Discussion of "A possible generating algorithm of the Voynich manuscript"

[Torsten]

The pseudo-code suggests that all paragraph-initial lines are generated from earlier paragraph-initial lines and all other lines are generated from earlier text on the same page.
This would make the proposed test: how many words actually comply with this, quite feasible to execute.

I have already answered this point in my last post. Anyway, even if you rephrase the "it must work like a computer simulation" argument, it is still wrong (see You are not allowed to view links. Register or Login to view.). We don't argue that the text was created by a computer program and we also don't argue that our program is able to simulate the complexity of human behavior.

See page 11 in Timm & Schinner 2019:
"However, note that any algorithmic description of the VMS must be seen as just one of many possible realizations. After all, the original VMS was not created by a computer program; the scribe had complete freedom to implement random personal esthetic preferences, spontaneous impulses, or even idiosyncrasies. The scope of this work is not the 'elemental deconstruction' of the VMS to an exact (and complete) set of rules. We rather demonstrate the feasibility to algorithmically create a text as rich and complex as the VMS, using the strikingly simple self-citation method (You are not allowed to view links. Register or Login to view., p. 11).

One could check how much edit distance is needed to explain the Voynich MS text under the auto-copy hypothesis. 
Of course, one has to apply reason to interpret such results. If only 5% of words don't fit, for the case of a small edit distance, then this should not be considered a show-stopper. Hoever, we have not seen such an analysis yet.

 
I have already given the reference to chapter 3 "Evidence" in Timm 2014. In this chapter I describe this type of test for two control samples: "In this paper all words occurring seven times and all words occurring eight times are used as two separate control samples. ..." (You are not allowed to view links. Register or Login to view., p. 12ff).

The 'evidence' of the network of words does not really support the auto-copy hypothesis, because the existence of such a network is a property of the vocabulary, not of the text.

It is one of the basic facts for the VMS that there are local variants. For instance Prescott H. Currier wrote in 1976: "These features are to be found generally in the other Sections of the manuscript although there are always local variations" (You are not allowed to view links. Register or Login to view.). You even write it yourself: "From this figure it seems more likely that the observed feature is page-related" (You are not allowed to view links. Register or Login to view.). 

See page 3 in Timm & Schinner 2019:
"Most interestingly, there appears to even exist an inherent relation between word similarity, with respect to the string edit distance and context: when we look at the three most frequent words on each page, for more than half of the pages two of three will differ in only one detail. ... However, all pages containing at least some lines of text do have in common that pairs of frequently used words with high mutual similarity appear." (You are not allowed to view links. Register or Login to view., p. 3).

Moreover, we have demonstrated a network graph for a single page as well as for the whole text (see Timm & Schinner 2019, p. 4). In both cases the result is the same. This means the text of the VMS is You are not allowed to view links. Register or Login to view.. With other words, it simply doesn't matter if you create a network graph for a single page, a bifolio, a quire in Currier A or B, or the whole VMS. 

See for instance the statics for the example in Figure 1: The core network for page You are not allowed to view links. Register or Login to view. contains 189 out of 277 words (=68.2 %). They represent 402 token out of 491 tokens (=81.8 %). Similar subnets can be constructed for all pages, but, of course, they are more instructive for pages containing lots of text. We even provide network graphs for all pages containing at least some lines in 1.3 "You are not allowed to view links. Register or Login to view." (see You are not allowed to view links. Register or Login to view.).

However, if one changes the English text by mapping words 1-to-1 with Voynichese words, it *might* look like one.
By re-organising this mapping, it is possible to make it into a text that looks like auto-copying. this was already shown before.

I have answered this point You are not allowed to view links. Register or Login to view.: "There is no contradiction between your experiment ...". You even say it yourself: "I think one can only analyse all proposed solutions by themselves and see if they 'work'" (You are not allowed to view links. Register or Login to view.).

Anyway, if you use a word dictionary substitution cipher to map the words 1-to-1 this would not affect the word frequencies and the word order. It would still be possible to identify high frequency words as functions words and grammar rules for words depending on each other. It is simply not possible to explain facts like that the line behaves as a functional entity or the correlation between frequency and word similarity with a 1-to-1 dictionary cipher. 

See for instance the word frequencies for tokens similar to <qokeedy>, <qotey>, <qokeey>, and <otedy>:
qokey   (107 times) okey   (63)  otey   (57)  qotey   (24)
qokeey  (308 times) okeey  (177) oteey  (140) qoteey  (42)
qokeedy (305 times) okeedy (105) oteedy (100) qoteedy (74)
qokedy  (272 times) okedy  (118) otedy  (155) qotedy  (91)

With other words, if similar tokens share the same meaning, sequences like in line f108v.P39 would only repeat the same information multiple times and if similar tokens not share the same meaning you would need a dictionary containing any word token instead of any word type. But this way the dictionary would be as long as the text and it would be sufficient to know the dictionary to reconstruct the source text.
<f108v.P.39> qokeedy qokeedy qokeedy qotey qokeey qokeey otedy