Best experienced payoff dynamics and cooperation in the centipede game

We study population game dynamics under which each revising agent tests each of his strategies a fixed number of times, with each play of each strategy being against a newly drawn opponent, and chooses the strategy whose total payoff was highest. In the centipede game, these best experienced payoff...

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Detalles Bibliográficos
Autores: Sandholm, William H., Izquierdo, Segismundo S., Izquierdo Millán, Luis Rodrigo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/5236
Acceso en línea:http://hdl.handle.net/10259/5236
Access Level:acceso abierto
Palabra clave:Evolutionary game theory
backward induction
centipede game
computational algebra
Economía
Economics
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spelling Best experienced payoff dynamics and cooperation in the centipede gameSandholm, William H.Izquierdo, Segismundo S.Izquierdo Millán, Luis RodrigoEvolutionary game theorybackward inductioncentipede gamecomputational algebraEconomíaEconomicsWe study population game dynamics under which each revising agent tests each of his strategies a fixed number of times, with each play of each strategy being against a newly drawn opponent, and chooses the strategy whose total payoff was highest. In the centipede game, these best experienced payoff dynamics lead to cooperative play. When strategies are tested once, play at the almost globally stable state is concentrated on the last few nodes of the game, with the proportions of agents playing each strategy being largely independent of the length of the game. Testing strategies many times leads to cyclical play.U.S. National Science Foundation (Grants SES-1458992 and SES- 1728853), the U.S. Army Research Office (Grants W911NF-17-1-0134 MSN201957), Project ECO2017-83147- C2-2-P (MINECO/AEI/FEDER, UE), and the Spanish Ministerio de Educación, Cultura, y Deporte (Grants PRX15/00362 and PRX16/00048)Econometric Society202020202019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/5236reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésTheoretical Economics. 2019, V. 14, n. 4, p. 1347–1386https://econtheory.org/ojs/index.php/te/issue/view/45info:eu-repo/grantAgreement/MINECO/ECO2017-83147- C2-2-P/info:eu-repo/grantAgreement/MECD/PRX15/00362info:eu-repo/grantAgreement/MECD/PRX16/00048Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/52362026-05-28T07:56:11Z
dc.title.none.fl_str_mv Best experienced payoff dynamics and cooperation in the centipede game
title Best experienced payoff dynamics and cooperation in the centipede game
spellingShingle Best experienced payoff dynamics and cooperation in the centipede game
Sandholm, William H.
Evolutionary game theory
backward induction
centipede game
computational algebra
Economía
Economics
title_short Best experienced payoff dynamics and cooperation in the centipede game
title_full Best experienced payoff dynamics and cooperation in the centipede game
title_fullStr Best experienced payoff dynamics and cooperation in the centipede game
title_full_unstemmed Best experienced payoff dynamics and cooperation in the centipede game
title_sort Best experienced payoff dynamics and cooperation in the centipede game
dc.creator.none.fl_str_mv Sandholm, William H.
Izquierdo, Segismundo S.
Izquierdo Millán, Luis Rodrigo
author Sandholm, William H.
author_facet Sandholm, William H.
Izquierdo, Segismundo S.
Izquierdo Millán, Luis Rodrigo
author_role author
author2 Izquierdo, Segismundo S.
Izquierdo Millán, Luis Rodrigo
author2_role author
author
dc.subject.none.fl_str_mv Evolutionary game theory
backward induction
centipede game
computational algebra
Economía
Economics
topic Evolutionary game theory
backward induction
centipede game
computational algebra
Economía
Economics
description We study population game dynamics under which each revising agent tests each of his strategies a fixed number of times, with each play of each strategy being against a newly drawn opponent, and chooses the strategy whose total payoff was highest. In the centipede game, these best experienced payoff dynamics lead to cooperative play. When strategies are tested once, play at the almost globally stable state is concentrated on the last few nodes of the game, with the proportions of agents playing each strategy being largely independent of the length of the game. Testing strategies many times leads to cyclical play.
publishDate 2019
dc.date.none.fl_str_mv 2019
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/5236
url http://hdl.handle.net/10259/5236
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Theoretical Economics. 2019, V. 14, n. 4, p. 1347–1386
https://econtheory.org/ojs/index.php/te/issue/view/45
info:eu-repo/grantAgreement/MINECO/ECO2017-83147- C2-2-P/
info:eu-repo/grantAgreement/MECD/PRX15/00362
info:eu-repo/grantAgreement/MECD/PRX16/00048
dc.rights.none.fl_str_mv Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución-NoComercial 4.0 Internacional
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Econometric Society
publisher.none.fl_str_mv Econometric Society
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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