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Redefining Fitness: Evolution as a Dynamic Learning Process

Evolution is the process of optimal adaptation of biological populations to their living environments. This is expressed via the concept of fitness, defined as relative reproductive success. However, it has been pointed out that this definition is incomplete and logically circular. To address this issue, several authors have called for new ways to specify fitness explicitly in terms of the relationship between phenotypes and their environment. Here, we show that fitness, defined as the likelihood function that follows from mapping population dynamics to Bayesian learning, provides a general solution to this problem. We show how probabilistic models of fitness can easily be constructed in this way, and how their averages acquire meaning as information. We also show how this approach leads to powerful tools to analyze challenging problems of evolution in variable environments, game theory, and selection in group-structured populations. The approach is general and creates an explicit bridge between population dynamics under selection, statistical learning theory, and emerging models of artificial intelligence.

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