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    1. Naturvetenskap och teknik
    2. Matematik och naturvetenskap
    3. Matematik
    4. Optimering

    Statistical Physics and Computational Methods for Evolutionary Game Theory

    AvMarco Alberto Javarone

    Häftad, Engelska, 2018

    Del i serien SpringerBriefs in Complexity

    701 kr

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    Fler format och utgåvor

    E-bok

    868 kr

    Beskrivning

    This book presents an introduction to Evolutionary Game Theory (EGT) which is an emerging field in the area of complex systems attracting the attention of researchers from disparate scientific communities. EGT allows one to represent and study several complex phenomena, such as the emergence of cooperation in social systems, the role of conformity in shaping the equilibrium of a population, and the dynamics in biological and ecological systems.Since EGT models belong to the area of complex systems, statistical physics constitutes a fundamental ingredient for investigating their behavior. At the same time, the complexity of some EGT models, such as those realized by means of agent-based methods, often require the implementation of numerical simulations. Therefore, beyond providing an introduction to EGT, this book gives a brief overview of the main statistical physics tools (such as phase transitions and the Ising model) and computational strategies for simulating evolutionary games (such as Monte Carlo algorithms on lattices).  This book will appeal to students and researchers in this burgeoning field of complex systems.

    Produktinformation

    • Utgivningsdatum:2018-01-22
    • Mått:155 x 235 x undefined mm
    • Format:Häftad
    • Språk:Engelska
    • Serie:SpringerBriefs in Complexity
    • Antal sidor:74
    • Förlag:Springer International Publishing AG
    • ISBN:9783319702049

    Utforska kategorier

    • Optimering inom Naturvetenskap och teknik
    • Fysik inom Naturvetenskap och teknik

    Mer om författaren

    Marco Alberto Javarone is a researcher in the area of Complex Systems and Statistical Physics, with a keen interest in interdisciplinary applications for Evolutionary Game Theory, Complex Networks, Socio-Economic Systems, and Information Theory. He graduated at Politecnico di Milano (2009), and then received two PhD’s at the University of Cagliari: the former in Computer Engineering (2013), studying Complex Networks and Machine Learning, and the latter in Mathematical Physics (2017), investigating different topics within Statistical Mechanics. He worked as postdoc researcher in Italy, and as an invited researcher in France. Currently, he is a Senior Research Fellow in the School of Computer Science at the University of Hertfordshire.

    Innehållsförteckning

    • Chapter 1. Introduction.- Chapter 2. Modeling Complex Systems.- Chapter 3. Evolutionary Games I: Statistical Physics.- Chapter 4 Evolutionary Games II: Applications.- Chapter 5. Conclusions.
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