An artificial economy based on reinforcement learning and agent based modeling
In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an informa...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2007
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- spa
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/10893
- Acceso en línea:
- https://doi.org/10.48713/10336_10893
http://repository.urosario.edu.co/handle/10336/10893
- Palabra clave:
- Economía
reinforcement learning
agent-based modeling
computational economics
Desarrollo económico
Modelos económicos
Crecimiento económico
Economía
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
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An artificial economy based on reinforcement learning and agent based modelingEconomíareinforcement learningagent-based modelingcomputational economicsDesarrollo económicoModelos económicosCrecimiento económicoEconomíaIn this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.Universidad del RosarioFacultad de Economía20072015-09-28T16:27:07Zinfo:eu-repo/semantics/workingPaperhttp://purl.org/coar/resource_type/c_8042[9 páginas]Recurso electrónicoapplication/pdfDocumentohttps://doi.org/10.48713/10336_10893 http://repository.urosario.edu.co/handle/10336/10893instname:Universidad del Rosarioinstname:Universidad del Rosarioreponame:Repositorio Institucional EdocURspahttps://ideas.repec.org/p/col/000092/003907.htmlhttp://purl.org/coar/access_right/c_abf2Lozano, FernandoLozano, JaimeGarcía, Mariooai:repository.urosario.edu.co:10336/108932021-06-03T00:46:36Z |
dc.title.none.fl_str_mv |
An artificial economy based on reinforcement learning and agent based modeling |
title |
An artificial economy based on reinforcement learning and agent based modeling |
spellingShingle |
An artificial economy based on reinforcement learning and agent based modeling Economía reinforcement learning agent-based modeling computational economics Desarrollo económico Modelos económicos Crecimiento económico Economía |
title_short |
An artificial economy based on reinforcement learning and agent based modeling |
title_full |
An artificial economy based on reinforcement learning and agent based modeling |
title_fullStr |
An artificial economy based on reinforcement learning and agent based modeling |
title_full_unstemmed |
An artificial economy based on reinforcement learning and agent based modeling |
title_sort |
An artificial economy based on reinforcement learning and agent based modeling |
dc.subject.none.fl_str_mv |
Economía reinforcement learning agent-based modeling computational economics Desarrollo económico Modelos económicos Crecimiento económico Economía |
topic |
Economía reinforcement learning agent-based modeling computational economics Desarrollo económico Modelos económicos Crecimiento económico Economía |
description |
In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function. |
publishDate |
2007 |
dc.date.none.fl_str_mv |
2007 2015-09-28T16:27:07Z |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/workingPaper |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_8042 |
dc.identifier.none.fl_str_mv |
https://doi.org/10.48713/10336_10893 http://repository.urosario.edu.co/handle/10336/10893 |
url |
https://doi.org/10.48713/10336_10893 http://repository.urosario.edu.co/handle/10336/10893 |
dc.language.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
https://ideas.repec.org/p/col/000092/003907.html |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.none.fl_str_mv |
[9 páginas] Recurso electrónico application/pdf Documento |
dc.publisher.none.fl_str_mv |
Universidad del Rosario Facultad de Economía |
publisher.none.fl_str_mv |
Universidad del Rosario Facultad de Economía |
dc.source.none.fl_str_mv |
instname:Universidad del Rosario instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR |
instname_str |
Universidad del Rosario |
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Universidad del Rosario |
reponame_str |
Repositorio Institucional EdocUR |
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Repositorio Institucional EdocUR |
repository.name.fl_str_mv |
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repository.mail.fl_str_mv |
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1803710473315549184 |