rankings are given an average value (i.e. a tie at 3 and 4 results in both being ranked 3.5).

Fixed length words
------------------

Here we discuss the symbol ranking evolution and known word counts for a sample ${\mathsf{F}}^{\stdW}_{\stdN,8}$ F-dictionary. Since the generation algorithm and word length chosen means that it is extremely unlikely that a word consisting of a single symbol will be in the dictionary, we see that many symbol discoveries are needed before *any* words from the dictionary are known. This delayed onset is a particular characteristic property of this kind of fixed-length word dictionary – or, indeed, of related types of dictionary which contain only finite minimum length words of randomly chosen symbols. Another characteristic is that – once started – the amount of word innovation accelerates as more symbols are discovered. However, note that this acceleration in innovation is reliant on the size of the dictionary being much less than the number of possible words.

The abbreviated symbol entropy curves occur because it cannot be calculated for sub-dictionaries which contain no words.

### Frequency order discovery (${\mathsf{F}}$)

For frequency-order symbol discovery, the main point to note is that the discovery process is highly dynamic, with frequent 