Publication:
Learning dynamics explains human behavior in Prisoner's Dilemma on networks

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dc.affiliation.dptoUC3M. Departamento de Matemáticases
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Análisis Aplicadoes
dc.contributor.authorCimini, Giulio
dc.contributor.authorSánchez, Angel
dc.date.accessioned2015-07-16T12:19:00Z
dc.date.available2015-07-16T12:19:00Z
dc.date.issued2014-03-31
dc.descriptionThe proceeding at: DPG-Frühjahrstagung (SOE: Fachverband Physik sozio-ökonomischer Systeme) = DPG Spring Meeting (Physics of Socio-Economic Systems), took place 2014 31- March, 04-April, in Dresden (Germany).en
dc.description.abstractCooperative behavior lies at the very basis of human societies, yet its evolutionary origin remains a key unsolved puzzle. Whereas reciprocity or conditional cooperation is one of the most prominent mechanisms proposed to explain the emergence of cooperation in social dilemmas, recent experimental findings on networked Prisoner's Dilemma games suggest that conditional cooperation also depends on the previous action of the player—namely on the 'mood' in which the player currently is. Roughly, a majority of people behave as conditional cooperators if they cooperated in the past, while they ignore the context and free-ride with high probability if they did not. However, the ultimate origin of this behavior represents a conundrum itself. Here we aim specifically at providing an evolutionary explanation of moody conditional cooperation. To this end, we perform an extensive analysis of different evolutionary dynamics for players' behavioral traits—ranging from standard processes used in game theory based on payoff comparison to others that include non-economic or social factors. Our results show that only a dynamic built upon reinforcement learning is able to give rise to evolutionarily stable moody conditional cooperation, and at the end to reproduce the human behaviors observed in the experiments.en
dc.description.sponsorshipThis work was supported by the Swiss Natural Science Fundation through grant PBFRP2_145872, by Ministerio de Economía y Competitividad (Spain) through grant PRODIEVO, by the ERA-Net on Complexity through grant RESINEE, and by Comunidad de Madrid (Spain) through grant MODELICO-CM.en
dc.description.statusPublicado
dc.format.extent8
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationJournal of the Royal Society Interface 11 (2014) 20131186, pp.1-8en
dc.identifier.doi10.1098/rsif.2013.1186
dc.identifier.issn1742-5689
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue20131186
dc.identifier.publicationlastpage8
dc.identifier.publicationtitleJournal of the Royal Society Interfaceen
dc.identifier.publicationvolume11
dc.identifier.urihttps://hdl.handle.net/10016/21441
dc.identifier.uxxiCC/0000022837
dc.language.isoeng
dc.publisherThe Royal Societyen
dc.relation.eventdate2014 31- March, 04-Aprilen
dc.relation.eventplaceDresden (Germany)en
dc.relation.eventtitleDPG-Frühjahrstagung (SOE: Fachverband Physik sozio-ökonomischer Systeme) = DPG Spring Meeting (Physics of Socio-Economic Systems)en
dc.relation.projectIDGobierno de España. FIS2011-22449/PRODIEVOes
dc.relation.projectIDComunidad de Madrid. S2009/ESP-1691/MODELICOes
dc.relation.publisherversionhttp://dx.doi.org/10.1098/rsif.2013.1186
dc.rights© 2014 The Authorsen
dc.rights.accessRightsopen access
dc.subject.ecienciaFísicaes
dc.subject.ecienciaMatemáticases
dc.subject.otherEvolutionary game theoryen
dc.subject.otherPrisoner's dilemmaen
dc.subject.otherSocial networksen
dc.subject.otherMoody conditional cooperationen
dc.subject.otherReinforcement learningen
dc.titleLearning dynamics explains human behavior in Prisoner's Dilemma on networksen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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