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.
Authors Daniel Friedman and Barry Sinervo show how to use theoretical developments in evolutionary game theory to build useful models describing parts of the worlds we live in - the natural world of biology, the social world of politics, ...
Evolutionary Psychology and Digital Games: Digital Hunter-Gatherers is the first edited volume that systematically applies evolutionary psychology to the study of the use and effects of digital games.
This is the side of game theory that is most relevant to biology; it also helps to explain how human societies evolve.
A comprehensive resource on the principles and techniques of virtual world design and programming covers everything from MUDS to MMOs and MMORPGs, explaining how virtual worlds work, creating games for multiple users, and the underlying ...
This interdisciplinary volume aims to provoke a new understanding of the important role that computer-generated ”virtual worlds” will play in domains such as science, business, computer games, education, training, and...
The recent boom experienced by this dscipline makes the book's systematic presentation of its essential contributions vital reading for newcomer to the field.
This encyclopedia provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering.
This book, Cultural Algorithms: Tools to Model Complex Dynamic Social Systems, is the foundation of that study. It showcases how we can use cultural algorithms to organize social structures and develop socio-political systems that work.
New York: Holmes & Meier. Axelrod, Robert. 1970. Conflict of Interest, A Theory of Divergent Goals with Applications to Politics. ... Behr, Roy L. 1981. “Nice Guys Finish Last—Sometimes.” Journal of Conflict Resolution 25:289300.
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