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 concepts in information theory, and noted that many of the insights in the philosophy of information theory first articulated by Dennett (1991) and later elaborated by other authors fit rather neatly with a picture of science as the study of patterns in the world. We looked at a few problem cases in pattern identification—including patterns that hold only approximately, and data-sets with partial information loss—and argued that even in cases like that, useful information can be gleaned from a close search for patterns; patterns neither need to be universal nor perfect in order to be informative. We tried to give an intuitive picture of what we might mean when we say that science looks for patterns that can be projected to unobserved cases. I’d like to now drop the abstraction from the discussion and make the implicit parallel with science that’s been lurking in the background of this discussion explicit. We should be able to draw on the machinery from Section 1.3 to make our earlier discussion of science more concrete, and to examine specific cases of how this model actually applies to live science.

Here’s the picture that I have in mind. Scientists are in the business of studying patterns in how the world changes over time. The method for identifying patterns varies from branch to branch of science; the special sciences differ in domain both from each other and from fundamental physics. In all cases, though, scientists proceed by making measurements of certain parts of the world, trying to identify patterns underlying those measurements, and then using those patterns to try to predict how unobserved cases—either future measurements or

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