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Information Geometry (Part 16) | Azimuth

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• John Baez, Biology as information dynamics, talk for Biological Complexity: Can it be Quantified?, a workshop at the Beyond Center, 2 February 2017. While preparing this talk, I discovered a cool fact. I doubt it’s new, but I haven’t exactly seen it elsewhere. I came up with it while trying to give a precise and general statement of ‘Fisher’s fundamental theorem of natural selection’. I won’t start by explaining that theorem, since my version looks rather different than Fisher’s, and I came up with mine precisely because I had trouble understanding his. I’ll say a bit more about this at the end. Here’s my version: The square of the rate at which a population learns information is the variance of its fitness. This is a nice advertisement for the virtues of diversity: more variance means faster learning. But it requires some explanation! Let’s start by assuming we have different kinds of self-replicating entities with populations As usual, these could be all sorts of things: • molecu

Information Geometry (Part 16) | Azimuth Azimuth Home About Information Geometry (Part 16) This week I’m giving a talk on biology and information: • John Baez, Biology as information dynamics , talk for Biological Complexity: Can it be Quantified? , a workshop at the Beyond Center , 2 February 2017. While preparing this talk, I discovered a cool fact. I doubt it’s new, but I haven’t exactly seen it elsewhere. I came up with it while trying to give a precise and general statement of ‘Fisher’s fundamental theorem of natural selection’. I won’t start by e

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