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Information Geometry and Evolutionary Game Theory
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The Shahshahani geometry of evolutionary game theory is realized as the information geometry of the simplex, deriving from the Fisher information metric of the manifold of categorical probability distributions. Some essential concepts in evolutionary game theory are realized information-theoretically. Results are extended to the Lotka-Volterra equation and to multiple population systems.
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Forward citations
Cited by 2 Pith papers
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Fisher Information and Dynamical Sampling I
The bias of Fisher information for large-n samples of dynamical curves is calculated, with clustering of degrees of freedom shown to reduce bias and improve reconstruction accuracy from the same data.
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Geometric tempering yields exponential convergence bounds for both Wasserstein and Fisher-Rao flows but produces no speedup in the Fisher-Rao metric, with new adaptive schedules derived from the tempered dynamics.
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