• Biology as information dynamics.

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Abstract.If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’—a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative to another, also known as the Kullback–Liebler divergence. Using this we can see evolution as a learning process, and give a clean general formulation of Fisher’s fundamental theorem of natural selection.

People have studied a variety of different equations of motion in evolutionary game theory:

• Samuel Alizon and Daniel Cownden, Replicator dynamics.

Some of these describe agents that look at what other agents are doing and copy them if those other guys are doing better than they are.

]]>Ah, my bad. I should have looked more closely (I assumed it was the original paper that was linked).

]]>In economics people use evolutionary game theory to model a population of people who adapt their mixed strategies to do well, instead of a population of replicators each with a pure strategy, who evolve to do well. Under some assumptions the two math problems are isomorphic. So I think this work could still fall under the heading of evolutionary game theory, though I’m not sure.

]]>I linked to that in my blog post. Yes, everyone should check it out! The commentary starts out being a bit vague and fuzzy, but gets more precise as it goes along.

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