This book’s goal is to introduce evolutionary game theory to applied researchers in a manner accessible to graduate students and advanced undergraduates in biology, economics, engineering, and allied disciplines. Chapters 1 through 6 present the basic ideas and techniques of this field, including fitness, replicator dynamics, memes and genes, single- and multiple-population games, Nash equilibrium and evolutionarily stable states, noisy best response and other adaptive processes, the Price equation, cellular automata, and estimating payoff and choice parameters from the data. Chapters 7 throug ... More

This book’s goal is to introduce evolutionary game theory to applied researchers in a manner accessible to graduate students and advanced undergraduates in biology, economics, engineering, and allied disciplines. Chapters 1 through 6 present the basic ideas and techniques of this field, including fitness, replicator dynamics, memes and genes, single- and multiple-population games, Nash equilibrium and evolutionarily stable states, noisy best response and other adaptive processes, the Price equation, cellular automata, and estimating payoff and choice parameters from the data. Chapters 7 through 14 collect exemplary applications from many fields, providing templates for applied work everywhere. These include a new co-evolutionary predator-prey learning model extending the rock-paper-scissors game; using human subject laboratory data to estimate models of learning in games; new approaches to plastic strategies and life cycle strategies, including estimates for male elephant seals; a comparison of machine-learning techniques for preserving diversity to those seen in the natural world; analyses of congestion in traffic networks (either Internet or highways)