  We present a method for predicting the large-scale evolution of a tag system
from its production rules. A tag system's evolution is first divided into
stages called `epochs' in which the tag system evolves monotonously. The
distribution of symbols in the queue at the beginning of each epoch determines
the tag system's large-scale properties, including growth rate and string
densities, during that epoch. We derive the symbol distribution for the next
epoch from the distribution for the current one, using this to make predictions
over multiple successive epochs. Finally, we compare predictions that were
obtained with this method to computer simulations and find that it retains
great accuracy over several epochs.
