that impact on a system. The capability of replicated structures to access high quality energy and eject disorder allows Landauer's principle, in conjunction with Algorithmic Information Theory, to quantify the entropy requirements to maintain a system far-from-equilibrium. Using Landauer's principle, where destabilising processes, operating under the second law of thermodynamics, change the information content or the algorithmic entropy of a system by ΔH bits, replication processes can access order, eject disorder, and counter the change without outside interventions. Both diversity in replicated structures, and the coupling of different replicated systems, increase the ability of the system (or systems) to self-regulate in a changing environment as adaptation processes select those structures that use resources more efficiently. At the level of the structure, as selection processes minimise the information loss, the irreversibility is minimised. While each structure that emerges can be said to be more entropically efficient, as such replicating structures proliferate, the dissipation of the system as a whole is higher than would be the case for inert or simpler structures. While a detailed application to most real systems would be difficult, the approach may well be useful in understanding incremental changes to real systems and provide broad descriptions of system behaviour.
authors.affiliation: Victoria Management School, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand. Electronic address: sean.devine@vuw.ac.nz.
authors.forename: Sean D
authors.initials: SD
authors.lastname: Devine
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004261:DNA Replication / Q000502:physiology*; D006128:Growth / Q000502:physiology*; D007257:Information Theory; D008954:Models, Biological; D015233:Models, Statistical; D014779:Virus Replication / Q000502:physiology*
keywords: Algorithmic Information Theory; Algorithmic entropy; Dynamics of evolution; Organization in complex systems; Replication