Journal article Open Access
Křivan V; Galanthay T. E.; Cressman R.
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.5281/zenodo.3303581</identifier> <creators> <creator> <creatorName>Křivan V</creatorName> <affiliation>Czech Academy of Sciences, Biology Centre, Institute of Entomology, Branišovská 31, České Budějovice 370 05, Czech Republic Department of Mathematics, Faculty of Sciences, University of South Bohemia, Branišovská 1760, České Budějovice 370 05, Czech Republic</affiliation> </creator> <creator> <creatorName>Galanthay T. E.</creatorName> <affiliation>Department of Mathematics, Ithaca College, Ithaca, NY, USA</affiliation> </creator> <creator> <creatorName>Cressman R.</creatorName> <affiliation>Department of Mathematics, Wilfrid Laurier University, Waterloo, Ontario, Canada</affiliation> </creator> </creators> <titles> <title>Beyond replicator dynamics: From frequency to density dependent models of evolutionary games</title> </titles> <publisher>Zenodo</publisher> <publicationYear>2018</publicationYear> <dates> <date dateType="Issued">2018-10-14</date> </dates> <resourceType resourceTypeGeneral="Text">Journal article</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3303581</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3303580</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/fourcmodelling690817</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>Game theoretic models of evolution such as the Hawk&ndash;Dove game assume that individuals gain fitness (which is a proxy of the per capita population growth rate) in pair-wise contests only. These models assume that the equilibrium distribution of phenotypes involved (e.g., Hawks and Doves) in the population is given by the Hardy&ndash;Weinberg law, which is based on instantaneous, random pair formation. On the other hand, models of population dynamics do not consider pairs, newborns are produced by singles, and interactions between phenotypes or species are described by the mass action principle. This article links game theoretic and population approaches. It shows that combining distribution dynamics with population dynamics can lead to stable coexistence of Hawk and Dove population numbers in models that do not assume&nbsp;<em>a priori</em>&nbsp;that fitness is negative density dependent. Our analysis shows clearly that the interior Nash equilibrium of the Hawk and Dove model depends both on population size and on interaction times between different phenotypes in the population. This raises the question of the applicability of classic evolutionary game theory that requires all interactions take the same amount of time and that all single individuals have the same payoff per unit of time, to real populations. Furthermore, by separating individual fitness into birth and death effects on singles and pairs, it is shown that stable coexistence in these models depends on the time-scale of the distribution dynamics relative to the population dynamics. When explicit density-dependent fitness is included through competition over a limited resource, the combined dynamics of the Hawk&ndash;Dove model often lead to Dove extinction no matter how costly fighting is for Hawk pairs.</p></description> </descriptions> <fundingReferences> <fundingReference> <funderName>European Commission</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier> <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/690817/">690817</awardNumber> <awardTitle>Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory to model realistic populations</awardTitle> </fundingReference> </fundingReferences> </resource>