Complexity, Information and Biological Organisation

Attila Grandpierre ()
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Attila Grandpierre: Konkoly Observatory of the Hungarian Academy of Sciences, Budapest, Hungary

Interdisciplinary Description of Complex Systems - scientific journal, 2005, vol. 3, issue 2, 59-71

Abstract: Regarding the widespread confusion about the concept and nature of complexity, information and biological organization, we look for some coordinated conceptual considerations corresponding to quantitative measures suitable to grasp the main characteristics of biological complexity. Quantitative measures of algorithmic complexity of supercomputers like Blue Gene/L are compared with the complexity of the brain. We show that both the computer and the brain have a more fundamental, dynamic complexity measure corresponding to the number of operations per second. Recent insights suggest that the origin of complexity may go back to simplicity at a deeper level, corresponding to algorithmic complexity. We point out that for physical systems Ashby's Law, Kahre's Law and causal closure of the physical exclude the generation of information, and since genetic information corresponds to instructions, we are faced with a controversy telling that the algorithmic complexity of physics is much lower than the instructions' complexity of the human DNA: I_algorithmic(physics) ~ 10^3 bit

Keywords: levels of complexity; the computer and the brain; algorithmic complexity; complexity and information; fundamental information paradox of the natural sciences (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:zna:indecs:v:3:y:2005:i:2:p:59-71

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