so that the currently known sub-dictionary of the world-dictionary will never (or is unlikely to) reach a state of total knowledge. Instead, the subset of known symbols would only ever provide a sort of moving window on to the world-dictionary.

Other distinct models worthy of note here are the agent-decision led, persuasion/contagion-like models of Weiss et al [@Weiss-PGPPBWN-2014prx], and the component-optimization view taken by McNerney et al [@McNerny-FRT-2011pnas] which led to nice power-law behaviour. However, as implied above, my model here follows more along the lines of “the system (dictionary) will only let me create a finite number of compounds (words), but I don’t know what the allowed (symbols and words) are, and will only be able to find out as I discover new things (symbols)”.

In what follows I will present and discuss the result of some numerical experiments based on algorithmically constructed dictionaries. Although real-world “dictionaries” are available, their underlying structure – the equivalent of the generating algorithm I use here to create my synthetic dictionaries – is unknown (or may not exist in any meaningful sense), which hampers analysis. In Sec. \[S-Dictionaries\] I will present the generating algorithms along with the averaged scaling behaviour of discovery processes. 