Voynich researchers without a significant maths grounding are often intimidated by the concept of entropy. But all it is is an aggregate measure of how [in]effectively you can predict the next token in a sequence, given a preceding context of a certain size. The more predictable tokens are (on average), the smaller the entropy: the more **un**predictable they are, the larger the entropy.

For example, if the first order (i.e. no context at all) entropy measurement of a certain text was 3.0 bits, then it would have almost exactly the same average information content-ness per character as a random series of eight different digits (e.g. 1-8). This is because entropy is a log2 value, and log2(8) = 3. (Of course, what is usually the case is that some letters are more frequent than others: but entropy is the bottom line figure averaged out over the whole text you’re interested in.)

And the same goes for second order entropy, with the only difference being that because we always know there what the preceding letter or token was, we can make a more effective guess as to what the next letter or token will be. For example, if we know the previous English letter was ‘q’, then there is a very high chance that the next letter will be ‘u’, and a far lower chance that the next letter will be, say, ‘k’. (Unless it just happens to be a text about the current Mayor of London with all the spaces removed.)

And so it should proceed beyond that: the longer the preceding context, the more effectively you should be to predict the next letter, and so the lower the entropy value.

As always, there are practical difficulties to consider (e.g. what to do across page boundaries, how to handle free-standing labels, whether to filter out key-like sequences, etc) in order to normalize the sequence you’re working with, but that’s basically as far as you can go with the concept of entropy without having to define the maths behind it a little more formally.

## Voynich Entropy

However, even a moment’s thought should be sufficient to throw up the flaw in using entropy as a mathematical torch to try to cast light on the Voynich Manuscript’s “Voynichese” text… that because we don’t yet know what makes up a single token, we don’t know whether or not the entropy values we get are telling us anything interesting.

EVA transcriptions are closer to stroke based than to glyph based: so it makes little (or indeed no) sense to calculate entropy values for EVA. And as for people who claim to be able to read EVA off the page as, say, mirrored Hebrew… I don’t think so. :-/

But what is the correct mapping or grouping for EVA, i.e. the set of rules you should apply to EVA to turn it into the set of tokens that will give us genuine results? Nobody knows. And, oddly, nobody seems to be even asking any more. Which doesn’t bode well.

All the same, entropy does sometimes yield us interesting glimpses inside the Voynichese engine. For example, looking at the Currier A pages only in the Takahashi transcription and using ch/sh/cth/ckh/cfh/cph as tokens (which is a pretty basic glyphifying starting point), you get [“h1” = first order entropy, “h2” = second order entropy]:

63667 input tokens, 56222 output tokens, h1 = 4.95, h2 = 4.03

This has a first order information content of 56222 x 4.95 = 278299 bits, and a second order information content of (56222-1) x 4.03 = 226571 bits.

If you then also replace all the occurrences of ain/aiin/aiiin/oin/oiin/oiiin with their own tokens, you get:

63667 input tokens, 51562 output tokens, h1 = 5.21, h2 = 4.01

This has a first order information content of 51562 x 5.21 = 268638 bits, and a second order information content of (51562-1) x 4.01 = 206760 bits. What is interesting here is that even though the h1 value increases a fair bit (as you’d expect from extending the post-parsed alphabet with additional tokens), the h2 value **decreases** very slightly, which I find a bit surprising.

And if, continuing in this vein, you also convert air/aiir/aiiir/sain/saiin/saiiin/dain/daiin/daiiin to glyphs, you get:

63667 input tokens, 50387 output tokens, h1 = 5.49, h2 = 4.04

This has a first order information content of 50387 x 5.49 = 276625 bits, and a second order information content of (50387-1) x 4.04 = 203559 bits. Again what I find interesting is that once again the h1 value increases a fair bit, but the h2 value barely moves.

And so it does seem to me that Voynich entropy may yet prove to be a useful tool in determining what is going on with all the different possible parsings. For example, I do wonder if there might be a practical way of exhaustively / hillclimbingly determining the particular parsing / grouping that maximises the post-parsed h1:h2 ratio for Voynichese. I don’t believe anyone has yet succeeded in doing this, so there may be plenty of room for good new work here – just a thought! 🙂

## Voynich Parsing

To me, the confounding beauty of Voynichese is that all the while we cannot even parse it into tokens, the vast modern cryptological toolbox normally at our disposal does us no good.

Even so, it’s obvious (I think) that ch and sh are both tokens: this is largely because EVA was designed to be able to cope with strikethrough gallows characters (e.g. cth, ckh etc) without multiplying the number of glyphs excessively.

However, if you ask whether or not qo, ee, eee, ii, iii, dy, etc should be treated as tokens, you’ll get a wide range of responses. And as for ar, or, al, ol, am etc, you won’t get a typical linguistic researcher to throw away their precious vowel to gain a token, but it wouldn’t surprise me if they were wrong there.

## The Language Gap

The Voynich Manuscript throws into sharp relief a shortcoming of our statistical toolbox: specifically, its excessive reliance on our having previously modelled the text stream accurately and reliably.

But if the first giant hurdle we face is parsing it, what kind of conceptual or technical tools should we be using to do this? And on an even more basic level, what kind of language should we as researchers use to try to collaborate on toppling this first statue? As problems go, this is a precursor both to cryptology and to linguistic analysis.

As far as cipher people and linguist people go: in general, both groups usually assume (wrongly) that all the heavy lifting has been done by the time they get a transcription in their hands. But I think there is ample reason to conclude that we’re not yet in the cinema, but are still stuck in the foyer, all the while there is a world of difference between a stroke transcription and a parsed transcription that few seem comfortable to acknowledge.