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

[Torsten]

What I did write was that you use your App as a key element in the evidence. 

"The scope of this work is not the 'elemental deconstruction' of the VMS to an exact (and complete) set of rules" (Timm & Schinner 2019, p. 11). With other words, the app is a simulation of the method. It demonstrates that with this method it is possible to generate a text that "reproduces some of the statistical key properties of the Voynich manuscript; in particular, both of Zipf’s laws are fulfilled" (Timm & Schinner 2019, p. 1).

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.

... do you mean the proposed test? I consider it the only possible proof that you could be right, and it would not be hard for you to do.

• I have executed this type of test in chapter 3 "Evidence" in Timm 2014 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. ..." (Timm 2015, p. 12ff).
  

• I have presented 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 "Graphs for individual pages" (see Additional Materials).
  

• I have presented the network graph for a single page as well as for the whole text (see Timm & Schinner 2019, p. 4).
  

• I have published the network graphs for any page in the VMS. You can just open the gephi projects (You are not allowed to view links. Register or Login to view.) for any page you want and calculate the number of word types and word tokens within the core network with a edit distance of 1: 
  
• For instance 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 %).  
  
• The core network for page You are not allowed to view links. Register or Login to view. contains 239 out of 354 words (=67.5 %). They represent 485 token out of 613 tokens (=79.1 %). 
  
• The core network for page You are not allowed to view links. Register or Login to view. contains 172 out of 267 words (=66.9 %). They represent 305 token out of 394 tokens (=77.4 %).
  
• ...
  

If you can't accept this answers for any reason you should explain yourself. Just to ignore the answers and too loop over your question doesn't help.

Again a more fundamental problem with the auto-copying process: everything that I see in the Voynich MS text speaks about planning. There is nothing arbitrary about it.

A word generated by self-citation is at the same time the result of the text generation method as well as a possible source for generating new words. Therefore the text is in the first place repetitive. That you interpret repetition as carefully planned is just an overinterpretation on your side.

... I don't see any reason to believe that. This is not the case in any language I know of. Change a single letter and the meaning changes completely. Even in tonal languages, just change the tone and the meaning changes completely. 

You just missed the point. Nobody has argued that a single letter can not change the meaning of a word in a natural language. Anyway, in your last post you have now argued yourself against your idea that "if one changes the English text by mapping words 1-to-1 with Voynichese words, it *might* look like one." (You are not allowed to view links. Register or Login to view.).