Thinking About Bioinformatics

I’m writing a textbook on Information Evolution. Or at least I thought I was — so far it mainly seems to be about the statistical inference side of “information”, as opposed to the “evolution” side. I suspect it will make more sense to make this focus on inference and methodology, and leave the science of how physical systems produce information for a later effort. If you have an interest in the basic issues I’m raising in the posts here, you may want to take a look at the first five draft chapters. That’s where the real meat is.

Does there exist an information metric with truly general utility? If so, a scientist could use it to choose which experiment to do: the best experiment is that one that yields the largest amount of information about the scientist’s question of interest (or, over the long-term, the highest information rate per unit time / expense). Indeed, if the metric were truly general, the scientist could use it to decide which research question is “most interesting” (again, compute the expected information yield for the different research directions). Actually, if such an information metric existed, the “scientist” could just be a robot, because all that is required is the ability to calculate this metric for different possible experiments (observations). This wouldn’t be artificial intelligence in the traditional sense of that field, but instead just a big statistical number-crunching computation. In a way, scientific computing at its dullest.
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