Fence-sitters protect cooperation in complex networks.
Identifieur interne : 000157 ( PubMed/Corpus ); précédent : 000156; suivant : 000158Fence-sitters protect cooperation in complex networks.
Auteurs : Yichao Zhang ; M A Aziz-Alaoui ; Cyrille Bertelle ; Shi Zhou ; Wenting WangSource :
- Physical review. E, Statistical, nonlinear, and soft matter physics [ 1550-2376 ] ; 2013.
Abstract
Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. In complex networks, because of the difficulty of formulating the replicator dynamics, most of the previous studies are confined to a numerical level. In this paper, we introduce a vectorial formulation to derive three classes of individuals' payoff analytically. The three classes are pure cooperators, pure defectors, and fence-sitters. Here, fence-sitters are the individuals who change their strategies at least once in the strategy evolutionary process. As a general approach, our vectorial formalization can be applied to all the two-strategy games. To clarify the function of the fence-sitters, we define a parameter, payoff memory, as the number of rounds that the individuals' payoffs are aggregated. We observe that the payoff memory can control the fence-sitters' effects and the level of cooperation efficiently. Our results indicate that the fence-sitters' role is nontrivial in the complex topologies, which protects cooperation in an indirect way. Our results may provide a better understanding of the composition of cooperators in a circumstance where the temptation to defect is larger.
DOI: 10.1103/PhysRevE.88.032127
PubMed: 24125233
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E, Statistical, nonlinear, and soft matter physics</title><idno type="eISSN">1550-2376</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. In complex networks, because of the difficulty of formulating the replicator dynamics, most of the previous studies are confined to a numerical level. In this paper, we introduce a vectorial formulation to derive three classes of individuals' payoff analytically. The three classes are pure cooperators, pure defectors, and fence-sitters. Here, fence-sitters are the individuals who change their strategies at least once in the strategy evolutionary process. As a general approach, our vectorial formalization can be applied to all the two-strategy games. To clarify the function of the fence-sitters, we define a parameter, payoff memory, as the number of rounds that the individuals' payoffs are aggregated. We observe that the payoff memory can control the fence-sitters' effects and the level of cooperation efficiently. Our results indicate that the fence-sitters' role is nontrivial in the complex topologies, which protects cooperation in an indirect way. Our results may provide a better understanding of the composition of cooperators in a circumstance where the temptation to defect is larger.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">24125233</PMID><DateCreated><Year>2013</Year><Month>10</Month><Day>15</Day></DateCreated><DateCompleted><Year>2014</Year><Month>05</Month><Day>20</Day></DateCompleted><DateRevised><Year>2013</Year><Month>10</Month><Day>15</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1550-2376</ISSN><JournalIssue CitedMedium="Internet"><Volume>88</Volume><Issue>3</Issue><PubDate><Year>2013</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Physical review. E, Statistical, nonlinear, and soft matter physics</Title><ISOAbbreviation>Phys Rev E Stat Nonlin Soft Matter Phys</ISOAbbreviation></Journal><ArticleTitle>Fence-sitters protect cooperation in complex networks.</ArticleTitle><Pagination><MedlinePgn>032127</MedlinePgn></Pagination><Abstract><AbstractText>Evolutionary game theory is one of the key paradigms behind many scientific disciplines from science to engineering. In complex networks, because of the difficulty of formulating the replicator dynamics, most of the previous studies are confined to a numerical level. In this paper, we introduce a vectorial formulation to derive three classes of individuals' payoff analytically. The three classes are pure cooperators, pure defectors, and fence-sitters. Here, fence-sitters are the individuals who change their strategies at least once in the strategy evolutionary process. As a general approach, our vectorial formalization can be applied to all the two-strategy games. To clarify the function of the fence-sitters, we define a parameter, payoff memory, as the number of rounds that the individuals' payoffs are aggregated. We observe that the payoff memory can control the fence-sitters' effects and the level of cooperation efficiently. Our results indicate that the fence-sitters' role is nontrivial in the complex topologies, which protects cooperation in an indirect way. Our results may provide a better understanding of the composition of cooperators in a circumstance where the temptation to defect is larger.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Yichao</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>Univ Normandy, France; ULH, LMAH, F-76600 Le Havre, FR CNRS 3335, ISCN, 25 rue Philippe Lebon, 76600 Le Havre, France and Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Aziz-Alaoui</LastName><ForeName>M A</ForeName><Initials>MA</Initials></Author><Author ValidYN="Y"><LastName>Bertelle</LastName><ForeName>Cyrille</ForeName><Initials>C</Initials></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Shi</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Wenting</ForeName><Initials>W</Initials></Author></AuthorList><Language>ENG</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2013</Year><Month>Sep</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Phys Rev E Stat Nonlin Soft Matter Phys</MedlineTA><NlmUniqueID>101136452</NlmUniqueID><ISSNLinking>1539-3755</ISSNLinking></MedlineJournalInfo></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>5</Month><Day>6</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>10</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>10</Month><Day>16</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2013</Year><Month>10</Month><Day>16</Day><Hour>6</Hour><Minute>1</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24125233</ArticleId><ArticleId IdType="doi">10.1103/PhysRevE.88.032127</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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