Speculative fraud hypothesis

[Torsten]

First, it would be a sure way to make Voynichese look like a real language.   It seems unlikely that a forger of the time could devise a gibberish generation method with that property.  The methods of Rugg and Thorsten&Timm could do that with proper parameters, but no one has shown how the VMS Author could have figured out the proper parameters.

There was no need to invent an artificial “gibberish generation” mechanism. As D’Imperio already observed, the natural way to produce sequences of meaningless text would be through iterative reuse and modification of existing material: “The scribe, faced with the task of thinking up a large number of such dummy sequences, would naturally tend to repeat parts of neighboring strings with various small changes and additions to fill out the line until the next message-bearing word or phrase” (D’Imperio 1978, p. 31).

It is simply more efficient for a scribe to copy and modify existing words than to continually invent entirely new ones. Consequently, a scribe attempting to generate language-like gibberish would, sooner or later, abandon the laborious task of perpetual invention in favor of the far easier strategy of reduplicating and adapting previously written material—and would ultimately adhere to this approach consistently. This tendency is further supported by the experimental findings of You are not allowed to view links. Register or Login to view..

One of the key advantages of the self-citation method is that statistical regularities—such as the Zipfian distribution—emerge organically from the process itself, without requiring deliberate adjustment. This is because the method is grounded directly in observations of the Voynich text. I begin by identifying fundamental, corpus-wide patterns—for example, the clustering of similar words across pages. These clusters suggest a mechanism of repetition and gradual variation. The central argument, therefore, is that an iterative process of copying and modification is sufficient to account for the statistical features. The observed word type frequencies can be interpreted as the outcome of a self-reinforcing resonance effect produced by continual copying. Frequently occurring words are more likely to be copied, thereby generating numerous variants. The accumulation of such variants, in turn, increases the probability that the original form will reappear during subsequent cycles of copying. This way the hypothesis accounts for the clustering of similar words within the Voynich Manuscript.

Therefore there is no need to determine or fine-tune external parameters. As noted in Timm & Schinner (2019):

We deliberately did not fine-tune the algorithm to pick an 'optimal' sample for this presentation. Such a strategy is by itself questionable. Nevertheless, an exhaustive scan of the parameter space (involving thousands of automatically analyzed text samples) verified the overall stability of the proposed algorithm. About 10-20% of the parameter space even yields excellent numerical conformity (≤ 10% relative error) with all considered key features of the real VMS text (entropy values, random walk exponents, token length distribution, etc.).