Over the last 25 years, evolutionary game theory has grown with theoretical contributions from the disciplines of mathematics, economics, computer science and biology. It is now ripe for applications. In this book, Daniel Friedman---an economist trained in mathematics---and Barry Sinervo---a biologist trained in mathematics---offer the first unified account of evolutionary game theory aimed at applied researchers. They show how to use a single set of tools to build useful models for three different worlds: the natural world studied by biologists; the social world studied by anthropologists, economists, political scientists and others; and the virtual world built by computer scientists and engineers.The first six chapters offer an accessible introduction to core concepts of evolutionary game theory. These include fitness, replicator dynamics, sexual 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, and cellular automata. The material connects evolutionary game theory with classic population genetic models, and also with classical game theory. Notably, these chapters also show how to estimate payoff and choice parameters from the data.The last eight chapters present exemplary game theory applications. These include a new coevolutionary predator-prey learning model extending rock-paper-scissors; models that use human subject laboratory data to estimate learning dynamics; 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) and the ?price of anarchy?; environmental and trade policy analysis based on evolutionary games; the evolution of cooperation; and speciation. As an aid for instruction, a web site provides downloadable computational tools written in the R programming language, Matlab, Mathematica and Excel.

PART I: BASICS1. Population Dynamics1.1 Fitness1.2 Tradeoffs and Fitness Dependence1.3 Dependence on environment, density and frequency1.4 State space geometry1.5 Memes and Genes1.6 Finite populations and randomness1.7 Replicator dynamics in discrete time1.8 Replicator dynamics in continuous time1.9 Steady states and stability1.10 Sexual dynamics1.11 Discussion1.12 Appendix A: Derivation of the Fisher equation1.13 Appendix B. Replicator dynamics, mean fitness, and entropy1.14 Exercises1.15 Endnotes1.16 Bibliography2. Simple Frequency Dependence2.1 The Hawk-Dove game2.2 H-D parameters and dynamics2.3 The three kinds of 2x2 games .2.4 Dilemmas played by viruses and eBay sellers2.5 Nonlinear frequency dependence2.6 RPS and the simplex2.7 Replicator dynamics for RPS2.8 Discussion2.9 Appendix A. Payoff differences in 3x3 games2.10 Exercises2.11 Endnotes2.12 Bibliography3. Dynamics in n-dimensional Games3.1 Sectoring the 2-d simplex3.2 Estimating 3x3 payoff matrices3.3 More strategies3.4 Nonlinear frequency dependence3.5 Two population games: the square3.6 Hawk-Dove with two populations3.7 Own population effects3.8 Higher dimensional games3.9 Alternative dynamics3.10 Discussion3.11 Appendix: Estimating 3x3 payoff matrices3.12 Exercises3.13 Notes3.14 Bibliography4. Equilibrium4.1 Equilibrium in 1 dimension4.2 Nash equilibrium with n strategies4.3 ESS with n strategies4.4 Equilibrium in multi-population games4.5 Fisherian runaway equilibrium4.6 Discussion4.7 Appendix A: Techniques to Assess Stability4.8 Exercises4.9 Notes4.10 Bibilography5. Social games5.1 Assortative matching5.2 Social Twists5.3 Inheritance from two parents5.4 The standard Price equation5.5 Group-structured Price equation and cooperation5.6 Group Structure and Assortativity in Lizards5.7 Price Equation in Continuous Time5.8 Discussion5.9 Appendix: Equilibrium in the Kirkpatrick (1982) model5.10 Exercises5.11 Notes5.12 Bibliography6. Cellular Automaton Games6.1 Specifying a CA6.2 Prisoner's Dilemma6.3 Snowdrift6.4 Public goods games with two strategies6.5 Spatial rock-paper-scissors dynamic6.6 Application to bacterial strains6.7 Buyer-seller game as a two population CA6.8 Exercises6.9 Notes6.10 BibliographyPART II: APPLICATIONS7. Rock-Paper-Scissors Everywhere7.1 Some RPS Theory7.2 Humans Play RPS in the Lab7.3 RPS Mating Systems7.4 Predators Learn7.5 A coevolutionary model of Predators and Prey7.6 Discussion7.7 Appendix7.8 Exercises7.9 Notes7.10 Bibliography8. Learning in Games8.1 Perspectives on learning and evolution8.2 An empirical example8.3 Learning rules8.4 Decision rules8.5 Estimating a model8.6 Results8.7 Learning in Continuous Time .8.8 Other Models of Learning8.9 Open Frontiers8.10 Appendix: Towards Models of Learning in Continuous Time8.11 Exercises8.12 Notes8.13 Bibliography9. Contingent Life Cycle Strategies9.1 Hawks, Doves and Plasticity9.2 Costly Plasticity9.3 Classic Life Cycle Analysis9.4 Strategic Life Cycle Analysis: Two Periods9.5 Strategic Life Cycle Analysis: More general cases9.6 Application: male elephant seals9.7 Discussion9.8 Appendix9.9 Exercises9.10 Notes9.11 Bibliography10. The Blessing and the Curse of the Multiplicative Updates (Contributed by Manfred K. Warmuth)10.1 Demonstrating the blessing and the curse10.2 Dispelling the curse10.3 Discussion10.4 Notes10.5 Bibliography11. Traffic Games (contributed by John Musacchio)11.1 Simple Non-Atomic Traffic Games11.2 Braess's Paradox11.3 The Price of Anarchy with Nonlinear Latency Functions11.4 Pigovian Taxes11.5 Selfish Pricing11.6 Circuit Analogy11.7 Discussion11.8 Exercises11.9 Endnotes11.10 Bibliography12. International Trade and the Environment (contributed by Matthew McGinty)12.1 Economics and evolutionary game theory12.2 Static Cournot model12.3 Green technology diffusion12.4 International trade12.5 International Trade and Pollution Taxation12.6 Other Economic Applications12.7 Exercises12.8 Notes12.9 Bibliography13. Evolution of Cooperation13.1 Coordination, cooperation and social dilemmas13.2 Solution K: Kin Selection13.3 Solution R: Bilateral reciprocity13.4 Social preferences: a problematic solution13.5 Early Human Niches13.6 Solution M: Moral memes13.7 Illustrative models13.8 Prehistoric and historic moral codes13.9 Discussion13.10 Exercises13.11 Notes13.12 Bibliography14. Speciation14.1 Long run evolution14.2 Adaptive Dynamics14.3 Morph loss in RPS14.4 Emergent Boundary Layers in Cellular Automata14.5 Speciation in Social and Virtual Worlds14.6 Discussion14.7 Exercises14.8 Endnotes14.9 BibliographyGlossaries