Self-Organization In Stellar Evolution: Size-Complexity Rule
arXiv:2202.02318 · doi:10.1007/978-3-030-69288-9
Abstract
Complexity Theory is highly interdisciplinary, therefore any regularities must hold on all levels of organization, independent on the nature of the system. An open question in science is how complex systems self-organize to produce emergent structures and properties, a branch of non-equilibrium thermodynamics. It has long been known that there is a quantity-quality transition in natural systems. This is to say that the properties of a system depend on its size. More recently, this has been termed the size-complexity rule, which means that to increase their size, systems must increase their complexity, and that to increase their complexity they must grow in size. This rule goes under different names in different disciplines and systems of different nature, such as the area-speciation rule, economies of scale, scaling relations (allometric) in biology and for cities, and many others. We apply the size-complexity rule to stars to compare them with other complex systems in order to find universal patterns of self-organization independent of the substrate. Here, as a measure of complexity of a star, we are using the degree of grouping of nucleons into atoms, which reduces nucleon entropy, increases the variety of elements, and changes the structure of the star. As seen in our previous work, complexity, using action efficiency, is in power law proportionality of all other characteristics of a complex system, including its size. Here we find that, as for the other systems studied, the complexity of stars is in a power law proportionality with their size - the bigger a system is, the higher its level of complexity is - despite differing explosion energies and initial metallicities from simulations and data, which confirms the size-complexity rule and our model.
arXiv comment20 pages, 3 figures, 11 tables, 9 equations, Conference on Complex Systems CCS2017, Cancun, Mexico, Sattelite meeting "Efficiency in complex systems"
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