24-08-2026, 05:29 PM
You're very focused on the model, saying things like, "I don't think the ledger itself needs to 'beat' every other model simply to be a valid." But a model isn't valid or invalid, instead claims about it are. And the claim that caused me to bring up the other models was, "The null hypothesis is that the ledger is just another way of describing the known local patterns in Voynichese and has no real predictive power." This is an obvious comparative to other models, which are the other ways of describing known local patterns in Voynichese. To distinguish this model and reject the null hypothesis you lay out here, it does in fact have to beat other models in some well-defined, mutually exclusive sense. And notice that this is not a condition that I imposed, this is something you took on by how you answered a particular question I asked about falsifiability claims.
What I would suggest for future iterations of this line of research is that you begin with a well-defined question and a model and analysis tailored to answer it. You might re-read Timm and Schinner for how they structure their argument because you are working in that tradition and the paper is quite well-argued whether one is ultimately persuaded to autocitation or not. It begins by laying out a number of claims researchers have made based on statistical features of the VMS and how those imply (but do not conclusively prove) meaningfulness. It then lays out a process for generating text that is both plausible for the technology of the time and is meaningless, trying to make as few assumptions as possible. It then shows that a meaningless text generated that way has the features that imply meaningfulness while being meaningless. It concludes by arguing that this counterexample provides a plausible alternative to the hypothesis that the text is meaningful based on those statistics. While the model is obviously crucial, not to mention interesting, notice it does not logically begin with the model, but instead begins with a set of claims made by proponents of the meaningfulness hypothesis and the model parameters fall out of trying to find a way to test those claims.
So my questions are these: What question is your model built to answer? Why is that a question worth asking? Why should we be persuaded it tests that? Then, is it is comparative to other models? (You have said both "yes" and "no" on this thread!) If it is comparative, the same questions repeat; what comparison is the model built to make; why is that a comparison worth checking; why should we be persuaded by those comparisons? This will focus your analysis and how you present it, and make it easier for people to see what you're contributing here
What I would suggest for future iterations of this line of research is that you begin with a well-defined question and a model and analysis tailored to answer it. You might re-read Timm and Schinner for how they structure their argument because you are working in that tradition and the paper is quite well-argued whether one is ultimately persuaded to autocitation or not. It begins by laying out a number of claims researchers have made based on statistical features of the VMS and how those imply (but do not conclusively prove) meaningfulness. It then lays out a process for generating text that is both plausible for the technology of the time and is meaningless, trying to make as few assumptions as possible. It then shows that a meaningless text generated that way has the features that imply meaningfulness while being meaningless. It concludes by arguing that this counterexample provides a plausible alternative to the hypothesis that the text is meaningful based on those statistics. While the model is obviously crucial, not to mention interesting, notice it does not logically begin with the model, but instead begins with a set of claims made by proponents of the meaningfulness hypothesis and the model parameters fall out of trying to find a way to test those claims.
So my questions are these: What question is your model built to answer? Why is that a question worth asking? Why should we be persuaded it tests that? Then, is it is comparative to other models? (You have said both "yes" and "no" on this thread!) If it is comparative, the same questions repeat; what comparison is the model built to make; why is that a comparison worth checking; why should we be persuaded by those comparisons? This will focus your analysis and how you present it, and make it easier for people to see what you're contributing here