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This text offers a systematic, rigorous, and unified presentation of evolutionary game theory, covering the core developments of the theory from its inception in biology in the 1970s through recent advances. Evolutionary game theory, which studies the behavior of large populations of strategically interacting agents, is used by economists to make predictions in settings where traditional assumptions about agents' rationality and knowledge may not be justified. Recently, computer scientists, transportation scientists, engineers, and control theorists have also turned to evolutionary game theory, seeking tools for modeling dynamics in multiagent systems. Population Games and Evolutionary Dynamicsprovides a point of entry into the field for researchers and students in all of these disciplines. The text first considers population games, which provide a simple, powerful model for studying strategic interactions among large numbers of anonymous agents. It then studies the dynamics of behavior in these games. By introducing a general model of myopic strategy revision by individual agents, the text provides foundations for two distinct approaches to aggregate behavior dynamics: the deterministic approach, based on differential equations, and the stochastic approach, based on Markov processes. Key results on local stability, global convergence, stochastic stability, and nonconvergence are developed in detail. Ten substantial appendixes present the mathematical tools needed to work in evolutionary game theory, offering a practical introduction to the methods of dynamic modeling.

Game theory --- Evolution --- Mathematical models --- Théorie des jeux --- Modèles mathématiques --- #SBIB:303H64 --- #SBIB:314H160 --- Games, Theory of --- Theory of games --- Mathematics --- Philosophy --- Creation --- Emergence (Philosophy) --- Teleology --- Speltheorie --- Bevolkingsstudies: onderzoeks- en analysemethoden --- Evolution - Mathematical models

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Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation. This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology.--Résumé de l'éditeur.

57.087.1 --- 575.85 --- Biometry. Statistical study and treatment of biological data --- Mechanics of evolution. Evolution according to levels of biological systems --- 575.85 Mechanics of evolution. Evolution according to levels of biological systems --- 57.087.1 Biometry. Statistical study and treatment of biological data --- Molecular evolution --- Phylogeny --- Mathematical models --- Statistical methods --- Molecular aspects --- Molecular evolution - Mathematical models --- Molecular evolution - Statistical methods --- Phylogeny - Molecular aspects

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This volume comprises ten thoroughly refereed and revised full papers originating from an interdisciplinary workshop on biocomputation entitled "Evolution as a Computational Process", held in Monterey, California in July 1992. This book is devoted to viewing biological evolution as a giant computational process being carried out over a vast spatial and temporal scale. Computer scientists, mathematicians and physicists may learn about optimization from looking at natural evolution and biologists may learn about evolution from studying artificial life, game theory, and mathematical optimization. In addition to the ten full papers addressing e.g. population genetics, emergence, artificial life, self-organization, evolutionary algorithms, and selection, there is an introductory survey and a subject index.

Génétique quantitative --- Kwantitatieve genetica --- Quantitative genetics --- Quantitative inheritance --- Population genetics --- Evolution (Biology) --- Mathematical models. --- Computer simulation. --- Mathematical models --- Evolution --- Computer simulation --- Evolution - Mathematical models. --- Population genetics - Computer simulation. --- Evolution - Computer simulation. --- Evolution (Biology). --- Information theory. --- Computer software. --- Artificial intelligence. --- Combinatorics. --- Evolutionary Biology. --- Theory of Computation. --- Algorithm Analysis and Problem Complexity. --- Artificial Intelligence. --- Mathematical and Computational Biology. --- Combinatorics --- Algebra --- Mathematical analysis --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Software, Computer --- Computer systems --- Communication theory --- Communication --- Cybernetics --- Animal evolution --- Animals --- Biological evolution --- Darwinism --- Evolutionary biology --- Evolutionary science --- Origin of species --- Biology --- Biological fitness --- Homoplasy --- Natural selection --- Phylogeny --- Population genetics - Mathematical models. --- Evolution (Biology) - Mathematical models. --- Evolution (Biology) - Computer simulation.

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Computational Biology --- Evolution, Molecular --- Genetics --- Molecular evolution --- Bioinformatics --- Evolution moléculaire --- Bio-informatique --- Mathematical models --- Modèles mathématiques --- Bioinformatics. --- Computational Biology. --- Evolution, Molecular. --- Genetics. --- Mathematical models. --- 57.087 --- 577.212.3 --- 575.852 --- bioinformatica --- moleculaire biologie --- genetische code --- genetische informatie --- genoom --- fylogenetica --- Methods and techniques for parameter estimation. Recording of biological data. Bioinformatics --- Nucleic acid base and sequence compositon. Experimental deciphering of genetic code. --- Biomacromolecules. Molecular evolution --- Basic Sciences. Bioinformatics --- Bioinformatics (General) --- Bioinformatics (General). --- 575.852 Biomacromolecules. Molecular evolution --- 577.212.3 Nucleic acid base and sequence compositon. Experimental deciphering of genetic code. --- Biomathematics. Biometry. Biostatistics --- Computer. Automation --- Evolution moléculaire --- Modèles mathématiques --- Biochemical evolution --- Chemical evolution --- Evolution --- Life --- Molecular biology --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Nucleic acid base and sequence compositon. Experimental deciphering of genetic code --- Origin --- Data processing --- Molecular evolution - Mathematical models