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Critical dynamics in the evolution of stochastic strategies for the iterated Prisoner's Dilemma
Keck Graduate Institute of Applied Life Sciences, Claremont, United States.ORCID iD: 0000-0002-4872-1961
2010 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 6, no 10, article id 1000948Article in journal (Refereed) Published
Abstract [en]

The observed cooperation on the level of genes, cells, tissues, and individuals has been the object of intense study by evolutionary biologists, mainly because cooperation often flourishes in biological systems in apparent contradiction to the selfish goal of survival inherent in Darwinian evolution. In order to resolve this paradox, evolutionary game theory has focused on the Prisoner's Dilemma (PD), which incorporates the essence of this conflict. Here, we encode strategies for the iterated Prisoner's Dilemma (IPD) in terms of conditional probabilities that represent the response of decision pathways given previous plays. We find that if these stochastic strategies are encoded as genes that undergo Darwinian evolution, the environmental conditions that the strategies are adapting to determine the fixed point of the evolutionary trajectory, which could be either cooperation or defection. A transition between cooperative and defective attractors occurs as a function of different parameters such as mutation rate, replacement rate, and memory, all of which affect a player's ability to predict an opponent's behavior. These results imply that in populations of players that can use previous decisions to plan future ones, cooperation depends critically on whether the players can rely on facing the same strategies that they have adapted to. Defection, on the other hand, is the optimal adaptive response in environments that change so quickly that the information gathered from previous plays cannot usefully be integrated for a response. © 2010 Iliopoulos et al.

Place, publisher, year, edition, pages
2010. Vol. 6, no 10, article id 1000948
Keywords [en]
article, environmental factor, evolution, evolutionary adaptation, gene mutation, genetic conservation, mathematical analysis, prediction, principal component analysis, probability, statistical analysis, biological model, biology, cellular, subcellular and molecular biological phenomena and functions, computer simulation, game, gene, genetics, methodology, mutation, physiology, population genetics, statistics, Cell Physiological Processes, Computational Biology, Game Theory, Genes, Genetics, Population, Models, Genetic, Stochastic Processes
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Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:du-37200DOI: 10.1371/journal.pcbi.1000948Scopus ID: 2-s2.0-78349282257OAI: oai:DiVA.org:du-37200DiVA, id: diva2:1557620
Available from: 2021-05-26 Created: 2021-05-26 Last updated: 2025-10-09Bibliographically approved

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Hintze, Arend

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf