Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61382
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChica, Manuelen
dc.contributor.authorHernandez, Juan Men
dc.contributor.authorManrique-De-Lara-Penate, Casianoen
dc.contributor.authorChiong, Raymonden
dc.date.accessioned2024-07-10T01:00:37Z-
dc.date.available2024-07-10T01:00:37Z-
dc.date.issued2021-
dc.identifier.citationIEEE Computational Intelligence Magazine, 16(2), p. 62-76en
dc.identifier.issn1556-6048en
dc.identifier.issn1556-603Xen
dc.identifier.urihttps://hdl.handle.net/1959.11/61382-
dc.description.abstract<p>This 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.</p>en
dc.languageenen
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.relation.ispartofIEEE Computational Intelligence Magazineen
dc.titleAn Evolutionary Game Model for Understanding Fraud in Consumption Taxes [Research Frontier]en
dc.typeJournal Articleen
dc.identifier.doi10.1109/MCI.2021.3061878en
local.contributor.firstnameManuelen
local.contributor.firstnameJuan Men
local.contributor.firstnameCasianoen
local.contributor.firstnameRaymonden
local.profile.schoolSchool of Science & Technologyen
local.profile.emailrchiong@une.edu.auen
local.output.categoryC1en
local.record.placeauen
local.record.institutionUniversity of New Englanden
local.publisher.placeUnited States of Americaen
local.format.startpage62en
local.format.endpage76en
local.peerreviewedYesen
local.identifier.volume16en
local.identifier.issue2en
local.contributor.lastnameChicaen
local.contributor.lastnameHernandezen
local.contributor.lastnameManrique-De-Lara-Penateen
local.contributor.lastnameChiongen
dc.identifier.staffune-id:rchiongen
local.profile.orcid0000-0002-8285-1903en
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.profile.roleauthoren
local.identifier.unepublicationidune:1959.11/61382en
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
dc.identifier.academiclevelAcademicen
local.title.maintitleAn Evolutionary Game Model for Understanding Fraud in Consumption Taxes [Research Frontier]en
local.output.categorydescriptionC1 Refereed Article in a Scholarly Journalen
local.search.authorChica, Manuelen
local.search.authorHernandez, Juan Men
local.search.authorManrique-De-Lara-Penate, Casianoen
local.search.authorChiong, Raymonden
local.uneassociationNoen
dc.date.presented2021-
local.atsiresearchNoen
local.sensitive.culturalNoen
local.year.published2021en
local.year.presented2021en
local.subject.for20204602 Artificial intelligenceen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.profile.affiliationtypeExternal Affiliationen
local.date.moved2024-08-26en
Appears in Collections:Journal Article
School of Science and Technology
Show simple item record

SCOPUSTM   
Citations

14
checked on Oct 26, 2024

Page view(s)

92
checked on Aug 3, 2024
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.