Crowd computing as a cooperation problem: an evolutionary approach

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dc.contributor.author Christoforou, Evgenia
dc.contributor.author Fernández Anta, Antonio
dc.contributor.author Georgiou, Chryssis
dc.contributor.author Mosteiro, Miguel A.
dc.contributor.author Sánchez, Angel
dc.date.accessioned 2015-07-09T08:15:46Z
dc.date.available 2015-07-09T08:15:46Z
dc.date.issued 2013-05
dc.identifier.bibliographicCitation Journal of Statistical Physics 151 (2013) 3-4, pp. 654-672
dc.identifier.issn 0022-4715 (Print)
dc.identifier.issn 1572-9613 (Online)
dc.identifier.uri http://hdl.handle.net/10016/21375
dc.description.abstract Cooperation is one of the socio-economic issues that has received more attention from the physics community. The problem has been mostly considered by studying games such as the Prisoner's Dilemma or the Public Goods Game. Here, we take a step forward by studying cooperation in the context of crowd computing. We introduce a model loosely based on Principal-agent theory in which people (workers) contribute to the solution of a distributed problem by computing answers and reporting to the problem proposer (master). To go beyond classical approaches involving the concept of Nash equilibrium, we work on an evolutionary framework in which both the master and the workers update their behavior through reinforcement learning. Using a Markov chain approach, we show theoretically that under certain----not very restrictive-conditions, the master can ensure the reliability of the answer resulting of the process. Then, we study the model by numerical simulations, finding that convergence, meaning that the system reaches a point in which it always produces reliable answers, may in general be much faster than the upper bounds given by the theoretical calculation. We also discuss the effects of the master's level of tolerance to defectors, about which the theory does not provide information. The discussion shows that the system works even with very large tolerances. We conclude with a discussion of our results and possible directions to carry this research further.
dc.description.sponsorship This work is supported by the Cyprus Research Promotion Foundation grant TE/HPO/0609(BE)/05, the National Science Foundation (CCF-0937829, CCF-1114930), Comunidad de Madrid grant S2009TIC-1692 and MODELICO-CM, Spanish MOSAICO, PRODIEVO and RESINEE grants and MICINN grant TEC2011-29688-C02-01, and National Natural Science Foundation of China grant 61020106002.
dc.format.extent 19
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.rights © 2013 Springer
dc.subject.other Evolutionary game theory
dc.subject.other Cooperation; Markov chains
dc.subject.other Crowd computing
dc.subject.other Reinforcement learning
dc.title Crowd computing as a cooperation problem: an evolutionary approach
dc.type article
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1007/s10955-012-0661-0
dc.subject.eciencia Matemáticas
dc.identifier.doi 10.1007/s10955-012-0661-0
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. FIS2011-22449/PRODIEVO
dc.relation.projectID Comunidad de Madrid. S2009/ESP-1691/MODELICO
dc.relation.projectID Gobierno de España. TEC2011-29688-C02-01
dc.relation.projectID Gobierno de España. FIS2006-01485/MOSAICO
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 654
dc.identifier.publicationissue 3-4
dc.identifier.publicationlastpage 672
dc.identifier.publicationtitle Journal of Statistical Physics
dc.identifier.publicationvolume 151
dc.identifier.uxxi AR/0000013131
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