Mostrar el registro sencillo del ítem
dc.contributor.author
Neñer, Julian
dc.contributor.author
Cardoso, Ben-Hur Francisco
dc.contributor.author
Laguna, Maria Fabiana
dc.contributor.author
Goncalves, Sebastián
dc.contributor.author
Iglesias, José Roberto
dc.date.available
2023-10-30T12:10:12Z
dc.date.issued
2022-04
dc.identifier.citation
Neñer, Julian; Cardoso, Ben-Hur Francisco; Laguna, Maria Fabiana; Goncalves, Sebastián; Iglesias, José Roberto; Study of taxes, regulations and inequality using machine learning algorithms; The Royal Society; Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences; 380; 2224; 4-2022; 1-13
dc.identifier.issn
1364-503X
dc.identifier.uri
http://hdl.handle.net/11336/216344
dc.description.abstract
Genetic machine learning (ML) algorithms to train agents in the Yard–Sale model proved very useful for finding optimal strategies that maximize their wealth. However, the main result indicates that the more significant the fraction of rational agents, the greater the inequality at the collective level. From social and economic viewpoints, this is an undesirable result since high inequality diminishes liquidity and trade. Besides, with very few exceptions, most agents end up with zero wealth, despite the inclusion of rational behaviour. To deal with this situation, here we include a taxation–redistribution mechanism in the ML algorithm. Previous results show that simple regulations can considerably reduce inequality if agents do not change their behaviour. However, when considering rational agents, different types of redistribution favour risk-averse agents, to some extent. Even so, we find that rational agents looking for optimal wealth can always arrive to an optimal risk, compatible with a particular choice of parameters, but increasing inequality. This article is part of the theme issue ‘Kinetic exchange models of societies and economies’.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
The Royal Society
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
AGENT-BASED MODEL
dc.subject
ECONOPHYSICS
dc.subject
WEALTH DISTRIBUTION
dc.subject.classification
Otras Ciencias Físicas
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Study of taxes, regulations and inequality using machine learning algorithms
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-10-27T16:18:03Z
dc.journal.volume
380
dc.journal.number
2224
dc.journal.pagination
1-13
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Neñer, Julian. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro | Universidad Nacional de Cuyo. Instituto Balseiro. Archivo Histórico del Centro Atómico Bariloche e Instituto Balseiro; Argentina
dc.description.fil
Fil: Cardoso, Ben-Hur Francisco. Universidade Federal de Santa Catarina; Brasil
dc.description.fil
Fil: Laguna, Maria Fabiana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina
dc.description.fil
Fil: Goncalves, Sebastián. Universidade Federal do Rio Grande do Sul; Brasil
dc.description.fil
Fil: Iglesias, José Roberto. Centro Brasileiro de Pesquisas Físicas; Brasil. Universidade Federal do Rio Grande do Sul; Brasil
dc.journal.title
Philosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1098/rsta.2021.0165
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://royalsocietypublishing.org/doi/10.1098/rsta.2021.0165
Archivos asociados