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Evolutionary game theory using agent-based methods
Michigan State University, East Lansing, United States.ORCID iD: 0000-0002-4872-1961
2016 (English)In: Physics of Life Reviews, ISSN 1571-0645, E-ISSN 1873-1457, Vol. 19, p. 1-26Article in journal (Refereed) Published
Abstract [en]

Evolutionary game theory is a successful mathematical framework geared towards understanding the selective pressures that affect the evolution of the strategies of agents engaged in interactions with potential conflicts. While a mathematical treatment of the costs and benefits of decisions can predict the optimal strategy in simple settings, more realistic settings such as finite populations, non-vanishing mutations rates, stochastic decisions, communication between agents, and spatial interactions, require agent-based methods where each agent is modeled as an individual, carries its own genes that determine its decisions, and where the evolutionary outcome can only be ascertained by evolving the population of agents forward in time. While highlighting standard mathematical results, we compare those to agent-based methods that can go beyond the limitations of equations and simulate the complexity of heterogeneous populations and an ever-changing set of interactors. We conclude that agent-based methods can predict evolutionary outcomes where purely mathematical treatments cannot tread (for example in the weak selection–strong mutation limit), but that mathematics is crucial to validate the computational simulations. © 2016 Elsevier B.V.

Place, publisher, year, edition, pages
Elsevier B.V. , 2016. Vol. 19, p. 1-26
Keywords [en]
Agent-based modeling, Evolutionary game theory, Autonomous agents, Computational methods, Stochastic systems, Agent-based model, Communication between agents, Computational simulation, Heterogeneous populations, Mathematical frameworks, Mathematical treatments, Spatial interaction, Game theory, algorithm, animal, computer simulation, evolution, game, Markov chain, mutation, population density, population dynamics, probability, theoretical model, Algorithms, Animals, Biological Evolution, Models, Theoretical, Stochastic Processes
National Category
Evolutionary Biology
Identifiers
URN: urn:nbn:se:du-37175DOI: 10.1016/j.plrev.2016.08.015Scopus ID: 2-s2.0-84994852202OAI: oai:DiVA.org:du-37175DiVA, id: diva2:1557884
Available from: 2021-05-27 Created: 2021-05-27 Last updated: 2021-05-27Bibliographically approved

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Hintze, Arend

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CiteExportLink to record
Permanent link

Direct link
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