Fence-sitters protect cooperation in complex networks.
Identifieur interne : 000468 ( Main/Exploration ); précédent : 000467; suivant : 000469Fence-sitters protect cooperation in complex networks.
Auteurs : Yichao Zhang [Royaume-Uni] ; 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
Affiliations:
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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><affiliations><list><country><li>Royaume-Uni</li></country></list><tree><noCountry><name sortKey="Aziz Alaoui, M A" sort="Aziz Alaoui, M A" uniqKey="Aziz Alaoui M" first="M A" last="Aziz-Alaoui">M A Aziz-Alaoui</name><name sortKey="Bertelle, Cyrille" sort="Bertelle, Cyrille" uniqKey="Bertelle C" first="Cyrille" last="Bertelle">Cyrille Bertelle</name><name sortKey="Wang, Wenting" sort="Wang, Wenting" uniqKey="Wang W" first="Wenting" last="Wang">Wenting Wang</name><name sortKey="Zhou, Shi" sort="Zhou, Shi" uniqKey="Zhou S" first="Shi" last="Zhou">Shi Zhou</name></noCountry><country name="Royaume-Uni"><noRegion><name sortKey="Zhang, Yichao" sort="Zhang, Yichao" uniqKey="Zhang Y" first="Yichao" last="Zhang">Yichao Zhang</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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