Tail universalities in rank distributions as an algebraic problem: The beta-like function

Although power laws of the Zipf type have been used by many workers to fit rank distributions in different fields like in economy, geophysics, genetics, soft-matter, networks, etc. these fits usually fail at the tail. Some distributions have been proposed to solve the problem, but unfortunately they do not fit at the same time the body and the tail of the distribution. We show that many different data in rank laws, like in granular materials, codons, author impact in scientific journal, etc. can be very well fitted by the integrand of a beta function (that we call beta-like function). Then we propose that such universality can be due to the fact that systems made from many subsystems or choices, present stretched exponential frequency-rank functions which qualitatively and quantitatively are well fitted with the beta-like function distribution in the limit of many random variables. We give a plausibility argument for this observation by transforming the problem into an algebraic one: finding the rank of successive products of numbers, which is basically a multinomial process. From a physical point of view, the observed behavior at the tail seems to be related with the onset of different mechanisms that are dominant at different scales, providing crossovers and finite size effects.