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dc.contributor.authorCALVANO, Emilio
dc.contributor.authorCALZOLARI, Giacomo
dc.contributor.authorDENICOLO, Vincenzo
dc.contributor.authorPASTORELLO, Sergio
dc.date.accessioned2022-01-20T11:53:49Z
dc.date.issued2021
dc.identifier.citationInternational journal of industrial organization, 2021, Vol. 79, Art. 102712, OnlineOnlyen
dc.identifier.issn0167-7187
dc.identifier.urihttps://hdl.handle.net/1814/73708
dc.descriptionPublished online: 14 February 2021en
dc.description.abstractWe show that if they are allowed enough time to complete the learning, Q-learning algorithms can learn to collude in an environment with imperfect monitoring adapted from Green and Porter (1984), without having been instructed to do so, and without communicating with one another. Collusion is sustained by punishments that take the form of “price wars” triggered by the observation of low prices. The punishments have a finite duration, being harsher initially and then gradually fading away. Such punishments are triggered both by deviations and by adverse demand shocks.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.ispartofInternational journal of industrial organizationen
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.titleAlgorithmic collusion with imperfect monitoringen
dc.typeArticleen
dc.identifier.doi10.1016/j.ijindorg.2021.102712
dc.identifier.volume79en
eui.subscribe.skiptrue
dc.embargo.terms2023-02-14
dc.date.embargo2023-02-14


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