Game-Theoretical Models in Biology

Mark Broom, Jan Rychtar

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March 27, 2013 by Chapman and Hall/CRC
Professional - 520 Pages - 86 B/W Illustrations
ISBN 9781439853214 - CAT# K12455
Series: Chapman & Hall/CRC Mathematical and Computational Biology

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Features

  • Focuses on the static aspects of game theory and various biological applications
  • Explores classical evolutionary games as well as a range of modern methods
  • Introduces the general mathematical theory
  • Presents mathematical models of real and important biological behaviors
  • Includes extensive references for further reading and numerous exercises at the end of each chapter
  • Provides source code for the MATLAB scripts and simulations at www.crcpress.com

Solutions manual available upon qualifying course adoption

Watch Jan Rychtár discuss the book.

Summary

Covering the major topics of evolutionary game theory, Game-Theoretical Models in Biology presents both abstract and practical mathematical models of real biological situations. It discusses the static aspects of game theory in a mathematically rigorous way that is appealing to mathematicians. In addition, the authors explore many applications of game theory to biology, making the text useful to biologists as well.

The book describes a wide range of topics in evolutionary games, including matrix games, replicator dynamics, the hawk-dove game, and the prisoner’s dilemma. It covers the evolutionarily stable strategy, a key concept in biological games, and offers in-depth details of the mathematical models. Most chapters illustrate how to use MATLAB® to solve various games.

Important biological phenomena, such as the sex ratio of so many species being close to a half, the evolution of cooperative behavior, and the existence of adornments (for example, the peacock’s tail), have been explained using ideas underpinned by game theoretical modeling. Suitable for readers studying and working at the interface of mathematics and the life sciences, this book shows how evolutionary game theory is used in the modeling of these diverse biological phenomena.