Let us begin by asking how science tells us anything at all. In my view, science is the recognition of patterns in data, and the generation of models that are adequate to delivering those patterns as explanation. The information in science, the "signal" from the physical world, is the information of measurement - Fisher Information, AKA the Cramer-Rao Bound (which is, roughly, where the second derivative of the estimate of the accuracy of a measurement is zero). In my view, science is induction from data, and the models retain the information content of the measurements just to the extent they are accurate. (Note: induction may not be a justification of models, but it sure as #### is the way we gather our data together so we can make reliable inferences; still, let's not open that can of undergraduate Humean worms.) The information content of a scientific explanation is just the preserved accuracy of the data in the model.
Anything that we know through science we know from empirical data. So a design inference has to be not only consonant with data, but licensed by the patterns that exist in the data. To be achievable, we need to understand (that is, have a model of) design and designers. (emphasis added) |