about whether chosen real items are individual components or compounds of other components.

In this paper I will investigate the synthetic datasets in a way broadly compatible with Fink et al. [@Fink-RPF-2016arxiv], but emphasizing discovery over determination of global properties, and relying primarily on numerical calculations. Notably, I will use some specific terminology that differs from theirs; this is in order to avoid unwanted inferences by clarifying the basic abstractions underpinning the dataset generation and its analysis. The total dataset will be called a *dictionary*, or world-dictionary, which is a list compiled of *words*. Each word in the dictionary is a string of *symbols* that has been randomly generated by an algorithm. As we *discover* extra symbols from the allowed list, we can *interrogate* the dictionary, finding “innovative” new words to add to our “sub-dictionary” of known words.

We will see how the process of ever-better interrogation of the dictionaries plays out in average terms and in some specific examples. Two different symbols discovery mechanisms are of interest here – most common first (i.e. frequency-order), and randomised. Of course, in many contexts we might think it most likely that the next-discovered symbol will be the most frequently occurring. Alternatively, when 