  We provide a general analytic solution to Herbert Simon's 1955 model for
time-evolving novelty functions. This has far-reaching consequences: Simon's is
a pre-cursor model for Barabasi's 1999 preferential attachment model for
growing social networks, and our general abstraction of it more considers
attachment to be a form of link selection. We show that any system which can be
modeled as instances of types---i.e., occurrence data (frequencies)---can be
generatively modeled (and simulated) from a distributional perspective with an
exceptionally high-degree of accuracy.
