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dc.contributor.author | Squartini. Tiziano | - |
dc.contributor.author | Ser-Giacomi, Enrico | - |
dc.contributor.author | Garlaschelli, Diego | - |
dc.contributor.author | Judge, George | - |
dc.date.accessioned | 2016-06-07T07:11:13Z | - |
dc.date.available | 2016-06-07T07:11:13Z | - |
dc.date.issued | 2015-05-06 | - |
dc.identifier | issn: 1932-6203 | - |
dc.identifier.citation | PLoS ONE 10(5): e0125077 (2015) | - |
dc.identifier.uri | http://hdl.handle.net/10261/133072 | - |
dc.description.abstract | © 2015 Squartini et al. In the context of agent based modeling and network theory, we focus on the problem of recovering behavior-related choice information from origin-destination type data, a topic also known under the name of network tomography. As a basis for predicting agents' choices we emphasize the connection between adaptive intelligent behavior, causal entropy maximization, and self-organized behavior in an open dynamic system. We cast this problem in the form of binary and weighted networks and suggest information theoretic entropy-driven methods to recover estimates of the unknown behavioral flow parameters. Our objective is to recover the unknown behavioral values across the ensemble analytically, without explicitly sampling the configuration space. In order to do so, we consider the Cressie-Read family of entropic functionals, enlarging the set of estimators commonly employed to make optimal use of the available information. More specifically, we explicitly work out two cases of particular interest: Shannon functional and the likelihood functional. We then employ them for the analysis of both univariate and bivariate data sets, comparing their accuracy in reproducing the observed trends. | - |
dc.description.sponsorship | TS acknowledges support from the Italian PNR project CRISIS-Lab. ESG acknowledges support from the European Commission Marie-Curie ITN program (FP7-320 PEOPLE-2011-ITN) through the LINC project (no. 289447). DG acknowledges support from the Dutch Econophysics Foundation (Stichting Econophysics, Leiden, the Netherlands). This work was also supported by the project MULTIPLEX (contract 317532) and the Netherlands Organization for Scientific Research (NWO/OCW). | - |
dc.publisher | Public Library of Science | - |
dc.relation | info:eu-repo/grantAgreement/EC/FP7/289447 | - |
dc.relation.isversionof | Publisher's version | - |
dc.rights | openAccess | - |
dc.title | Information recovery in behavioral networks | - |
dc.type | artículo | - |
dc.identifier.doi | 10.1371/journal.pone.0125077 | - |
dc.relation.publisherversion | http://dx.doi.org/10.1371/journal.pone.0125077 | - |
dc.date.updated | 2016-06-07T07:11:13Z | - |
dc.description.version | Peer Reviewed | - |
dc.language.rfc3066 | eng | - |
dc.rights.license | http://creativecommons.org/licenses/by/4.0/ | - |
dc.contributor.funder | Netherlands Organization for Scientific Research | - |
dc.contributor.funder | Stichting Econophysics | - |
dc.contributor.funder | European Commission | - |
dc.relation.csic | Sí | - |
dc.identifier.funder | http://dx.doi.org/10.13039/501100000780 | es_ES |
dc.identifier.pmid | 25946169 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
item.openairetype | artículo | - |
Aparece en las colecciones: | (IFISC) Artículos |
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information_recovery_Squartini.PDF | 313,02 kB | Adobe PDF | Visualizar/Abrir |
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