  n-tuple power law widely exists in language, computer program code, DNA and
music. After a vast amount of Zipf analyses of n-tuple power law from empirical
data, we propose a model to explain the n-tuple power law feature existed in
these information translational carriers. Our model is a preferential selection
approach inspired by Simon's model which explained scaling law of single symbol
in a sequence Zipf analysis. The kernel mechanism is neat and simple in our
model. It can be simply described as a randomly copy and paste process, that
is, randomly select a random segment from current sequence and attach it to the
end repeatedly. The simulation of our model shows that n-tuple power law exists
in model generated data. Furthermore, two estimation equations: the Zipf
exponent and the minimal length of n-tuple for power law appears all correspond
to empirical data well. Our model can also reproduce the symmetry breaking
process of ATGC number differences in DNA data.
