Evolutionary games and population dynamics

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Last edited by MARC Bot
April 30, 2025 | History

Evolutionary games and population dynamics

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Every form of behaviour is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realised how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centred not on the concept of rational players but on the population dynamics of behavioural programmes. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behaviour, and of the closely related interactions between species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions which can alter the basis of their success, id est to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions which punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.

Publish Date
Language
English
Pages
323

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Edition Availability
Cover of: Evolutionary games and population dynamics
Evolutionary games and population dynamics
1998, Cambridge University Press
in English

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Book Details


Edition Notes

Includes bibliographical references (p. 301-320) and index.

Published in
Cambridge, New York, NY
Other Titles
Evolutionary games and replicator dynamics

Classifications

Dewey Decimal Class
576.8/01/5118
Library of Congress
QH371 .H624 1998

The Physical Object

Pagination
xxvii, 323 p. :
Number of pages
323

Edition Identifiers

Open Library
OL682377M
Internet Archive
evolutionarygame00hofb
ISBN 10
0521623650, 052162570X
LCCN
97029058
OCLC/WorldCat
37437460
LibraryThing
378930
Wikidata
Q98276839
Goodreads
1659003
4503822

Work Identifiers

Work ID
OL2698963W

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