Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/107085
Campo DC Valoridioma
dc.contributor.authorChica, Manuelen_US
dc.contributor.authorHernández Guerra, Juan Maríaen_US
dc.contributor.authorManrique De Lara Peñate, Casianoen_US
dc.contributor.authorChiong, Raymonden_US
dc.date.accessioned2021-05-05T08:39:39Z-
dc.date.available2021-05-05T08:39:39Z-
dc.date.issued2021en_US
dc.identifier.issn1556-603Xen_US
dc.identifier.otherScopus-
dc.identifier.urihttp://hdl.handle.net/10553/107085-
dc.description.abstractThis paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player's payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other?s payoff. We study the model with a wellmixed population and different scalefree networks. Model parameters were calibrated using real-world data of VAT declarations by businesses registered in the Canary Islands region of Spain. We analyzed several scenarios of audit probabilities for high and low transactions and their prevalence in the population, as well as social rewards and penalties to find the most efficient policy to increase the proportion of cooperators. Two major insights were found. First, increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Computational Intelligence Magazineen_US
dc.sourceIEEE Computational Intelligence Magazine [ISSN 1556-603X], v. 16 (2), p. 62-76, (Mayo 2021)en_US
dc.subject530202 Modelos econométricosen_US
dc.subject530101 Política fiscal y deuda públicaen_US
dc.subject.otherTax compilanceen_US
dc.subject.otherValue added taxen_US
dc.subject.otherGame modelsen_US
dc.subject.otherSubjective probabilityen_US
dc.titleAn evolutionary game model for understanding fraud in consumption taxes [Research Frontier]en_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/MCI.2021.3061878en_US
dc.identifier.scopus85104382782-
dc.contributor.authorscopusid24723574600-
dc.contributor.authorscopusid7403026151-
dc.contributor.authorscopusid6506539491-
dc.contributor.authorscopusid23395951300-
dc.identifier.eissn1556-6048-
dc.description.lastpage76en_US
dc.identifier.issue2-
dc.description.firstpage62en_US
dc.relation.volume16en_US
dc.investigacionCiencias Sociales y Jurídicasen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.date.coverdateMayo 2021en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-ECOen_US
dc.description.sjr3,594
dc.description.jcr9,809
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
dc.description.miaricds10,7
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR TIDES: Economía, medioambiente, sostenibilidad y turismo-
crisitem.author.deptIU de Turismo y Desarrollo Económico Sostenible-
crisitem.author.deptDepartamento de Métodos Cuantitativos en Economía y Gestión-
crisitem.author.deptGIR Economía, Comercio y Transporte Marítimo-
crisitem.author.deptDepartamento de Análisis Económico Aplicado-
crisitem.author.orcid0000-0001-6897-5179-
crisitem.author.orcid0000-0003-0809-7823-
crisitem.author.parentorgIU de Turismo y Desarrollo Económico Sostenible-
crisitem.author.parentorgDepartamento de Análisis Económico Aplicado-
crisitem.author.fullNameHernández Guerra, Juan María-
crisitem.author.fullNameManrique De Lara Peñate, Casiano Alberto-
Colección:Artículos
miniatura
Adobe PDF (2,06 MB)
Vista resumida

Citas SCOPUSTM   

14
actualizado el 17-nov-2024

Citas de WEB OF SCIENCETM
Citations

13
actualizado el 17-nov-2024

Visitas

167
actualizado el 01-nov-2024

Descargas

271
actualizado el 01-nov-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.