Aaron Goodman of the Stanford Complexity Group invited me to give a talk there on Thursday April 20th. If you’re nearby—like in Silicon Valley—please drop by! It will be in Clark S361 at 4:20 pm.

Here’s the idea. Everyone likes to say that biology is all about information. There’s something true about this—just think about DNA. But what does this insight actually do for us, quantitatively speaking? To figure this out, we need to do some work.

Biology is also about things that make copies of themselves. So it makes sense to figure out how information theory is connected to the replicator equation—a simple model of population dynamics for self-replicating entities.

To see the connection, we need to use ‘relative information’: the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Then everything pops into sharp focus.

It turns out that free energy—energy in forms that can actually be used, not just waste heat—is a special case of relative information Since the decrease of free energy is what drives chemical reactions, biochemistry is founded on relative information.

But there’s a lot more to it than this! Using relative information we can also see evolution as a learning process, fix the problems with Fisher’s fundamental theorem of natural selection, and more.

So this what I’ll talk about! You can see my slides here:

Fisher metric of Information Geometry could be introduced by Hessian of Free Energy, and by Massieu Characteristic Function:

See:

• Frederic Barbaresco, Geometric theory of heat from Souriau Lie groups thermodynamics and Koszul Hessian geometry: applications in information geometry for exponential families, Entropy18 (2016), 386. Available at http://www.mdpi.com/1099-4300/18/11/386.

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Fisher metric of Information Geometry could be introduced by Hessian of Free Energy, and by Massieu Characteristic Function:

See:

• Frederic Barbaresco, Geometric theory of heat from Souriau Lie groups thermodynamics and Koszul Hessian geometry: applications in information geometry for exponential families,

Entropy18(2016), 386. Available at http://www.mdpi.com/1099-4300/18/11/386.