Published September 14, 2016 | Version v1
Journal article Open

Why Darwin would have loved evolutionary game theory

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Description

Humans have marvelled at the fit of form and function, the way organisms’
traits seem remarkably suited to their lifestyles and ecologies. While natural
selection provides the scientific basis for the fit of form and function, Darwin
found certain adaptations vexing or particularly intriguing: sex ratios, sexual
selection and altruism. The logic behind these adaptations resides in
frequency-dependent selection where the value of a given heritable phenotype
(i.e. strategy) to an individual depends upon the strategies of others. Game
theory is a branch of mathematics that is uniquely suited to solving such
puzzles. While game theoretic thinking enters into Darwin’s arguments and
those of evolutionists throughmuch of the twentieth century, the tools of evolutionary
game theory were not available to Darwin or most evolutionists until
the 1970s, and its full scope has only unfolded in the last three decades. As a
consequence, game theory is applied and appreciated rather spottily. Game
theory not only applies to matrix games and social games, it also applies to
speciation, macroevolution and perhaps even to cancer. I assert that life and
natural selection are a game, and that game theory is the appropriate logic
for framing and understanding adaptations. Its scope can include behaviours
within species, state-dependent strategies (such as male, female and so much
more), speciation and coevolution, and expands beyond microevolution to
macroevolution. Game theory clarifies aspects of ecological and evolutionary
stability in ways useful to understanding eco-evolutionary dynamics, niche
construction and ecosystem engineering. In short, I would like to think that
Darwin would have found game theory uniquely useful for his theory of
natural selection. Let us see why this is so.

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Funding

European Commission
FourCmodelling - Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory to model realistic populations 690817